Looking at each data series we see an outlier in Gini coefficient computed for 1965 for `labour income`.
We will smooth our data and take an average of the data either side of it for the time being.
```{code-cell} ipython3
ginis["l_income"][1965] = (ginis["l_income"][1962] + ginis["l_income"][1968]) / 2
```
Now we can compare net wealth and labour income.
```{code-cell} ipython3
---
mystnb:
figure:
caption: Gini coefficients of US net wealth and labour income
name: gini_wealth_us2
---
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.legend()
plt.show()
```
We see that, by this measure, inequality in both wealth and income has risen
substantially since 1980.
Previously we had compared
labour incomeandincome(from the World bank) Gini coefficients but they exibit different trends.Further research into explaining these differences is required.