Latin American Journal of Central Banking最新文献

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.

Chile implemented large pension withdrawals during the pandemic relative to other countries. Afterwards, Chile increased non-contributory benefits in a quasi-universal scheme. Simulating the future pensions, I show that the average loss in contributory pension income is 27.9%, with losses of 23.9% and 31.4% for men and women, respectively. After accounting for public transfers, the average loss in total pension income is just 6.2%, with losses of 7.5% and 5.2% for men and women, respectively. Current retirees lost just 1.1% of their pension income after accounting for the government transfers. The state may end up covering 92% of the total value of the pension withdrawals through the increased transfers.

The output gap, while inherently unobservable, plays a pivotal role in informing policymakers due to its significant implications for forecasting inflation rates and understanding the mechanisms of monetary policy transmission. Traditional filters frequently employed in estimating the output gap face criticism for being purely statistical and lacking economic theory. Conversely, estimates derived from Dynamic Stochastic General Equilibrium (DSGE) models encounter challenges stemming from the assumption of constant parameters over time. This study focuses on estimating the output gap for Brazil, employing a full specified DSGE model that incorporates Markov-Switching elements (MS-DSGE) to account for potential regime shifts. We introduce four model versions, some of which incorporate variations in volatilities and Taylor’s rule parameters. In order to compare our output gap estimate with other approaches, we perform prediction tests, both with the central bank’s reaction function and with the free price inflation Phillips curve. Our results in the first test indicate that the HP (Hodrick–Prescott) filter estimate performs better in the short and mid-term, but the MS-DSGE estimate presented better results in the long run. In the second exercise, no output gap series stands out among the approaches considered. In this context, the estimated output gap from the MS-DSGE framework emerges as a valuable asset within the arsenal available to policymakers, contributing meaningfully to their analytical toolkit.

This paper estimates a standard Dynamic Stochastic General Equilibrium (DSGE) model that includes a wage and price Phillips curves with different expectation formation processes for Brazil and the USA. Other than the standard rational expectation process, we also use a limited rationality process, the adaptative learning model. In this context, we show that the separate inclusion of a labor market in the model helps to anchor inflation even in a situation of adaptive expectations, a positive output gap and inflation above target. The estimation results show that the adaptive learning model does a better job in fitting the data in Brazil. In addition, the estimation shows that expectations are more backward-looking and started to drift away sooner in 2021 in Brazil than in the USA. We then conduct optimal policy exercises that prescribe front-loading monetary policy tightening and easing earlier than the estimated monetary policy rule in the context of positive output gaps and inflation far above the central bank target.

Emerging Market and Developing Economies (EMDE) countries are the leading destinations of Foreign Direct Investment (FDI). We investigate whether prudent monetary and fiscal policy through indicators that reflect the expectations concerning the central bank's commitment to a target and the sustainability of government finance affects FDI inflows. Based on a large sample of 75 EMDE countries from 1990 to 2019, we provide empirical evidence through panel data analysis that prudent macroeconomic policies are an essential driver of FDI inflows. The findings support the view that using prudent monetary and fiscal policy can help enhance FDI inflows in EMDE countries.