The Rise in Danish Unemployment: Reallocation or Mismatch?

Two key relations in theoretical work on labor market flows are analyzed; the hiring function and the uv-curve. The relations are identified in a cointegrated VAR-model framework. The system comprising unemployment, vacancies and hirings is driven by a stochastic trend in the vacancies, corresponding to the status of vacancies as the driving force in models of labor market flows. The drift in the relations is modeled by smooth transition functions and is identified as increased mismatch problems in the Danish labor market as opposed to increased structural change.hiring function; UV-curve; cointegrated VAR-Model; smooth transition functions; mismatch

The Rise in Danish Unemployment: Reallocation or Mismatch?

The two main competing theories for the outward shift in the uv-curve are investigated: Increased separations from employment at a given employment level (reallocation) and decreased levels of hires, given unemployment and vacancies (mismatch). Shifts in the uv-curve and the hiring function are modelled by smooth transition functions, and the hypothesis of analogous shifts in the two curves is tested and accepted. This is interpreted as evidence in favor of the mismatch hypotheses.

Conductance spectroscopy on Majorana wires and the inverse proximity effect

Recent experimental searches for signatures of Majorana-like excitations in proximitized semiconducting nanowires involve conductance spectroscopy, where the evidence sought after is a robust zero-bias peak (in longer wires) and its characteristic field-dependent splitting (in shorter wires). Although experimental results partially confirm the theoretical predictions, commonly observed discrepancies still include (i) a zero-bias peak that is significantly lower than the predicted value of $2e^2/h$ and (ii) the absence of the expected "Majorana oscillations" of the lowest-energy modes at higher magnetic fields. Here, we investigate how the inevitable presence of a normal drain lead connected to the hybrid wire can affect the conductance spectrum of the hybrid wire. We present numerical results using a one-band model for the proximitized nanowire, where the superconductor is considered to be in the diffusive regime, described by semi-classical Green functions. We show how the presence of the normal drain could (at least partially) account for the observed discrepancies, and we complement this with analytic results providing more insights in the underlying physics.Comment: 10 pages, 7 figure

Phase-tunable Majorana bound states in a topological N-SNS junction

We theoretically study the differential conductance of a one-dimensional normal-superconductor-normal-superconductor (N-SNS) junction with a phase bias applied between the two superconductors. We consider specifically a junction formed by a spin-orbit coupled semiconducting nanowire with regions of the nanowire having superconducting pairing induced by a bulk $s$-wave superconductor. When the nanowire is tuned into a topologically non-trivial phase by a Zeeman field, it hosts zero-energy Majorana modes at its ends as well as at the interface between the two superconductors. The phase-dependent splitting of the Majorana modes gives rise to features in the differential conductance that offer a clear distinction between the topologically trivial and non-trivial phases. We calculate the transport properties of the junction numerically and also present a simple analytical model that captures the main properties of the predicted tunneling spectroscopy.Comment: 11 pages, 7 figure

Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice

This paper uses factor models to identify and estimate distributions of counterfactuals. We extend LISREL frameworks to a dynamic treatment effect setting, extending matching to account for unobserved conditioning variables. Using these models, we can identify all pairwise and joint treatment effects. We apply these methods to a model of schooling and determine the intrinsic uncertainty facing agents at the time they make their decisions about enrollment in school. Reducing uncertainty in returns raises college enrollment. We go beyond the “Veil of ignorance” in evaluating educational policies and determine who benefits and loses from commonly proposed educational reforms.Factor models; model of schooling; distributions of counterfactual outcomes

Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies

This paper summarizes our recent research on evaluating the distributional consequences of social programs. This research advances the economic policy evaluation literature beyond estimating assorted mean impacts to estimate distributions of outcomes generated by different policies and determine how those policies shift persons across the distributions of potential outcomes produced by them. Our approach enables analysts to evaluate the distributional effects of social programs without invoking the 'Veil of Ignorance' assumption often used in the literature in applied welfare economics. Our methods determine which persons are affected by a given policy, where they come from in the ex-ante outcome distribution and what their gains are. We apply our methods to analyze two proposed policy reforms in American education. These reforms benefit the middle class and not the poor.

The Effect of Schooling and Ability on Achievement Test Scores

This paper develops two methods for estimating the effect of schooling on achievement test scores that control for the endogeneity of schooling by postulating that both schooling and test scores are generated by a common unobserved latent ability. These methods are applied to data on schooling and test scores. Estimates from the two methods are in close agreement. We find that the effects of schooling on test scores are roughly linear across schooling levels. The effects of schooling on measured test scores are slightly larger for lower latent ability levels. We find that schooling increases the AFQT score on average between 2 and 4 percentage points, roughly twice as large as the effect claimed by Herrnstein and Murray (1994) but in agreement with estimates produced by Neal and Johnson (1996) andWinship and Korenman (1997). We extend the previous literature by estimating the impact of schooling on measured test scores at various quantiles of the latent ability distribution.

Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College

This paper uses factor models to identify and estimate distributions of counterfactuals. We extend LISREL frameworks to a dynamic treatment effect setting, extending matching to account for unobserved conditioning variables. Using these models, we can identify all pairwise and joint treatment effects. We apply these methods to a model of schooling and determine the intrinsic uncertainty facing agents at the time they make their decisions about enrollment in school. Reducing uncertainty in returns raises college enrollment. We go beyond the Veil of Ignorance' in evaluating educational policies and determine who benefits and who loses from commonly proposed educational reforms.