Local Labour Market Areas Research Articles

Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy

SummaryUnemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed.

Agglomeration and the Italian North–South divide

Abstract This article offers new evidence on agglomeration economies by examining the link between total factor productivity (TFP) and employment density in Italy. We investigate whether and how the TFP–density nexus contributes to explaining a relevant share of the marked productivity gap between the northern and the southern Italian regions. We estimate TFP for a large sample of manufacturing firms and then aggregate it at the level of local labour market areas. We tackle the endogeneity issues stemming from the presence of omitted covariates and reverse causation with an innovative set of diagnostic tests and an instrumental variable approach that relies on geological and historical data. Our estimate of the TFP elasticity to the spatial concentration of economic activities is about 0.045, a magnitude comparable to those measured for other developed countries. We also show that no significant heterogeneity emerges in the intensity of agglomeration economies across the country and that the positive TFP difference in favour of the firms located in the North is not due to the tougher competition taking place in those areas.

Temporality in the delimitation of functional regions: the use of mobile phone location data

ABSTRACT This paper evaluates the role of ‘temporality’ in defining functional regions. Functional regions are viewed as relatively closed in terms of selected population flows (or more generally concerning spatial interactions). They are usually defined by the daily commuting to work and are therefore commonly referred to as local labour market areas or travel-to-work areas. Using mobile phone location data, however, it is possible to work with population flows in a broader temporal and spatial context. Then we can talk about the temporality alternatives of functional regions depending on whether we base them on regular daily population flows, irregular daily population flows (which, according to the data analysis, are irregular from an individual’s point of view but regular from a spatial unit’s point of view between which they take place) or weekend population flows. Thus, several functional region’s versions can be defined for a single regional system, where the different population movement’s rhythm lengths movements limit their length and also determine their hierarchy. All functional regions’ temporal alternatives according to mobile phone location data are defined based on data from the Czech Republic.

The Use of Spatial Information in Area-level Models: An Evaluation Based on Auxiliary Data Availability

The small area estimation (SAE) theory is widely used when local or domain-specific reliable estimates based on survey data are needed. Small area model-based estimates use a model that links the response variable to some auxiliary information borrowing strength from the related areas. When geographical information on the areas of interest is available, the specification of a spatial area level model can increase the estimates’ efficiency, depending on available auxiliary data. In this article, we first review the most popular area level spatial models, and we then compare their performance under two alternative scenarios of auxiliary information availability to estimate the average equivalized household income in Italian Local Labour Market Areas (LLMAs) using the EU-SILC (European Union Statistics on Income and Living Conditions) survey data. Our findings suggest that the spatial information can “fill the gap” when the covariates do not have a high predictive power, a crucial result when there is lack of auxiliary data. AMS Subject Classification: 62D05, 62G05, 62H11

The local labour market effects of earthquakes

ABSTRACT Using a balanced panel of local labour market areas (LLMAs) and adopting a new difference-in-differences approach with multiple time periods and multiple groups, this paper assesses the causal effects on local labour market outcomes of the earthquakes that occurred in Italy in 2009, 2012 and 2016. The results show a strong heterogeneity: the 2009 event had significant negative and persistent impacts on the employment rate of the LLMAs involved, while there were no adverse effects for the LLMAs affected by the earthquakes in 2012 and 2016. We also extend the analysis at the industry level in order to highlight the sectoral shifts that typically characterise the aftermath of these natural disasters.

Robots and the productivity of local manufacturing systems in Emilia-Romagna: the mediating role of occupational similarity and complexity

ABSTRACT During the last decade, a growing literature has been assessing the impact of industrial robots on productivity and employment. At the local level, the labour-displacing effect of robot adoption can be counterbalanced by a task reallocation between manufacturing industries characterized by similar/related occupational spaces. This reallocation process can be also influenced by the average degree of occupational complexity of the local labour markets. To test for the mediating role of similarity and complexity in the robot-employment-productivity relationship, we develop a two-step analysis mixing information from different data sources on 15 industries and 39 local labour market areas of the Emilia-Romagna region in Italy, for the period 2008–2017. Our analysis shows that higher exposure to robots does not affect the aggregate level of labour productivity, but, rather, is associated with a decline in (high routine) employment after three years. We further show that the negative relationship between robots and employment decreases, or even vanishes, in local labour markets with high cross-industry occupational similarity and low occupational or task complexity, and within industrial districts.

Expectile Regression for Multi-Category Outcomes with Application to Small Area Estimation of Labour Force Participation

AbstractIn many applications of small area estimation, dichotomous or categorical outcomes are the targets of statistical inference. For example, in the analysis of labour markets, proportions of working-age people in the various labour market statuses are of interest. In this paper, in line with the recent literature, we consider a classification with more than three statuses and estimate related population parameters for 611 local labour market areas using data from the 2012 Italian Labour Force Survey, administrative registers and the 2011 Census. As for the methodology, we propose multinomial expectile regression models. These models provide a means to utilise M-quantile type approaches, which have been shown to be a useful alternative to mixed model approaches when parametric assumptions on the distribution of random effects cannot be met. Via a large-scale simulation study, we show how this novel approach is much faster and provides reliable results when compared to multinomial mixed model approaches, and works for any number of categories rather than just a small number of categories as is more commonly the case with existing methods. Furthermore, the proposed approach potentially provides a framework for developing other methods for prediction with multi-category outcomes.

Delineating and comparing local labor market geographies of Millennials, Generation Xers, and Baby Boomers

There has been an increasing share of older workers in America’s workforce as Baby Boomers continue to age into this group. This raises questions about what their local labor markets look like in space and how they differ from Millennials and Generation Xers who are also active in today’s workforce. However, even though significant generational (or age) differences in commuting distance have been well documented, questions about generational disparities in local labor market geographies have not been investigated. Although several definitions of market areas exist, they were delineated using all commuting trips made by all workers in a region and thus may not be representative of subgroups of workers. The aim of this research is to delineate generation-specific local labor market areas and compare how they differ from one another. Using flow visualizations and three employment self-containment indices applied to a Longitudinal Employer-Household Dynamics dataset of Florida, this research finds that the core-based statistical areas, a popular definition of local labor market areas, understate the true market geographies across generational cohorts. It then delineates generation-specific market areas using a popular community detection method and compares their geographies using the self-containment indices. Results demonstrate largely consistent market geographies across the three generational cohorts in most regions in Florida. Most of these consistent market areas encompass two or more metros, indicating the emergence of functional megaregions centering on major metropolitan hubs. Substantial generational differences in market geographies do exist and concentrate in regions appealing to seniors along the Gulf of Mexico.

How to map industrial waste metabolism at a geographical level? A proposal for a composite indicator

Waste overproduction is a relevant issue in industrialised countries. Most empirical analyses investigate the problem using household consumption and urban waste generation data to identify behavioural patterns for policy design. However, industrial production and its geographical distribution significantly contribute to territorial waste generation and require specific treatments affecting waste metabolism. This paper focuses on the supply-side of waste production in three regions, namely Lombardy, Emilia-Romagna and Veneto, which are considered to be the industrial core of Italy. To further refine the analysis, we use a sub-regional territorial unit, the local labour market area (LLMA), which depicts the local configuration of waste production and treatment and supports estimating the territorial capacity to absorb and treat industrial waste. Combining data on industrial waste production and treatment to the socio-economic features of LLMA over the period 2015–2017, a composite indicator proxying the main waste metabolism constraints is proposed, considering three main dimensions: waste items’ hazardousness and quantities, their main treatment in terms of disposal or recovery, and the physical distance travelled from the manufacturing site to the waste treatment plant. Once spatialized, the obtained index provides a geographical representation of waste-based territorial weaknesses. Results show that the density of manufacturing plants, the type of industrial specialisations, and the technological characteristics of such specialisations are the primary sources of constraints to industrial waste metabolism. Overall, the paper contributes to the field by offering an original tool to assess local production systems' main sources of industrial waste vulnerability. Accordingly, the use of the index can support place-based waste policies to improve manufacturing clusters' ecological transition.

Distance from diasporas and immigrants’ location choice: evidence from Italy

ABSTRACT Diasporas play a fundamental role in explaining the location choice of new immigrants. We investigate the spatial dimension of diaspora externalities focusing on immigrants in Italian local labour market areas (LLMAs). We show that the net pull effect of diasporas spills over an estimated average distance of 82 km. We find evidence of negative spatial spillovers at greater geographical distances, suggesting a competition effect from neighbouring diasporas. Ethnic-specific labour markets and ethnic consumption externalities are important channels through which the distance–decay effects of diasporas take place. We also find that the spatial effects of diasporas are highly heterogeneous across gender and origin countries.

Education, routine, and complexity-biased Key Enabling Technologies: evidence from Emilia-Romagna, Italy

ABSTRACT \\We analyse the relationship between the endowment of Key Enabling Technologies (KETs) and the demand for occupations, tasks, and skills in the local labour market areas (LLMAs) of Emilia-Romagna, Italy. We merge three data sources, and we compute the share of highly educated employees, of employees accomplishing low- versus high-routine tasks, and three novel indicators measuring the complexity of occupations, tasks, and skills. Our panel estimates show that a larger share of KETs not only corresponds to a higher demand for workers holding a tertiary education degree, or accomplishing less routinary tasks, but also to a higher demand for a wider, and more exclusive, set of occupations, tasks, and skills. These results are also robust to unobserved heterogeneity and reverse causality.

How do occupational relatedness and complexity condition employment dynamics in periods of growth and recession?

ABSTRACT Related diversification has generated interest in policy (Smart Specialisation) and academic (regional branching) circles, linking path creation to regional capabilities and performance. We develop measures of occupational relatedness and complexity for local labour market areas in Sweden over the period 2002–12 to examine whether these constructs are helpful in explaining spatial and temporal variations in employment growth. The results indicate that increases in occupational relatedness are positively related to employment growth, while changes in occupational complexity have no significant impact. Separating the results either side of the global financial crisis shows that the influence of relatedness on employment is stronger pre-2008, while after the crisis building specialization in more complex occupations was positively associated with employment growth.

Internal migration and technical efficiency: the case of Italy

ABSTRACT The aim of the paper is to contribute to the study of the determinants of the internal mobility of people. To study the determinants of internal migration, we introduce a new explanatory variable, i.e. technical efficiency of Local Labour Market Areas. This variable is particularly relevant, as it allows us to summarize the entrepreneurial capacity connected to the cultural and social context of an area. Our results, for the case of Italy, suggest that technical efficiency contributes greatly to explaining internal migratory flows and the various patterns of migration among different groups of individuals. By looking more specifically at different groups of individuals, we also uncover important differences between natives and foreigners and among different age-groups.

Local and Global Agglomeration Patterns in the Banking Sector: The Calm in the Mid of a Storm

We compare the spatial agglomeration of banks’ branches in Italy across local areas (as identified by local labor market areas) to that of other services. Banks branches appear to be only weakly spatially agglomerated, their spatial distribution being similar to that of other services and to that of the firms from whom the demand for banking services is most likely to stem from. These findings have been stable throughout the period 1991-2015, despite the dramatic changes occurring in that time span (liberalization, ICT and the great recession). On the other hand, local areas with a higher (lower) presence of banking branches tend to be geographically clustered, displaying also a moderately decreasing pattern in this polarization. IC technologies partially contributed to this trend.

Agglomeration and the Italian North-South Divide

This paper offers novel evidence on agglomeration economies by examining the link between total factor productivity (TFP) and employment density in Italy. TFP is estimated for a large sample of manufacturing firms and then aggregated at the level of Local Labor Market Areas (LLMAs). We tackle the endogeneity issues stemming from the presence of omitted covariates and reverse causation with an instrumental variable (IV) approach that relies on his-torical and geological data. Our estimate of the TFP elasticity with respect to the spatial con-centration of economic activities is about 6%, a magnitude comparable to that measured for other developed countries. We find that the TFP-density nexus contributes to explaining a large share of the substantial productivity gap between the northern and southern regions of Italy. We also show that no significant heterogeneity emerges in the intensity of agglomera-tion economies across the country and that the positive TFP difference in favor of the firms located in the North is not due to the tougher competition taking place in those areas.

Population Change and International and Internal Migration in Italy, 2002-2017: Ravenstein Revisited

In 1885, Ravenstein formulated his “laws” of migration, based on the experience of the British Isles. In a further 1889 paper, he extended his analysis as a tour d’horizon of migration and population changes in other nations, including Italy. Even if social and economic processes including globalisation and rising mobility have changed the world since then, Ravenstein’s “laws” remain a point of reference today. Harnessing theoretical and methodological advances made since the 19th century, this paper describes and seeks to explain the role of international and internal migration in regional population change in Italy from 2002-2017. This paper provides the first geographically detailed migration analysis for the country’s 611 Local Labour Market Areas (LLMAs), using register-based migration and population data. Our contribution focuses on several of Ravenstein’s “laws” relating to gender (differences between men and women), natives and non-natives (differences between the Italian and the foreign population), distance migrated from origin to destination, and the role of the economy in shaping push and pull factors of migration. The results show that international migration is more prominent among men than women. In the case of internal moves, the rates of migration among men and women are similar, and internal migration is more prominent among the foreign than the native Italian population. Overall, international migration gains contribute substantially more to population change than internal migration gains and losses do. In Italy, the effects of persistent economic imbalances and of distance on migration patterns are not in line with Ravenstein’s hypotheses: not all areas with high unemployment show an effect of dispersion, nor does distance always act as a deterrent to migration. The geographically detailed analysis presented here illustrates the temporal and spatial coexistence of diverse international and internal migration processes depending on local characteristics, as well as the importance of the economic or administrative centres as the driving force behind national patterns. Our results show that, even 130 years after their formulation, Ravenstein’s migration “laws” (more accurately called “hypotheses” today) are still a valuable starting point in assessing and understanding migration processes and their role in regional population change.
 
 * This article belongs to a special issue on “Internal Migration as a Driver of Regional Population Change in Europe: Updating Ravenstein”.

What drives migration moves to urban areas in Spain? Evidence from the Great Recession

ABSTRACT During the Great Recession international emigration skyrocketed while internal migration strongly declined. However, when the focus is on migration to the larger urban areas in Spain, an increase in population since the outbreak of the crisis is observed. This study determines the effect of labour market factors on migration flows to 45 Spanish functional urban areas for the recent recession period. The results indicate that wages and employment rates greatly influence migration to cities. The results are presented by considering both provinces and local labour market areas as flows’ origins. From the results, the strong role of labour markets in migration to cities can be confirmed.

The regional dynamics of internal migration intensities in Italy

AbstractThis paper considers internal migration in Italy in terms of current patterns and longer term trends in migration intensity. It systematically reviews the Italian situation by considering internal migration patterns by citizenship and gender using a geography of 611 local labour market areas (LLMAs), distinguishing between short‐ and long‐distance moves. Attention is given, in particular, to how migration intensities have changed between the categories of LLMAs and across different demographic subgroups. The paper also provides descriptive analyses of the influence on migration patterns of the persistent socio‐economic disparities, which exist between LLMAs. Finally, it discusses the role of delayed youth transitions from home, showing how this phenomenon—also observed in other countries—manifests itself in the Italian national and regional contexts.

Delineating urban functional regions by considering interaction cohesiveness and function diversity

Abstract. A functional region is usually defined as a region characterized by not only interaction cohesiveness but also function diversity. The former indicates that places within a region should share a cohesive interacting pattern of human movements, such as commute or daily travel; the latter means that the places should possess diverse functions like residence, commerce, etc. Due to these two characteristics, people living and moving in one region can acquire almost all their daily needs, and they do not need to frequently move to other regions. In other words, a functional region resembles a life circle. Based on this definition, many previous studies have developed different methods to analyse human flow data so that they can properly profile different types of functional regions like Local labour market area (LLMA) or daily urban system (DUS), which helps people better understand how functional regions compose an area. Existing methods have considered interaction cohesiveness in a functional region, but function diversity is ignored. This is because these studies only used specific-purpose-trip data, such as journey to work or to shop. These kinds of trips describe people moving from their home to their office or to shopping malls, so each trip has an inherent distinction of functions between its origin and destination; thus, a functional region consisting of these trips indirectly owns function diversity. However, this framework has some problems. First, it cannot profile a functional region shaped by composite functions, for only one trip purpose can be taken into account each time. Second, collecting the data of specific-purpose trips needs either some questionnaires or some surveys, so it is often costly and time consuming. Finally, the purpose-unrecorded-trip data provided by rapidly developed smart card systems in several countries can be collected quickly, yet it cannot properly profile functional regions through the aforementioned framework due to the lack of trip purposes. Therefore, we propose a new spatial partitioning algorithm which can simultaneously consider both interaction cohesiveness and function diversity in profiling functional regions. Compared with previous methods, the proposed algorithm can better profile functional regions from many purpose-unrecorded-trip data based on the inclusion of land-use data. Our results provide deeper insights for understanding how various functions of lands and human movements together shape the boundary of a region. It could be significant reference for developing policies for urban planning or public transportation management.

Analyzing Resilience in Local Labor Market Areas: A Spatial Analysis for the Case of Italy

Abstract The aim of the paper is to analyse if and in which way specialization, geographical localization and spill-over effects affect resilience. The research is carried out using LLMAs (Local Labor Market Areas) as observational unit and spatial data analysis techniques (Anselin 1999, LeSage & Pace, 2009) in Italy. Resilience literature focalized its attention on regions. Despite this, there is no general agreement regarding the most appropriate observation unit. Our aim is not only to investigate the relationship between specialization and resilience at smaller scale using the LLMAs as observation unit but also to explore the spatial relationship among them. Results highlight a strong spatial correlation among LLMAs. As consequence resilience is not only influenced by specialization but also by geographical localization through spill-over effects. JEL Classifications: R10, R12, C23, C33 Spatial analysis; Resilience; Labor Market Area; Italy