Wealth Constraints and Self-Employment: Evidence from Birth Order

I revisit the question of whether entrepreneurs face liquidity constraints in business formation. The principle challenge is that wealth is correlated with unobserved ability, and adequate instruments are often difficult to identify. This paper uses the son’s birth order as an instrument for household wealth. The instrument would likely not be useful in Western data, but it is in many non-Western cultures where primogeniture remains important. I exploit the data available in the Korean Labor and Income Panel Study, and find evidence of liquidity constraints associated with self-employment in South Korea.Liquidity constraints, self-employment, instrument, birth order.

Gravitational field of a slowly rotating black hole with phantom global monopole

We present a slowly rotating black hole with phantom global monopole by solving Einstein's field equation and find that presence of global monopole changes the structure of black hole. The metric coefficient $g_{t\phi}$ contains hypergeometric function of the polar coordinate $r$, which is more complex than that in the usual slowly rotating black hole. The energy scale of symmetry breaking $\eta$ affects the black hole horizon and a deficit solid angle. Especially, the solid angle is surplus rather than deficit for a black hole with the phantom global monopole. We also study the correction originating from the global monopole to the angular velocity of the horizon $\Omega_H$, the Kepler's third law, the innermost stable circular orbit and the radiative efficiency $\epsilon$ in the thin accretion disk model. Our results also show that for the phantom black hole the radiative efficiency $\epsilon$ is positive only for the case $\eta\leq \eta_c$. The threshold value $\eta_c$ increases with the rotation parameter $a$.Comment: 14 pages, 6 figure

Unfolding Hidden Barriers by Active Enhanced Sampling

Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of microscopic configurations, originating from the orthogonal space perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly reduce the efficiency of CV-based sampling. Here we demonstrate that systematic machine learning CV, through enhanced sampling, can iteratively lift such degeneracies on the fly. We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler. Our active enhanced sampling (AES) algorithm is capable of identifying the least informative regions based on a historical sample, forming a positive feedback loop between the CV learner and sampler. This approach is able to globally preserve kinetic characteristics by incrementally enhancing both sample completeness and CV quality.Comment: 5 pages, 3 figure