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This paper analyzes the impact of the Italian Start Up Act which entered into force in October 2012. This public policy provides a unique bundle of benefits, such as tax incentives, public loan guarantees, and a more flexible labor law, for firms registered as “innovative startups” in Italy. This legislation has been implemented by the Italian government to increase innovativeness of small and young enterprises by facilitating access to (external) capital and (high-skilled) labor. Consequently, the goal of our evaluation is to assess the impact of the policy on equity, debt, and employment. Using various conditional difference-in-difference models, we find that the Italian innovative startup policy has met its primary objectives. The econometric results strongly suggest that Italian innovative startups are more successful in obtaining equity and debt capital and they also hire more employees because of the program participation.

In this paper, we document how the productivity gap between firms in the North and in the South of Italy is essentially due to a difference in local competitive pressures, lower in the South. In fact, we find that lower average productivity in the South is driven by the presence of relatively more inefficient firms on the left tail of the distributions, while no significant difference is found among the most productive firms across the country, after controlling for industrial specialization patterns, timeinvariant geographic characteristics and different institutional environments at the province-level. In addition, firms' entry and exit dynamics are more frequent in the provinces of the North, where the average productivity is already higher, pointing to a stronger market selection process than in the South. Eventually, after entry, new firms register productivity levels not different from local incumbent firms, i.e. higher where the average productivity is already higher. Finally, we argue that policies that do not consider the emergence of firm-level heterogeneity at the local level fail in tackling regional disparities.

Multi-regional input–output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (parts of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and matrix completion with a LASSO-like nuclear norm penalty, to predict missing entries of a partially unknown I/O matrix. Through analyses based on both real-world and synthetic I/O matrices, we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input–Output Database (WIOD) tables—which are an example of industry-by-industry I/O tables—is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.

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