nep-geo New Economics Papers
on Economic Geography
Issue of 2014‒08‒16
seven papers chosen by
Andreas Koch
Institut für Angewandte Wirtschaftsforschung

  1. Spatial Aspects of Innovation Activity in the US By Drivas, Kyriakos; Economidou, Claire; Karkalakos, Sotiris
  2. Labor Pooling as a Determinant of Industrial Agglomeration By Najam uz Zehra Gardezi
  3. A Spatial Analysis of Agricultural Land Prices in Bavaria By Feichtinger, Paul; Salhofer, Klaus
  4. A J-Test for Panel Models with Fixed Effects, Spatial and Time By Harry H. Kelejian; Gianfranco Piras
  5. Regional economies - shape, performance and drivers By Eaqub, Shamubeel; Stephenson, John
  6. Localisation of industrial activity across England’s LEPs: 2008 & 2012 By Michael Anyadike-Danes; Karen Bonner; Cord-Christian Drews; Mark Hart
  7. This Mine is Mine! How minerals fuel conflicts in Africa By Nicolas Berman; Mathieu Couttenier; Dominic Rohner; Mathias Thoenig

  1. By: Drivas, Kyriakos; Economidou, Claire; Karkalakos, Sotiris
    Abstract: This paper studies the effects of spatial concentration of innovation activity on local production of patents in the US. In doing so, we augment the standard knowledge production function with a structure that allows for spatial effects, accounting along with bilateral also for multilateral influences across states. Our findings corroborate with past evidence on the important role of state’s own R&D stock and human capital in producing new inventions. In addition, external knowledge, via spatial interactions, is also a purveyor of local innovation production. The effect is stronger when we consider spatial influences from all states, in particular from the most innovative ones, and to a lesser extent from close neighboring states. Finally, spillovers are more likely to occur between states with similar technological specialization, which share common technological knowledge and pour similar technological effort.
    Keywords: patents, innovation, knowledge production, spatial
    JEL: C21 O31 R12
    Date: 2014–08–10
  2. By: Najam uz Zehra Gardezi (Lahore School of Economics, Lahore, Pakistan.)
    Abstract: This paper analyzes the agglomeration behavior exhibited by manufacturing firms in Punjab. Employing a unique dataset, it constructs a distance-based measure of agglomeration to verify the existence of localization economies. The M function—the industry-level measure of concentration—is regressed on a number of industry characteristics that measure the presence of positive externalities. In particular, a measure of each industry’s potential for labor pooling is used to determine whether firms that experience greater fluctuations in employment are likely to be more concentrated. The results provide evidence of the importance of labor pooling in explaining the high level of concentration within industries.
    Date: 2013
  3. By: Feichtinger, Paul; Salhofer, Klaus
    Abstract: This paper empirically analyses a dataset of more than 7,300 agricultural land sales transactions from 2001 and 2007 to identify the factors influencing agricultural land prices in Bavaria. We use a general spatial model, which combines a spatial lag and a spatial error model, and in addition account for endogeneity introduced by the spatially lagged dependent variable as well as other explanatory variables. Our findings confirm the strong influence of agricultural factors such as land productivity, of variables describing the regional land market structure, and of non-agricultural factors such as urban pressure on agricultural land prices. Moreover, the involvement of public authorities as a seller or buyer increases sales prices in Bavaria. We find a significant capitalisation of government support payments into agricultural land, where a decrease of direct payments by 1% would decrease land prices in 2007 and 2001 by 0.27% and 0.06%, respectively. In addition, we confirm strong spatial relationships in our dataset. Neglecting this leads to biased estimates, especially if aggregated data is used. We find that the price of a specific plot increases by 0.24% when sales prices in surrounding areas increase by 1%.
    Keywords: Land Economics/Use,
    Date: 2013–06–17
  4. By: Harry H. Kelejian (Department of Economics, University of Maryland); Gianfranco Piras (Regional Research Institute, West Virginia University)
    Abstract: In this paper we suggest a J-test in a spatial panel framework of a null model against one or more alternatives. The null model we consider has fixed effects, along with spatial and time dependence. The alternatives can have either fixed or random effects. We implement our procedure to test the specifications of a demand for cigarette model. We find that the most appropriate specification is one that contains the average price of cigarettes in neighboring states, as well as the spatial lag of the dependent variable. Along with formal large sample results, we also give small sample Monte Carlo results. Our large samples results are based on the assumption N ? 8 and T is fixed. Our Monte Carlo results suggest that our proposed J-test has good power, and proper size even for small to moderately sized samples.
    Keywords: spatial panel models, fixed effects, time and spatial lags, non-nested j-test
    JEL: C01 C12
    Date: 2013–03
  5. By: Eaqub, Shamubeel (New Zealand Institute of Economic Research); Stephenson, John (New Zealand Institute of Economic Research)
    Abstract: Economic performance is uneven across New Zealand’s regions. This paper highlights the similarities and differences in regional economies, the drivers of past performance, and how that performance is shared in the community (GDP versus household income, for example).
    Keywords: New Zealand; regional eocnomies
    JEL: H70
    Date: 2014–08–05
  6. By: Michael Anyadike-Danes (Aston Business School); Karen Bonner (Aston Business School); Cord-Christian Drews (Aston Business School); Mark Hart (Aston Business School)
    Abstract: BIS commissioned the Enterprise Research Centre (ERC) to use the new Local Enterprise Partnerships (LEPs) as the sub-national spatial frame in England to provide data on industrial clusters. The analysis is designed as an information source for the LEPS as they prepare their new strategic economic plans. We use a very simple Location Quotient (LQ) measure which is designed to show the extent to which a particular activity is over- or under-represented in each LEP relative to the GB national average. We do this for 2008 and 2012 using the local unit or workplace version of the Office of National Statistics Business Structure Database. For the detailed 5-digit standard industrial classification (SIC) we present for each LEP two tables for each year. First, a table of the top 20 sectors by LQ score with details of the number of workplaces and total employment in the sector and the overall GB share of employment in the LEP. Second, a table of the top 20 sectors by jobs. Viewed together they provide an overall summary of the nature and scale of the clusters in each LEP and an indication of their importance in terms of jobs. The calculation of LQs is of course just a first step in the process of cluster identification since there are many other dimensions of a cluster it does not capture (for example the fact that strategically important supply chains extend beyond individual 5-digit SIC boundaries). A large LQ is not sufficient to indicate a policy-relevant cluster since many contribute only very small job numbers in a LEP. A commentary is provided for each of the 39 English LEPs, and although most activities are broadly distributed, there are some industries where particular LEPs have particular concentrations in terms of employment. The identification of these local concentrations of industrial activity is, of course, just a starting point for a much more detailed discussion in order to understand how the analysis can be interpreted and connected to local economic strategies. In particular, there is no simple 'read-through' from a ‘cluster’ identified by a high LQ to a strategic focus.
    Keywords: location quotients, industrial clusters, localisation of industry
    JEL: R11 R12 R58
    Date: 2013–12–01
  7. By: Nicolas Berman; Mathieu Couttenier; Dominic Rohner; Mathias Thoenig
    Abstract: This paper studies empirically the impact of mining on conflicts in Africa. Using novel data, we combine geo-referenced information over the 1997-2010 period on the location and characteristics of violent events and mining extraction of 27 minerals. Working with a grid covering all African countries at a spatial resolution of 0.5 x 0.5 degree, we find a sizeable impact of mining activity on the probability/intensity of conflict at the local level. This is both true for low-level violence (riots, protests), as well as for organized violence (battles). Our main identification strategy exploits exogenous variations in the minerals' world prices; however the results are robust to various alternative strategies, both in the cross-section and panel dimensions. Our estimates suggest that the historical rise in mineral price observed over the period has contributed to up to 21 percent of the average country-level violence in Africa. The second part of the paper investigates whether minerals, by increasing the financialcapacities of fighting groups, contribute to diffuse violence over time and space, therefore affecting the intensity and duration of wars. We find direct evidence that the appropriation of a mining area by a group increases the probability that this group perpetrates future violence elsewhere. This is consistent with "feasibility" theories of conflict. We also find that seccessionist insurgencies are more likely in mining areas, which is in line with recent theories of secessionist conflict.
    Keywords: Minerals, Mines, Conflict, Natural Resources, Rebellion
    JEL: C23 D74 Q34
    Date: 2014

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