Determinants of spatial knowledge spillovers in Italian provinces

Statistical evidence suggests that the relevance of knowledge spillovers has increased over time. In this paper we focus on regional knowledge spillovers and adopt a new econometric transformation that allows inference on potential inter-regional knowledge spillovers, accounting for spatial interdependencies.Determinants of inter-regional knowledge spillovers are explained with a sample of 103 Italian provinces. We find that a region’s absorptive capacity, measured by local R&D expenditure and social capital, implies a reduction of outward knowledge spillovers.Identification is based on the use of Two Stages Least Squares and Fixed Effects estimates.     

Endogenous qualifications and firms'' agglomeration

This paper analyzes firms' location when workers endogenously choose to qualify for professional skills but when they remain uncertain about the potential match between their personal abilities and/or affinities and the firms' specific production tasks. By qualifying in a region where firms agglomerate, workers benefit from higher prospects of good match. At the equilibrium, we show that firms may locate in a single cluster, symmetric clusters or even asymmetric clusters. Comparative statics with respect to product market demand and labor supply parameters are provided.     

A political economy model of road pricing

In this paper, we use a simple majority voting model to study the introduction of urban congestion tolls. The model allows for different types of uncertainty and considers different uses of the toll revenues. The following results are obtained. First, we show that individual uncertainty with respect to modal substitution costs may imply that a majority votes against road pricing ex ante, although a majority would have been in favor after its introduction ex post. Moreover, if a majority is against road pricing ex ante, there will also be no majority for organizing an experiment that would take away the individual uncertainty. Second, political uncertainty with respect to the use of the revenues corroborates the finding that ex ante more voters will be against the introduction of tolls. Third, both types of uncertainty suggest that fewer voters are against road pricing when toll revenues are used to subsidize public transport than when they are redistributed to all voters. Importantly, the results of this paper are consistent with a number of recent empirical observations on efforts to introduce road pricing, including the systematic rejection of road pricing in referenda, the more favorable attitudes towards road pricing after than before its introduction, and tying the toll revenues to support public transport.     

The origin of spatial interaction

This paper uses spatial empirical methods to detect and analyze trade patterns in a historical data set on Chinese rice prices. Our results suggest that spatial features were important for the expansion of interregional trade. Geography dictates, first, over what distances trade was possible in different regions, because the costs of ship transport were considerably below those for land transport. Spatial features also influence the direction in which a trading network is expanding. Moreover, our analysis captures the impact of new trade routes both within and outside the trading areas.     

Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration

Yu et al. (2008) establish asymptotic properties of quasi-maximum likelihood estimators for a stable spatial dynamic panel model with fixed effects when both the number of individuals n and the number of time periods T are large. This paper investigates unstable cases where there are unit roots generated by temporal and spatial correlations. We focus on the spatial cointegration model where some eigenvalues of the data generating process are equal to 1 and the outcomes of spatial units are cointegrated as in a vector autoregressive system. The asymptotics of the QML estimators are developed by reparameterization, and bias correction for the estimators is proposed. We also consider the 2SLS and GMM estimations when T could be small.     

Efficient estimation of the semiparametric spatial autoregressive model

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non-stochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as the ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered, relying on somewhat different regularity conditions. A Monte Carlo study of finite sample performance is included.     

Efficient,Pareto-improving processes

We give two optimization programs for determining whether Pareto improving local changes are possible. When they are, the programs compute them. Any procedure generating efficient and Pareto improving changes can be replicated by these programs. The two programs are dual to each other. We apply the programs to Pareto improving exchange processes and to Pareto-improving tax-tariff reforms.     

An efficient GMM estimator of spatial autoregressive models

In this paper, we consider GMM estimation of the regression and MRSAR models with SAR disturbances. We derive the best GMM estimator within the class of GMM estimators based on linear and quadratic moment conditions. The best GMM estimator has the merit of computational simplicity and asymptotic efficiency. It is asymptotically as efficient as the ML estimator under normality and asymptotically more efficient than the Gaussian QML estimator otherwise. Monte Carlo studies show that, with moderate-sized samples, the best GMM estimator has its biggest advantage when the disturbances are asymmetrically distributed. When the diagonal elements of the spatial weights matrix have enough variation, incorporating kurtosis of the disturbances in the moment functions will also be helpful.     

The productivity of transport infrastructure investment: A meta-analysis of empirical evidence

Investments in transport infrastructure have been widely used by decision makers to encourage economic growth, particularly during periods of economic downturn. There has been extensive research on the linkage between transport infrastructure and economic performance since the late 1980s, characterised by widely varying evidence. We conduct a meta-analysis of the empirical evidence on the output elasticity of transport infrastructure, based on a sample of 563 estimates obtained from 33 studies. Previous meta-analyses have focused on total public capital and hence cannot appropriately explain the wide variation in the productivity effect of transport infrastructure nor provide guidance to policymakers on the returns to investment in different types of transport infrastructure. Our results indicate that the existing estimates of the productivity effect of transport infrastructure can vary across main industry groups, tend to be higher for the US economy than for European countries, and are higher for roads compared to other modes of transport. The variation in the estimates of the output elasticity of transport is also explained by differences in the methods and data used in previous studies. Failing to control for unobserved heterogeneity and spurious associations tends to result in higher values, while failing to control for urbanisation and congestion levels leads to omitted variable bias. These findings can be used to inform future research on the choice of model specification and estimation and transport-related policy making.     

On the equivalence of location choice models: Conditional logit, nested logit and Poisson

It is well understood that the two most popular empirical models of location choice - conditional logit and Poisson - return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region’s gain is the other regions’ loss. In contrast, the Poisson model implies a positive-sum economy, in which one region’s gain is no other region’s loss. We also show that all intermediate cases can be represented as a nested logit model with a single outside option. The nested logit turns out to be a linear combination of the conditional logit and Poisson models. Conditional logit and Poisson elasticities mark the polar cases and can therefore serve as boundary values in applied research.     

GMM and 2SLS estimation of mixed regressive,spatial autoregressive models

The GMM method and the classical 2SLS method are considered for the estimation of mixed regressive, spatial autoregressive models. These methods have computational advantage over the conventional maximum likelihood method. The proposed GMM estimators are shown to be consistent and asymptotically normal. Within certain classes of GMM estimators, best ones are derived. The proposed GMM estimators improve upon the 2SLS estimators and are applicable even if all regressors are irrelevant. A best GMM estimator may have the same limiting distribution as the ML estimator (with normal disturbances).     

Supersizing supercenters? The impact of Walmart Supercenters on body mass index and obesity

Researchers have linked the rise in obesity to technological progress reducing the opportunity cost of food consumption and increasing the opportunity cost of physical activity. We examine this hypothesis in the context of Walmart Supercenters, whose advancements in retail logistics have translated to substantial reductions in the prices of food and other consumer goods. Using data from the Behavioral Risk Factor Surveillance System matched with Walmart Supercenter entry dates and locations, we examine the effects of Supercenters on body mass index (BMI) and obesity. We account for the endogeneity of Walmart Supercenter locations with an instrumental variables approach that exploits the unique geographical pattern of Supercenter expansion around Walmart’s headquarters in Bentonville, Arkansas. An additional Supercenter per 100,000 residents increases average BMI by 0.24 units and the obesity rate by 2.3% points. These results imply that the proliferation of Walmart Supercenters explains 10.5% of the rise in obesity since the late 1980s, but the resulting increase in medical expenditures offsets only a small portion of consumers’ savings from shopping at Supercenters.     

The phantom of the opera: Cultural amenities, human capital, and regional economic growth

We analyze the extent to which endogenous cultural amenities affect the spatial equilibrium share of high-human-capital employees. To overcome endogeneity, we draw on a quasi-natural experiment in German history and exploit the exogenous spatial distribution of baroque opera houses built as a part of rulers' competition for prestigious cultural sights. Robustness tests confirm our strategy and strengthen the finding that proximity to a baroque opera house significantly affects the spatial equilibrium share of high-human-capital employees. A cross-region growth regression shows that these employees induce local knowledge spillovers and shift a location to a higher growth path.     

Primary prevention and health outcomes: Treatment of residential lead-based paint hazards and the prevalence of childhood lead poisoning

In order to gain a better understanding of the effects of an investment in primary prevention on health, I investigate the impact of treatment of lead-based paint hazards in housing units (the preventive action) on childhood lead poisoning (the health outcome) at the census tract level in Chicago, IL. I use the findings from the analysis to simulate and then weigh the costs of lead interventions against the potential benefits of reducing blood lead levels in children. Childhood lead poisoning presents an interesting case study of the potential of preventive care in reducing the prevalence of a disease. There is a clear, well-defined pathway of exposure (deteriorating lead paint in older homes) and no method of secondary care that effectively mitigates the negative health effects. I find that a one-tenth percentage point increase in the proportion of older housing units that have been remediated is associated with a four-tenths percentage point reduction in the prevalence of childhood lead poisoning, an elasticity of roughly 0.5. Citywide, this is roughly 2.5 cases of lead poisoning averted for every housing unit remediated. Furthermore, I find evidence that the effect of remediations in preventing the disease has improved over time. The lower bound estimates of the benefits associated with the reduction in lead poisoning - increased expected lifetime earnings and reduced medical expenditures - are two to twenty times the estimated costs of the remediations.     

Real option value over a housing market cycle

This paper analyzes how the dynamics of house prices are affected by the option to rebuild or enlarge existing dwellings. The nonlinear functional form for option value and zoning limits provides identification of changes in option value over the cycle. For homes with high development potential, our results show that about 40% of the price increases during the boom years after the fall of the Berlin Wall were related to increased option value. In the subsequent bust about 50% of their price decline was associated with decreased option value. For dwellings with low redevelopment potential 12% of the decline in real value can be attributed to changing option value.     

Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris

This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrate these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period 1990-2003. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics.     

The agglomeration of headquarters

This paper uses a micro data set on auxiliary establishments from 1977 to 1997 in order to investigate the determinants of headquarter agglomerations and the underlying economic base of many larger metro areas. The significance of headquarters in large urban settings is their ability to facilitate the spatial separation of their white collar activities from remote production plants. The results show that separation benefits headquarters in two main ways: the availability of differentiated local service input suppliers and the scale of other headquarter activity nearby. A wide diversity of local service options allows the headquarters to better match their various needs with specific experts producing service inputs from whom they learn, which improves their productivity. Headquarters also benefit from other headquarter neighbors, although such marginal scale benefits seem to diminish as local scale rises.