nep-geo New Economics Papers
on Economic Geography
Issue of 2023‒12‒04
five papers chosen by
Andreas Koch, Institut für Angewandte Wirtschaftsforschung

  1. Regional productivity differences in the UK and France: From the micro to the macro By Bridget Kauma; Giordano Mion
  2. Tradability, Productivity, and Regional Disparities: theory and UK evidence By Anthony J. Venables; Patricia G.Rice
  3. Agglomeration decay in rural areas By Rasmus Bøgh Holmen
  4. The Economic Geography of Lifecycle Human Capital Accumulation: The Competing Effects of Labor Markets and Childhood Environments By Ben Sprung-Keyser; Sonya Porter
  5. Joint model for longitudinal and spatio-temporal survival data By Victor Medina-Olivares; Finn Lindgren; Raffaella Calabrese; Jonathan Crook

  1. By: Bridget Kauma; Giordano Mion
    Abstract: We propose a new data resource that attempts to overcome limitations of standard firm-level datasets for the UK (like the ARD/ABS) by building on administrative data covering the population of UK firms with at least one employee. We also construct a similar dataset for France and use both datasets to: 1) Provide some highlights of the data and an overall picture of the evolution of aggregate UK and French productivity and markups: 2) Analyse the spatial distribution of productivity in both countries at a fine level of detail - 228 Travel to Work Areas (TTWAs) for the UK and 297 Zones da'emploi (ZEs) for France - while focusing on the role of economic density. Our findings suggest that differences in firm productivity across regions are magnified in the aggregate by an increasing productivity return of density along the productivity distribution.
    Keywords: firm-level dataset, merging, BSD, FAME, VAT, FICUS, FARE, productivity, markups, UK, France, regional disparities, density
    Date: 2023–11–01
  2. By: Anthony J. Venables; Patricia G.Rice
    Abstract: Spatial variation in the productivity of different sectors is a determinant of sectoral location, with consequences for wages, rents and the cost-of-living in each area. This paper develops an analytical framework which shows how productivity advantage in a highly tradable sector translates into higher nominal wages, rents, and cost of living in an area; in contrast, high physical productivity in non-tradables may result in lower wages, rents and revenue productivity. The theory’s prediction that an area’s bias towards highly tradable activities is positively correlated with its earnings is confirmed by empirical analysis of earnings data for the ITL3 areas of GB. As suggested by the theory, two factors drive this effect. Approximately one-third is a direct result of sectoral composition – on average across GB, tradable sectors pay higher wages. The remaining two-thirds is an equilibrium effect, arising as a productivity advantage in tradables translates into higher local employment and factor prices. While our primary analysis is on recent data, we show that our approach also captures the impact of the structural change that occurred in Britain during the 1970s and 1980s on regional earnings disparities.
    Date: 2023–01–09
  3. By: Rasmus Bøgh Holmen (Institute of Transport Economics - UiO - University of Oslo)
    Abstract: Spatial proximity to other economic activities – occasionally labeled as ‘market access' and ‘economic density' – is associated with good economic performance. How the impulses from economic activities diminish over space is known as ‘agglomeration decay' or ‘distance decay'. Although market access functions and the associated agglomeration decay constitute an important topic within spatial economic research, the phenomenon is seldom studies in a rural setting or addressed by non-linear estimation techniques. In this paper, we estimate the market access function in the relatively rural regions of Southern parts of Norway. We approximate market access in the national road network by alternative market access functions with power and exponential distance decay, applying ordinary non-linear least squares (NLS) and non-linear mixed effects (NLME). We apply labor productivity as the outcome variable, employment and population as alternative measures for potential market connections and traveling time as distance measure. In the regression, we control for capital intensity, industry structure and annual growth trend, as well as mixed effect in case of the NLME model. Compared to previous findings in the literature, we find evidence of relative sharp agglomeration decay in a rural setting, involving power and exponential distance decay parameters of about 2.3 and 0.07 respectively. Comparisons of the log likelihood from the estimation of market access functions suggest that exponential distance decay involve a slightly better fit than power distance decay. In addition, employment involves slightly more explanatory power than population as a measure for potential market connections.
    Keywords: Urban economics, Rural economics, Productivity, Wider economic impacts, Market access, road constructions, agglomeration decay, Distance decay
    Date: 2022–09–30
  4. By: Ben Sprung-Keyser; Sonya Porter
    Abstract: We examine how place shapes the production of human capital across the lifecycle. We ask: do those places that most effectively produce human capital in childhood also have local labor markets that do so in adulthood? We begin by modeling wages across place as driven by 1) location-specific wage premiums, 2) adult human capital accumulation due to local labor market exposure, and 3) childhood human capital accumulation. We construct estimates of location wage premiums using AKM style estimates of movers across US commuting zones and validate these estimates using evidence from plausibly exogenous out migration from New Orleans in response to Hurricane Katrina. Next, we examine differential earnings trajectories among movers to construct estimates of human capital accumulation due to labor market exposure. We validate these estimates using wage changes of multi-time movers. Finally, we estimate the impact of place on childhood human capital production using age variation in moves during childhood. Crucially, our estimates of location wage premiums and adult human capital accumulation allow us to construct estimates of the causal effect of place during childhood that are not confounded by correlated labor market exposure. Using these estimates, we show there is a tradeoff between those places that most effectively produce human capital in childhood and the local labor markets that do so in adulthood. We find that each 1-rank increase in earnings due to adult labor market exposure trades off with a 0.43 rank decrease in earnings due to the local childhood environment. This pattern is closely linked to city size, as adult human capital accumulation generally increases with city size, while childhood human capital accumulation falls. These divergent trajectories are associated with differences in both the physical structure of cities and the nature of social interaction therein. There is no tradeoff present in the largest cities, which provide greater exposure to high-wage earners and higher levels of local investment. Finally, we examine how these patterns are reflected in local rents. Location wage premia are heavily capitalized into rents, but the determinants of lifecycle human capital accumulation are not.
    Date: 2023–11
  5. By: Victor Medina-Olivares; Finn Lindgren; Raffaella Calabrese; Jonathan Crook
    Abstract: In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower's time-to-event. When the time-varying drivers are endogenous, modelling jointly the evolution of the survival time and the endogenous covariates is the most appropriate approach, also known as the joint model for longitudinal and survival data. In addition to the temporal component, credit risk models can be enhanced when including borrowers' geographical information by considering spatial clustering and its variation over time. We propose the Spatio-Temporal Joint Model (STJM) to capture spatial and temporal effects and their interaction. This Bayesian hierarchical joint model reckons the survival effect of unobserved heterogeneity among borrowers located in the same region at a particular time. To estimate the STJM model for large datasets, we consider the Integrated Nested Laplace Approximation (INLA) methodology. We apply the STJM to predict the time to full prepayment on a large dataset of 57, 258 US mortgage borrowers with more than 2.5 million observations. Empirical results indicate that including spatial effects consistently improves the performance of the joint model. However, the gains are less definitive when we additionally include spatio-temporal interactions.
    Date: 2023–11

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