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Presidential candidate Donald Trump ran for office promising a ‘total and complete shutdown’ of Muslims entering the United States. This essay, based on policy research, data analysis and interviews, provides extensive details of what became of that promise from legal, social and humanistic perspectives. Issued during his first week in office as US President, the ‘Muslim Ban’ Executive Order immediately produced chaos at airports globally, as US visas and ‘green cards’ suddenly became invalid for entry to the United States for persons travelling on the passports of seven Muslim majority countries. Over time, the Trump administration amended the Muslim Ban through new executive orders and proclamations that removed unlawful components, changed the countries affected, or altered the policy's justification. Although all these iterations faced legal challenges, a majority of the US Supreme Court ultimately acquiesced to President Trump and ruled in favour of the ban's legality. Throughout this period, the US immigration process rattled on like a machine, encouraging would-be (but banned) migrants to continue pursuing their paperwork and paying their fees, even though entry visas would prove unavailable. Waivers for family reunification were overwhelmingly denied at the consular level, and tens of thousands of otherwise eligible migrants lost substantial amounts of money in pursuit of the elusive visa. Protests erupted at US airports when the ban was initially implemented, revealing a political solidarity with Muslims rarely seen before. These events ended when enforcement of the ban was moved to remote locations, to US consulates abroad.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Anyone who has recently watched television or movies can tell you that transgender, gender nonbinary or gender expansive people are becoming more visible in these media. This trend reflects the reality that younger generations are increasingly identifying with more fluid and nonbinary gender and sexual identities and are progressively expressing those identities in a more flexible and changing manner (Herman et al., 2022; Wilson & Meyer, 2021). Unsurprisingly then, those individuals are also more visible at work, including in workplaces with employer-mandated dress codes. Indeed, in 2020 the US Supreme Court decided a case involving a transgender woman, Aimee Stephens, who was fired because her employer, a funeral home, required her to conform to its gender-binary dress policy and wear clothing mandatory for people assigned male at birth, rather than appropriate for her female gender identity ( Bostock v. Clayton County, 2020).

However, as the description of Aimee Stephens's own experience illustrates, often these employer appearance codes are based on a binary and fixed conception of gender and gender identity and expression at odds with the increasing number of workers who do not identify within those rigid parameters. Moreover, even when an employee, like Aimee Stephens herself, could have fit within her employer's dress code, the improper application of that policy to her, or employer concerns about customer or co-worker discomfort with an employee's appearance under the policy may mean that a worker's identity and expression may still conflict with a workplace appearance code. For gender nonbinary or nonconforming individuals, these complications are magnified.

This chapter explores the practical problems and barriers that employer dress codes have on employees whose gender identity and/or presentation move beyond the traditional male/female binary. Using insights from queer theory, gender expansive employees serve to interrogate fundamental assumptions behind workplace dress policies and the formal and informal ways in which these policies are policed. The chapter will explore that discordance, examine possible employer resolutions, and evaluate the strengths and weaknesses of those responses.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.