Sarah Fritz (p), Eva Dettmann

Harm-Jan Rouwendal

Despite the European Union's efforts to foster economic convergence of less developed regions, spatial disparities across European regions are profound and persistent. Using a novel data set and improving on previous research from a methodological point of view, we provide new evidence on the impact of the EU Cohesion Policy on regional GDP per capita growth and employment reallocation. We apply a Fuzzy Regression Discontinuity Design, exploiting that the definition of less developed regions gives rise to a discontinuity in the probability of treatment assignment. Using digitized historical data employed by the European Commission for the demarcation of eligible regions, we are able to precisely replicate the policy rule and correctly classify regions according to their compliance status. Our panel comprises the years from 1989 to 2020, hence covering all five completed funding periods. We use the variation across regions within funding periods, accounting for period-specific influences by flexibly modelling the relationship between initial GDP per capita and changes in the outcome variable. We show that the EU’s place-based policy indeed fostered GDP per capita growth of the less developed regions. However, the implied growth effects are much smaller than previously documented. Looking at effect heterogeneity, we find insignificant effects for the subsample of EU-15 member states.

Yifei Wang (p)

Sarah Fritz

Investments in research and development (R&D) are well-known to be essential for firm success. As research is a learning process, investments in R&D may not only lead to an increase in a firm’s stock of capabilities, but also in the human capital of workers. Yet, despite the well-documented impacts of R&D investments on firm growth, we lack evidence on the individual-level effects of R&D. In other words, for workers who benefited from firm expansion, we know little about their labour market trajectories, and especially whether the experience of working for R&D-investing firms leads to better labour market outcomes.
Combining a biennial survey series on R&D investments with a longitudinal matched employer-employee dataset from Statistics Sweden, this study examines the labour market outcomes of workers who were hired when their employers make investments in R&D. We also consider how such outcomes would differ in different regional contexts. Results show that R&D is a learning process for individuals. On average, compared to employees working for non-investing firms, research personnel recruited by R&D investors have a higher probability of upgrading their occupations during later job switches. Among the rest workforce, managers from R&D investing firms have a higher chance of joining start-up firms. Importantly, both labour market outcomes have a strong geographical dimension, as they are shown to be particularly applicable in periphery regions, triggering important regional industrial dynamics. Results strengthen our current knowledge of the learning effects of R&D, and provide important implications for regional policies.

Nino Javakhishvili-larsen (p), Bence János Bøje-Kovács

Yifei Wang

This study aims to develop parameters to identify the effects of involuntary lockdowns during Covid-19 on the local economy and labour market. The paper is an exploratory study that generates Lockdown (LDI) and Home-Office (HOI) indexes and constructs a dummy parameter – Covid-Shock. In this article, we have three main goals. Firstly, we aim to develop parameters using Danish micro register data per person from 2020 and 2021 to identify the individuals that could be affected by the involuntary lockdown requirements during the Covid-19 pandemic period and to identify the parameters that measure their resilience on the local labour markets. We approach this as an explorative experiment, as the micro register data does not contain any direct information that can indicate how individual in the labour market was affected by the Covid pandemic. Secondly, the aim is to develop an indicator that can estimate the Covid impact on a person in the labour market – we refer to it as Covid-Shock dichotomous dummy. Thirdly, The Covid-Shock variable is applied to estimate the probability of the effect of the pandemic on the working population and provide a comparison of the Covid-Shock impact in urban and rural types of municipalities in Denmark. In this article, we apply longitudinal micro register data from Statistics Denmark for 2020 and 2021 and a Probit model to observe significant probabilities of the person's resilience concerning their characteristics and conduct the pairwise comparative analyses of marginal effects of the Covid Shock on the individuals regarding their workplace municipality's urbanisation type.

Johan Klaesson (p), Lina Bjerke (p)

Nino Javakhishvili-larsen

The aim of this paper is to continue the path to tie together two large strands of literature. The first literature is concerned with the observation that people tend to earn higher wages in cities compared to the countryside. This observation is often called the urban wage premium. The literature is voluminous, and scholars have been very active during the last two decades.
The second strand of literature is that dealing with the so-called labor market polarization. This literature has its roots in the early years of the 2000s and shows an extensive documentation of polarization in the last 40 years at the U.S and European labor markets (Autor et. al. 2006, Goos and Manning 2007, Goos et. al. 2009). The polarization literature generally finds that the middle of the jobs/skill distribution is hollowing out.
We estimate the dependence of job growth and wages on economic density in one unified framework. We disaggregate the effects on a finer geographical level. In this way we can disentangle the relative importance of number of jobs and labor productivity for the economic magnitude of different occupation/sector groups, and on different types of locations.
First, we decompose the change in total economic activity in different jobs (occupation-industry combinations) groups into the number of employees and average wages. Then we relate these two changes to economic density overall in regional labor markets. This way we can gauge the relative importance of urban density for productivity and growth/decline of the number of jobs in the change of these in economic size. In a second step relate these results to the relative growth and decline of jobs. In this way we disentangle how much of the overall job polarization that can be related to urbanization effects.

Harm-Jan Rouwendal (p), Teresa Farinha, Sierdjan Koster

Johan Klaesson

This paper studies AI adoption in regional labour markets. We use the demand for AI-related skills in online job vacancies for the period 2010-2020 for the Netherlands as a measure of AI adoption in firms. We document a rapid increase of AI related vacancies in the past decade, especially in the professional and technical occupations and in urban areas. Moreover, we find spatial differences in AI adoption that can only be partly explained by sector structure and the effect of agglomeration economies. We hypothesize that firms are more prone to adopt AI if similar firms in same regions already use AI. This would indicate that local knowledge spillovers remain important in the regional adoption of new digital technologies and the subsequent automation process.