review in jupyter-lab

lectures/_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv

@@ -1,21 +1,21 @@
 year,n_wealth,t_income,l_income
-1950,0.825733203436636,0.44248654139458754,0.5342948198773422
-1953,0.8059487586599333,0.42645440609359464,0.5158978980963698
-1956,0.8121790488050616,0.4442694287339925,0.5349293526208134
-1959,0.7952068741637915,0.43749348077061606,0.5213985948309418
-1962,0.8086945076579374,0.4435843103853642,0.5345127915054336
-1965,0.7904149225687952,0.43763715466663355,0.7487860020887757
-1968,0.7982885066993517,0.42086207944388965,0.5242396427381534
-1971,0.7911574835420259,0.42333442460902565,0.5576454812313468
-1977,0.7571418922185198,0.46187678800902515,0.5704448110072063
-1983,0.7494335400643009,0.43934561846446973,0.5662220844385935
-1989,0.7715705301674317,0.5115249581654214,0.6013995687471423
-1992,0.7508126614055307,0.47406506720767516,0.5983592657979556
-1995,0.7569492388110264,0.48965523558400864,0.5969779516716902
-1998,0.7603291991801189,0.49117441585169025,0.5774462841723348
-2001,0.7816118750507017,0.5239092994681133,0.604273964496734
-2004,0.7700355469522374,0.48843503839032487,0.5981432201792718
-2007,0.7821413776486984,0.5197156312086194,0.6263452195753234
-2010,0.8250825295193427,0.5195972120145639,0.6453653328291896
-2013,0.8227698931835266,0.5314001749843371,0.6498682917772642
-2016,0.8342975903562223,0.5541400068900839,0.6706846793375303
+1950,0.8257332034366344,0.44248654139458693,0.5342948198773417
+1953,0.8059487586599325,0.42645440609359514,0.5158978980963705
+1956,0.8121790488050629,0.4442694287339931,0.5349293526208135
+1959,0.7952068741637919,0.4374934807706156,0.5213985948309419
+1962,0.8086945076579375,0.4435843103853644,0.5345127915054356
+1965,0.7904149225687939,0.43763715466663433,0.7487860020887759
+1968,0.7982885066993506,0.42086207944389026,0.5242396427381537
+1971,0.7911574835420256,0.42333442460902515,0.5576454812313486
+1977,0.7571418922185218,0.46187678800902543,0.5704448110072071
+1983,0.749433540064304,0.43934561846446973,0.5662220844385909
+1989,0.7715705301674298,0.51152495816542,0.6013995687471444
+1992,0.7508126614055317,0.4740650672076807,0.5983592657979544
+1995,0.7569492388110282,0.48965523558400603,0.596977951671693
+1998,0.7603291991801175,0.4911744158516888,0.5774462841723299
+2001,0.7816118750507037,0.5239092994681126,0.6042739644967319
+2004,0.7700355469522371,0.48843503839032426,0.5981432201792735
+2007,0.782141377648699,0.5197156312086187,0.6263452195753223
+2010,0.8250825295193419,0.5195972120145633,0.6453653328291933
+2013,0.8227698931835327,0.5314001749843346,0.6498682917772663
+2016,0.8342975903562247,0.5541400068900854,0.670684679337527

lectures/inequality.md

@@ -303,6 +303,8 @@ ax.plot(f_vals_nw[-1], l_vals_nw[-1], label=f'net wealth')
 ax.plot(f_vals_ti[-1], l_vals_ti[-1], label=f'total income')
 ax.plot(f_vals_li[-1], l_vals_li[-1], label=f'labor income')
 ax.plot(f_vals_nw[-1], f_vals_nw[-1], label=f'equality')
+ax.set_xlabel("household percentile")
+ax.set_ylabel("income/wealth percentile")
 ax.legend()
 plt.show()
 ```
@@ -356,23 +358,18 @@ mystnb:
     name: lorenz_gini
 ---
 fig, ax = plt.subplots()
-
 f_vals, l_vals = lorenz_curve(sample)
 ax.plot(f_vals, l_vals, label=f'lognormal sample', lw=2)
 ax.plot(f_vals, f_vals, label='equality', lw=2)
-
-ax.legend()
-
 ax.vlines([0.8], [0.0], [0.43], alpha=0.5, colors='k', ls='--')
 ax.hlines([0.43], [0], [0.8], alpha=0.5, colors='k', ls='--')
-
 ax.fill_between(f_vals, l_vals, f_vals, alpha=0.06)
-
 ax.set_ylim((0, 1))
 ax.set_xlim((0, 1))
-
 ax.text(0.04, 0.5, r'$G = 2 \times$ shaded area')
-  
+ax.set_xlabel("household percentile")
+ax.set_ylabel("income/wealth percentile")
+ax.legend()
 plt.show()
 ```
 
@@ -391,21 +388,17 @@ mystnb:
     name: lorenz_gini2
 ---
 fig, ax = plt.subplots()
-
 f_vals, l_vals = lorenz_curve(sample)
-
 ax.plot(f_vals, l_vals, label='lognormal sample', lw=2)
 ax.plot(f_vals, f_vals, label='equality', lw=2)
-
 ax.fill_between(f_vals, l_vals, f_vals, alpha=0.06)
 ax.fill_between(f_vals, l_vals, np.zeros_like(f_vals), alpha=0.06)
-
 ax.set_ylim((0, 1))
 ax.set_xlim((0, 1))
-
 ax.text(0.55, 0.4, 'A')
 ax.text(0.75, 0.15, 'B')
-
+ax.set_xlabel("household percentile")
+ax.set_ylabel("income/wealth percentile")
 ax.legend()
 plt.show()
 ```
@@ -711,7 +704,9 @@ We will smooth our data and take an average of the data either side of it for th
 
 ```{code-cell} ipython3
 ginis["l_income"][1965] = (ginis["l_income"][1962] + ginis["l_income"][1968]) / 2
-ginis["l_income"].plot()
+ax = ginis["l_income"].plot()
+ax.set_ylabel("Gini coefficient")
+plt.show()
 ```
 
 Now we can focus on US net wealth
@@ -728,7 +723,7 @@ mystnb:
 fig, ax = plt.subplots()
 ax.plot(years, ginis["n_wealth"], marker='o')
 ax.set_xlabel("year")
-ax.set_ylabel("gini coefficient")
+ax.set_ylabel("Gini coefficient")
 plt.show()
 ```
 
@@ -747,7 +742,7 @@ fig, ax = plt.subplots()
 ax.plot(years, ginis["l_income"], marker='o', label="labor income")
 ax.plot(years, ginis["t_income"], marker='o', label="total income")
 ax.set_xlabel("year")
-ax.set_ylabel("gini coefficient")
+ax.set_ylabel("Gini coefficient")
 ax.legend()
 plt.show()
 ```
@@ -759,7 +754,7 @@ fig, ax = plt.subplots()
 ax.plot(years, ginis["n_wealth"], marker='o', label="net wealth")
 ax.plot(years, ginis["l_income"], marker='o', label="labour income")
 ax.set_xlabel("year")
-ax.set_ylabel("gini coefficient")
+ax.set_ylabel("Gini coefficient")
 ax.legend()
 plt.show()
 ```