Spatial Lag Term Research Articles

Effects of construction activities on residential electricity consumption: Evidence from Singapore's public housing estates

This study aims to empirically test the effects of negative environmental externalities (i.e. noise pollution) due to construction activities within half to one kilometer (km) radius and how households react to such externalities by increasing the use of air-conditioners to mitigate noise from the construction work. We use a unique dataset of electricity consumption by public housing residents in Singapore measured at the building level and merge it with the dataset of construction sites for the periods from 2009 to 2011. Using a difference-in-differences approach, we find that electricity consumption by the households living close to the construction sites increases by 6% compared to the households who are not affected by noises from construction sites during the construction periods, after controlling for building and month of the year fixed effects. The results remain robust after controlling for spatial autocorrelated lag and error terms. The economic cost of the construction externalities for each household amounts to approximately S$98 per annum. We also find that the increases in electricity consumption of the affected households were persistent, and the electricity consumption of the affected households did not revert to the pre-construction levels, after the removal of the negative externality.

Random Effects, Fixed Effects and Hausman's Test for the Generalized Mixed Regressive Spatial Autoregressive Panel Data Model

This article suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive panel data model. This extends the generalized spatial panel model of Baltagi et al. (2013) by the inclusion of a spatial lag term. The estimation method utilizes the Generalized Moments method suggested by Kapoor et al. (2007) for a spatial autoregressive panel data model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the performance of these estimators as well as the corresponding Hausman test.

Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead–zinc smelter

High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world's fourth largest lead-zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12-15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6-14.7 μg/dl) and urine cadmium (0.18-1.14 μg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28-0.93 μg/g creatinine) and uranium (0.07-0.13 μg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods.

Effects of Construction Activities on Residential Electricity Consumption: Evidence from Singapore's Public Housing Estates

This study aims to empirically test the effects of negative environmental externalities (i.e. noise pollution) due to construction activities within half to one kilometer (km) radius and how households react to such externalities by increasing the use of air-conditioners to mitigate noise from the construction work. We use a unique dataset of electricity consumption by public housing residents in Singapore measured at the building level and merge it with the dataset of construction sites for the periods from 2009 to 2011. Using a difference-in-differences approach, we find that electricity consumption by the households living close to the construction sites increases by 6% compared to the households who are not affected by noises from construction sites during the construction periods, after controlling for building and month of the year fixed effects. The results remain robust after controlling for spatial autocorrelated lag and error terms. The economic cost of the construction externalities for each household amounts to approximately S$98 per annum. We also find that the increases in electricity consumption of the affected households were persistent, and the electricity consumption of the affected households did not revert to the pre-construction levels, after the removal of the negative externality.

Growth spillover through trade: A spatial dynamic panel data approach

This paper empirically examines the international spillover of economic growth through bilateral trade. We extend the Solow growth model with a spatial autoregressive term and a spatial time lag term, and estimate such a model with a sample of 26 OECD countries over the period 1971–2005. We find that there is a positive spillover effect of growth from one country to its trade partners. The implied rate of convergence is higher after including the spatial terms.

Spatial Dependence of Technology Spillovers among Trading Partners

Previous literature suggests that trade contributes to knowledge and technology spillovers among trading partners. Using panel data and country-specific fixed effects, we show that the technology of a country is explained by existing technology of its major trading partners. We build an endogenous growth model for OECD countries for the 1960–2010 period; we draw the residuals to measure the Total Factor Productivity (TFP) of each country. Then, using spatial econometrics, we regress the TFP of each country on previous TFP of its major trading partners. In addition, we run a Random Coefficient Model, to let this relationship vary randomly by country. Finally, we run the endogenous growth model again, but now it includes the spatial lag term as an explanatory variable.

Structural Breaks and Spatial Linkages in FDI: Further Evidence in OECD Countries

This paper evaluates the knowledge–capital model (KCM) of foreign direct investment (FDI) as defined by Braconier et al. (Rev Int Econ 13(4):770-786, 2005). The model predicts that there are both horizontal and vertical motivations for engaging in FDI. The horizontal motives regard access to new markets in the case of trade frictions, whereas the vertical motivations imply that multinational enterprises seek to take advantage of relative skill differences in different countries. The impact of the vertical component in the KCM has been largely debated and thereby is of the greatest interest. Another issue addressed in the study is the relevance of spatial interdependencies in FDI flow. To check for these kinds of FDI determinants the KCM model is augmented with a surrounding market potential and a spatial lag term. By using large data sets for FDI stocks among OECD countries from 1982 to 2003 along with structural breaks and spatial econometric techniques, the paper evaluates the significance of traditional and spatial FDI determinants. The results show that the vertical component is indeed important, even for FDI among OECD countries, but that this importance is clearly decreasing over time. The findings point towards the existence of spatial linkages that are also compatible with export-platform motivations.

International Real Estate Review

Literature regarding transit’s impact on land values reports mixed results concerning the economic benefits of accessibility to subway stations, specifically regarding commercial properties. After examining 731 commercial land values in Seoul, Korea, this study suggests a possible explanation for the mixed results: transit’s discrimination impact on land values by location in a built-up urban area. The regression coefficient for distance to station in the central business district is the highest, the subcenters are next, and other areas are lowest – apparently a strong correlation with higher centrality and development densities of submarkets. Also, the inclusion of spatial lag and error term variables greatly improves the goodness of fit of the regression equations lowering the spatial autocorrelation in the ordinary least squares residuals as well as reduces overestimation of value premiums in association with rail transit stations, which enables a regression model to produce a more accurate and efficient estimator for transit’s impact on commercial land values.

Hedonic Analysis of Impacts of Traffic Volumes on Property Values

This research attempts to quantify the cumulative impacts of vehicle traffic, both passenger cars and trucks, by using the hedonic price analysis of the relationship between property values and the traffic along selected arterial corridors in Chicago, Illinois. The traffic characteristics are derived from the traffic counts data obtained from the City of Chicago and include average daily traffic, maximum daily peak, and nighttime volumes, calculated separately for trucks and for total traffic. Autoregressive models, with assessed property value as the dependent variable and traffic characteristics along with other determinants of property value as the independent variables, are constructed. The models include a spatial-lag term to control for the spatial autocorrelation and are estimated using the two-stage least squares. The regression results from the final models suggest that although the characteristics for total traffic have modest but statistically significant impact on property values, the traffic characteristics for trucks are statistically insignificant. Also, the strong evidence of spatial dependence in the data set underscores the importance of paying close attention to the model specification and controlling the autocorrelation in the hedonic price analysis.

Hedonic Analysis of Impacts of Traffic Volumes on Property Values

This research attempts to quantify the cumulative impacts of vehicle traffic, both passenger cars and trucks, by using the hedonic price analysis of the relationship between property values and the traffic along selected arterial corridors in Chicago, Illinois. The traffic characteristics are derived from the traffic counts data obtained from the City of Chicago and include average daily traffic, maximum daily peak, and nighttime volumes, calculated separately for trucks and for total traffic. Autoregressive models, with assessed property value as the dependent variable and traffic characteristics along with other determinants of property value as the independent variables, are constructed. The models include a spatial-lag term to control for the spatial autocorrelation and are estimated using the two-stage least squares. The regression results from the final models suggest that although the characteristics for total traffic have modest but statistically significant impact on property values, the traffic ...

Joining the Nazi Party

The relative strength of competing explanations of Nazism is examined by using a sample of Nationalist Socialist German Workers' Party (NSDAP) members joining from 1925 to 1933, culled from the Berlin Document Center. Results of a national multivariate model, regressing NSDAP membership rate at the Kreise (county) level on key indicators of competing theories, lends support to what recent analysts have concluded about the Nazi electorate. The NSDAP is best characterized as a catchall party with strongest membership support found in Bavaria and Northwest Germany. Tests for spatial autocorrelation reveal spatial clustering. A spatial-effects model is estimated and results support the political-confessionalism thesis, with mass-society and lower-middle-class explanations invalidated. The spatial lag term is significant, which may represent the importance of preexisting networks to Nazi mobilization and the likelihood that the joining process was one of hierarchical diffusion. Finally, results from regional models emphasize the varied political geography of Nazi membership.