UO's 'marginal' health insurance proposal

insurance_swindle.py

# modeling this ridiculous healthcare proposal

import numpy as np
import matplotlib.pyplot as plt

def healthcare(steps=11, initial_cost=2500000., mean_increase=6., sd_increase=2.):
    # stash initial values
    cost = initial_cost
    uo_cost = cost*0.95
    increase_pct = 0.
    uo_pct = 0.95

    # yield out the initial values
    yield cost, uo_cost, increase_pct, uo_pct

    step = 0
    while step < steps:
        # randomly raise the damn costs ya filthy insurance company
        increase_pct = np.random.normal(loc=mean_increase, scale=sd_increase)
        increase_pct = increase_pct / 100.
        increase = increase_pct*cost

        # calculate the marginal increase
        uo_increase = 0.
        if increase_pct < 0:
            # actually have no idea what happens if costs go down...
            pass
        if increase_pct < .02:
            # straight up 95%
            uo_increase += .95*increase
        elif increase_pct < .03:
            # take 95% of the first 2%
            uo_increase += .95*.02*cost
            # then 75% of the rest
            uo_increase += .75*(increase_pct-.02)*cost
        elif increase_pct < .05:
            # etc.
            uo_increase += .95*.02*cost
            uo_increase += .75*.01*cost
            uo_increase += .20*(increase_pct-.03)*cost
        else:
            # if above .05,
            uo_increase += .95*.02*cost
            uo_increase += .75*.01*cost
            uo_increase += .20*.02*cost

        cost += increase
        uo_cost += uo_increase
        uo_pct = uo_cost/cost
        step += 1

        # yield em up
        yield cost, uo_cost, increase_pct, uo_pct

def hc_costs(**kwargs):
    # lil wrapper that yields a numpy array
    hc_costs = [(cost, uo_cost, increase_pct, uo_pct) for
                 cost, uo_cost, increase_pct, uo_pct in healthcare(**kwargs)]
    return np.row_stack(hc_costs)



def plot_costs(costs):
    fig, ax = plt.subplots(3,1, gridspec_kw={'height_ratios':[2,1,1]})
    ax[0].plot(range(costs.shape[0]), costs[:,0], color='black')
    ax[0].plot(range(costs.shape[0]), costs[:,1], color='blue')
    ax[0].plot(range(costs.shape[0]), costs[:,0]-costs[:,1], color='red')
    ax[0].set_title('Health Insurance Costs')
    ax[0].legend(['Total Cost', 'UO Cost', 'GTFF Cost'])

    ax[1].plot(range(costs.shape[0]), costs[:,3], color='black')
    ax[1].set_title("Percent UO Contribution")

    ax[2].plot(range(costs.shape[0]), costs[:, 2], color='black')
    ax[2].set_title("Percent Annual Cost Increase")

    plt.tight_layout()

plot_costs(hc_costs(initial_cost=2500000., steps=11, mean_increase=6, sd_increase=0))
plot_costs(hc_costs(initial_cost=2500000., steps=11, mean_increase=6, sd_increase=4))
plot_costs(hc_costs(initial_cost=2500000., steps=11, mean_increase=8, sd_increase=4))

# calc mean increases 
increases = []
for i in range(500):
    c = hc_costs(initial_cost=2500000., steps=11, mean_increase=8, sd_increase=4)
    increases.append(c[5,0]-c[5,1])

np.mean(increases)