Using a hedonic property price approach, we estimate the amenity value associated with proximity to habitats, designated areas, domestic gardens and other natural amenities inEngland. There is a long tradition of studies looking at the effect of environmental amenities and disamenities on property prices. But, to our knowledge, this is the first nationwide study of the value of proximity to a large number of natural amenities in England. We analysed 1 million housing transactions over 1996-2008 and considered a large number of environmental characteristics. Results reveal that the effects of many of these environmental variables are highly statistically significant, and are quite large in economic magnitude. Gardens, green space and areas of water within the census ward all attract a considerable positive price premium. There is also a strong positive effect from freshwater and flood plain locations, broadleaved woodland, coniferous woodland and enclosed farmland. Increasing distance to natural amenities such as rivers, National Parks and National Trust sites is unambiguously associated with a fall in house prices. Our preferred regression specifications control for unobserved labour market and other geographical factors using Travel to Work Area fixed effects, and the estimates are fairly insensitive to changes in specification and sample. This provides some reassurance that the hedonic price results provide a useful representation of the values attached to proximity to environmental amenities in England. Overall, we conclude that the house market in England reveals substantial amenity value attached to a number of habitats, designations, private gardens and local environmental amenities.

Abstract:This paper seeks to understand how and why the determinants of economic growth (including spatial spillovers) in Brazil may manifest themselves differently at different spatial scales (municipalities, micro-regions, spatial clusters, and states) between 1991 and 2000. Analysing this issue it sheds light on the geography of the structural process underlying the economic growth at different scales. It means that the definition of each scale level could have a well-defined role in the economic growth process. A complementary approach is related to the Modifiable Areal Unit Problem (MAUP) and Ecological Fallacy Problem. These two measurement problems stem from the fact that there is an aggregation problem which might prevent us from identifying the actual scale at which processes operate. This paper suggests a general framework that allows dealing with multiple spatial scales, spatial autocorrelation and model uncertainty. The analysis reveals that if single regression is estimated at the different scale levels, the results change as scale level changes. However, the robustness test was able to identify variables that are simultaneously significant at different spatial scales: higher education and health capital and better local infra-structure are related to higher economic growth rates. Among other results, this paper identifies that spatial spillovers are operating especially at finer scales. At municipal level, several variables exhibit externality effects across space in Brazil, such as physical capital, education and health capital and local infrastructure. Finally, the study also concludes that Brazil is a country that regions (at all scale levels) converged too slowly over the nineties.

The goal of this paper is to evaluate the results of regional economic growth model estimations at multiple spatial scales using spatial panel data models. The spatial scales examined are minimum comparable areas, microregions, mesoregions and states between 1970 and 2000. Alternative spatial panel data models with fixed effects were systematically estimated across those spatial scales to demonstrate that the estimated coefficients change with the scale level. The results show that the conclusions obtained from growth regressions depend on the choice of spatial scale. First, the values of spatial spillover coefficients vary according to the spatial scale under analysis. In general, such coefficients are statistically significant at the MCA, microregional and mesoregional levels, however, at state level those coefficients are no longer statistically significant, suggesting that spatial spillovers are bounded in space. Moreover, the positive average-years-of-schooling direct effect coefficient increases as more aggregate spatial scales are used. Population density coefficients show that higher populated areas are harmful to economic growth, indicating that congestion effects are operating in all spatial scales, but their magnitudes vary across geographic scales. Finally, the club convergence hypothesis cannot be rejected suggesting that there are differences in the convergence processes between the north and south in Brazil. Furthermore, the paper discusses the potential theoretical reasons for different results found across estimations at different spatial scales. JEL Classification C23 · O18 · R11 MotivationThe goal of this paper was to evaluate the results of regional economic growth estimates at multiple spatial scales using alternative spatio-temporal models recently proposed in the spatial econometrics literature. During the last two decades, an increasing dissemination of spatial econometrics techniques has been observed among regional scientists, economists, and researchers in several fields (Anselin 1988;Lesage 1999;Conley 1999). The vast research of applied spatial econometrics on the interdependencies among spatial units and their effects on, among others, regional economic growth, trade flows, knowledge spillovers, migration, housing prices, tax interactions, and city's growth controls 1 is well known. However, this literature still lacks a better understanding of the potential reasons why models estimated at different geographic scales yield different results in the context of regional economic growth empirics. 2 Resende (2011) engages in an initial discussion on the determinants of Brazil's regional economic growth at a variety of geographic scales using a cross-sectional data set over the 1990s period. Resende (2013) improves this analysis by using standard panel data models across several spatial scales, but the process of economic growth in Brazil is only examined using non-spatial panel data models. This investigation refers back to the modifiable areal unit problem (MAUP), 3 but it s...

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