Do Governments Tax Agglomeration Rents?

Using the German local business tax as a testing ground, we empirically investigate the impact of firm agglomeration on municipal tax setting behavior. The analysis exploits a rich data source on the population of German firms to construct detailed measures for the communities’ agglomeration characteristics. The findings indicate that urbanization and localization economies exert a positive impact on the jurisdictional tax rate choice which confirms predictions of the theoretical New Economic Geography (NEG) literature. Further analysis suggests a qualification of the NEG argument by showing that a municipality’s potential to tax agglomeration rents depends on its firm and industry agglomeration relative to neighboring communities. To account for potential endogeneity problems, our analysis exploits long-lagged population and infrastructure variables as instruments for the agglomeration measures.agglomeration rents, corporate taxation, regional differentiation

The winner gives it all: Unions, tax competition and offshoring

This paper analyzes competition for capital between welfare-maximizing gov- ernments in a framework with agglomeration tendencies and asymmetric union- ization. We find that a unionized country's government finds it optimal to use tax policy to induce industry to relocate towards a location with a competitive labor market instead of realizing the benefits from higher wage income while exporting part of the wage burden to foreign consumers. Via the tax regime effect, which favors the factor capital, and the efficiency effect, consumers and producers alike benefit from off-shoring industry towards a low-cost country. Our result qualifies first intuition that defending high wage industries is beneficial to a country as part of the associated cost is shifted to foreign consumers.tax competition, trade unions, agglomeration

Cusp Ion Structures and Their Relation to Magnetopause Processes

Dispersed ion structures observed near the magnetosphere cusps have long been used to infer locations and properties of reconnection at the Earth\u27s magnetopause. However, observations are often difficult to interpret since spacecraft move relative to a cusp ion structure, creating temporal/spatial ambiguity in the observations. Models of cusp ion structures are also limited to the cases during stable solar wind and IMF because empirical models are used to obtain the Earth\u27s electromagnetic fields. In this dissertation, we develop a dynamic model of cusp ion structures usable for non-steady solar wind and IMF cases by using the Liouville Theorem Particle Tracer (LTPT) with the OpenGGCM 3D global MHD model. We first test our model\u27s validity by reconstructing cusp ion structures observed from Cluster and Polar satellites. Our model faithfully reproduces various observed cusp ion structures, such as normal dispersion, reverse dispersion, double dispersions, and stepped dispersion. We also demonstrate our model\u27s ability to investigate magnetopause processes that relate to the cusp structures. By analyzing the precipitating pattern of cusp ions and the magnetopause movement, we find that sudden increase of solar wind pressure, non-steady reconnection rates, and change of IMF clock angle cause the various dispersions in the Cluster and Polar observations. After the model validation test, we study the general relation between cusp ion structures and magnetopause processes during four different IMF clock angles of 0°, 60°, 120°, and 180°. Our model produces a reverse dispersion, double reverse dispersions, a flat and dispersed structure, and a normal dispersion under each IMF condition, respectively. From the detailed study of the ion entry points and the reconnection patterns on the magnetopause, we find that lobe reconnection, recurring FTE formation, coexistence of component and anti-parallel reconnection, and subsolar reconnection cause each cusp structure. We also find that cusp ions during northward IMF originate from an anti-parallel reconnection zone whose shear angle is over 170°. Conversely, during southward IMF ions precipitate not only from a high shear angle zone but also from a very low shear angle zone

Approach to the cellular automaton interpretation using deep learning

Recently, there has been significant research on the connection between physics theory and machine learning. As a way to approach physics theory from machine learning, there has been a study on the universe that learns its own laws based on the fact that quantum field theory and learning system are expressed as a matrix model in much the same way. In the opposite position, certain familiar symmetries have been required for conventional convolutional neural networks (CNNs) for performance improvement, and as a result, CNNs have come to be expressed in a covariant form that physics theory must satisfy. These positive signals can be a driving force for studying physics theory using machine learning, but in reality, there are several difficulties in implementing a working system. First of all, just because the convolution can be expressed in covariant form, it is not obvious to implement the algorithm corresponding to that expression. At the beginning of this paper, we show that it is possible to reach covariant CNNs through the proposed method without implementing the specific algorithm. However, the more serious problem is that there is still insufficient discussion on how to collect a well-defined data set corresponding to the law to be learned. Therefore, in the current situation, it would be best to simplify the problem to satisfy some physical requirements and then see if it is possible to learn with the corresponding neural-networks architecture. In this point of view, we demonstrate to learning process of cellular automata (CA) that could satisfy locality, time-reversibility through CNNs. With simple rules that satisfy the above two conditions and an arbitrary dataset that satisfies those rules, CNNs architecture that can learn rules were proposed and it was confirmed that accurate inference, that is, an approximation of the equation was made for simple examples.Comment: 12 pages, 7 figure

The impact of U.S. foreign aid on human rights conditions in post-Cold War era

During the Cold War, U.S. foreign aid was mainly used to fight against the potential Soviet military threat and to support allies. Containing Communism was the non-negotiable goal in U.S. foreign policy. With the end of the Cold War and the rising force of globalization, aid-providing developed countries in the West, including the United States, emphasized political conditionality attached to aid in order to encourage political reforms, such as democratic political process and securing human rights, in aid-recipient developing countries. This study uses pooled cross-sectional time series data covering 112 countries for the post-Cold War years of 1990-2009 to examine the effects of U.S. foreign aid allocation on human rights, especially physical integrity rights. The findings suggest that U.S. foreign aid [economic, military, and total aid] did have an impact on a government\u27s respect for human rights in recipient countries, but that the association was negative: an increase in foreign aid from the United States is associated with less protection of human rights. Even though the good will of the chief administrators to promote human rights was explicit, implementations to achieve such a goal through foreign aid seem to fall far short of their promises

Identifying the Distribution of Treatment Effects under Support Restrictions

The distribution of treatment effects (DTE) is often of interest in the context of welfare policy evaluation. In this paper, I consider partial identification of the DTE under known marginal distributions and support restrictions on the potential outcomes. Examples of such support restrictions include monotone treatment response, concave treatment response, convex treatment response, and the Roy model of self-selection. To establish informative bounds on the DTE, I formulate the problem as an optimal transportation linear program and develop a new dual representation to characterize the identification region with respect to the known marginal distributions. I use this result to derive informative bounds for concrete economic examples. I also propose an estimation procedure and illustrate the usefulness of my approach in the context of an empirical analysis of the effects of smoking on infant birth weight. The empirical results show that monotone treatment response has a substantial identifying power for the DTE when the marginal distributions of the potential outcomes are given