This paper examines if money matters in education by looking at whether missing resources due to corruption affect student outcomes. We use data from the auditing of Brazil's local governments to construct objective measures of corruption involving educational block grants transferred from the central government to municipalities. Using variation in the incidence of corruption across municipalities and controlling for student, school, and municipal characteristics, we find a significant negative association between corruption and the school performance of primary school students. Students residing in municipalities where corruption in education was detected score 0.35 standard deviations less on standardized tests, and have significantly higher dropout and failure rates. Using a rich dataset of school infrastructure and teacher and principal questionnaires, we also find that school inputs such as computer labs, teaching supplies, and teacher training are reduced in the presence of corruption. Overall, our findings suggest that in environments where basic schooling resources are lacking, money does matter for student achievement.

This article analyzes the determinants of local institutions in Brazil. We show

that institutional quality and distribution of land are partly inherited from the

colonial histories experienced by different areas of the country. The sugar cane

boom—characterized by an oligarchic society—is associated with more land

inequality. The gold boom—characterized by a heavily inefficient presence of

the Portuguese state—is associated with worse governance and access to justice.

We do not find similar effects for a postcolonial boom (coffee). We also find

that the colonial episodes are correlated with lower provision of public goods

What are the costs and benefits of mandatory dividend rules? On the one hand, they make it harder for controlling shareholders to divert corporate assets. On the other hand, they reduce the internal funds available for firms to invest, possibly leading to the loss of valuable projects. To assess this trade-off, we look at investment and dividend decisions in a sample of public firms in Brazil. We show that a significant fraction of these firms use loopholes of Brazil's mandatory dividend rules to avoid paying dividends. And yet, the dividend rules are effective. They help explain why the average dividend yield in Brazil is higher than in the U.S., without making it harder for firms to invest.

This paper investigates to what extent the persistence of Microsoft Windows in the market for server operating systems is due to lock-in or unobserved preferences. While the hypothesis of lock-in plays an important role in the antitrust policy debate for the operating systems market, it has not been extensively documented empirically. To account for unobserved preferences, we use a panel data identification approach based on time-variant group fixed effects, and estimate the dynamic discrete choice panel data model developed by Arellano and Carrasco (2003). Using detailed establishment-level data, we find that once we account for unobserved preferences, the estimated magnitudes of lock-in are considerably smaller than those from the conventional approaches, suggesting that unobserved preferences play a major role in the persistence of Windows. Further robustness checks are consistent with our findings.

Since the conquest of hyperinflation, with the Real Plan, in 1994, the Brazilian financial system has grown from early infancy to late adolescence. We describe the process of maturing with emphasis on the defining features of the Brazilian financial system over the last 20 years: (1) stabilization and the subsequent financial crisis; (2) universality of banks; (3) market segmentation through public lending; (4) institutional improvement. Further paraphrasing Diaz-Alejandro (1985), we raise some hypotheses on why, this time, the financial boom has not (at least yet) turned into a financial crash.

This paper argues that conflicting results from previous literature—related

to the effect of economic conditions on child labor—derive from different

income and substitution effects implicit in different types of income variation.

We use agricultural shocks to local economic activity in Brazil (coffee

production) to distinguish between increases in household income and increases

in the opportunity cost of time. Results show that higher household

wealth is associated with lower child labor and higher schooling. Nevertheless,

temporary increases in local economic activity are associated with

higher child labor and lower schooling, particularly for children with poor

economic backgrounds.

In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.

Most models of optimalincometaxenforcement assume that income is either random or solely remunerates labor, neglecting that auditing strategies may depend on observable inputs. This paper outlines a model to optimally monitor self-employed entrepreneurs when, in addition to reported profits, the taxcollection agency also observes the number of workers employed (or any other input variable) at each firm. We show that, by conditioning the monitoring strategy only on labor input, it is optimal for the IRS to audit firms in a way that generates some empirical regularities, like the missing middle. We also show that theoptimal direct mechanism can be implemented by an indirect monitoring strategy that is consistent with actual IRS practices. In particular, the IRS calculates inputted income as function of labor. Whenever an entrepreneur reports profits that are lower than inputted income, she is randomly monitored. Finally, we formalize a model of optimal presumption taxation, in which inputted income is the tax base, to compare revenue collection across tax systems.

In September 2008, a six-year-old article about the 2002 bankruptcy of United Airlines' parent company resurfaced on the Internet and was mistakenly believed to be reporting a new bankruptcy filing by the company. This episode caused the company's stock price to drop by as much as 76% in just a few minutes, before NASDAQ halted trading. After the “news” had been identified as false, the stock price rebounded, but still ended the day 11.2% below the previous close. We explore this natural experiment by using a simple asset-pricing model to study the aftermath of this false news shock. We find that, after three trading sessions, the company's stock was still trading below the two-standard-deviation band implied by the model and that it returned to within one standard deviation only during the sixth trading session. On the seventh day after the episode, the stock was trading at the level predicted by the asset-pricing model. We investigate several potential explanations for this finding, but fail to find empirical evidence supporting any of them. We also document that the false news shock had a persistent negative effect on the stock prices of other major airline companies. This is consistent with the view that contagion effects would have dominated competitive effects had the bankruptcy actually taken place.

In this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find: (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes' structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging). Copyright © 2010 John Wiley & Sons, Ltd.

Nonlinear regressionmodels have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular,smoothtransitionregression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discussmoment-based methods for estimating STR models. The paper analyzes the properties of the STR modelwith endogenousvariables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models withendogenousvariables can be specified and estimated by a straightforward application of existing results in the literature.

In this paper, we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high-frequency intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.

We show that political institutions affect corruption levels. We use audit reports in Brazil to construct new measures of political corruption in local governments and test whether electoral accountability affects the corruption practices of incumbent politicians. We find significantly less corruption in municipalities where mayors can get reelected. Mayors with reelection incentives misappropriate 27 percent fewer resources than mayors without reelection incentives. These effects are more pronounced among municipalities with less access to information and where the likelihood of judicial punishment is lower. Overall our findings suggest that electoral rules that enhance political accountability play a crucial role in constraining politician's corrupt behavior

Whether campaign advertising influences election outcomes is an open question; a paradox given the amount spent on campaigning in general and TV advertising in particular. We argue that such “absence of documentation” is due to the focus of the empirical literature on the U.S., where the allocation of campaign spending and advertising is decentralized. We explore a quasi-natural experiment that enables us to mitigate the omitted variables and reverse causality problems caused by decentralized allocation. In Brazil, gubernatorial elections work in a two-round system. In the first round, candidates' TV time shares are determined by their coalitions' share of seats in the National Parliament. In the second round, TV time is split equally between the first-round winner and runner-up. Using differences between rounds as a source of variation, we find a large causal effect of TV advertising on election outcomes.

We study the purchasing power parity (PPP) puzzle in a multisector, two-country, sticky-price model. Sectors differ in the extent of price stickiness, leading to heterogeneous sectoral real exchange rate dynamics. Deviations from PPP are more volatile and persistent than in an otherwise identical one-sector world economy with the same average frequency of price changes. Under the empirical distribution of price stickiness of the US economy, the model produces PPP deviations with a half-life of 39 months. We provide a structural interpretation of the approaches found in the empirical literature on aggregation and PPP, and reconcile its apparently conflicting findings.

This paper considers the problem faced by two regulators in providing incentives to a common (privately informed) regulated firm under various degrees of coordination. In the model, the firm exerts effort toward cost reduction and self-dealing, and incentives can be input-based (monitoring) and output-based (demanded cost targets). Full coordination between the regulators leads to the second best allocation. A setting in which the regulators do not fully coordinate leads to (i) higher overall monitoring (more aggressive input-based incentives) and (ii) higher demanded cost targets (i.e., more lenience in terms of output-based incentives). As a consequence of (i,0), in all possible equilibria, the effort toward cost reduction will be smaller when the agent reports to two regulators who do not coordinate. (i) and (ii) imply that the impact on the effort toward self-dealing activities is ambiguous. In our leading example, self-dealing will be larger if the regulators coordinate on monitoring levels but smaller if they choose monitoring levels independently.