very least some region/area specific control for births and deaths could be added to population change and ideally a control for the effects of changing age cohorts would be added to the model. This would attempt to control for the effects of both growth and decline occurring as a result of births and deaths and growth and decline occurring as a result of changing cohort sizes. Secondly, naturalamenities may be valued differently according to accessibility. Adding distance to the nearest airport could help to establish which areas have naturalamenities with easy accessibility. Thirdly, this analysis does not include any urban amenities. Urban amenities play a big factor in migration where different age groups move toward education, healthcare and employment opportunities. This study would benefit from the inclusion of the access to healthcare as well as some kind of control for economic growth. These additions would help to provide a more accurate prediction of the effects of naturalamenities on population growth.
Department of Agriculture, with the counties deemed as most amenity attractive assigned a 7. The ranking is based on an amenity scale derived from the relationships between population growth during the period of 1970 and1996 and six natural amenity indicators (McGranahan, 1999): (1) average January temperature; (2) average January days of sun; (3) a measure of temperate summers; (4) average July humidity; (5) topographic variation; and (6) water area as a proportion of total county area (including coastal waters). Of the twelve indicators of naturalamenities examined by McGranahan (1999), these six are the only ones related to population growth during the 1970-1996 period. Topographic variation is found to be the most correlated with population growth over the period, followed by temperate summer and low July humidity (McGranahan, 1999). McGranahan et al. (2011) find forest cover to be related to their measure of natural amenity attractiveness. But Rickman and Rickman (2011) report that the counties with the highest natural amenity ranking also have the largest forest cover in the county. 2 Because
Nord and Cromartie (1997) and McGranahan (1999) develop natural amenity maps using the natural amenity index and focusing on climatic characteristics, topography, and water areas. Isserman (2001) includes natural areas, outdoor recreation, broad vistas, and peaceful sunsets, which naturalamenities are viewed in rural America as a source of competitive advantage that can create new economic opportunities. In addition, Marcouiller et al. (2004) use the Gini coefficient to explain income distribution in terms of naturalamenities, whether land-based, river-based, lake-based, warm weather-based, or cold weather-based. Kim et al. (2005) analyze the spatial autocorrelation of naturalamenities and find that the spatial patterns of both human activities and naturalamenities validate the suggested spatial econometric models (Kim et al. 2005). In sum, the amenity characteristics of natural resources are becoming accepted as important growth determinants for regions endowed with such amenities (Deller et al. 2001; English et al. 2000; McGranahan 1999).
The relationship between amenities and population constituted an important stream in amenity literature. Clark and Cosgrove (1991) and McGranahan (1999) presume that population change patterns are affected by climatic amenities. Glaeser et al (2001) found that naturalamenities such as climate and coastal proximity are dominant predictors of population density inside US cities, they notes that high amenity cities have grown faster than low amenity cities. Large differences in American and European cities are strongly caused by differences in consumption amenities; recent empirical results suggest that physical infrastructures, such as cultural institutions, architecture and other historical amenities are key factors that determine the localization choice of people (Rappaport 2008; Albouy 2012).
Recently, there has been an increasing consideration of urban amenities as a determinant of both residential (Smith et al., 1988) and productive locational decisions (Gottlieb, 1994, 1995; Granger & Blomquist, 1999), as well as of aspects associated with urban and regional growth and development. Sivitanidou & Sivitanides (1995) relate amenities to the intracity distribu- tion of R&D activities; Malecki (1984), Markusen et al. (1986) and Angel (1989) identify amenities as a key factor in the at- traction of qualified migrants, particularly scientists; Herzog & Schlottmann (1993) and Blomquist et al. (1988) take amenities as determinants of quality of life and even of the scale of urban centers; Knapp & Graves (1989) analyze the relation between amenities, migration and regional development. Clark & Kahn (1988), in turn, tried to define the social benefits of particularly cultural urban amenities. Vandell & Lane (1989) aim to identify the influence of design and architecture in the dynamism and valorization of urban areas. Glaeser et al. (2001) relate higher rates of urban growth with the presence of high amenities in selected urban regions. Recently, Clark (2004) edited a book with a suggestive title (“The city as an entertainment machine”), in which several authors deal with the topic of entertainment industry taken as the main contemporary urban amenity, and also discuss the limits and possibilities of taking amenities as a drive for development. McGranahan & Wojan (2007) associate regional development to the presence of cultural density (crea- tive class as in Florida, 2002) and naturalamenities. Ahlfeldt & Maennig (2010) found a strong relation between housing prices, quality of life and proximity to cultural amenities, particularly to historic heritage sites.
The intent of this applied research study is to more formally introduce the problem of model specification into the rural growth literature, with specific attention paid to the role of amenities within the Appalachian region of the United States. Rural Appalachia is a particularly inter- esting region for focused analysis for several rea- sons. Rural Appalachia’s traditional dependence on mining, agriculture, and forestry has caused it to lag behind the rest of rural America. Yet rural Appalachia is endowed with tremendous scenic beauty, wildlife, and recreational opportunities. If a new engine of economic growth is the non-con- sumptive use of natural resources, has rural Ap- palachia been able to take advantage of its re- sources? Can the growth patterns affecting, for example, the mountainous west, be applied to rural Appalachia? Is access to naturalamenities sufficient to ensure growth, or are human-built amenities required to take advantage of those naturalamenities and quality of life attributes? Work by Green, Deller, and Marcouiller (2005) suggest that simply having access to naturalamenities is not sufficient to ensure growth. They conclude that some basic economic infrastructure (or built amenities), such as recreational busi- nesses, need to be in place to capture economic activity. Does this same general conclusion apply to rural Appalachia?
Key Words: amenities, migration, spatial econometrics
Naturalamenities such as open spaces, scenic lakes, rivers, beaches, mountain vistas, and mild temper- atures are widely believed to be important factors considered by migrants, as are the types of amenities that are provided only in larger cities—such as Broadway musicals and theatre productions. While previous studies have examined the effects of nat- ural and related amenities on migration (e.g., Knapp and Graves, 1989; Mueser and Graves, 1995), or population change (Deller et al., 2001), the effects on migration decisions of adverse local environ- mental and health conditions have been largely ignored in the literature. 1 Using a laboratory exper- imental setting, Greenwood, McClelland, and Schulze (1997) found that the presence of a nuclear waste facility in Yucca Mountain in Nevada may affect employment-related migration decisions, for example. Our study expands upon this and other previous work on the determinants of (net) migra- tion using U.S. county-level data by systematically including health and environmental risks in migra-
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segregation. In the classical monocentric city model of Alonso (1964), Mills (1967) and Muth (1967), it is assumed that households choose a residential location conditional on a workplace location. Rich households sort themselves in locations close to the central business district, depending on their preferences for house size and commuting costs (see e.g. Glaeser and Kahn, 2003; Glaeser et al., 2008). Wheaton (1977) shows that the income elasticity of housing demand and commuting time costs are of about equal value. Therefore, the monocentric city model fails to explain why income levels are generally increasing in distance to the city centre in the US, while this pattern is the reverse in most European cities (Mills and Lubuele, 1997; Anas et al., 1998; Brueckner et al., 1999). So, other factors than commuting costs, such as agglomeration economies, building attributes and external urban amenities, are likely to play a role in explaining easy-to-observe variation in household income over space.
In a first, attempt was made to represent the official data of Indian Railways and at large concerning Passenger Amenities. Then the economic aspects have been weighed and explored. Subsequently, preferences of commuters and emergence of any trend of shift/pattern, if any have been traced. The established model of SERVQUAL through a Pilot Survey have been undertaken and elaborated with its finding and limitations. A remodelled version of it with variables & factors- as identified by the respondents of the Pilot Survey- have suggested and taken forward with. Lastly, an overview of Final Survey is tabulated and figured.
Wyndham hotels have launched a ‘Wyndham At Home’ retail programme that offers discounts to its frequent stay programme members. This online programme offers for sale not only guest room amenities but the furniture and fittings that guests enjoy and experience when they stay at a Wyndham hotel (Internet Wire, 2008). Non-take away amenities are also ‘sold’ with Higgins (2000) describing hotel staff efforts to engage in personal selling to up-sell personalised spa and massage treatments and to offer guests ‘technology’ and ‘bath’ butler services. Weinstein & Scoviak-Lerner (2002) outline the concept behind a hotel lobby gift shop that merchandises hotel branded items and selected luxury goods to hotel guests and passers-by, and stress the importance of having the available stock displayed on-line and in a catalogue in guest bedrooms. This initiative could be likened to the catalogues containing duty free products and
While lot size and building attributes receive direct payments, subdivision specific amenities, location and neighborhood characteristics receive no direct payments. However, the consumer’s willingness to pay for a house is affected by these attributes. It is to be expected that payments increase with the desired attributes of the subdivision, location and neighborhood, and decrease with undesired ones. On the other hand, it is often the case that the land developers are constrained by some state and county policies. Therefore, we include these sorts of variables in our empirical hedonic analysis. The following section discusses the issues related to the
A QQ-plot constructed from residuals of POLS revealed problems with normality. Also, though White tests indicated no heteroscedasticity, the traditional and more reliable method of visual inspection proved to be more difficult than anticipated. Due primarily to the issues of non- normality, a natural log transformation of the response variable, Lot Value was implicated for additional POLS analysis. Residual plots indicate that the transformation was successful in normalizing the data, while it also facilitates better inferences concerning heteroscedasticity. White test results and residual plots for the 750 meter range are available in the second section of the appendix.
time to the effect that the relationship between urban amenities and urban population growth has weakened (Glaeser and Shapiro, 2003; Glaeser, Gyourko and Saks, 2005). With the above brief discussion of the spatial equilibrium condition in mind, this raises the question where and how the effect of urban amenities shows up for Dutch cities. In theory, the difference between urban wages and urban housing rents thus captures the urban amenity premium. In the Dutch case, this is not a very useful indicator because regional wage differences are rather limited. Again, as with the population growth, this points to a structural difference with other countries like the USA. Wage setting in the Dutch case is highly centralized and the result of bargaining between employers’ and labor unions at the national level. This means that urban wages in for instance cities in the center, like Amsterdam, are only marginally higher than in peripheral cities and municipalities in the North or South.
main contribution of these so-called sorting models to the valuation litera- ture, until now little attention has been paid to the efficiency of the market equilibrium assumed, in terms of the consumption of space. This is surpris- ing, because social interactions as endogenous amenities might alternatively be interpreted as positive external effects. As such, they are likely to result in an oversupply of land in a competitive market. The dominant character- isation of the equilibrium on the land (or housing) market in sorting models is market clearing, given a fixed supply. In this paper, the total amount of land used in the market clearing equilibrium will be compared with the competitive market equilibrium and the allocation by a benevolent social planner maximising social welfare. It is shown that under relatively general conditions and allowing for endogenous amenities, locational sorting models with a fixed supply make strong assumptions regarding the optimal total amount of land used and that in a competitive market this amount is larger than in the case of land use planning. This result suggests that in public policy recommendations, sorting models could benefit from complementing the valuation methodology with the internalisation of external effects for optimising land use.
The first analysis, which is carried out in Chapter 3, explains firm location by an area’s endowment with localised consumption amenities. Berlin internet start-ups are found to be attracted by cultural amenities: An increase in the amenity density by 1% causes an increase in the probability of a young web firm to locate in a block by 1.2%. The positive impact remains significant across a number of robustness checks, testing for alternative explanations such as centrality and for the validity of the applied instrumental variables. Moreover, according to robustness specifications using placebo firms, it is indeed creative firms like agencies (probability increase of 0.84% as a response to a 1% rise in amenity density) and consultancies (with an increased probability of 0.57%) which are attracted by cultural amenities, whilst traditional service industries such as financial advisories (-0.55%) respond negatively. I contribute to an understudied field of research by being the first to explain firm location with highly endogenous urban amenities. It is shown that cultural amenities, a city’s diversity and tolerance play an important role in attracting start-ups. It is therefore not very likely that young innovative firms will (re-)locate at (to) artificially created science parks in the periphery but will stay in more central and amenity-rich areas. Due to data availability this analysis is limited to cross- sectional estimation techniques. Time-variation would allow for estimating the start-up model in differences differentiating out time invariant unobservables and also for investigating the dynamics of the amenity effect. This could also enable us to disentangle potential agglomeration effects from the amenity effects. Moreover, it would be interesting not just to distinguish between service sectors but also between types of amenities. This is left for further research.
Abstract— A key enabler for Electric Vehicles (EVs) is destination charging – allowing users to charge their vehicles while parked at amenities such as supermarkets, gyms, cinemas and shopping centres – leaving their vehicles for periods ranging from 10 minutes to 3 hours. This paper presents a Monte Carlo (MC)-based method for the characterization of likely demand profiles of EV destination charging at these locations based on smartphone users’ anonymised positional data captured in the Google Maps Popular Times feature. Unlike the majority of academic works on the subject, which tend to rely on users’ responses to surveys, these data represent individuals’ actual movements rather than how they might recall or divulge them. Through a smart charging approach proposed in this paper, likely electrical demand profiles for EV destination charging at different amenities are presented. The method is demonstrated by way of two case studies. Firstly, it is applied to a large GB shopping centre to show how the approach can be used to derive suitable specifications for large charging infrastructure to maximise revenue or EV service provision. Secondly, it is applied to a GB supermarket in a residential area to show how the approach can be used to examine network impact for a distribution-connected destination charging facility.
Creating farmland preservation programs involves familiar processes of government: passing enabling legislation, securing funding from general revenues or from a dedicated stream, and allocating funding through a bureaucracy. When these processes are motivated by consideration of an underlying model such as in equations (1), they can offer evidence as to what objectives and amenities produced by farm- land are considered the most important to preserve. Examination of PDR ranking mechanisms can reveal the relative importance of protecting differ- ent parcel characteristics and their associated rural amenities. Although not motivated exclusively by public preferences for amenities, each of the steps that occur in the creation and implementation of farmland protection programs reveals something about which amenities are considered to be the most important to protect. Hence, we examine these pro- grams for evidence as to what rural amenities are most likely to be protected, and how these vary as socioeconomic and geophysical factors vary. 9,10
The paper makes the following contributions. First, it adds a new perspective into the discussion of water allocation eciency in Israel. Contrary to most literature on Israel, the paper supports the continued use of an administrative water allocation mechanism. Second, it suggests a simple method, which can be used to evaluate and improve water allocation decisions also in other countries. Finally, the paper imputes the value of agricultural ameni- ties in Israel, adding to the discussion by Fleischer and Tsur (2009); Kan et al. (2009) on the economic value of agricultural amenities. Thus, the paper relates to two areas of research. It has relevance for natural resource management that aims to improve the ecient use of a scarce resource, and it has implications for political and social policy.