This article gives a brief introduction to \LaTeX\ and related tools. The aim is to give an overview, to demonstrate the flexibility and versatility of the software, and to assist the reader taking first steps using it. The article links to a number of valuable resources for further information.
This primer provides a practical guide to get started with spatial interaction modeling using the SpInt module in the python spatial analysis library (PySAL).
I have given this paper the title ‘Regional Science in a time of uncertainty’ because that is what it appears to be, very much a time of uncertainty. Uncertainty is always with us, but what I mean is the future is more uncertain than usual. There are multiple unanticipated and threatening shocks to our economic, social and environmental systems. For example, global climate change appears to be upon us right now, so what is the future especially for the world’s poorest people, living at the margins of existence? The after-effects of the 2007 shock to the global economy are still very much with us. In an era of very low demand, Central Banks are running out of policy options. We are moving into an era of experimental and unconventional fixes, such as negative interest rates. But these could have dangerous, unanticipated consequences. Also the upheavals in the Middle East are now being manifest as unforeseen mass migrations. Closer to home, the vote to exit the UK from the EU was based on a referendum dominated by claims and counterclaims about the effects of Brexit. Now the UK has voted to leave, the true consequences remain uncertain.
We move beyond the nation-state as the unit of analysis and use subnational spatial variation to study the effect of the institutional environment on international trade. Additionally, we address the heterogeneous effect of trade agreements on different regions within a country. Employing a gravity model approach, we use a region-to-country dataset to estimate the determinants of Spanish regional exports and we apply quantile regressions for panel data. We find that better institutional quality of trade agreements leads to an increase in both the intensive and the extensive margins of trade. The institutional quality of trade agreements exerts a differential effect on regional exports at different locations within a country, although differences across Spanish regions seem to be larger for the intensive margin than for the extensive margin. We do, however, find a common trend: for the relatively more important exporting regions, the institutional quality of TAs is less relevant for trade margins. Therefore, our results posit that subnational spatial variation should be added to the analysis of the determinants of international trade flows.
Pride in one’s city is an individual, and collective as well as institutional response to urban conditions which may be harnessed in support of expanding urban facilities and services. Pride is likely to be felt most keenly by those who have a stake in the city and for this reason anecdotal reporting of urban pride in the media is subject to likely bias in favour of vested interests. In practice however we know very little about urban pride. The vast literature on urbanism does not appear to have identified any role for urban pride let alone indicating which cities gather pride or who among its inhabitants exhibit such prideThis paper applies a multi-level statistical model to large random sample of residents in twelve New Zealand cities. From the results we learn that, although financial stake holding is relevant, urban pride is concentrated more broadly among those whose social and cultural identity is closely tied to the city. Where financial stake holding is most influential is when it is absent, for those experiencing financial difficulties are the most likely to disavow urban pride. Urban pride is a therefore a distributional property of cities in which the currencies are emotional and cultural as well as financial. Urban pride is relatively absent among those who fail to have a stake in the city as well as being weaker among those who live in relatively unattractive cities, and less attractive neighbourhoods. As a barometer of rewards to living and investing in the city, urban pride certainly warrants closer attention than it has received to date.
We propose an innovative methodology to measure inequality between cities. If an even distribution of amenities across cities is assumed to increase the average well-being in a given country, inequality between cities can be evaluated through a multidimensional index of the Atkinson (1970) type. This index is shown to be decomposabe into the sum of inequality indices computed on the marginal distributions of the amenities across cities, plus a residual term accounting for their correlation. We apply this methodology to assess inequality between Italian cities in terms of the distribution of public infrastructures, local services, economic and environmental conditions.
The literature on life satisfaction in transition countries, and in particular on Romania, demonstrated that life satisfaction significantly differs across rural communities and cities of different size. The question addressed in this paper is whether these imbalances are stable over time or, instead, they become manifest in the presence of strong divergences in the economic growth rates of different kinds of communities. Results point out that in the period of sharp economic growth led by large urban areas, as the one experienced by Romania on the road to EU accession, rural/urban disparities in life satisfaction widened, favoring cities of intermediate size.
The paper describes an empirical analysis to understand the main drivers of economic growth in the European Union (EU) regions in the past decade. The analysis maintains the traditional factors of growth used in the literature on regional growth - stage of development, population agglomeration,transport infrastructure, human capital, labour market and research and innovation - and incorporates the institutional quality and two variables which aim to reflect the macroeconomic conditions in which the regions operate. Given the scarcity of reliable and comparable regional data at the EU level, large part of the analysis has been devoted to build reliable and consistent panel data on potential factors of growth. Two non-parametric, decision-tree techniques, randomized Classication and Regression Tree and Multivariate Adaptive Regression Splines, are employed for their ability to address data complexities such as non-linearities and interaction eects, which are generally a challenge for more traditional statistical procedures such as linear regression. Results show that the dependence of growth rates on the factors included in the analysis is clearly non-linear with important factor interactions. This means that growth is determined by the simultaneous presence of multiple stimulus factors rather than the presence of a single area of excellence. Results also conrm the critical importance of the macroeconomic framework together with human capital as major drivers of economic growth of countries and regions. This is overall in line with most of the economic literature, which has persistently underlined the major role of these factors on economic growth but with the novelty that the macroeconomic conditions are here incorporated. Human capital also has an important role, with low-skilled workforce having a higher detrimental eect on growth than high-skilled. Not surprisingly, other important factors are the quality of governance and, in line with the neoclassical growth theory, the stage of development, with less developed economies growing at a faster pace than the others. The evidence given by the model about the impact of other factors on economic growth such as those on the quality of infrastructure or the level of innovation seems to be more limited and inconclusive. The analysis conclusions support the reinforcement of the EU economic governance and the conditionality mechanisms set in the new architecture of the EU regional funds 2014-2020 whose rationale is that the eectiveness of the expenditure is conditional to good institutional quality and sound economic policies.
Regional development has been in the centre of interest among both academics but also decision makers in the central and local governments of many European countries. Identifying the key problems that regions face and considering how these findings could be effectively used as a basis for planning their development process are essential in order to improve the conditions in the European Union regions. For a long period of time a country’s or a region’s development has been synonymous with its economic growth. Over the last years, however, economies and societies have been undergoing dramatic changes. These changes have led to the concept of sustainable development, which refers to the ability of our societies to meet the needs of the present without sacrificing the ability of future generations to meet their own needs. Measuring sustainable development means going beyond a purely economic description of human activities; requires integration of economic, social and environmental concerns. New techniques are required in order to benchmark performance, highlight leaders and laggards on various aspects of development and facilitate efforts to identify best practices. Furthermore, new tools have to be designed so as to make sustainability decision-making more objective, systematic and rigorous. The growth or decline of a country or region depends on its power to pull and retain both business and the right blend of people to run them. Working in this context, we have so far defined a variable which is called the Image of a region and quantifies this pulling power. The region’s Image is a function of a multitude of factors physical, economic, social and environmental, some common for all potential movers and some specific for particular groups of them and expresses its present state of development and future prospects. The paper examines a number of south European countries and focuses on their NUTS 2 level regions. Its objective is to:Estimate the Basic Image values of those regions.Group those regions into different clusters on the basis of the values of the various factors used to define their respective Basic Images.Present and discuss the results.