Inference for Quantiles of a Finite Population: Asymptotic versus Resampling Results

The aim of the paper is to study the problem of estimating the quantile function of a finite population. Attention is first focused on point estimation, and asymptotic results are obtained. Confidence intervals are then constructed, based on both the following: (i) asymptotic results and (ii) a resampling technique based on rescaling the ‘usual’ bootstrap. A simulation study to compare asymptotic and resampling‐based results, as well as an application to a real population, is finally performed.     

Unpaid labour and territorial extraction in digital value networks

Production in knowledge and data-intensive industries is powered by work that can, in theory, be done from anywhere, via cloudwork platforms. Cloudwork platforms govern data value chains in distinct ways to concentrate power and extract value at the global scale. We argue that unpaid labour is a systemic mechanism of accumulation in these digital value networks. In this paper we demonstrate how it is tied to platform business models and facilitated by elements of platform governance including monopsony power, a high degree of spatial flexibility in sourcing labour, regulatory unaccountability and digital enclosure. We draw on a survey of 699 workers on 14 platforms in 74 countries to show that unpaid labour is an engine of South–North value extraction, and workers in the global South perform more unpaid labour than counterparts in the global North. Our findings have important ramifications our understanding of the changing international division of labour and platform capitalism.     

Response improvement in complex experiments by co-information composite likelihood optimization

We propose an adaptive procedure for improving the response outcomes of complex combinatorial experiments. New experiment batches are chosen by minimizing the co-information composite likelihood (COIL) objective function, which is derived by coupling importance sampling and composite likelihood principles. We show convergence of the best experiment within each batch to the globally optimal experiment in finite time, and carry out simulations to assess the convergence behavior as the design space size increases. The procedure is tested as a new enzyme engineering protocol in an experiment with a design space size of order 10⁷.