Purpose This paper aims to analyze the role of learning and the diffusion of new ideas and technologies as major drivers of growth, especially in developing countries, by incorporating the costs and specific requirements (vintage specific) of technology adoption. Design/methodology/approach The study presents an AK model with embodied capital technology, where new ideas or technologies are embodied in capital goods. To capture the human and physical requirements of adoption, the model employs a Nelson-Phelps catch-up equation. Findings The model reveals complex dynamics, including the potential for catch-up and leapfrogging within the AK structure and the possibility of negative growth and non-monotonic transitions toward a balanced growth path due to the adoption cost. The optimal pace of technology adoption generates a trade-off between short-run costs and long-run benefits. Practical implications Policymakers in developing countries have a range of policy options to foster growth, including reducing adoption costs, promoting new technologies or accelerating the diffusion and learning of new technologies. The model highlights the need to balance the trade-off between technological complexity (and its short-run costs) and long-run gains when choosing the optimal policy mix. Originality/value This work is original in integrating adoption costs and vintage-specific technology requirements into an AK growth model with a Nelson-Phelps catch-up mechanism. The results are particularly relevant for policymakers in developing countries, highlighting that the benefits from technology adoption may only materialize after the economy has gained a deeper understanding of the new technology.

The present study responds to the following question: What factors determined the location of manufacturing industries in Peruvian regions for the years 1963 and 1974? Using the data of the Economic Censuses conducted in 1963 and 1974 by the Peruvian government, we evaluate the factors that influenced the location decisions. For this aim, we apply the methodology proposed by Midelfart-Knarvik et al. (2000, 2001), which integrates in a model the factors that the Heckscher-Ohlin (H-O) and the New Economic Geography (NEG) theories consider important to explain industrial location decisions. Among them, they consider the influence of the regional endowment of resources and the intensity of their use in industries (H-O theory), as well as the influence of the market potential and the backward or forward linkages between industries or the economies of scale in industries (NEG theory). Our findings indicate that for this period of analysis in Peru, the factors related to agricultural endowment, electrical energy, financial capital (components related to the H-O theory), and the economies of scale (components related to the NEG theory) were influential in determining the industrial location decisions of the manufacturing sector across regions. The results also indicate that the two components associated with the NEG theory have the highest weighted impact on manufacturing location decisions. Another relevant aspect is that our findings allow us to partially understand the agglomeration of industries in some regions, particularly in the capital of the country, Lima.

Following the outbreak of the COVID-19 pandemic, most economic indicators experienced an increase in observed volatility, reducing the accuracy of nowcasting econometric models. In this paper, we propose a new specification for a mixed-frequency dynamic factor model used to nowcast the quarterly GDP growth rate of the Spanish economy –the Spain-STING–. With the aim of improving the predictive capacity of the model, we consider three proposals: (i) the relationship between the indicators and the estimated common factor is now contemporaneous, and not leading for some of the indicators; (ii) the variance of the common component is estimated by a stochastic process to allow it to vary over time; (iii) the set of variables is revised with the aim of including only those that add the most relevant information to the nowcast of the quarterly GDP growth rate. All these three modifications imply a notable improvement in the nowcasting performance during the period after the COVID-19 pandemic, while maintaining the accuracy obtained before it. These proposals could be also useful to revise other forecasting models.

This paper examines how business environment distortions and informal competition contribute to the persistence of low-scale formal firms in Peru. Using data from the 2015 National Enterprise Survey, the analysis estimates an ordered probit model with instrumental variables to assess these effects. Results show that limited access to working-capital credit and competition from informal businesses increase the probability of being a micro enterprise by 18 and 16 percentage points (pp), respectively. Likewise, complex tax regulations increase this probability by 10 pp, while inadequate infrastructure and institutional weaknesses raise it by 8 pp. However, simultaneous improvements in credit access, tax simplification, and institutional and infrastructure quality could reduce the share of micro enterprises by 39 pp while increasing the shares of small and medium/large enterprises by 27 and 12 pp, respectively.

Purpose We analyze how academic credentials relate to career outcomes using linked microdata on Brazilian economists, focusing on two credentials (admission test scores and program prestige) and multiple outcomes spanning the labor market (e.g. wages) and research productivity (e.g. publication incidence). Design/methodology/approach We follow 888 master’s graduates over nearly a decade to track their career development. To address endogeneity and sample-selection issues, we estimate a set of Heckman selection models with endogenous regressors. Findings Wages are positively associated with admission test scores but not with program prestige. Research productivity shows mixed associations with these credentials. Overall, the estimates are suggestive of – but do not establish – a sorting mechanism whereby a larger share of high-scoring students from top programs select into non-academic, higher-paying careers, whereas graduates from lower-ranked programs are relatively more likely to pursue academic careers within Brazil. Research limitations/implications Partial observability and the context-specific nature of the data limit the generalizability of our findings; future work should lengthen the observation window and follow additional cohorts. Practical implications Findings inform applicants, programs and policymakers about potential trade-offs between prestige and career paths. Social implications Insights into sorting between academic and non-academic careers can inform policies to retain research talent. Originality/value We provide novel evidence on Brazilian economists’ early careers using linked administrative and bibliometric microdata, combining sample-selection correction with endogenous regressors.

Purpose The purpose of the study is to estimate the impact of household income diversification on household welfare in a developing rural Nigerian economy. Design/methodology/approach The study used a panel fractional probit correlated random-effects technique to achieve the 1st-stage and 2nd-stage regression estimations. Four waves of the Nigerian General Household Survey panel for the periods 2010/2011, 2012/2013, 2015/2016 and 2018/2019 were used. Income diversification measures used are the Simpson diversification index and the count index, while household welfare is proxied by dietary diversity score and household consumption spending per adult equivalent. Findings The empirical results of the instrumental variables estimation suggest that income diversification positively and significantly impacts rural household welfare. The study further tests whether income diversification has non-homogeneous effects on consumption using quantile regression and found that the positive connection between income diversification and household welfare is across all percentiles, and the magnitude of the impact is slightly higher for non-poor households than for poor households. One of the major findings is that the choice of income diversification proxy significantly influences the welfare effect on rural households. Practical implications The results emphasize the need for Nigerian policymakers to design and implement social protection programs targeted at households for income diversification. Originality/value The study becomes the curtain raiser in literature to employ four waves of the Nigerian General Household Survey panel data.

of Postgraduate Programs in Economics (Anpec), was held in

Purpose This study examines the impact of macroeconomic shocks on the formal labor market in Brazil, segmented by workers’ education levels. Design/methodology/approach We estimate a factor-augmented vector autoregression (FAVAR) model identified via heteroskedasticity based on the two-step method of Bernanke et al. (2005), along with the identification approach proposed by Brunnermeier et al. (2021). Findings Various types of macroeconomic shocks, such as those related to monetary policy and expectations, are identified. Our empirical results support the theory of heterogeneous agents for the Brazilian formal labor market over the business cycle, showing that the impacts of these shocks on more educated workers were smaller than other groups. Additionally, the findings suggest that the primary adjustment mechanism of firms is through hiring rather than separations and reveal a pro-cyclical pattern in turnover. Originality/value Unlike previous studies, this paper applies a FAVAR model identified via heteroskedasticity to analyze the effects of macroeconomic shocks on the formal labor market in Brazil, offering additional evidence on how these effects vary across workers with different education levels.

Purpose This paper aims to contribute to the literature on the labor market impacts of Uber’s entry in Brazilian capital cities. The analysis first characterizes patterns of employment flows, earnings dynamics and shifts in the composition of self-employed drivers using descriptive statistics and transition analysis. Subsequently, it leverages the staggered rollout of Uber and 99 across cities using a difference-in-differences framework to estimate the causal effects of platform entry on labor market outcomes in Brazil. Design/methodology/approach Using microdata for 2012–2021 from Brazil’s Continuous National Household Sample Survey, a nationally representative, quarterly rotating labor-force survey, we combine (1) descriptive statistics, (2) year-to-year transition matrices and (3) a staggered difference-in-differences design that exploits the city-level timing of app entry to isolate causal effects on unemployment, overall employment, working hours, earnings and hourly earnings. Findings Platform entry triggered rapid growth of self-employed drivers, disproportionately Black and secondary-educated. Most entrants were already employed and remained driving, while a modest share of unemployed workers transitioned into the occupation, suggesting a buffer role. The difference-in-differences estimates reveal no statistically significant effect of Uber and 99’s market entry on city-level unemployment rates. Among self-employed drivers, employment counts increase in later periods, yet the initial earnings uptick proves transitory: average labor income and working hours decline over time, resulting in statistically unchanged hourly earnings. Research limitations/implications City-level samples limit statistical power, and the one-year panel may miss longer-term mobility. Future research could aim at investigating the impact on the formal labor market, trying to grasp how much the flexibility benefit of this type of occupation crowded out employment in a formal labor market context. Originality/value This study provides one of the first causal assessments of ride-sharing platforms’ labor-market impacts in Brazil and supplements it with detailed descriptive and transition analyses drawn from nationally representative survey microdata. It sheds light on how digital platforms reshape employment dynamics in economies with persistent informality.

Purpose This study aims to explore the asymmetric effects of cryptocurrencies returns on climate policy uncertainty (CPU). We also wanted to explore cryptocurrencies’ safe-haven and hedging properties to mitigate the climate risk during the financial turmoil period. Design/methodology/approach The study uses the monthly time series data of the CPU index and five major cryptocurrencies’ data from July 2015 to September 2023. The study applied cross-quantilogram (CQ) to assess the asymmetric quantile based dependence between the CPU index and cryptocurrencies. The CQ results confirm the non-linear quantile based dependence between cryptocurrencies and the CPU index. Findings The finding of the heatmap reveals that Bitcoin and Tether are strong safe havens, while Ripple served as a weak safe haven for the CPU index during the bearish quantile (0.05). Dogecoin and Ethereum have a strong dependence with the CPU during the different quantiles. Lastly, the non-linear Granger confirms the asymmetric role of cryptocurrencies in causing CPU. Practical implications Our study findings provide useful insights for market investors and policymakers. Policymakers can focus on developing a policy to limit CO2 emissions during cryptocurrencies mining because, during the high CPU period, investors can save their investments by investing in cryptocurrencies. Originality/value The present paper has a number of unique contributions in the literature. Firstly, our study is the first to investigate the asymmetric quantile dependence between cryptocurrencies and CPU during the bullish bearish and normal period. The study also investigated the hedging and safe-haven properties of cryptocurrencies to manage climate risk.