Estimating Equilibrium Models of Sorting across Locations

With the growing recognition of the role played by geography in all sorts of economic problems, there is strong interest in measuring the size and scope of local spillovers (i.e., simple anonymous agglomeration or congestion effects, or more complicated interactions between individuals or firms of specific types). It is well-understood, however, that such spillovers cannot be distinguished from unobservable local attributes using just the observed location decisions of individuals or firms. We propose an empirical strategy for recovering estimates of spillovers in the presence of unobserved local attributes for a broadly applicable class of equilibrium sorting models. This approach relies on an instrumental variables strategy derived from the internal logic of the sorting model itself. We show practically how the strategy is implemented, provide intuition for our instrumental variables, and discuss the role of effective choice-set variation in identifying the model, and carry-out a series of Monte Carlo experiments to demonstrate the instruments' performance in small samples.Local Spillovers, Location Choice, Economic Geography, Natural Advantage, Social Interactions, Network Effects, Endogenous Sorting, Discrete Choice Models, Agglomeration, Congestion

A Note on the Equilibrium Properties of Locational Sorting Models

A central feature of many models of location choice -- whether of firms or households, within or across cities -- is the role of local interactions or spillovers, whereby the payoffs from choosing a location depend in part on the number or attributes of other individuals or firms that choose the same or nearby locations in equilibrium. The main goal of this paper is to develop the equilibrium properties of a broadly applicable and readily estimable class of sorting models that allow the location decision to depend on both fixed local attributes (including unobserved attributes) and such local interactions. In particular, we prove uniqueness in the case of congestion effects and use a series of simulations to demonstrate that a unique equilibrium is more likely to obtain (i) the smaller are any agglomeration effects, (ii) the larger are the set of choices available to the agents, (iii) the more "meaningful variation" there is in those choices, and (iv) the more heterogeneous are the agents themselves. This is encouraging for the use of our model to describe the sorting of individuals and firms over geographic space, where the number of choices is usually large and variation in exogenous fixed attributes can be important. Moreover, these results conveniently coincide with the conditions required for econometric identification of our model.Local Spillovers, Social Interactions, Economic Geography, Natural Advantage, Endogenous Sorting, Discrete Choice Models, Agglomeration, Congestion, Random Utility

Choosing international organizations: when do states and the World Bank collaborate on environmental projects?

While international cooperation research emphasizes institutional design, states mostly interact with existing organizations. How do states choose organizations for cooperation? We develop a theory of agency choice for development projects, emphasizing the importance of domestic institutions, the scope of cooperation, and the resources of the implementing agency. If states are to cooperate with funding agencies that have abundant resources, such as the World Bank, they must accept more stringent conditions on project implementation. We argue states accept the stringent conditions that resourceful organizations demand if the public goods from project implementation are highly valuable. Empirically, this is the case for democratic states, large projects, and projects that produce national instead of global public goods. We test this theory using data on 2,882 Global Environment Facility (GEF) projects, 1991–2011. The GEF offers an ideal case because various implementing agencies are responsible for the actual projects. States implement projects in collaboration with the World Bank, which has the most expertise and resources among the GEF’s implementing agencies, if their regime type is democracy, the project size is large, and the benefits are primarily national. Qualitative evidence sheds light on causal mechanisms

When international organizations bargain: evidence from the global environment facility

Who gets what in bargaining between states and international organizations (IOs)? Although distributional conflict is unavoidable in international cooperation, previous research provides few empirical insights into the determinants of bargaining outcomes. We test a simple bargaining model of cooperation between states and IOs. We expect that nonegalitarian international organizations, such as the World Bank, secure more gains from bargaining with economically weak than with economically powerful states. For egalitarian international organizations, such as most United Nations (UN) agencies, the state’s economic power should be less important. We test these hypotheses against a novel data set on funding shares for 2,255 projects implemented under the auspices of the Global Environment Facility, from1991 to 2011. The data allow us to directly measure bargaining outcomes. The results highlight the importance of accounting for the interactive effects of international organization and state characteristics

Leveraging private capital for climate mitigation: evidence from the clean development mechanism

To mitigate climate change, states must make significant investments into energy and other sectors. To solve this problem, scholars emphasize the importance of leveraging private capital. If states create institutional mechanisms that promote private investment, they can reduce the fiscal cost of carbon abatement. We examine the ability of different international institutional designs to leverage private capital in the context of the Kyoto Protocol's Clean Development Mechanism (CDM). Empirically, we analyze private capital investment in 3749 climate mitigation projects under the CDM, 2003–2011. Since the CDM allows both bilateral and unilateral implementation, we can compare the two modes of contracting within one context. Our model analyzes equilibrium private investment in climate mitigation. When the cost of mitigation is high, unilateral project implementation in one host country, without foreign collaboration, draws more investment than bilateral contracting, whereby foreign investors participate in the project

Migration and Hedonic Valuation: The Case of Air Quality

Conventional hedonic techniques for estimating the value of local amenities rely on the assumption that households move freely among locations. We show that when moving is costly, the variation in housing prices and wages across locations may no longer reflect the value of differences in local amenities. We develop an alternative discrete-choice approach that models the household location decision directly, and we apply it to the case of air quality in U.S. metro areas in 1990 and 2000. Because air pollution is likely to be correlated with unobservable local characteristics such as economic activity, we instrument for air quality using the contribution of distant sources to local pollution %u2013 excluding emissions from local sources, which are most likely to be correlated with local conditions. Our model yields an estimated elasticity of willingness to pay with respect to air quality of 0.34 to 0.42. These estimates imply that the median household would pay $149 to$185 (in constant 1982-1984 dollars) for a one-unit reduction in average ambient concentrations of particulate matter. These estimates are three times greater than the marginal willingness to pay estimated by a conventional hedonic model using the same data. Our results are robust to a range of covariates, instrumenting strategies, and functional form assumptions. The findings also confirm the importance of instrumenting for local air pollution.

A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles