This paper documents the persistence of human capital over time and its association with long-term development. We exploit variation induced by a state-sponsored settlement policy that attracted immigrants with higher levels of schooling to particular regions of Brazil in the late nineteenth and early twentieth century. We show that one century after the policy, municipalities that received settlements had higher levels of schooling and higher income per capita. We provide evidence that long-run effects worked through higher supply of educational inputs and shifts in the structure of occupations toward skill-intensive sectors

We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso‐type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics as well as the performance of the proposed models for forecasting the realized covariance matrices of the 30 Dow Jones stocks. We find that the dynamics are not stable as the data are aggregated from the daily to lower frequencies. Furthermore, we are able beat our benchmark by a wide margin. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts. Copyright © 2016 John Wiley & Sons, Ltd.

In the aftermath of the Taper Tantrum, the Central Bank of Brazil announced a major program of sterilized foreign exchange intervention. We use a synthetic control approach to estimate its impact on the level and volatility of the exchange rate. Our counterfactual results, based on the experience of other emerging markets, indicate the program led to an appreciation of the Brazilian real in excess of 10 percent. Some of our estimates also point to a decline in the option-implied volatility. A second announcement extending the program had more muted effects, and subsequent extensions had little or no impact.

In this paper, we show the validity of the adaptive least absolute shrinkage and selection operator (LASSO) procedure in estimating stationary autoregressive distributed lag(p,q) models with innovations in a broad class of conditionally heteroskedastic models. We show that the adaptive LASSO selects the relevant variables with probability converging to one and that the estimator is oracle efficient, meaning that its distribution converges to the same distribution of the oracle-assisted least squares, i.e., the least square estimator calculated as if we knew the set of relevant variables beforehand. Finally, we show that the LASSO estimator can be used to construct the initial weights. The performance of the method in finite samples is illustrated using Monte Carlo simulation.

All things equal, interest rates should increase with the borrower's risk. And yet, Klapper, Laeven, and Rajan (2012) cannot find such a positive relation in a broad sample of trade credit contracts. We shed some light on this puzzle by arguing that competition between informed and uninformed suppliers weakens the link between the trade credit cost and the borrower's creditworthiness. Our model implies that trade credit rates are more likely to increase with the borrower's risk if suppliers are less profitable, have high cost of funds, or sell inputs to firms plagued by moral hazard and financial distress.

Once driven by large-scale clearings, Amazon deforestation now occurs mostly in small increments. Did this result from the emergence of a new group of agents or from a strategic adaptation in the behavior of those who led deforestation in the past? We address this question using georeferenced data on private rural properties and deforestation. We cross property-level and forest clearing data in an empirical setting designed to detect shifts towards clearing patches that were knowingly invisible to the monitoring system. We are therefore able to assess not only whether deforesters were responding strategically to stricter monitoring of deforestation, but also how this response differed across actor types. Results suggest that centralized policy efforts introduced starting in the mid-2000s inhibited medium- and large-scale deforestation, but had heterogeneous effects on small-scale deforestation. Although the relative participation of small deforestation polygons increased in both sample states, the relative participation of smallholders in total state deforestation increased in Pará, while remaining constant in Mato Grosso. We interpret these results as suggestive --- albeit not causal --- evidence that landholders strategically responded to the monitoring system by adapting their forest clearings practices to elude monitoring in both Mato Grosso and Pará. In the latter, however, the increase in smallholders' share of annual deforestation suggests that their clearing practices were relatively less affected by what effectively contained deforestation in large properties. The apparent similarity in scale of deforestation across states conceals relevant baseline differences between the agents engaging in forest clearing in each locality. Tailoring policy to account for such differences could strengthen Brazilian conservation policy

The British war effort in the Second World War depended on US Lend-Lease and the accumulation of sterling balances by other countries, including the Empire. By the end of the war outstanding balances were equivalent to 60 per cent of British net receipts under Lend-Lease. Of the total sterling balances, about a third was accumulated by India. This article seeks to evaluate the costs incurred by India in the reduction of balances after the war. The accumulation of balances and their use to repatriate India's sterling debt is described. British efforts to convince India to accept a partial cancellation of the balances are analysed, singling out the crucial role of Keynes. The negotiations after independence are detailed, including releases, transfers to Pakistan, settlement of pensions, purchase of military stores, and gold sales. The possible contribution of British divestment to reduce outstanding balances is assessed. The Indian case is compared with those of other holders, such as Portugal, Brazil, Argentina, and Egypt. The links between the accumulation of sterling balances and inflation are considered. In the end there was a significant reduction in the purchasing power of sterling balances, but not for the reasons anticipated by London.

We show that high-dimensional econometric models, such as shrinkage and complete subset regression, perform very well in the real-time forecasting of inflation in data-rich environments. We use Brazilian inflation as an application. It is ideal as an example because it exhibits a high short-term volatility, and several agents devote extensive resources to forecasting its short-term behavior. Thus, precise forecasts made by specialists are available both as a benchmark and as an important candidate regressor for the forecasting models. Furthermore, we combine forecasts based on model confidence sets and show that model combination can achieve superior predictive performances.

This article explores the effect of an industry’s market structure on the liquidation value of assets. We show that when firms with financial constraints compete for the gains arising from market concentration, they expend insufficient efforts to deploy assets across industries, leading to significant liquidation discounts when compared with an efficient benchmark. Equilibrium distress costs and private costs of leverage should increase with the rents linked to concentration in the product market

This paper evaluates the impact of climate change on agricultural productivity. Cross-sectional variation in climate among Brazilian municipalities is used to estimate an equation in which geographical attributes determine agricultural productivity. The Intergovernmental Panel on Climate Change (IPCC) predictions based on atmosphere-ocean, coupled with general circulation models (for 2030-2049), are used to simulate the impacts of climate change. Our estimates suggest that global warming under the current technological standards is expected to decrease the agricultural output per hectare in Brazil by 18 per cent, with the effects onmunicipalities ranging from -40 to +15 per cent. 

Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level. 

We develop a multisector model in which capital and labor are free to move across firms within each sector, but cannot move across sectors. To isolate the role of sectoral specificity, we compare our model with otherwise identical multisector economies with either economy-wide or firm-specific factor markets. Sectoral factor specificity generates within-sector strategic substitutability and tends to induce across-sector strategic complementarity in price setting. Our model can produce either more or less monetary non-neutrality than those other two models, depending on parameterization and the distribution of price rigidity across sectors. Under the empirical distribution for the U.S., our model behaves similarly to an economy with firm-specific factors in the short-run, and later on approaches the dynamics of the model with economy-wide factor markets. This is consistent with the idea that factor price equalization might take place gradually over time, so that firm-specificity may serve as a reasonable short-run approximation, whereas economy-wide markets are likely a better description of how factors of production are allocated in the longer run.

We show that high-dimensional models produce, on average, smaller forecasting errors for macroeconomic variables when we consider a large set of predictors. Our results showed that a good selection of the adaptive LASSO hyperparameters also reduces forecast errors

We show that data-driven instrument selection based on the LASSOestimator can perform well comparative to the usual ad hoc instrument set for single equation estimation of a forward-looking Phillips Curve, when the overall identification condition is strong or in cases when the instruments are not very weak. We conclude that in face of model uncertainty and/or some potentially weak instruments within a large number of candidates, data-driven selection may provide a disciplined and more reliable estimation strategy.

In many economic applications, it is often of interest to categorize, classify, or label individuals by groups based on similarity of observed behavior. We propose a method that captures group affiliation or, equivalently, estimates the block structure of a neighboring matrix embedded in a Spatial Econometric model. The main results of the Least Absolute Shrinkage and Selection Operator (Lasso) estimator shows that off-diagonal block elements are estimated as zeros with high probability, property defined as “zero-block consistency.” Furthermore, we present and prove zero-block consistency for the estimated spatial weight matrix even under a thin margin of interaction between groups. The tool developed in this article can be used as a verification of block structure by applied researchers, or as an exploration tool for estimating unknown block structures. We analyzed the U.S. Senate voting data and correctly identified blocks based on party affiliations. Simulations also show that the method performs well.

I investigate the optimal auditing scheme for a revenue-maximizing tax collection agency that observes not only reported profits, but also a single factor of production at each firm. Each firm is owned by a single entrepreneur whose managerial ability is random. The optimal auditing scheme is discontinuous and nonmonotone in ability. In intermediate audit costs, less productive entrepreneurs face auditing probabilities that increase in ability, whereas the ablest ones are not audited. Finally, the effective tax rate is higher in the middle of the managerial ability distribution; thus, the overall regressive (or progressive) bias that arises from evasion is unknown.

In this paper we use high-dimensional models, estimated by the Least Absolute Shrinkage and Selection Operator (LASSO), to forecast the Brazilian inflation. The models are compared to  benchmark specifications such as linear autoregressive (AR) and the factor models based on principal components. Our results showed that the LASSO-based specifications have the smallest errors for short-horizon forecasts. However, for long horizons the AR benchmark is the best model with respect to point forecasts. The factor model also produces some good long horizon forecasts in a few cases. We estimated all the models for the two most important Brazilian inflation measures, the IPCA and the IGP-M indexes. The results also showed that there are differences on the selected variables for both measures. Finally, the most important variables selected by the LASSO based models are, in general, related to government debt and money. On the other hand, variables such as unemployment and production were rarely selected by the LASSO.

We study the impact of emigration on local labor markets, based on the construction of a 1,087km road in northeastern Brazil. The new road has changed population substantially, creating new cities along its path and increasing internal migration flows. We first use a reduced-form approach to estimate the effect of emigration on skill groups (defined by education and experience) - a 10 percentage point increase in the proportion of emigrants raises wages by 5%. Then, using a structural approach, we estimate cross effects among groups - although emigration typically raises wages, complementary effects determine negative impacts in some municipalities.