The Effect of (Mostly Unskilled) Immigration on the Innovation of Italian Regions

We use small Italian regions (i.e. provinces) to investigate the causal effect of foreign immigration on innovation during 2003-2008. Using instrumental variables estimation (based on immigrants' enclaves), we find that the overall stock of immigrants did not have any effect on innovation. However, decomposing the overall effect into the contributions of low- and high-skilled migrants shows that an increase of 1 percentage point in the share of low-skilled migrants on the population reduces patent applications by about 0.2%. By contrast, the impact of high-skilled immigrants on innovation is positive, in line with the previous literature, but cannot be precisely estimated

The Effect of (Mostly Unskilled) Immigration on the Innovation of Italian Regions

Immigration has recently been at the centre of the political and economic agenda. Economists have studied extensively the impact of immigration on several economic and social indicators of host countries. The effect of immigration on innovation and technical change is, however, not much studied. The existing work on the effect of immigrants on innovation is generally limited to the role played by highly educated immigrants, generally immigrants with at least tertiary education, and is mostly focused on the US. Yet, although in anglosaxon countries skilled immigration is a sizeable phenomenon -- according to the Docquier and Marfouk (2006) data the percentages of tertiary educated immigrants were in 2001 40.3% for Australia, 58.8% for Canada, 34.9% for the UK, ad 42.7% for the US -- this is much less the case in European countries, for which just the minority of immigrants are skilled. Just to take a few figures, according to the same source, the percentages of tertiary-educated immigrants were 16.4% for France, 21.8% for Germany, 15.4% for Italy and 18.5% for Spain. Now, although the existing literature has emphasized why there are good reasons to expect positive effects of skilled immigrants on the innovation of the receiving countries, it has much less to say about the general effect of immigrants, or of low-educated immigrants. In this paper, we make an attempt to partly fill the gap concerning the effects of overall immigration on innovation, and in particular of low-skilled immigrants, existing in the literature. In addition to providing evidence for a country which was exposed to a very fast and large wave of immigration during the 2000s -- Italy --, and for which evidence is scant, we also use a very small geographical scale of analysis -- Italian provinces corresponding to NUTS-3 regions --, which presumably enables us to better control for differences in institutional and socio-economic factors which are difficult to observe but which may simultaneously contribute to both attracting new immigrants and to increasing the innovation potential of a region. More importantly, unlike most papers in the literature which only investigated the effect of skilled immigration, (i) we first focus on the general impact of immigration on innovation, and then (ii) separately look at the effects of low-educated and high-educated immigrants on innovation. Last but not least, we tackle potential endogeneity issues by using a well established instrumental variables (IVs, hereafter) strategy based on immigrants' enclaves

The Appropriateness of the Poolability Assumption for Multiproduct Technologies: Evidence from the English Water and Sewerage Utilities

The empirical literature on the cost structure of multiproduct firms (e.g., public utilities providing in combination gas, water, and electricity) traditionally assumes a common technology across different products and stages of production, letting the issue of poolability unexplored. The appropriateness of this assumption is tested here by estimating a General cost function for samples of UK specialized and sewerage-diversified water utilities. The results show the existence of both aggregate scale economies and diseconomies of scope; more interestingly, the hypothesis that the two groups of water companies share the same technological parameters is rejected. Given the implications of this finding in terms of optimal industry configuration and possible restructuring policies (e.g., mergers and/or divestitures), our test suggests caution in pooling samples when undertaking empirical studies on data which refer to multiproduct technologies.Multiproduct technologies; Water and sewerage utilities; Poolability; General cost function

Employment protection, temporary contracts and firm-provided training: Evidence from Italy

In this study we leverage on Italy’s size-contingent firing restrictions in order to identify the causal effect of employment protection legislation (EPL) on firm-provided training using a regression discontinuity design. Our analysis demonstrates that higher levels of EPL reduce firms’ incentives to invest in workers’ training. The number of trained workers falls by about 1.5-1.9 units at the threshold: this is a non-negligible effect, corresponding to a 16-20 per cent reduction in the number of trained workers. The results are robust to several sensitivity checks and controls for potential confounding factors (e.g., worker councils). The EPL effect on training is not mediated by different levels of investment in physical capital or propensities to innovate, while it is mostly accounted for by larger workers’ turnover and use of temporary contracts, which entail lower training, in firms with higher firing costs. Our study points to potential adverse effects of EPL on workers’ training in dual labor markets, owing to larger firms seeking to avoid higher EPL costs by means of temporary contracts.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Did the EU Airport Charges Directive Lead to Lower Aeronautical Charges? Empirical Evidence From a Diff-in-Diff Research Design

Abstract In this study we analyse the impact of the EU Airport Charges Directive on the level of aeronautical charges for EU airports serving between 2 and 20 million passengers, over the period 2008–2017, using a difference-in-differences research design. We find that the transposition of the Airport Charges Directive into national legislation has led to a statistically significant reduction in the level of airport charges, but only after a few years. We also find the existence of heterogeneous treatment effects that depend on the quality of transposition of the Directive

Why tackling late government payments to businesses should be a key priority

Every year, many businesses across Europe go bankrupt as a result of payment delays. For this reason, the EU established a Late Payment Directive in 2011. Maurizio Conti, Leandro Elia, Antonella Ferrara and Massimiliano Ferraresi assess the impact of the directive, finding it has had some notable positive effects for the financial position of firms. Given the strain many businesses are under as a result of the Covid-19 outbreak, it is now more vital than ever for policymakers to address the problem

Degree-level determinants of university student performance

Although features of the higher education degree programmes in which students are enrolled are likely to have an impact on their academic (and future working) careers, primarily because of data limitations research has mainly focused on the individual, household and higher education institution's drivers of student performance. To fill this knowledge gap, using administrative data on the complete supply of higher education degrees in Italy during 2013-2018, this chapter carries out an analysis of the degree-level determinants of university student performance, as measured by the national Agency for the Evaluation of the University System and Research (ANVUR) “quality” indicators. After controlling for detailed degree subject–geographic macro area fixed effects, our analysis uncovers several significant degree-programme predictors of university students’ degree performances, among which a degree’s type of access (i.e. selectivity), language of instruction, composition of the teaching body, percentage of teachers in “core” subjects, teachers’ research performance (for master degrees) and university spatial competition

Vocabulary-free Image Classification

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction.Comment: Accepted at NeurIPS2023, 19 pages, 8 figures, code is available at https://github.com/altndrr/vi