Model estimates, alternative specification: Different bounds for V distribution moments |$ \underline{V},\overline{V}$|
. | $5,000 . | $50,000 . |
---|---|---|
Bunching | 0.4075 | 0.167 |
(0.0981) | (0.075) | |
Elasticity | 0.05 | 0.0349 |
(0.0119) | (0.015) | |
Nonoptimizer value | 0.8435 | 0.8512 |
(0.0185) | (0.0272) | |
Optimal compensation | 0.027 | 0.019 |
(0.0063) | (0.008) | |
Fraction of nonoptimizers | 0.432 | |
Nonoptimizer participation sensitivity | –0.002 |
. | $5,000 . | $50,000 . |
---|---|---|
Bunching | 0.4075 | 0.167 |
(0.0981) | (0.075) | |
Elasticity | 0.05 | 0.0349 |
(0.0119) | (0.015) | |
Nonoptimizer value | 0.8435 | 0.8512 |
(0.0185) | (0.0272) | |
Optimal compensation | 0.027 | 0.019 |
(0.0063) | (0.008) | |
Fraction of nonoptimizers | 0.432 | |
Nonoptimizer participation sensitivity | –0.002 |
Starting with the baseline parameters in Table 3, I change the parameters |$ \underline{V}$| and |$ \overline{V}$|. I change |$ \underline{V}$| from $3,500 ($35,000) to $3,600 ($36,000) for the estimation using data near the kink $5,000 ($50,000). I change |$ \overline{S}$| from $4,000 ($40,000) to $3,900 ($39,000) for the estimation using data near the kink $5,000 ($50,000). I then reestimate (1) bunching; (2) the elasticity parameter e; (3) the nonoptimizer value δ; and (4) the optimal commission. This table presents the resultant estimates. The first column lists the statistics. The second column displays my estimate of each statistic when I use data near the kink K = $5,000. The third column displays my estimate of each statistic when I use data near the kink K = $50,000. I bootstrap the estimation procedure 500 times and present bootstrapped standard errors in parentheses. See Table 4 for details.
Model estimates, alternative specification: Different bounds for V distribution moments |$ \underline{V},\overline{V}$|
. | $5,000 . | $50,000 . |
---|---|---|
Bunching | 0.4075 | 0.167 |
(0.0981) | (0.075) | |
Elasticity | 0.05 | 0.0349 |
(0.0119) | (0.015) | |
Nonoptimizer value | 0.8435 | 0.8512 |
(0.0185) | (0.0272) | |
Optimal compensation | 0.027 | 0.019 |
(0.0063) | (0.008) | |
Fraction of nonoptimizers | 0.432 | |
Nonoptimizer participation sensitivity | –0.002 |
. | $5,000 . | $50,000 . |
---|---|---|
Bunching | 0.4075 | 0.167 |
(0.0981) | (0.075) | |
Elasticity | 0.05 | 0.0349 |
(0.0119) | (0.015) | |
Nonoptimizer value | 0.8435 | 0.8512 |
(0.0185) | (0.0272) | |
Optimal compensation | 0.027 | 0.019 |
(0.0063) | (0.008) | |
Fraction of nonoptimizers | 0.432 | |
Nonoptimizer participation sensitivity | –0.002 |
Starting with the baseline parameters in Table 3, I change the parameters |$ \underline{V}$| and |$ \overline{V}$|. I change |$ \underline{V}$| from $3,500 ($35,000) to $3,600 ($36,000) for the estimation using data near the kink $5,000 ($50,000). I change |$ \overline{S}$| from $4,000 ($40,000) to $3,900 ($39,000) for the estimation using data near the kink $5,000 ($50,000). I then reestimate (1) bunching; (2) the elasticity parameter e; (3) the nonoptimizer value δ; and (4) the optimal commission. This table presents the resultant estimates. The first column lists the statistics. The second column displays my estimate of each statistic when I use data near the kink K = $5,000. The third column displays my estimate of each statistic when I use data near the kink K = $50,000. I bootstrap the estimation procedure 500 times and present bootstrapped standard errors in parentheses. See Table 4 for details.
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