Cinque (2020) presents a unified theory positing that various types of relative clauses (RCs) originate from a single, double-headed universal structure via raising or matching. The Frame Noun-Modifying Clause (FRC) as described and analyzed by Matsumoto et al. (2017a, 2017b) presents a significant challenge to Cinque's framework, as it does not conform to any of Cinque's identified RC types, which include amount RCs, kind(-defining) RCs, restrictive RCs and non-restrictive RCs. The FRC eludes derivation via the proposed matching or raising mechanisms. Determining the semantic link between the head noun and the FRC, as well as its external merger position, remains elusive. One might suggest that inserting additional material into the FRC, which incorporates a plausible internal head, could clarify their connection. This approach falls short of providing a systematic and coherent syntactic criterion, relying instead on semantic intuition that lacks operational reliability.

In this paper, we analyze two types of refutations for Unit Two Variable Per Inequality (UTVPI) constraints. A UTVPI constraint is a linear inequality of the form: $a_{i}\cdot x_{i}+a_{j} \cdot x_{j} \le b_{k}$, where $a_{i},a_{j}\in \{0,1,-1\}$ and $b_{k} \in \mathbb{Z}$. A conjunction of such constraints is called a UTVPI constraint system (UCS) and can be represented in matrix form as: ${\bf A \cdot x \le b}$. UTVPI constraints are used in many domains including operations research and program verification. We focus on two variants of read-once refutation (ROR). An ROR is a refutation in which each constraint is used at most once. A literal-once refutation (LOR), a more restrictive form of ROR, is a refutation in which each literal ($x_i$ or $-x_i$) is used at most once. First, we examine the constraint-required read-once refutation (CROR) problem and the constraint-required literal-once refutation (CLOR) problem. In both of these problems, we are given a set of constraints that must be used in the refutation. RORs and LORs are incomplete since not every system of linear constraints is guaranteed to have such a refutation. This is still true even when we restrict ourselves to UCSs. In this paper, we provide NC reductions between the CROR and CLOR problems in UCSs and the minimum weight perfect matching problem. The reductions used in this paper assume a CREW PRAM model of parallel computation. As a result, the reductions establish that, from the perspective of parallel algorithms, the CROR and CLOR problems in UCSs are equivalent to matching. In particular, if an NC algorithm exists for either of these problems, then there is an NC algorithm for matching.

Participants drank either regular root beer or sugar-free diet root beer before working on a probability-learning task in which they tried to predict which of two events would occur on each of 200 trials. One event (E1) randomly occurred on 140 trials, the other (E2) on 60. In each of the last two blocks of 50 trials, the regular group matched prediction and event frequencies. In contrast, the diet group predicted E1 more often in each of these blocks. After the task, participants were asked to write down rules they used for responding. Blind ratings of rule complexity were inversely related to E1 predictions in the final 50 trials. Participants also took longer to advance after incorrect predictions and before predicting E2, reflecting time for revising and consulting rules. These results support the hypothesis that an effortful controlled process of normative rule-generation produces matching in probability-learning experiments, and that this process is a function of glucose availability.

Crop insurance has been linked to changes in farm production decisions. In this study, we examine the effects of crop insurance participation and coverage on farm input use. Using a 1993–2016 panel of Kansas farms, evidence exists that insured farms apply more farm chemicals and seed per acre than uninsured farms. We use a fixed effects instrumental variable estimator to obtain the effects of change in crop insurance coverage on farm input use accounting farm-level heterogeneity. Empirical evidence suggests that changes in the levels of crop insurance coverage do not significantly affect farm chemical use. Thus, moral hazard effects from purchasing crop insurance are not large on a per acre basis but can lead to expenditures of $6,100 per farm.

This paper studies the structure and origin of prenominal and postnominal restrictive relative clauses in Pharasiot Greek. Though both patterns are finite and introduced by the invariant complementizer tu, they differ in two important respects. First, corpus data reveal that prenominal relatives are older than their postnominal counterparts. Second, in the present-day language only prenominal relatives involve a matching derivation, whereas postnominal ones behave like Head-raising structures. Turning to diachrony, we suggest that prenominal relatives came into being through morphological fusion of a determiner t- with an invariant complementizer u. This process entailed a reduction of functional structure in the left periphery of the relative clause, to the effect that the landing site for a raising Head was suppressed, leaving a matching derivation as the only option. Postnominal relatives are analyzed as borrowed from Standard Modern Greek. Our analysis corroborates the idea that both raising and matching derivations for relatives must be acknowledged, sometimes even within a single language.

Motivated by applications to a wide range of areas, including assemble-to-order systems, operations scheduling, healthcare systems, and the collaborative economy, we study a stochastic matching model on hypergraphs, extending the model of Mairesse and Moyal (J. Appl. Prob.53, 2016) to the case of hypergraphical (rather than graphical) matching structures. We address a discrete-event system under a random input of single items, simply using the system as an interface to be matched in groups of two or more. We primarily study the stability of this model, for various hypergraph geometries.

We introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.

Overseas study is a global phenomenon and a major business internationally. But does overseas study pay off? Using data from the 2015 China Household Finance Survey (CHFS), we examine the labour market performance of overseas returnees in China. To obtain more accurate results, we matched each returnee with a local so that the domestic group is as similar as possible to the returnee group. We then conducted empirical analyses of the matched data. We find that compared with domestic postgraduates, returnee postgraduates earn about 20 per cent more annually. Moreover, the salary premiums paid for foreign graduate degrees can be attributed principally to the superior human capital gained from overseas education rather than from any “signalling” effect. Also, returnees with graduate degrees are more likely to enter high-income professions and foreign-funded ventures, and to reach higher positions in those organizations. However, we find no significant differences in income, occupation choices and positions between returnee and local bachelor's degree recipients. As such, we suggest that Chinese students and their families are best served when the students obtain a local undergraduate degree and then go overseas for graduate training.

The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.

Multilayer graphs consist of several graphs, called layers, where the vertex set of all layers is the same but each layer has an individual edge set. They are motivated by real-world problems where entities (vertices) are associated via multiple types of relationships (edges in different layers). We chart the border of computational (in)tractability for the class of subgraph detection problems on multilayer graphs, including fundamental problems such as maximum-cardinality matching, finding certain clique relaxations, or path problems. Mostly encountering hardness results, sometimes even for two or three layers, we can also spot some islands of computational tractability.

We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.