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.

In a school choice problem, each school has a priority ordering over the set of students. These orderings depend on criteria such as whether a student lives within walking distance or has a sibling at the school. A priority ordering provides a ranking of students but nothing more. I argue that this information is sufficient when priority is based on merit but not when priority is based on criteria such as walking distance. I propose an extended formulation of the problem wherein a ‘priority matrix’, indicating which criteria are satisfied by each student-school pair, replaces the usual priority orderings.

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties of existing theories, such as assumptions hidden in data analyses rather than stated up front, asymptotics, unfamiliar estimators, and complex variance calculations. Our theory of inference makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments.

We study a one-parameter class of examples of optimal transport problems between a two-dimensional source and a one-dimensional target. Our earlier work identified a nestedness condition on the surplus function and marginals, under which it is possible to solve the problem semi-explicitly. In the family of examples we consider, we classify the values of parameters which lead to nestedness. In those cases, we derive an almost explicit characterisation of the solution.

We characterize the structural impediments to the existence of Borel perfect matchings for acyclic locally countable Borel graphs admitting a Borel selection of finitely many ends from their connected components. In particular, this yields the existence of Borel matchings for such graphs of degree at least three. As a corollary, it follows that acyclic locally countable Borel graphs of degree at least three generating μ-hyperfinite equivalence relations admit μ-measurable matchings. We establish the analogous result for Baire measurable matchings in the locally finite case, and provide a counterexample in the locally countable case.

We introduce and study a new model that we call the matching model. Items arrive one by one in a buffer and depart from it as soon as possible but by pairs. The items of a departing pair are said to be matched. There is a finite set of classes 𝒱 for the items, and the allowed matchings depend on the classes, according to a matching graph on 𝒱. Upon arrival, an item may find several possible matches in the buffer. This indeterminacy is resolved by a matching policy. When the sequence of classes of the arriving items is independent and identically distributed, the sequence of buffer-content is a Markov chain, whose stability is investigated. In particular, we prove that the model may be stable if and only if the matching graph is nonbipartite.

Forest conservation incentives are a popular approach to combatting tropical deforestation. Here we consider a case where direct economic incentives for forest conservation were offered to newly titled smallholders in a buffer zone of a protected area in the northeastern Ecuadorian Amazon. We used quasi-experimental impact evaluation methods to estimate changes in forest cover for 63 smallholders enrolled in Ecuador's Socio Bosque program compared to similar households that did not enroll. Focus group interviews in 15 communities provided insight into why landowners enrolled in the program and how land use is changing. The conservation incentives program reduced average annual deforestation by 0.4–0.5% between 2011 and 2013 for those enrolled, representing as much as a 70% reduction in deforestation attributable to Socio Bosque. Focus group interviews suggested that some landowners chose to ‘invest’ in conservation because the agricultural capacity of their land was limited and economic incentives provided an alternative livelihood strategy. Interviews, however, indicated limits to increasing enrollment rates under current conditions, due to lack of trust and liquidity constraints. Overall, a hybrid public–private governance approach can lead to larger conservation outcomes than restrictions alone.

In his seminal, 1973 paper, published in the Journal of Political Economy, Gary Becker emphasizes marriage as a crucial, yet understudied issue to which economic analysis could, therefore should, be applied. In his words: