Suchergebnisse

Numerous attempts to explain the decision making framework of public agencies with multiple goals have been published in recent years. The central problem of the multiple goal decision process of public agencies is the problem of unvalued benefits stemming from projects which the agency could potentially implement. Regardless of the complexity of the problem, the fact remains that choices must be made between projects. As a result, several alternative procedures for analyzing the issue have been suggested. These may be subdivided into three more or less different approaches: (1) estimation of value indexes or weights for the arguments of the objective function, (2) constrained optimization models, (3) political decision procedures. The last alternative provides the setting for the discussion that follows. The object is to suggest procedures for incorporating better information in valued and non-valued benefits into public resource. Agency programs of the United States Forest Service are analyzed in terms of their valued benefits and their impact on regional employment. Given that there are significant dependencies in certain Colorado communities on the timber industry, how important is Forest Service timber-harvesting policy for these industries and communities? This question is the basis for the objectives of this thesis. These objectives may be stated as: 1. to quantify the cost of providing for regional income objectives via timber harvesting and investment, and 2. To determine the distribution of benefits from public timber harvesting-investment programs. Are the benefits accruing to particular local economies, or are they leaking out of the region? A present value model is used to value timber investment alternatives. When present values are negative and it can be argued that the investments were undertaken to provide for timber originating employment objectives. To analyze incidence of benefits, regional ownership of capital resources in lumber manufacturing and harvesting are first identified. The ...

The motivation of this dissertation is multi-fold. Firstly, regional income disparity is widely considered to be a central concern among economists and policy makers. This responds to many facts. Usually, when regional specialisation takes place, only a few regions are able to attract modern industrial activity and high value-added services, causing an increase in regional inequality over the long term, as these activities generally enjoy increasing returns (which, in turn, makes this pattern very difficult to be reversed). Moreover, regional inequality is, all things being equal, highly correlated to inequality among individuals, which is also a very relevant issue for economists and policy makers. Lastly, regional inequality has high political relevance because it may be a source of political instability, which can result in social and economic crisis. Therefore, there is a great deal of scientific literature concerned with the evolution and causes of regional inequality. The interest in regional inequality is shared by the Economic History literature, especially by that based on quantitative methods, which has developed a number of innovative research strategies to analyse the main forces behind the long-term evolution of regional inequality. However, this line of research has mainly focused on high-income industrialised economies, such as the US and some Western European countries, and there is still a significant gap in our knowledge of the long-term trends of regional inequality in low and middle-income economies. This leads to the second motivation of this project. Even though there has been some recent work on long-term regional inequality in middle and low-income economies, this is still a rather understudied field, where new hypotheses and interpretations –different from those developed for the industrialised countries– need to be developed. For instance, in developing countries, industrial location and agglomeration economies may not have had such a central role as drivers of regional income ...