Spatial econometrics

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns.

We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.

According to specialist literature, the current development of agglomerations is by large
urban areas evolving by the expansion and structural transition of larger urban zones,
where “the formerly hierarchic division of settlements with a different size and role is
replaced by horizontal cooperation, linking into a network” (Enyedi 2012, p. 17). The
aim of the present paper is to examine, how equal the domestic large cities and their
agglomeration can be considered by their development and competitiveness, and if there
is an economic basis for them to cooperate horizontally in a network in the present case.
The authors use different methodological approaches to examine the development and
competitiveness of cities and their agglomeration, and spatial autocorrelation
circumstances to model the economic base of outlined cooperation.

The paper is concerned with the theoretical and practical approaches to administrative
regionalisation. The authors attempt to present a new methodology, which on one hand is
theoretical and from the other, enables the design of an optimal spatial system in terms of
the demands of the population. This is presented on through the example of Hungarian
districts (the level between municipalities and counties in the administrative system).

This research is concerned with a statistical method that has recently become widespread
in the international literature; although, it is still limited in Hungarian research. The method
is geographically weighted regression (GWR), which is demonstrated through an example
of its application. GWR is a local model that is founded on the basis of regression,
prominently taking into consideration the geographical distance. Since it does not calculate
the global relations of the whole data, but concentrates on the relationship of the dependent
and independent variables locally within a determined search area, it allows consideration
of the spatially varying processes. Simply, GWR is a developed version of the global
regression model, since, through its use, it is possible to take into account the local features
that are hidden by the global approach.

The research paper emphasizes the importance of export from the aspect of economic
development. In the first part, the theoretical background of the regional development
connected to export-led growth is introduced. As a bridge between the theoretical and
empirical part, the territorial sense of export data and economic spaces is highlighted. In
connection to the research, actual domestic research examining spatial differences and
inequalities in Hungary is presented. The reviewed researches (Dusek–Lukács–Rácz
2014, Nemes Nagy–Tagai 2011, Obádovics 2013, Pénzes 2012) concur on the main
territorial features in Hungary: territorial hegemony of the capital, increasing differences
between the capital and rural areas, emergence of the ‘West–East decline’, and varied
development patterns of micro-regions and settlements. Dusek–Lukács–Rácz (2014)
describe the regional disparities in the country on NUTS-2 level, pointing out that Central
Hungary is the most advanced region of Hungary, followed by Western Transdanubia
and Central Transdanubia, and with significant lag, Southern Transdanubia, Southern
Great Plain, Northern Great Plain and Northern Hungary trail the list. In the primary
research, the aim is to explore the conditions of export-led growth in Hungary, and
through an examination of the spatial distribution of the 500 companies with the best
sales performance (TOP 500 list) an attempt is made to demonstrate the reasons for
regional inequalities in Hungary. The other scope of the research is to demonstrate the
concentration of different sectors in NUTS-3 level, as well as highlighting their export
orientation. Furthermore research examines associations between different measures. The
pool of data used for carrying out the research is derived from HVG (Heti Világgazdaság
– a dominant economic weekly review in Hungary) about the first 500 companies with
the best sales performance in 2012 (the TOP 500 companies) and from the Hungarian
Central Statistical Office. Our research findings emphasize the importance of export for
regional development (export-led growth), and resembles the secondary research outcomes
about the development of NUTS-2 and NUTS-3 regions.

The study examines how the recent economic crisis and the related unfavourable economic
features affect poverty. As economic crisis is usually associated with many economic and
social problems, it tries to determine to what extent it influences poverty. The paper
attempts to prove that economic recession contributes not only to the impoverishment of a
significant section of society, but also increases the depth of poverty significantly. If the
research supports this hypothesis, it is worth examining to what extent one percent
economic growth or economic decline can decrease or increase the rate of the poor and the
depth of poverty. Besides the effect of economic growth on the given area, the paper also
analyses the effect of the economic growth of the neighbouring areas. The initial
hypothesis states that the economic growth of the neighbouring regions can also alleviate
poverty. As for spatial effects, spatial autocorrelation is examined in the average income
level to reveal how the economic growth of the neighbouring areas affects a given region.
The study examines Northern Hungary, one of the most backward regions in Hungary
(based on GDP per capita). Eurostat (2010) reports this region is among the poorest twenty
regions within the European Union (based on GDP per capita PPP, Northern Hungary is
the 259th among the 271 regions of the European Union).

Knowledge of whether, and over what range of output, there are economies or
diseconomies of scale in providing local communal services is an important question from
a theoretical, practical and regional political point of view. The theoretical side of the
question is connected to the primary research regarding the optimal city size. If an optimal
city size can actually be established, then a valid policy argument can be made for fostering
its approximation. However, theoretical considerations are sometimes based on those types
of assumptions, which are often not valid in reality. Therefore, empirical investigations are
essential in this research area.
After a short theoretical overview, the paper initially gives a general outline of previous
controversial empirical evidence on economies of scale regarding community size and the
provision of local communal services. In the second part, the empirical findings are
presented; these concern economies of scale in local communal services based on a large
and detailed database, which consist of almost 300 Hungarian settlements. During the
research, a number of methodological questions have occurred. For example, there are
several solutions for the organisational structure providing local services, from a large
holding to the smaller individual companies. Considering this and some other issues, the
main results suggest that on a community level, there are moderate economies of scale in
water supply, sewage disposal and district heating and a moderate diseconomy of scale in
refuse collection until 5 thousand inhabitants. Above this level there is no connection
between the settlement size and the average cost of services. The results are mainly
consistent with the previous findings: there are economies of scale under a certain
threshold, but after this, unit cost reduction is not feasible. This threshold can be different
in the different types of services.

Spatial lag dependence in a regression model is similar to the inclusion of a serially
autoregressive term for the dependent variable in a time-series context. However, unlike
in the time-series model, the implied covariance structure matrix from the spatial
autoregressive model can have a very counterintuitive and improbable structure. A single
value of spatial autocorrelation parameter can imply a large band of values of pair-wise
correlations among different observations of the dependent variable, when the weight
matrix for the spatial model is specified exogenously. This is illustrated using cigarette
sales data (1963–1992) of 46 US states. It can be seen that that two "close" neighbours can
have very low implied correlations compared to distant neighbours when the weighting
scheme is the first-order contiguity matrix. However, if the weight matrix can capture the
underlying dependence structure of the observations, then this unintuitive behaviour of
implied correlation is corrected to a large extent. From this, the possibility of constructing
the weight matrix (or the overall spatial dependence in the data) that is consistent with the
underlying correlation structure of the dependent variable is explored. The suggested
procedures produced very positive results indicating further research.

Rapid, large-scale U.S. deployment of wind turbines is expected to continue in the coming years. Because some of that deployment is expected to occur in relatively populous areas, concerns have arisen about the impact of turbines on nearby home values. Previous research on the effects of wind turbines on surrounding home values has been limited by small home-sale data samples and insufficient consideration of confounding home-value factors and spatial dependence. This study examines the largest set of turbine-proximal sales data to date: more than 50,000 home sales including 1,198 within 1 mile of a turbine (331 of which were within a half mile). The data span the periods well before announcement of the wind facilities to well after their construction. We use ordinary least squares and spatial-process difference-in-difference hedonic models to estimate the home-value impacts of the wind facilities, controlling for value factors existing prior to the wind facilities’ announcements, the spatial dependence of home values, and value changes over time. A series of robustness models provide greater confidence in the results. We find no statistical evidence that home values near turbines were affected in the turbine post-construction or post-announcement/pre-construction periods.

This paper studies the relationship between migration and trade, with the aim of measuring both direct and indirect network effects. We analyze trade of diferentiated and homogeneous goods using an econometric approach inspired by spatial econometrics, proposing a new way to define country neighbors based on the most  intense links in the migration network. We find that migration significantly affects trade across categories both in direct and in indirect way. The indirect impact highlights a stronger competitive effect of third country migrants for homogeneous goods. We also confirm that the effect of migration channels is higher on differentiated goods.

Agricultural policy and farm lobby groups often stress the role of farm production in sustaining local economies. This paper considers the spatial pattern of upstream and downstream agricultural transactions of farms in North East Scotland and, in particular, the extent to which they take place within the locality of the farm holding. Three alternative definitions of “local” are considered: a distance-based measure; a measure which takes into account the location of the farm in relation to the nearest town; and a measure which takes into account the location of agribusinesses, defining a transaction as local if the farmer buys from (sells to) the nearest available input supplier (output purchaser). The results highlight the importance of allowing for context when explaining farmer purchasing and sales decisions. They also reveal a highly complex pattern of production-related linkages in the region, with many farmers choosing to bypass their most proximate agribusinesses. Certain towns are found to dominate agriculture-related transactions in the region, reflecting the spatial concentration of upstream and downstream agribusinesses. The findings provide new insights into theoretical debates on the role of small towns in the urban system and the changing importance of geographical distance in determining business transactions.