nep-geo New Economics Papers
on Economic Geography
Issue of 2013‒09‒26
seven papers chosen by
Andreas Koch
Institute for Applied Economic Research

  1. Does economic globalization affect regional inequality? A cross-country analysis By Ezcurra, Roberto; Rodríguez-Pose, Andrés
  2. Trade openness and spatial inequality in emerging countries By Ezcurra, Roberto; Rodríguez-Pose, Andrés
  3. The Location of the UK Cotton Textiles Industry in 1838: a Quantitative Analysis By Nicholas Crafts; Nikolaus Wolf
  4. Geography, Transparency and Institutions By Mayshar, Joram; Moav, Omer; Neeman, Zvika
  5. European migration, national origin and long-term economic development in the US By Rodríguez-Pose, Andrés; von Berlepsch, Viola
  6. The Causal Impact of Common Native Language on International Trade: Evidence from a Spatial Regression Discontinuity Design By Egger, Peter; Lassmann, Andrea
  7. Estimating spatial panel models using unbalanced panels By Gordon Hughes

  1. By: Ezcurra, Roberto; Rodríguez-Pose, Andrés
    Abstract: This paper investigates the relationship between economic globalization and regional inequality in a panel of 47 countries over the period 1990-2007, using a measure of globalization that distinguishes the different dimensions of economic integration. The results show that there is a positive and statistically significant association between economic globalization and the magnitude of regional disparities. Countries with a greater degree of economic integration with the rest of the world tend to register higher levels of regional inequality. This finding is robust to the inclusion of additional explanatory variables and to the choice of the specific measure used to quantify the relevance of spatial inequality within the sample countries. Our analysis also reveals that the spatial impact of economic globalization is greater in low- and middle-income countries, whose levels of regional disparities are on average significantly higher than in high-income countries.
    Keywords: Economic globalization; Regional inequality
    JEL: F15 R11 R12
    Date: 2013–07
  2. By: Ezcurra, Roberto; Rodríguez-Pose, Andrés
    Abstract: Emerging world countries have experienced over the last two decades a significant change in their trade patterns. Bold trade reforms have been followed by rapid rises in international trade levels. However, despite these radical changes, we know remarkably little about how changes in trade patterns are affecting the evolution of regional inequality in the developing world. This paper addresses the link between trade openness and spatial inequality across 22 emerging countries over the period between 1990 and 2006. Our findings show that changes in international trade bring about a significant rise in within country inequality across the developing world and that this impact is greatest in the poorest countries. This result is robust to the inclusion of a number of control variables, and to changes in the specification of the sample and in the measure used to quantify the level of regional disparities. Consequently, the increase in trade exposure across the emerging world, while possibly benefiting the countries involved in the process in aggregate terms, is generating winning and losing regions.
    Keywords: convergence/divergence; developing world; economic growth; emerging countries; spatial inequality; trade
    JEL: F14 F43 O18 R11
    Date: 2013–04
  3. By: Nicholas Crafts (University of Warwick); Nikolaus Wolf (Humboldt University Berlin)
    Abstract: We examine the geography of cotton textiles in Britain in 1838 to test claims about why the industry came to be so heavily concentrated in Lancashire. Our analysis considers both first and second nature aspects of geography including the availability of water power, humidity, coal prices, market access and sunk costs. We show that some of these characteristics have substantial explanatory power. Moreover, we exploit the change from water to steam power to show that the persistent effect of first nature characteristics on industry location can be explained by a combination of sunk costs and agglomeration effects.
    Keywords: agglomeration; cotton textiles; geography; industry location
    JEL: N63 N93 R12
    Date: 2013–09
  4. By: Mayshar, Joram; Moav, Omer; Neeman, Zvika
    Abstract: We propose a theory by which geographic variations in the transparency of the production process explain cross-regional differences in the scale of the state, in its hierarchical structure, and in property rights over land. The key linkage between geography and these institutions, we posit, is via the effect of transparency on the state's extractive capacity. We apply our theory to explain institutional differences between ancient Egypt and ancient Upper and Lower Mesopotamia. We also discuss the relevance of our theory to analyses of the deep rooted factors affecting economic development and the growth of taxation in the modern age.
    Keywords: Geography; Institutions; Land Tenure; State Capacity; State Concentration; Transparency
    JEL: D02 D82 H10 O43
    Date: 2013–09
  5. By: Rodríguez-Pose, Andrés; von Berlepsch, Viola
    Abstract: Have Irish, German or Italian settlers arriving in the US at the turn of the 20th century left an institutional trace which determines economic development differences to this day? Does the national origin of migrants matter for long-term development? This paper explores whether the distinct geographical settlement patterns of European migrants according to national origin affected economic development across US counties. It uses micro-data from the 1880 and 1910 censuses in order to identify where migrants from different nationalities settled and then regresses these patterns on current levels of economic development, using both OLS and instrumental variable approaches. The analysis controls for a number of factors which would have determined both the attractiveness of different US counties at the time of migration, as well as current levels of development. The results indicate that while there is a strong and positive impact associated with overall migration, the national origin of migrants does not make a difference for the current levels of economic development of US counties.
    Keywords: Counties; Economic Development; Ethnic/National Origin; Institutions; Migration; USA
    JEL: F22 N91 O15 R23
    Date: 2013–04
  6. By: Egger, Peter; Lassmann, Andrea
    Abstract: This paper studies the causal effect of sharing a common native language on international trade. Switzerland is a multilingual country that hosts four official language groups of which three are major (French, German, and Italian). These groups of native language speakers are geographically separated, with the corresponding regions bordering countries which share a majority of speakers of the same native language. All of the three main languages are understood and spoken by most Swiss citizens, especially the ones residing close to internal language borders in Switzerland. This unique setting allows for an assessment of the impact of common native (rather than spoken) language as a cultural aspect of language on trade from within country-pairs. We do so by exploiting the discontinuity in various international bilateral trade outcomes based on Swiss transaction-level data at historical language borders within Switzerland. The effect on various margins of imports is positive and significant. The results suggest that, on average, common native language between regions biases the regional structure of the value of international imports towards them by 18 percentage points and that of the number of import transactions by 20 percentage points. In addition, regions import 102 additional products from a neighboring country sharing a common native language compared to a different native language exporter. This effect is considerably lower than the overall estimate (using aggregate bilateral trade and no regression discontinuity design) of common official language on Swiss international imports in the same sample. The latter subsumes both the effect of common spoken language as a communication factor and of confounding economic and institutional factors and is quantitatively well in line with the common official (spoken or native) language coefficient in many gravity model estimates of international trade.
    Keywords: Common language; Culture; International trade; Quasi-randomized experiments; Regression discontinuity design
    JEL: C14 C21 F14 R12 Z10
    Date: 2013–04
  7. By: Gordon Hughes (University of Edinburgh)
    Abstract: Econometricians have begun to devote more attention to spatial interactions when carrying out applied econometric studies. In part, this is motivated by an explicit focus on spatial interactions in policy formulation or market behavior, but it may also reflect concern about the role of omitted variables that are or may be spatially correlated. The Stata user-written procedure xsmle has been designed to estimate a wide range of spatial panel models, including spatial autocorrelation, spatial Durbin, and spatial error models using maximum likelihood methods. It relies upon the availability of balanced panel data with no missing observations. This requirement is stringent, but it arises from the fact that in principle, the values of the dependent variable for any panel unit may depend upon the values of the dependent and independent variables for all the other panel units. Thus even a single missing data point may require that all data for a time period, panel unit, or variable be discarded. The presence of missing data is an endemic problem for many types of applied work, often because of the creation or disappearance of panel units. At the macro level, the number and composition of countries in Europe or local government units in the United Kingdom has changed substantially over the last three decades. In longitudinal household surveys, new households are created and old ones disappear all the time. Restricting the analysis to a subset of panel units that have remained stable over time is a form of sample selection whose consequences are uncertain and that may have statistical implications that merit additional investigation. The simplest mechanisms by which missing data may arise underpin the missing-at-random (MAR) assumption. When this is appropriate, it is possible to use two approaches to estimation with missing data. The first is either simple or, preferably, multiple imputation, which involves the replacement of missing data by stochastic imputed values. The Stata procedure mi can be combined with xsmle to implement a variety of estimates that rely upon multiple imputation. While the combination of procedures is relatively simple to estimate, practical experience suggests that the results can be quite sensitive to the specification that is adopted for the imputation phase of the analysis. Hence, this is not a one-size-fits-all method of dealing with unbalanced panels, because the analyst must give serious consideration to the way in which imputed values are generated. The second approach has been developed by Pfaffermayr. It relies upon the spatial interactions in the model, which means that the influence of the missing observations can be inferred from the values taken by nonmissing observations. In effect, the missing observations are treated as latent variables whose distribution can be derived from the values of the nonmissing data. This leads to a likelihood function that can be partitioned between missing and nonmissing data and thus used to estimate the coefficients of the full model. The merit of the approach is that it takes explicit account of the spatial structure of the model. However, the procedure becomes computationally demanding if the proportion of missing observations is too large and, as one would expect, the information provided by the spatial interactions is not sufficient to generate well-defined estimates of the structural coefficients. The missing-at-random assumption is crucial for both of these approaches, but it is not reasonable to rely upon it when dealing with the birth or death of distinct panel units. A third approach, which is based on methods used in the literature on statistical signal processing, relies upon reducing the spatial interactions to immediate neighbors. Intuitively, the basic unit for the analysis becomes a block consisting of a central unit (the dependent variable) and its neighbors (the spatial interactions). Because spatial interactions are restricted to within-block effects, the population of blocks can vary over time and standard nonspatial panel methods can be applied. The presentation will describe and compare the three approaches to estimating spatial panel models as implemented in Stata as extensions to xsmle. It will be illustrated by analyses of i) state data on electricity consumption in the U.S. and ii) gridded historical data on temperature and precipitation to identify the effects of El Niño (ENSO) and other major weather oscillations.
    Date: 2013–09–16

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