Escola de Economia de São Paulo

This chapter provides a comprehensive overview of decomposition methods that have been developed since the seminal work of Oaxaca and Blinder in the early 1970s. These methods are used to decompose the difference in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, our main focus is on other distributional statistics besides the mean such as quantiles, the Gini coefficient or the variance. We discuss the assumptions required for identifying the different elements of the decomposition, as well as various estimation methods proposed in the literature. We also illustrate how these methods work in practice by discussing existing applications and working through a set of empirical examples throughout the paper.

Using a high‐stakes field experiment conducted with a financial brokerage, we implement a novel design to separately identify two channels of social influence in financial decisions, both widely studied theoretically. When someone purchases an asset, his peers may also want to purchase it, both because they learn from his choice (“social learning”) and because his possession of the asset directly affects others' utility of owning the same asset (“social utility”). We randomize whether one member of a peer pair who chose to purchase an asset has that choice implemented, thus randomizing his ability to possess the asset. Then, we randomize whether the second member of the pair: (i) receives no information about the first member, or (ii) is informed of the first member's desire to purchase the asset and the result of the randomization that determined possession. This allows us to estimate the effects of learning plus possession, and learning alone, relative to a (no information) control group. We find that both social learning and social utility channels have statistically and economically significant effects on investment decisions. Evidence from a follow‐up survey reveals that social learning effects are greatest when the first (second) investor is financially sophisticated (financially unsophisticated); investors report updating their beliefs about asset quality after learning about their peer's revealed preference; and, they report motivations consistent with “keeping up with the Joneses” when learning about their peer's possession of the asset. These results can help shed light on the mechanisms underlying herding behavior in financial markets and peer effects in consumption and investment decisions.

Changes in agricultural land use have important implications for environmental services. Previous studies of agricultural land-use futures have been published indicating large uncertainty due to different model assumptions and methodologies. In this article we present a first comprehensive comparison of global agro-economic models that have harmonized drivers of population, GDP, and biophysical yields. The comparison allows us to ask two research questions: (1) How much cropland will be used under different socioeconomic and climate change scenarios? (2) How can differences in model results be explained? The comparison includes four partial and six general equilibrium models that differ in how they model land supply and amount of potentially available land. We analyze results of two different socioeconomic scenarios and three climate scenarios (one with constant climate). Most models (7 out of 10) project an increase of cropland of 10–25% by 2050 compared to 2005 (under constant climate), but one model projects a decrease. Pasture land expands in some models, which increase the treat on natural vegetation further. Across all models most of the cropland expansion takes place in South America and sub-Saharan Africa. In general, the strongest differences in model results are related to differences in the costs of land expansion, the endogenous productivity responses, and the assumptions about potential cropland.

According to the development approach to exchange rates, competitive currencies have been a key factor in most East and Southeast Asian successful growth strategies. There is also today an important empirical literature that relates overvaluations to low per capita growth rates. While the econometric literature on this issue is relatively rich, theoretical analysis of channels through which real exchange rate levels could affect economic development are very scarce. This paper intends to contribute to the debate by bringing more theoretical elements and providing new econometric evidence to the connections between real exchange rate levels and development.

This paper estimates the welfare effects of Brexit in the medium to long run, focusing on trade and fiscal transfers. We use a standard quantitative general equilibrium trade model with many countries and sectors and trade in intermediates. We simulate a range of counterfactuals reflecting alternative options for European Union (EU)–United Kingdom (UK) relations following Brexit. Welfare losses for the average UK household are 1.3% if the UK remains in the EU’s Single Market like Norway (a ‘soft Brexit’). Losses rise to 2.7% if the UK trades with the EU under World Trade Organization rules (a ‘hard Brexit’). A reduced-form approach that captures the dynamic effects of Brexit on productivity more than triples these losses and implies a decline in average income per capita of between 6.3% and 9.4%, partly via falls in foreign investment. The negative effects of Brexit are widely shared across the entire income distribution and are unlikely to be offset from new trade deals.

Abstract We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specification searching when the number of pre‐treatment periods is in line with common SC applications, and when alternative specifications commonly used in SC applications are also considered. This suggests that such lack of guidance generates a substantial level of discretion in the choice of the comparison units in SC applications, undermining one of the advantages of the method. We provide recommendations to limit the possibilities for specification searching in the SC method. Finally, we analyze the possibilities for specification searching and provide our recommendations in a series of empirical applications.

This article provides field-experimental evidence on status goods. We work with an Indonesian bank that markets platinum credit cards to high-income customers. In a first experiment, we show that demand for the platinum card exceeds demand for a nondescript control product with identical benefits, suggesting demand for the pure status aspect of the card. Transaction data reveal that platinum cards are more likely to be used in social contexts, implying social image motivations. In a second experiment, we provide evidence of positional externalities from the consumption of these status goods. A final experiment provides suggestive evidence that increasing self-esteem causally reduces demand for status goods, indicating that social image might be a substitute for self-image.

We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favourably with existing IPW estimators, including augmented IPW estimators, in terms of efficiency, robustness, and higher-order bias. We illustrate our method with a study of the relationship between early Black–White differences in cognitive achievement and subsequent differences in adult earnings. In our data set, the early childhood achievement measure, the main regressor of interest, is missing for many units.

We use a growth model in which saving, fertility and labour market participation are endogenous, to quantify the cost that barriers to female labour force participation impose in terms of an economy’s output. The model is calibrated to mimic the U.S. economy’s behavior in the long-run. We find that a 50 percent increase in the gender wage gap leads to a 35 percent decrease in income per capita in steady-state. Using independent estimates of the female to male earnings ratio for a wide cross-section of countries, we construct an economy with parameters similar to those calibrated for the U.S. economy, except for the degree of gender barriers. Higher discrimination decreases steady-state output per capita for two distinct reasons: a direct effect due to the decrease in female labour market participation, and an indirect effect working through an increase in fertility. For several countries, a large fraction of the difference between the country’s output and U.S. output can be ascribed to differences in gender discrimination. In addition, we find that close to half of the overall decrease in output per capita is due to the effect of gender discrimination in fertility.

We derive an inference method that works in differences-in-differences settings with few treated and many control groups in the presence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method remains valid in this case. Our test can also be combined with feasible generalized least squares, providing a safeguard against misspecification of the serial correlation.

An extensive survey data set of Brazilian households is used to test whether intrahousehold gender bias affects the decisions of mothers and fathers to send their sons and daughters to work and to school. An intrahousehold allocation model is examined in which fathers and mothers may affect the education investment and the child labor participation of their sons and daughters differently because of differences in parental preferences or differences in how additional schooling affects sons' and daughters' acquisition of human capital. Brazilian household survey data for 1998 are used to estimate the impact of each parent's education on the labor market participation and school attendance of their sons and daughters. For labor market participation, the father's education has a greater negative impact than the mother's education on the labor status of sons. The father's education also has a greater impact on sons' labor status than on daughters'. For schooling decisions, the mother's education has a greater positive impact than the father's education on daughters' school attendance, but fathers have a greater positive impact on sons' school attendance than on daughters'.

Summary Abadie et al. (2010) derive bounds on the bias of the synthetic control estimator under a perfect balance assumption on both observed covariates and pre-treatment outcomes. In the absence of a perfect balance on covariates, we show that it is still possible to derive such bounds, albeit at the expense of relying on stronger assumptions about the effects of observed and unobserved covariates and of generating looser bounds. We also show that a perfect balance on pre-treatment outcomes does not generally imply an approximate balance for all covariates, even when they are all relevant. Our results have important implications for the implementation of the method.

Purpose -To describe the use of specific lags (and/or temporal differences) of the original regressors as instrumental variables in a succinct and practical way, showing, by means of a theoretical discussion illustrated by an original simulation exercise, how combining these with adequate modeling of firm and time fixed effects can address not only the dynamic endogeneity problem, but also those derived from the presence of omitted variables, measurement errors, and simultaneity between dependent and independent variables.

We propose to smooth the objective function, rather than only the indicator on the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur–Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient quantile regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and quantile level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed quantile regression estimator indeed performs very well in finite samples. Supplementary materials for this article are available online.

This paper assesses the effects of market-based mechanisms and carbon emission restrictions on the Brazilian energy system by comparing the results of six different energy-economic or integrated assessment models under different scenarios for carbon taxes and abatement targets up to 2050. Results show an increase over time in emissions in the baseline scenarios due, largely, to higher penetration of natural gas and coal. Climate policy scenarios, however, indicate that such a pathway can be avoided. While taxes up to 32 US$/tCO2e do not significantly reduce emissions, higher taxes (from 50 US$/tCO2e in 2020 to 16 2US$/tCO2e in 2050) induce average emission reductions around 60% when compared to the baseline. Emission constraint scenarios yield even lower reductions in most models. Emission reductions are mostly due to lower energy consumption, increased penetration of renewable energy (especially biomass and wind) and of carbon capture and storage technologies for fossil and/or biomass fuels. This paper also provides a discussion of specific issues related to mitigation alternatives in Brazil. The range of mitigation options resulting from the model runs generally falls within the limits found for specific energy sources in the country, although infrastructure investments and technology improvements are needed for the projected mitigation scenarios to achieve actual feasibility.

This paper presents estimators of distributional impacts of interventions when selection to the program is based on observable characteristics. Distributional impacts are calculated as differences in inequality measures of the marginal distributions of potential outcomes of receiving and not receiving the treatment. The estimation procedure involves a first non-parametric estimation of the propensity score. In the second step weighted versions of inequality measures are computed using weights based on the estimated propensity score. Consistency, semi-parametric efficiency and validity of inference based on the percentile bootstrap are shown for the estimators. Results from Monte Carlo exercises show its good performance in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

Using a gravity-like approach, we study how COVID-19 deaths and lockdown policies affected countries' imports from China during 2020. We find that a country's own COVID-19 deaths and lockdowns significantly reduced its imports from China, suggesting that the negative demand effects prevailed over the negative supply effects of the pandemic. On the contrary, COVID-19 deaths in the main trading partners of a country (excluding China) induce more imports from China, partially offsetting countries' own effects. The net effect of moving from the pre-pandemic situation to another where the main variables are evaluated at their 2020 mean is, on average, a reduction of nearly 10% in imports from China. There is also significant heterogeneity. For example, the negative own effects of the pandemic vanish when we restrict the sample to medical goods and are significantly mitigated for products with a high 'work-from-home' share or a high contract intensity for products exported under processing trade and for capital goods. We also find that deaths and lockdowns in previous months tend to increase current imports from China, partially offsetting the contemporaneous trade loss, suggesting that trade is not simply 'destroyed,' but partially 'postponed'.

This paper provides evidence of the causal impact of oil discoveries on local development. Novel data covering the universe of oil wells drilled in Brazil allow us to exploit a quasi-experiment: Municipalities where oil was discovered constitute the treatment group, while municipalities with drilling but no discovery are the control group. The results show that oil discoveries significantly increase local production and have positive spillovers. Workers relocate from informal, low productivity agriculture to higher value-added activities in formal services, increasing urbanization. The results are consistent with greater local demand for non-tradable services driven by highly paid oil workers.

How do political leader's words and actions affect people's behavior? We address this question in the context of Brazil by combining electoral data and geo-localized mobile phone data for more than 60 million devices throughout the entire country. We find that after Brazil's president publicly and emphatically dismisses the risks associated with the COVID-19 Pandemic and advises against isolation, social distancing measures of citizens in pro-government localities reduce relative to those places in which his support is weaker, while pre-event effects are insignificant. The impact is large and robust to different empirical model specifications. We also find suggestive evidence that this impact is driven by localities with relatively higher levels of media penetration.

ABSTRACT Context: 25 years after it was coined, the triple bottom line (TBL) is now considered a failure by its own author. The concept can be considered the foundational base for the development of a necessary new business model for sustainable operations management. Objective: this paper aims to present systematic literature updates, controversies, limitations, and future framework developments of the TBL concept presented by Elkington in 1998. Methodology: through a systematic literature review spanning from 1998 to 2019, considering two main bibliographical databases, it was possible to evaluate the use of the concept in the sustainability literature. Results: the main results present that the concept has not lost its credibility; on the contrary, it reached its peak in the past five years, due to environmental and societal pressures. Also, it has been used inadequately considering only two of its three spheres (either financial and social, or financial and environmental). Conclusion: the study also exposes capabilities that if included to the TBL concepts can result into success of the business model. Therefore, our aim is to scrutinize how the concept has been used along these years, reflect on its impact in the academia and the business segment, and draw some conclusions on future research agenda and the transition toward a holistic framework for sustainable operations.