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This study frames the international disability movement – NGOs, foreign donors, and transnational networks focused on promoting the 2006 UN Convention on the Rights of Persons with Disabilities – as an organizational environment. As the movement expands into the Global South, it actively pressures local grassroots associations to adopt a new organizational model in order to become membership-based advocacy organizations. Many groups, however, are embedded in local civic environments that expect them to act as self-help and social support organizations. As such, grassroots associations are caught between two organizational environments, each promoting different models and practices.

This analysis draws upon 18 months of participant observation and 69 interviews gathered from a local coalition of seven grassroots disability associations in Nicaragua. This ethnographic approach is combined with sociological institutionalism, an analysis that emphasizes the way organizations conform to organizational models that spread across a field.

The local associations responded in a variety of ways to the advocacy model promoted by the international movement. Organizations either conformed, resisted, or developed hybrid organizational models on the basis of internal characteristics that determined how they straddled the two organizational environments.

This paper highlights the way international models may be ineffective in local environments that have civic traditions and lower levels of governmental capacity than found in the West. Some disability associations, however, will creatively combine local and international models to create new initiatives that make a positive impact in the lives of persons with disabilities at the grassroots.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. Monte Carlo experiments are provided to compare the performance of spatial entropy estimators relative to classical estimators. Finally, the estimators are applied to an illustrative model allocating agricultural disaster payments.

We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.

Purpose – This chapter considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure.

Methodology/approach – We analyze the small sample properties of a generalized moments estimation approach for that model. This spatial Hausman–Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial fixed effects estimator is known to be consistent, but its disadvantage is that it wipes out the effects of time-invariant variables which are important for most empirical studies.

Findings – Monte Carlo results show that the spatial Hausman–Taylor estimator performs well in small samples.

This chapter investigates the impact of different state correlation assumptions for out-of-sample performance of unobserved components (UC) models with stochastic volatility. Using several measures of US inflation the author finds that allowing for correlation between inflation’s trend and cyclical (or gap) components is a useful feature to predict inflation in the short run. In contrast, orthogonality between such components improves the out-of-sample performance as the forecasting horizon widens. Accordingly, trend inflation from orthogonal trend-gap UC models closely tracks survey-based measures of long-run inflation expectations. Trend dynamics in the correlated-component case behave similarly to survey-based nowcasts. To carry out estimation, an efficient algorithm which builds upon properties of Toeplitz matrices and recent advances in precision-based samplers is provided.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.