This paper looks at capacity expansion relating to an airport and the derived tourist demand that this facilitates. The context is the airport relocation planned for the tourist destination of Eilat, Israel. The paper addresses three issues. First, using a multi-regional input output model for Israel, we estimate the magnitude of the static inter-sectoral impacts associated with airport construction and operation and their impact on the regional and national economy. Second, we focus on the lag effects in this process as increased tourism demand does not elicit an immediate response on the supply side in terms of new hotel investment. Third, on the demand side, we estimate additional tourism expenditure in non-hotel activities over the period that the market adjusts and beyond.
This paper presents a systematic framework for assessing the costs of sea-level rise (SLR) and extreme flooding at the local level. The method is generic and transferable. It is built on coupling readily available GIS capabilities with quantitative estimates of the effects of natural hazards. This allows for the ex ante monetization of the main costs related to different scenarios of permanent inundation and periodic flooding. This approach can be used by coastal zone planners to generate vital information on land use, capital stock and population at risk for jurisdictions of different sizes. The simple mechanics of the method are presented with respect to two examples: one relates to the two largest coastal cities in Israel (Tel Aviv and Haifa) and the other to the Northern Coastal Strip region containing a variety of small towns and rural communities. The paper concludes with implications for coastal zone planning praxis.
This paper suggests a model of obtaining estimates of capital stock based on the theory of ‘flexible accelerator’. However, this represents a rather ‘indirect’ method independently for each year and each region. Clearly this is an unrealistic condition, especially for regional economies characterized by mutual spatial dependence. To add an extra injection of realism, we illustrate how a national model of capital stock (the stock –flow model) can effectively be ‘regionalized’
We use moments from the covariance matrix for spatial panel data to estimate the parameters of the spatial autoregression model, including the spatial connectivity matrix W. In the unrestricted spatial autoregression model, the parameters are underidentified by one when W is symmetric. We show that a special case exists in which W is asymmetric and its parameters are exactly identified. If the panel data are stationary and ergodic, spatially and temporally, the estimates of W and the spatial autoregression coefficients are consistent. Spatial panel data for house prices in Israel are used to illustrate this methodology.
Spatial impulses are derived for SAR models containing a spatial unit root. Analytical solutions are obtained for lateral space where the number of spatial units tends to infinity. Numerical solutions are obtained for finite regular lattices where edge-effects are shown to influence spatial impulses, and for irregular lattices. Monte Carlo simulation methods are used to compute critical values for spatial unit root tests in SAR models estimated from spatial cross-section data for regular and irregular lattices. We also compute critical SAC values for spatial cointegration tests for cross-section data that happen to be spatially nonstationary. We show that parameter estimates in spatially cointegrated models are ‘superconsistent’.
We present a simple reproducible methodology for constructing regional capital stock data, which we apply to Israel. We find that capital deepening has been sigma-convergent since 1985. This process is “inverted” since capital stocks and capital–labor ratios in the richer center have been catching-up with their counterparts in the poorer periphery. We explain this phenomenon in terms of fundamental changes in regional policy. Despite this, regional wages have not been sigma-convergent because other wage determinants have been sigma-divergent.
Over the past decade, the welfare evaluation of local economic development activities has become increasingly sophisticated. Projected or realized gains have been broken down by wage levels, household income levels, and race. However, relatively little attention has been paid to the distribution of gains by gender. In parallel, the gender literature has recognized the distribution of economic development activity by income group but not by vacancies. The authors present an evaluation approach—the job chains model—that combines the two. Occupations with a high proportion of women are identified and isolated at each wage level. The authors estimate the proportion of job chain vacancies induced by new “female” jobs and their welfare impacts. Findings suggest that women are underrepresented in welfare gains associated with both male and female high-wage jobs. The applicability of the authors’ approach for evaluating alternative industrial targets is demonstrated.
Most models of regional agglomeration are based on the new economic geography (NEG) model in which returns to scale are pecuniary. We investigate the implications for regional agglomeration of a ‘Marshallian’ model in which returns to scale derive from technological externalities. Workers are assumed to have heterogeneous ‘home region’ preferences. The model is designed to explain how ‘second nature’ determines regional wage inequality and the regional distribution of economic activity. We show that agglomeration is not a necessary outcome of Marshallian externalities. However, if centrifugal or positive externalities are sufficiently strong relative to their centripetal or negative counterparts, the model generates multiple agglomerating equilibria. These equilibria multiply if, in addition, there are scale economies in amenities. A dynamic version of the model is developed in which external economies and inter-regional labour mobility grow over time. Regional wage inequality overshoots its long run equilibrium and, there is more agglomeration in the long run.
Many countries evade the formal valuation of real property for taxation purposes by using qualitative and spatial criteria in order to pursue an equitable distribution of burden. This paper evaluates the performance of a prototypical setup as such, by analyzing the relationship between property value, household income and the actual tax paid, in the exact framework of which the qualitative criteria are set to determine tax assessment. Drawing on detailed data from the Israeli Household Expenditure Surveys 1997–2005, the strong correlation between the three variables is evident. Yet, the limited differences in rates, compared with large variation in property value, make it regressive. Policy implications are relevant for many other countries using non-ad valorem taxation.
A strong inter-dependence exists between the decision to develop land and the expected returns to be gained from that development. Current practice in UrbanSim modeling treats developer behavior and the emergence of land prices as independent processes. This assumes that land prices are exogenous to the interaction between buyers and sellers—an assumption that is difficult to sustain in urban economics and real estate research. This paper presents an attempt to model the two processes as occurring simultaneously. Using the UrbanSim model for metropolitan Tel Aviv, we compare the results of forecasts for densities (residential and non-residential) and land values for the period 2001–2020. Our results show that simultaneous estimation tends to produce more accentuated outcomes and volatile trends. The validity of these results and the implications of this approach in the wider context of land use modeling are discussed.
We “spatialize” residual-based panel cointegration tests for nonstationary spatial panel data in terms of a spatial error correction model (SpECM). Local panel cointegration arises when the data are cointegrated within spatial units but not between them. Spatial panel cointegration arises when the data are cointegrated through spatial lags between spatial units but not within them. Global panel cointegration arises when the data are cointegrated both within and between spatial units. Spatial error correction arises when error correction occurs within and between spatial units. We use nonstationary spatial panel data on the housing market in Israel to illustrate the methodology. We show that regional house prices in Israel are globally cointegrated in the long run and there is evidence of spatial error correction in the short run.
The paper looks at the sensitivity of commonly used income inequality measures to changes in the ranking, size and number of regions into which a country is divided. During the analysis, several test distributions of populations and incomes are compared with a ‘reference’ distribution, characterized by an even distribution of population across regional subdivisions. Random permutation tests are also run to determine whether inequality measures commonly used in regional analysis produce meaningful estimates when applied to regions of different population size. The results show that only the population weighted coefficient of variation (Williamson’s index) and population-weighted Gini coefficient may be considered sufficiently reliable inequality measures, when applied to countries with a small number of regions and with varying population sizes.
The paper stresses the importance of accounting for regional heterogeneity in the dynamic analysis of regional economic disparities. Studies of regional growth mainly presume that regions are homogeneous in their socio-demographic composition. It is argued that the analysis of regional convergence needs to be tested conditionally, i.e. conditional upon the socio-demographic structure of the workers in the various regions. To this end, various measures of conditional regional earnings inequality are estimated using Israeli regional data for the period 1991–2002. The results show that about half of regional earnings inequality may be accounted for by the conditioning variables. Conditioning also makes a large difference to estimates of Gini and beta-convergence. Conditional beta and Gini mobility are about half their unconditional counterparts.
The tourism industry is characterized by severe shifts in demand that play havoc with forecasting future investment. Within the tourism industry, the need for large-scale initial capital investment in the hotel sector, make the latter particularly vulnerable to the vagaries of the tourism market. Given an up-turn in demand, the hotel industry cannot always respond immediately and its' response is likely to vary across regions. There is therefore a need for a forecasting tool that can estimate the magnitude of the demand 'push' that can stimulate the hotel sector into new investment and the extent to which this response is regionally differentiated. Using a multi-regional input output (MRIO) augmented by an investment matrix, this paper demonstrates the capabilities of such an approach. Regional hotel industry outputs for four classes of hotels in the six regions of Israel are estimated. Expected regional rates of return to hotel investment are compared with actual (reported) rates of return and the discrepancy between the two explained. Regional hotel (per room) capacity coefficients are also estimated and regional responses to an increase in demand of 100,000 extra tourists are calculated in terms of additional hotel rooms and capital investment.
In theory, new regional jobs yield two distinct sources of welfare gains to workers: (1) mobility gains achieved by workers as they move up job chains and (2) traditional Marshallian surpluses enjoyed by all workers as labor markets tighten. In the past, we have argued that the second channel is likely to be small relative to the first. This paper integrates a chain model (using PSID job change data) with a modified-Marshallian model based on “wage curves” (estimated from CPS data) to formalize and test that argument. High wage jobs with modest wage–unemployment elasticities show Marshallian effects only 10 percent to 20 percent the size of mobility effects. Low wage jobs with somewhat higher elasticities show Marshallian effects from 40 percent to 70 percent the size of mobility effects.
Social scientists have long used ‘chain’ metaphors, yet their methodological justification remains somewhat hazy. This paper offers a rationale for using chains to measure changes in economic welfare in urban and regional contexts. In contrast to the Marshallian surplus, which well describes situations in which price changes generate rents in a single market, chains are especially useful in markets where changes lead to the transmission of demand or supply through a series of markets characterized by sticky prices and markups. This argument is illustrated by reference to chain-driven analyses of local production, labor, and housing markets. The institutional structures that underpin chain models are stressed.
Employment deconcentration has become a major issue on the policy and planning agenda in many metropolitan areas throughout the western world. In recent years, growing evidence indicates that in many developed countries, the deconcentration of employment - particularly of retail centres and offices - has become a key planning issue. This chapter uses the UrbanSim forecasting and simulation model in order to investigate some of the projected changes in land use, land value and sociodemographic characteristics of metropolitan areas undergoing employment deconcentration. The process of model application in the Tel Aviv metropolitan context is described. Two land-use scenarios of very different scales are simulated: a macro-level scenario relating to the imposition of an ‘urban growth boundary’ and a micro-level scenario simulating the effects of a shopping mall construction in different parts of the metropolitan area. The results are discussed in terms of the potential and constraints of microsimulation for analyzing metropolitan growth processes.