Knowledge of the determinants of self‐employment income is critical to entrepreneurial development strategies if the development goal is to increase incomes not just employment. Using American Community Survey data, unconditional quantile regression is used to investigate differences in the relationship between entrepreneurial income and an array of individual, industry, and regional characteristics across the self‐employment income distribution. Personal attributes, such as education, race, age, and gender, both explain differences in self‐employment income and vary in importance across the income distribution. Regional agglomerative effects are significantly positive and stronger at the upper end of the self‐employed income distribution.

"Traditional Folk Song in Modern Japan" published on 31 Jan 2008 by Brill.

Quality assurance programs (QAPs) may add value to wines through reassuring consumers of wine quality prior to purchase and consumption. In Tennessee, the potential to use a wine QAP is being explored as a means to improve the industry’s economic sustainability. To date, several QAPs exist, but studies directly related to their impact on consumer behavior for wines from emerging areas are scarce. We used an online survey instrument to elicit consumer perceptions of QAPs importance on wine purchasing decisions and how the presence of a QAP may impact their purchasing behavior for a wine from an emerging area (e.g., Tennessee). Wine involvement, QAP perceptions, and familiarity were also measured. A mixed-process model was used to estimate the two ordered probit models for importance of QAPs and the influence of QAPs on Tennessee wine purchases, where QAP importance was treated as a latent variable influencing Tennessee wine purchases. Ordinal probit estimates suggest that greater on-site spending and greater perceived benefits of QAPs (as indicators of quality and standardization) resulted in a higher probability of perceived QAP importance when making wine purchasing decisions. Reduced form estimates of the model of Tennessee wine purchases indicated distance to wine producing areas, female gender, and familiarity with Tennessee wines each negatively influenced the potential impact of QAPs on Tennessee wine purchases. However, on-site spending at wineries and perceived benefits of a Tennessee QAP increased likelihood of Tennessee wine purchases.

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher-order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split-sample jackknife, result in larger higher-order variances in the i.i.d. setting we focus on. For both a cross-sectional MLE and a panel model with individual fixed effects, we show that the split-sample jackknife has a higher-order variance term that is twice as large as that of the `leave-one-out' jackknife.