Abstract
PurposeThis study aims to estimate the impact of refugee inflows on host countries' entrepreneurial rates. The refugee crisis led to an increased scientific and public policy interest in the impact of refugee inflows on host countries. One important perspective of such an impact, which is still underexplored, is the impact of refugee inflows on host countries entrepreneurial rates. Given the high number of refugees that flow to some countries, it would be valuable to assess the extent to which such countries are likely to reap the benefits from increasing refugee inflows in terms of (native and non-native) entrepreneurial talent enhancement. Design/methodology/approachResorting to dynamic (two-step system generalized method of moments) panel data estimations, based on 186 countries over the period between 2000 and 2019, this study estimates the impact of refugee inflows on host countries' entrepreneurial rates, measured by the total early-stage entrepreneurial activity (TEA) rate and the self-employment rate. FindingsIn general, higher refugee inflows are associated with lower host countries' TEA rates. However, refugee inflows significantly foster self-employment rates of medium-high and high income host countries and host countries located in Africa. These results suggest that refugee inflows tend to enhance necessity related new ventures and/ or new ventures (from native and non-native population) operating in low value-added, low profit sectors. Originality/valueThis study constitutes a novel empirical contribution by providing a macroeconomic, quantitative assessment of the impact of refugee from distinct nationalities on a diverse set of host countries' entrepreneurship rates in the past two decades resorting to dynamic panel data models, which enable to address the heterogeneity of the countries and deal with the endogeneity of the variables of the model.

Abstract
A growing body of empirical literature has considered very long-time horizons when studying the sources of a country's economic growth. Nevertheless, the growth experiences of emerging economies (EEs) have been overlooked. This study examines to what extent human capital, structural change, and institutional quality contribute to the economic growth of one of the largest EEs in the world, Brazil, between 1822 and 2019. Resorting to the ARDL cointegration technique, the results suggest that years of schooling (human capital) have a positive and long-lasting impact on Brazil's economic growth. Moreover, there is solid evidence that sectoral changes toward more advanced and sophisticated manufacturing basis is growth-enhancing in the country. Finally, institutional quality does not constitute over the very long-run, a significant booster of Brazilian economic growth. © 2023 The Author(s)

Abstract
A growing body of empirical literature has considered very long-time horizons when studying the sources of a country's economic growth. Nevertheless, the growth experiences of emerging economies (EEs) have been overlooked. This study examines to what extent human capital, structural change, and institutional quality contribute to the economic growth of one of the largest EEs in the world, Brazil, between 1822 and 2019. Resorting to the ARDL cointegration technique, the results suggest that years of schooling (human capital) have a positive and long-lasting impact on Brazil's economic growth. Moreover, there is solid evidence that sectoral changes toward more advanced and sophisticated manufacturing basis is growth-enhancing in the country. Finally, institutional quality does not constitute over the very long-run, a significant booster of Brazilian economic growth.

Abstract
This paper assesses Brazil's real convergence (1822-2019) through unit root tests and Markov Regime-Switching (MS) models in three different scenarios: towards (i) other six Latin American countries (LA6); (ii) Portugal; and (iii) the technological frontier country, the US. The extended unit root test results favour Brazil's very long-run real convergence towards LA6 and Portugal, but not the US. The estimated MS models, involving two different regimes, real convergence and real non-convergence/divergence, capture institutional quality's positive effect in promoting Brazil's real convergence.

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

Abstract
The full realization of the potential of a technology requires good understanding of its imple-mentation. During implementations, lack of compatibility between technology and its adopters require dynamic sequences of alignment. This process is understood to be central to the success in technology assimilation. This paper proposes a configurational model to explain and predict the alignment process during technology implementations, derived from a multiple case research of the implementation of a retinopathy screening program in networks of healthcare providers. It builds on and expands previous research capturing in a holistic way the alignment process and its nature of adaptation over time.