Narae Lee (p)

Emanuele Pugliese

Does hiring credit for firms increase jobs? Literature questions the effectiveness and the cost-effectiveness of the policy. However, demand-side intervention still holds promise for developing economies of its labor supply surplus, long-run hiring uncertainty, and regional labor market segregations due to limited infrastructure. This study evaluates the Colombian First Job Act of 2010 to assess the impact of hiring subsidies in developing economies. The policy fine-tunes administrative details by linking the beneficiary selection to the government payroll tax registry. The Difference-in-Differences (DID) estimates with and without Nearest Neighbor (NN-1) Propensity Score Matching (PSM) consistently show a 13.9%p increase for a 'vulnerable' formal employment with a wage increase of 3%p. Does the hiring subsidy generate an equal impact in urban and rural regions? Estimates suggest that the same dose and duration of the intervention yields higher job effects in urban states. The First Job Act paralleled a small firm promotion in three Amazon forest states to enhance the rural job impacts. Does the policy bundling catch up with the impact disparities or offset each other? I explore the bundling effects and underlying causes.

Bernardo Caldarola (p), Dario Mazzilli, Aurelio Patelli, Angelica Sbardella

Narae Lee

Structural change consists of industrial diversification towards more productive, knowledge-intensive activities. When countries upgrade their productive structure, they also move to sectors with higher knowledge intensity, and lower labour requirements. This may have consequences on the wage and functional distribution of income, as well on the creation of new jobs and the destruction of old ones. In this paper, we investigate the consequences of structural change – defined in terms of labour shifts towards more complex industries – on employment growth, wage inequality and functional distribution of income, the latter being defined by the labour ratio of the economy. The analysis is conducted for European countries over the period 2010 – 2018, and relies mainly on Eurostat’s Structural Business Survey data on industrial employment. First, we identify patterns of industrial specialisation by validating the country-industry industrial employment matrix using a bipartite weighted configuration model (BiWCM), overcoming some limitations imposed by the Balassa method. Secondly, we introduce a measure of labour-weighted Fitness, which sums up complexity of industries weighted by their employment share. This measure can be decomposed in such a way as to identify the contribution to changes in labour-weighted Fitness coming from the movement of labour towards more complex industries. We identify such component as the one linked to structural change from a labour viewpoint – the structural change component. Thirdly, we link the structural change component to a number of economic outcomes at the country level: i) employment growth, ii) wage inequality, and iii) functional distribution of income (labour share of the economy. The results of OLS panel regressions with country and time fixed effects indicate that our structural change measure is associated negatively with employment growth, corroborating the evidence that highly complex industries have lower labour requirements. However, it is also associated with lower income inequality – measured in terms of the ratio of average wages in the ninth and first deciles of the wage distribution. As countries move to more complex industries, they drop the least complex ones, so the (low-paid) jobs in the least complex sectors disappear, making the 1st decile of the salary distribution go up. Finally, structural change predicts a higher labour ratio of the economy; however, this is likely to be due to the increase in salaries rather than by job creation.

Aurelio Patelli (p)

Bernardo Caldarola

The evolution of economic and innovation systems at the national scale is shaped by a complex dynamic related to the activities in which they are proficient. Nestedness, a footprint of a complex dynamics, emerges as a persistent feature across multiple activities. We observe that, in the layers of innovation and trade, the competitiveness of countries correlates unambiguously with their diversification, while the science layer shows some peculiar features. The evolution of the scientific domain leads to an increasingly modular structure, in which the most developed nations become relatively less active in the less advanced scientific fields, where emerging countries acquire prominence. This observation is in line with a capability-based view of the evolution of economic systems, but with a slight twist.
Furthermore, given the capability structure found, we characterize the temporal dynamics of Scientific Fitness, as defined by the Economic Fitness and Complexity (EFC) framework, and R&D expenditures at the geographical scale of nations. Our analysis highlights common patterns across similar research systems and shows how developing nations (China in particular) are quickly catching up with the developed world. This paints the picture of a general growth of scientific and technical capabilities of nations induced by the spreading of information typical of the scientific environment. Shifting the focus of the analysis to the regional level, we find that even developed nations display a considerable level of inequality in the Scientific Fitness of their internal regions.

Angelo Cuzzola, Daniele Moschella (p)

Aurelio Patelli

In this paper, we investigate the region- and firm-specific characteristics that allow firms to react to external shocks. In particular, exploiting a dynamic factor model estimated on export transactions of French firms from 1993 to 2017 at a monthly frequency, we first introduce a new measure of resilience to export shocks. Then, we investigate the micro and regional determinants of firms' resilience.

Emanuele Pugliese (p), Dario Diodato, Lorenzo Napolitano, Andrea Tacchella

Daniele Moschella

Complexity analyses is nowadays accepted for country level macroeconomic analyses by many institutions (Pugliese & Tacchella, 2021). To better inform industrial policy however it is crucial to look at regional systems of innovation (Balland, Boschma, Crespo, & Rigby, 2018). Indeed, on one hand most of innovation and industrial policy happens at the regional level, and several relevant policy instruments require understanding not just market opportunities at the country level, but also which regions are better prepared in terms of technological capabilities. On the other hand, in particular with respect to the regional redistribution of European and National funds, the issue of efficiency is balanced by the need for a cohesive distribution that would help regions with fewer capabilities to find their own opportunities. It is therefore crucial to identify not only absolute advantage in specific products, but also relative regional advantage.
The framework we develop here is designed to help policymakers to identify knowledge-based investment priorities and the potential feasibility of the several options they have. It uses the machine learning algorithms developed within the paradigm of economic complexity to highlight which technologies may be feasibly developed by a region or a country, based on their current capabilities. It is a quantitative tool, whose aim is to provide orientation for policymakers from the early phases of the strategy design and throughout its implementation.