The effects of adult literacy on earnings and employment

This paper provides evidence of the effects of adult literacy on individuals’ income and employability in Brazil based on information obtained from the monthly employment survey (PME). The OLS results indicate that after controlling for observable characteristics, there is a 21.25% increase in wages for individuals who become literate; however, there is no significant impact on employability. Moreover, the findings show an 8.1% increase in the probability of being employed in the formal sector. We also explore the longitudinal structure of the dataset to control for unobservable fixed characteristics of individuals. The fixed-effects estimators show smaller effects compared to the OLS estimators. We find that literacy has a 4.4% effect on wages and a 4.3% impact on the probability of being formally employed. The effects are significantly different from zero.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Uma técnica para modelagem de cenários consistentes na previsão da demanda por energia elétrica

As técnicas qualitativas disponiveis para a modelagem de cenários têm sido reconhecidas pela extrema limitação, evidenciada no principio das atividades do processo, como a fase inicial de concepção. As principais restrições têm sido: • inexistência de uma ferramenta que teste a consistência estrutural interna do modelo, ou pela utilização de relações econômicas com fundamentação teórica mas sem interface perfeita com o ambiente, ou pela adoção de variações binárias para testes de validação; • fixação "a priori" dos possíveis cenários, geralmente classificados sob três adjetivos - otimista, mais provável e pessimista - enviesados exatamente pelos atributos das pessoas que fornecem esta informação. o trabalho trata da utilização de uma ferramenta para a interação entre uma técnica que auxilia a geração de modelos, suportada pela lógica relacional com variações a quatro valores e expectativas fundamentadas no conhecimento do decisor acerca do mundo real. Tem em vista a construção de um sistema qualitativo de previsão exploratória, no qual os cenários são obtidos por procedimento essencialmente intuitivo e descritivos, para a demanda regional por eletricidade. Este tipo de abordagem - apresentada por J. Gershuny - visa principalmente ao fornecimento de suporte metodológico para a consistência dos cenários gerados qualitativamente. Desenvolvimento e estruturação do modelo são realizados em etapas, partindo-se de uma relação simples e prosseguindo com a inclusão de variáveis e efeitos que melhoram a explicação do modelo. o trabalho apresenta um conjunto de relações para a demanda regional de eletricidade nos principais setores de consumo residencial, comercial e industrial bem como os cenários resultantes das variações mais prováveis das suas componentes exógenas. Ao final conclui-se que esta técnica é útil em modelos que: • incluem variáveis sociais relevantes e de dificil mensuração; • acreditam na importância da consistência externa entre os resultados gerados pelo modelo e aqueles esperados para a tomada de decisões; • atribuem ao decisor a responsabilidade de compreender a fundamentação da estrutura conceitual do modelo. Adotado este procedimento, o autor aqui recomenda que o modelo seja validado através de um procedimento iterativo de ajustes com a participação do decisor. As técnicas quantitativas poderão ser adotadas em seguida, tendo o modelo como elemento de consistência.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

The effects of small firm tax incentives on employment levels

This paper aims to evaluate the impact on employment growth of a tax incentive program targeting Brazilian manufacturing small businesses (SIMPLES). This evaluation is conducted for two distinct periods: for the year 1997, when the program was first implemented, and for the year 1999, when the eligibility rule was modified to allow the eligibility of a broader group of firms. The evaluation takes into account two distinct channels through which the charted effects operate. The first is the employment variation in the firms that became eligible for the incentives, and the second is the change in the survival probability experienced by the same group of firms. Moreover, each of these channels can be activated either by the tax reduction dimension of the program or by its dimension of red tape simplification. Our results identify positive effects on employment growth for the tax incentive program only in the dimension of red tape simplification and its effects on the 1997 sample.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

The effects of openness on industrial employment in Brazil

Este artigo tem dois objetivos principais. Primeiramente, apresentamos as pnnClpms evidências de que a baixa qualidade do emprego é o maior problema do mercado de trabalho brasileiro. Mostra-se que o país tem absorvido um crescimento da oferta de trabalho significativo ao longo dos últimos anos sem registrar um aumento da taxa de desemprego. No entanto, os postos de trabalho no Brasil são, em média, extremamente precários. Em grande medida, a precariedade do emprego no Brasil está relacionada à alta rotatividade da mão-de-obra, que desincentiva o investimento em treinamento, impedindo o crescimento da produtivid::;de do trabalho. De acordo com os indicadores passíveis de comparação internacional, o Brasil apresenta uma das maiores taxas de rotatividade do mundo. Em segundo lugar, estuda-se a evolução recente do emprego industrial, uma vez que o setor industrial está tradicionalmente associado à geração de bons empregos no Brasil. Mostra-se que o nível de emprego industrial tem se reduzido de forma praticamente contínua desde o início desta década. A estimação de um modelo de ajustamento parcial do emprego nos permite explicar este fenômeno. Mudanças estruturais significativas na elasticidade custo salarial do emprego e no coeficiente de tendência são detectadas a partir do início da década de 90. Um simples modelo teórico é usado de forma a interpretar estas mudanças estruturais como a resposta ótima das empresas industriais ao ambiente de maior competição externa, devido à abertura comercial. Isto, junto com o uso crescente de tecnologias poupadoras de mão-de-obra, são suficientes para explicar a brutal queda do emprego industrial no Brasil ao longo dos últimos seis anos.

The effects of the minimum wage on earnings and employment in Brazil

This paper analyzes the effects of the mlmmum wage on both, eammgs and employment, using a Brazilian rotating panel data (Pesquisa Mensal do Emprego - PME) which has a similar design to the US Current Population Survey (CPS). First an intuitive description of the data is done by graphical analysis. In particular, Kemel densities are used to show that an increase in the minimum wage compresses the eamings distribution. This graphical analysis is then forrnalized by descriptive models. This is followed by a discussion on identification and endogeneity that leads to the respecification of the model. Second, models for employment are estimated, using an interesting decomposition that makes it possible to separate out the effects of an increase in the minimum wage on number of hours and on posts of jobs. The main result is that an increase in the minimum wage was found to compress the eamings distribution, with a moderately small effect on the leveI of employment, contributing to alleviate inequality.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.

Automatic model selection for forecasting Brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

How to manage macroeconomic policies to foster growth and employment and prevent financial and external crises

My presentation focuses on the implementation of a macroeconomic policy regime which, I believe, is capable of simultaneously attaining several targets, including the promotion of growth and employment and the prevention of external and financial crises.