Latin American Journal of Central Banking最新文献

It is widely accepted that episodes of social unrest, conflict, political tensions and policy uncertainty affect the economy. Nevertheless, the real-time dimension of such relationships is less studied, and it remains unclear how to incorporate them in a forecasting framework. This can be partly explained by a certain divide between the economic and political science contributions in this area, as well as the traditional lack of availability of timely high-frequency indicators measuring such phenomena. The latter constraint, though, is becoming less of a limiting factor through the production of text-based indicators. In this paper we assemble a dataset of such monthly measures of what we call “institutional instability”, for three representative emerging market economies: Brazil, Colombia and Mexico. We then forecast quarterly GDP by adding these new variables to a standard macro-forecasting model using different methods. Our results strongly suggest that capturing institutional instability above a broad set of standard high-frequency indicators is useful when forecasting quarterly GDP. We also analyse relative strengths and weaknesses of the approach.

In late 2021 and early 2022, CEMLA conducted a thorough survey involving 12 central banks to analyze key aspects related to the potential implementation of a Central Bank Digital Currency (CBDC). The findings revealed prevalent issues and objectives regarding digital payments in the region, as well as promising avenues for central bank involvement. However, it highlighted the challenges of creating a universal CBDC framework due to significant disparities among neighboring countries. Nonetheless, the collaborative research approach promises valuable insights for future efforts. To protect participant anonymity, each country’s responses were anonymized with randomly assigned letters.

The survey extensively examined awareness and understanding of CBDCs across diverse stakeholders in Latin American nations, including the general public, corporations, and financial institutions. These insights could shape educational campaigns and communication strategies, enabling policymakers and central banks to effectively share information about CBDCs.

Moreover, this research effort sheds light on public perceptions, expectations, and concerns about CBDCs in Latin America. By exploring various dimensions such as awareness, interest, adoption potential, concerns, preferences, and regulatory considerations, the survey offers a comprehensive understanding of the CBDC landscape in the region. This holistic approach provides valuable insights for policymakers, central banks, and stakeholders to navigate the complexities of CBDC implementation effectively and responsibly.

We analyze the repercussions of different kinds of uncertainty on cash demand, including uncertainty of cashless infrastructures, confidence crises of the financial system, natural disasters, political uncertainties, and inflationary crises. Based on a comprehensive literature survey and complemented by case studies, we derive a classification scheme how cash holdings typically evolved in each of these types of uncertainty over the past 30 years by separating between demand for domestic and international cash as well as between transaction and store of value balances. Hereby, we focus on the stabilizing macroeconomic properties of cash and recommend guidelines for cash supply by central banks and the banking system. Finally, we exemplify our analysis with five case studies from the developing world, namely Venezuela, Zimbabwe, Afghanistan, Iraq, and Libya.

We extend a standard multivariate filter used to estimate the Output Gap (OG) in Chile to account for large economic shocks, such as those observed during the COVID-19 crisis. We propose two methodological extensions. First, we introduce exogenous supply shocks in the dynamics of potential output growth. Second, we add “system” prior distributions on a set of characteristics of the model. We show that these extensions improve several desirable properties of Chile’s OG, such as stability, accuracy, consistency and also reduce the uncertainty surrounding the estimates. Next, we generalize our results by applying our methodology to a panel of countries, showing that the extended filter yields a tighter relationship between inflation developments and the OG, when compared to its standard counterpart.

Households in emerging economies are subject to significant income risk and have low access to financial markets. Leveraging multiple administrative microdata sources, this paper documents significant heterogeneity in asset holdings, income, and income cyclicality across the distribution of Chilean households, as well as considerable income risk. Considering this evidence, we compare the transmission mechanisms between Heterogeneous-Agent New-Keynesian models with search and matching (SAM) and sticky wage frictions (SW), and between one-liquid-asset (OA) and two-asset (TA) specifications. We propose a decomposition of consumption responses into direct, indirect, average, and cross-sectional effects. We show that the transmission mechanisms depend on the labor market setup: in SAM-OA the transmission operates through average and direct effects, while in SW-OA it is through cross-sectional effects. Assets also matter, the transmission in the SW-TA has stronger direct and average effects than SW-OA.

This paper analyzes how monetary policy surprises in Chile affects the real and financial sector separating between a pure monetary policy shock and an information shock. Using inter-day movements of futures of interest rate in the banking system, we identify an information shock when labor data is released and a pure monetary policy shock when the central bank reveals their interest rate decision, and their effects are quantified through an external vector autoregression model. Our results suggest that a pure monetary policy shock produce an appreciation of nominal exchange rate, and contractionary effects on the economy. However, an information shock does not necessarily produce adverse effects. This paper contribute to the literature in two dimensions: studying the effect of the main driver behind the central bank announcements, and their transmission to the banking sector and consequently to the real and monetary sector.

This research introduces an innovative GDP nowcasting strategy tailored for developing countries, specifically addressing challenges related to limited data timeliness. The study centers on Bolivia, where the official monthly indicator of economic growth is released with a substantial delay of up to six months. The proposed nowcast estimates effectively narrow this gap from six to two months. This advancement is achieved through the integration of machine learning techniques with data comprising indicators from traditional sources and statistics derived from satellite imagery. The robustness of this approach is rigorously validated using various criteria, including performance comparisons with conventional econometric methods and sensitivity assessments to different feature sets. Beyond enhancing the understanding of Bolivia’s economic dynamics, this research establishes a framework for analogous analyses in regions grappling with information availability challenges.

In a descriptive way, this paper analyzes the transitions in the Bolivian labor market during the Covid19 pandemic to comprehend the patterns and trends of worker transitions across states related to employment, unemployment, temporarily inactive, permanently inactive; also for employed people like salaried, self-employed, unpaid family worker, other and finally for different economic activities. The information used corresponds to the Continuous Employment Survey reported by the National Institute of Statistics of Bolivia which has the virtue of following the same individuals in more than two periods. Labor transition probabilities according to the Markov chain process between the first and third quarter for 2019 and 2020 were obtained and allowed to observe important changes in the Bolivian labor market during the Covid-19 pandemic. Particularly, unemployed persons were in a more vulnerable situation than those who were inactive; however, due to the measures implemented during the peak of the pandemic the probability to flow to inactivity was higher. On the other hand, at first the emergency reduces the possibility for self-employed people to remain in this category; nevertheless, in the following period the self-employed status was an advantageous one.

We provide evidence on the evolution of worker reallocation in Italy and Spain during the pandemic. In both countries, job-to-job transition rates fell in 2020 but improved in 2021 in aggregate. We then focus on cross-sectoral mobility following job separation. In Italy, we find a modest increase in sectoral reallocation in 2020, reaching 4 percentage points at the end of 2021—around 11 percent of the pre-pandemic average reallocation rate. In contrast, there are no statistically significant differences in the reallocation probability with respect to the pre-pandemic average in Spain.