This study investigates the determinants of financial literacy among women entrepreneurs in Pakistan’s informal sector and its implications for their entrepreneurial success. Using data from five districts in Punjab, a Bayesian hierarchical logistics model estimated through Hamiltonian Monte Carlo (HMC) and the No-U-Turn Samplers (NUTS) was applied for robust interference. Results show that 55-59 % of respondents were financially literate, with an overall mean of 61 %. Education, access to credit, and business facilities were positively associated with financial literacy, whereas gender-related constrains and lack of formal education had negative effects. Cultural constrained showed mixed influences, and multiple roles had a slight positive impact. The findings highlight the need for targeted financial literacy programs, focusing on budgeting, financial management, and investment skills, to enhance women’s entrepreneurial capacity and support inclusive economic development.
This study examines the long-run relationships and short-run adjustment dynamics among financial inclusion, unemployment, poverty, and public debt in Nigeria using annual time series data spanning 1990 to 2023. Given the non-stationary nature of the variables, the analysis employs the Augmented Dickey–Fuller unit root test, Johansen cointegration technique, Fully Modified Ordinary Least Squares (FMOLS), and Vector Error Correction Model (VECM) within a multivariate time-series framework. The results confirm that all variables are integrated of order one and exhibit a stable long-run equilibrium relationship. The FMOLS estimates indicate that financial inclusion and public debt are negatively associated with unemployment in the long run, while poverty is positively associated with unemployment. The VECM results further reveal the presence of short-run adjustment dynamics toward the long-run equilibrium following temporary shocks. These findings highlight the interconnected nature of financial inclusion, fiscal conditions, and social welfare indicators in shaping labour market outcomes. The study contributes to the literature by providing a unified empirical framework that captures both equilibrium relationships and dynamic adjustments among key macroeconomic variables in Nigeria. Policy implications should be interpreted within the context of long-run macroeconomic coordination rather than direct causal effects, particularly with respect to financial inclusion strategies, debt management, and poverty reduction efforts.
This paper develops an open-economy model with banks operating under monopolistic competition, two types of credit subject to credit risk—corporate and mortgage—and Basel-type capital requirements. The model is calibrated and estimated using Bayesian methods and Peruvian data. The estimated model is then used for historical shock decompositions, variance decompositions, and impulse response analysis following monetary and fiscal shocks. It also serves to evaluate the effects of regulatory frameworks such as the IRB approaches under Basel II and III.
This paper seeks to explore developments in the Latin American financial system over the last decade by analyzing the factors that determined non-performing loans during 2015-2024 and, above all, by showing that banks responded differently depending on internal and external factors. We explore financial and macroeconomic variables for Brazil, Colombia, Chile, Mexico, and Peru. Specifically, when we split the sample, the results are heterogeneous, with some variables affecting non-performing loans more than others. The split is based on the pandemic, which led to structural changes in the performance of the financial system in emerging countries.
PurposeThis paper aims to analyze the existence and spatial scope of the effects of human capital agglomeration on firms’ location decisions within the context of Brazilian urban centers.Design/methodology/approachBy leveraging unique micro-geographic data for all Brazilian manufacturing activities, we estimate the local determinants of new establishments’ location choices as a function of the concentration of college-educated workers within different distance bands, along with other economic and environmental characteristics. To achieve this, we employ a Poisson model with instrumental variables for the local number of college-educated workers and a control function approach.FindingsThe main findings indicate that the externalities associated with human capital concentration are spatially constrained in scope. The external gains of human capital are strongest within 1 km of the firm’s chosen location and dissipate entirely beyond 5 km. The results remain robust even when accounting for a comprehensive set of controls for both observable and unobservable local characteristics and align with the idea that human capital spillovers are more significant over shorter distances.Originality/valueOur results provide the first evidence on the spatial scope of human capital spillovers and their influence on the location choices of new establishments in Brazil. Our findings contribute to the relatively scarce literature on the spatial extent of agglomeration economies in developing countries and carry significant policy implications, particularly for urban areas characterized by similar structural patterns and deficiencies in transportation infrastructure. Specifically, understanding the spatial scope of agglomeration gains can help design more effective regional policies.
Theoretically, the effect of innovation on employment is ambiguous. There is a negative impact due to labor-saving efficiency and a positive impact through increasing competitiveness, leading to business expansion and new hiring. In this regard, we investigated the effect of innovation support on employment for Brazilian firms that received public support for innovation from Finep. We use a novel database by merging Finep and RAIS microdata, which allows for a better identification of the causal effect. Moreover, we use a staggered difference-in-differences (DiD) method recently developed by Callaway and Sant’Anna (2021), specifically designed for situations where treatments occur in different years. We found a statistically significant effect of innovation support on formal employment, especially on firms seeking financing for process innovations (but not product innovations). Unlike previous studies assessing the causal effect of Brazilian government support on innovation, we used as a control group companies that applied for support but were not selected. This novel strategy provides a better identification of the causal effect.
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. 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. 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. 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. 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.
PurposeThis study examines the impact of macroeconomic shocks on the formal labor market in Brazil, segmented by workers’ education levels.Design/methodology/approachWe 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).FindingsVarious 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/valueUnlike 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.
PurposeThis 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/approachThe 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.FindingsThe 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 implicationsOur 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/valueThe 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.
Purpose This paper aims to demonstrate how the level of data aggregation affects the estimation of the income elasticity of demand and potentially influences the distinction between luxury and necessary goods. We explore the estimation of the income elasticity of outpatient medicine demand in Brazil. Medicines may compete with expenditures on essential goods, which turns them into a frequent target of public policies worldwide.Design/methodology/approach We used a single data set (the Brazilian Household Budget Survey – BHBS, 2017–2018) with varying levels of data aggregation and an identical method of estimation to assess how the level of aggregation affects elasticity coefficient estimations.Findings The elasticity estimated based on per person microdata significantly differed from that estimated on a per capita basis by primary sampling unit and by State, but was similar to per capita estimates by household.Originality/value Although many academic studies still use aggregate data to estimate associations between variables that represent individual characteristics or choices, this procedure is incorrect and can lead to inconsistent results. Data aggregation level matters when it comes to understand and estimate demand elasticity. Elasticities based on aggregate data provide overestimations unfit both for crafting public policies and for projecting future demand.
PurposeThis paper evaluates the effectiveness of price incentives in reducing residential water demand during the 2014 water shortage.Design/methodology/approachWe utilized a fixed effects panel data model and a diff-in-diff analysis.FindingsThe results revealed a significant decrease in water consumption during the early months of the water crisis in all municipalities, with a more pronounced effect in the MRSP compared to cities where price incentives were not applied. We estimate that the price incentives program reduced monthly water consumption by approximately 0.5 m3 per household. We also provide evidence that the impact of the water pressure reduction in pipelines was similar, while the financial burden program appears to have had a weaker effect. Finally, after all the adopted policies had ceased, the water consumption remained lower than the pre-crisis level in all municipalities.Originality/valueCompared to the existing literature, we used a different control group, based on water consumption in municipalities outside the MRSP and achieved opposing results.
PurposeThis paper seeks to identify whether there was a decrease in the wages of young Brazilian adults of recent birth cohorts in relation to older birth cohorts.Design/methodology/approachWe applied quantile regression and quantile decomposition models to the historical series of PNADs.FindingsThere was a decrease in the relative wages for males at the top of the wage distribution. But there was an increase in wages for those in the bottom and middle of the wage distribution.Originality/valueThe result for Brazil differs from the results of the most developed countries. This difference could be attributed to structural changes in the country, such as the education expansion and the increase of female labor participation.
Hyman Minsky's financial instability hypothesis provides a theoretical framework to understand the emergence of endogenous crises in modern economies and how capital flows amplify accumulated imbalances and exacerbate financial constraints in economic units. This inquiry operationalizes the Financial Instability Hypothesis within the Colombian non-listed manufacturing sector through the estimation of discrete-state dynamics and distributional sensitivities. The methodological design constructs two distinct fragility taxonomies to interrogate the determinants of the Hedge, Speculative, and Ponzi classifications. The first specification applies an open-economy cash-flow model derived from Castro (2011), which explicitly internalizes the valuation effects of nominal exchange rate fluctuations on debt service obligations. The second taxonomy, grounded in Nishi (2019), evaluates solvency through the interaction of a flow-based profitability margin and a stock-based liquid asset buffer. To parse the transmission of meso-level economic impulses, the analysis deploys multinomial logit models equipped with a Mundlak correction for correlated random effects alongside recentered influence function regressions. Estimation outputs from the first model confirm that the deterioration of the interest coverage ratio functions as the primary determinant to the Ponzi state, while pre-existing dependence on imported capital acts as a specific transmission channel for currency shocks. The margin-of-safety specification reveals that stock-based liquidity buffers absorb solvency shocks effectively, rendering specific currency exposure variables redundant as predictors of distress. Finally, the dynamic analysis uncovers a temporal asymmetry where contemporaneous sectoral expansions ameliorate immediate default risk through the revenue channel, whereas lagged growth accumulation is associated with the endogenous generation of future fragility. This validates the core thesis of Minsky's framework: that stability breeds instability.
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 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), backward linkages 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.
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.
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.
Corporations with persistently negative profitability, excessive leverage, and declining real revenue growth, are colloquially referred to as "zombie firms" due to their economically unviable nature. Such financially distressed entities operate in both developed economies and emerging markets. This paper quantifies the financial performance of zombie firms within Colombia's petrochemical cluster using a five-component methodology. The research applies a probit regression, a comparative analysis of financial constraint indices, an investment-cash flow sensitivity model, a corporate flow of funds analysis, and a cash flow sensitivity of cash model. The probit model establishes that indebtedness, minimal asset tangibility, and inadequate operating cash flow function as the primary statistically determinant predictors of zombie status. The comparative analysis of financial constraint indices confirms the superior discriminatory power of the Whited-Wu index over the Kaplan-Zingales and Size-Age alternatives for classifying these corporations. Furthermore, the investment-cash flow sensitivity model establishes that zombie firms systematically curtail capital expenditures in response to their distressed nature, a behavior not observed in their solvent counterparts. The corporate flow of funds analysis derives a structural explanation: an inability to generate internal cash flow and volatility in working capital primarily cause the financing deficit of underperforming entities, whereas these factors are not determinant for solvent corporations. Finally, the cash flow sensitivity of cash model confirms that a precautionary motive dictates the cash accumulation policies of zombie firms, a behavior absent in financially viable entities.
