London School of Economics and Political Science

About: London School of Economics and Political Science is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Politics & Population. The organization has 8759 authors who have published 35017 publications receiving 1436302 citations.

Spatial Inequality and Development

Abstract: 1. Introduction 2. Regional Output Differences in International Perspective 3. Are Neighbors Equal? Estimating Local Inequality in Three Developing Countries 4. Market Size, Linkages and Productivity: A Study of Japanese Regions 5. Externalities in Rural Development: Evidence for China 6. Opening the Convergence Black Box: Measurement Problems and Demographic Aspects 7. Adverse Geography and Differences in Welfare in Peru 8. How Responsive is Poverty to Growth? A Regional Analysis of Poverty, Inequality and Growth in Indonesia, 1984-1999 9. Reforms, Remoteness and Risk in Africa: Understanding Inequality and Poverty During the 1990s 10. Economic Polarization Through Trade: Trade Liberalization and Regional Growth in Mexico 11. International Trade, Location and Wage inequality in China 12. Spatial Inequality for Manufacturing Wages in Five African Countries 13. Regional Poverty and Income Inequality in Central and Eastern Europe: Evidence from the Luxembourg Income Study 14. Quo Vadis: Inequality and Poverty Dynamics Across Russian Regions

Weak and Strong Sustainability in the SEEA: Concepts and Measurement

Abstract: In this paper, we explain how the latest international handbook on environmental accounting, the System of Integrated Environmental and Economic Accounting or SEEA (United Nations, European Commission, International Monetary Fund, Organisation for Economic Co-operation and Development and World Bank (2003). Handbook of National Accounting, Studies in Methods. New York, United Nations.), can be used to measure weak and strong sustainability. We emphasise the importance of understanding the conceptual differences between weak and strong sustainability. We then outline what we consider to be current best practice in measurement, all the time flagging the relationship between our discussion and that of the SEEA-2003. This is an important task in our view, because, despite covering a very wide range of relevant conceptual and empirical issues, the handbook is by design not meant to provide clear guidelines for the purpose of measuring sustainability in either its weak or strong version.

Estimating Missing Observations in Economic Time Series

Abstract: Two related problems are considered. The first concerns the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation. The second concerns the estimation of the missing observations. Both problems can be solved by setting up the model in state space form and applying the Kalman filter.

Sales Taxes and Prices: an Empirical Analysis

Abstract: One of the most fundamental questions in public finance is who bears the burden of taxes -- the incidence of taxation' Our understanding of incidence from an empirical standpoint is quite meager Indeed, there seems to be little evidence even in the case that is theoretically the easiest -- partial equilibrium commodity taxes Are taxes levied on commodities completely shifted into their prices, or does the incidence also fall on firms? How long does the shifting process take? In this paper we employ a unique data source to examine the incidence of sales taxes The main idea is to take information on the prices of specific commodities in different US cities and to examine the extent to which differences in tax rates and bases are reflected in prices, controlling for other factors (such as costs) We find a surprising variety of shifting patterns For some commodities, the after-tax price increases by exactly the amount of the tax, a result consistent with the standard competitive model However, taxes on other commodities are overshifted -- an increase in tax revenue of one dollar per unit increases the price by more than one dollar

The Effects of Seat Belt Legislation on British Road Casualties: A Case Study in Structural Time Series Modelling

Abstract: Monthly data on road casualties in Great Britain are analyzed in order to assess the effect on casualty rates of the seat belt law introduced on January 31, 1983. Such analysis is known technically as intervention analysis. The form of intervention analysis that is used in this paper is based upon structural time series modelling and differs in significant respects from standard intervention analysis based upon ARIMA modelling. The relative merits of the two approaches are compared. Structural modelling intervention techniques are used to estimate the changes in casualty rates for various categories of road users following the introduction of the seat belt law. We first note the high rate of compliance with the seat belt law. By February 1983, the wearing rate had jumped to 90 percent and the rate has remained at approximately 95 percent from March 1983 onwards. There can be no doubt of the success of the law as regards to compliance. In considering the casualty figures we distinguish between those directly affected by the law, namely car drivers and front seat passengers, and those not directly affected by the law, that is car rear seat passengers, pedestrians and cyclists. Taking first numbers killed and seriously injured (KSI), we found a reduction of 23 percent for car drivers and 30 percent for front seat passengers. Thus, for those directly affected by the law, there have been substantial reductions. For rear seat passengers KSI we found a rise of 3 percent, for pedestrians a fall of one-half percent, and for cyclists an increase of 5 percent -- all three of these of these values being statistically insignificant. We conclude that there is no significant evidence of a change in numbers of KSI of those not directly affected. For numbers killed, we found for those directly affected a reduction of 18 percent for car drivers and 25 percent for front seat passengers. However, for those indirectly affected by the law, our model gave an increase of 27 percent for rear seat passengers, 8 percent for pedestrians, and 13 percent for cyclists. The value for rear seat passengers is highly significant and the other two values are on the borderline of significance. We conclude that there was an increase in fatalities of those not directly affected. We are unable to provide a completely satisfactory explanation of the difference between the figures for KSI and killed for rear seat passengers, pedestrians and cyclists. The article contains 13 pages of comments by discussants and 3 pages of authors' response. Language: en