Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Profit efficiency analysis under limited information with an application to German farm types

Lack of information about technology and prices often hampers the empirical assessment of the profit maximization hypothesis (viz. by measuring the degree of profit efficiency). The non-parametric Data Envelopment Analysis (DEA) methodology can deal with such incomplete information. We exploit the implicit but largely neglected profit interpretation of the DEA model that builds on assumptions of monotone and convex production possibility sets. We show how its embedded assessment of necessary conditions for profit maximization can be strengthened given partial information in the form of monetary sub-cost/-revenue data (that are often easier obtained than the pure quantity data). Finally, we argue that a ‘mix’ efficiency analysis is naturally complementary to such a profit efficiency analysis. An application to German farm types complements our methodological discussion. By using non-parametric statistical tests, we further demonstrate the potential of the non-parametric approach in deriving strong and robust statistical evidence while imposing minimal structure on the setting under study. In particular, we look for significant efficiency variation over regions.     

Sharing inventory risk in supply chain: The implication of financial constraint

A supply chain may operate under either preorder mode, consignment mode or the combination of these two modes. Under preorder, the retailer procures before the sale and takes full inventory risk during the sale, while under consignment, the retailer sells the product for the supplier with the supplier taking the inventory risk. The combination mode shares the risk in the supply chain. The existing research has examined the supply chain modes from various operational aspects. However, the impact of financial constraint is neglected. This paper examines the impact of financial constraint and investigates the supply chain efficiency under each mode. Based on a Stackelberg game with the supplier being the leader, we show that without financial constraint the supplier always prefers the consignment mode, taking full inventory risk. Whereas, in the presence of financial constraint, the supplier will sell part of the inventory to the retailer through preorder, which shares the inventory risk in the supply chain. We show that with financial constraint, the combination mode is the most efficient mode even if the retailer earns zero internal capital.     

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction. An abridged version of this paper was presented at the Workshop on European Indicators and Scoreboards, organised by DG Education and the Joint Research Centre within the auspices of CRELL, in Brussels, October 24–25, 2005.     

The Collective Model of Household Consumption: A Nonparametric Characterization

We provide a nonparametric characterization of a general collective model for household consumption, which includes externalities and public consumption. Next, we establish testable necessary and sufficient conditions for data consistency with collective rationality that only include observed price and quantity information. These conditions have a similar structure as the generalized axiom of revealed preference for the unitary model, which is convenient from a testing point of view. In addition, we derive the minimum number of goods and observations that enable the rejection of collectively rational household behavior.     

Sharing Rule Identification for General Collective Consumption Models

We propose a method to set identify bounds on the sharing rule for a general collective household consumption model. Unlike the effects of distribution factors, the level of the sharing rule cannot be uniquely identified without strong assumptions on preferences across households. Our new results show that, though not point identified without these assumptions, strong bounds on the sharing rule can be obtained. We get these bounds by applying revealed preference restrictions implied by the collective model to the household's continuous aggregate demand functions. We obtain informative bounds even if nothing is known about whether each good is public, private, or assignable within the household, though having such information tightens the bounds. We apply our method to US PSID data, obtaining narrow bounds that yield useful conclusions regarding the effects of income and wages on intrahousehold resource sharing, and on the prevalence of individual (as opposed to household level) poverty.     

Justice in Indonesia: The Social Life of a Momentous Concept

In this article we argue that efforts in Indonesia to improve access to justice for the disadvantaged would greatly benefit from a pragmatic approach that takes local circumstances of custom, values and social relations into account at least as much as legal reform and bureaucratic transparency. We maintain that in post-Suharto Indonesia ‘justice’ can be conceptualised as the inverse of injustice and is manifested in terms of sovereign interests. Ideal justice, such as associated with rule of law implementing programs, assumes a functioning of government and judiciary that might bring about such results. Building on our own research as well as on the articles in this special issue we argue that engaging with the role and meaning of justice should involve solid ethnography of justice-seekers' life-worlds, understanding of the strategies and institutions that provide justice, and paying attention to the networks and interactions that connect actors in an ever moving field.     

Between Land Grabbing and Farmers' Benefits: Land Transfers in West Kalimantan,Indonesia

Rapid growth of oil palm cultivation in West Kalimantan, Indonesia has increased both the pace and size of land transfers from farmers to big companies and city-based investors. This process has been widely conceptualised as ‘top-down land grabbing’ but this perspective largely neglects the agency of the farmers. This paper seeks to shed light on the agency of farmers with respect to how they respond to land transfer schemes. Through a study of land transfers under nucleus estate schemes (NES), schemes that were introduced by the Indonesian government in which smallholding farms are integrated parts of a modern, large size plantation company, we will show that agrarian dynamics are shaped by notions of what is ‘fair’ and ‘just’.     

Collective consumption models with restricted bargaining weights: an empirical assessment based on experimental data

We use experimental data to analyze consumption decisions by groups of individuals who have to reach a consensus on spending a joint budget. Our experiment involves dyads (i.e. two-member groups) who have to compose consumption bundles consisting of three commodities (wine, orange juice and M&Ms). We focus on the collective consumption model to describe group behavior. This model represents group decisions as Pareto optimal outcomes of a within-group bargaining process, with rational group members who are each characterized by individual bargaining weights. We also consider specifications of the collective model that restrict the variation of these bargaining weights. A distinguishing feature of our study is that we use revealed preference testing tools to assess the goodness-of-fit and discriminatory power of alternative specifications of the collective model. Our experimental results suggest that the most appropriate model specification allows for a limited variation of the bargaining weights.     

A test procedure for detecting super-heavy tails

The aim of this work is to develop a test to distinguish between heavy and super-heavy tailed probability distributions. These classes of distributions are relevant in areas such as telecommunications and insurance risk, among others. By heavy tailed distributions we mean probability distribution functions with polynomially decreasing upper tails (regularly varying tails). The term super-heavy is reserved for right tails decreasing to zero at a slower rate, such as logarithmic, or worse (slowly varying tails). Simulations are presented for several models and an application with telecommunications data is provided.