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Many retailers practice an extreme form of discrete pricing defined as quantum prices: differentiated products are priced using few and sparse price buckets. To show this, online data was collected for 350,000 products from over 65 fashion retailers in the U.S. and the U.K. This pricing strategy is observed within categories and across categories (i.e., similar or even disparately distinct products like jeans and bags have an identical price), as well as in product introductions, where new products come in at previous price buckets. Normalized indices indicate substantial price clustering after controlling for popular prices, convenient prices, assortment size, or digit endings. Quantum prices have implications for price adjustments through product shares, markdown prices, and for the law-of-one-price. A behavioral model of price salience and recall is discussed.

This paper investigates the divergence of environmental, social, and governance (ESG) ratings based on data from six prominent ESG rating agencies: Kinder, Lydenberg, and Domini (KLD), Sustainalytics, Moody’s ESG (Vigeo-Eiris), S&P Global (RobecoSAM), Refinitiv (Asset4), and MSCI. We document the rating divergence and map the different methodologies onto a common taxonomy of categories. Using this taxonomy, we decompose the divergence into contributions of scope, measurement, and weight. Measurement contributes 56% of the divergence, scope 38%, and weight 6%. Further analyzing the reasons for measurement divergence, we detect a rater effect where a rater’s overall view of a firm influences the measurement of specific categories. The results call for greater attention to how the data underlying ESG ratings are generated.

COVID-19 highlighted the weaknesses in the supply chain. Many have argued that a more resilient or robust supply chain is needed. But what does a robust supply chain mean? And how do firms’ decisions change when taken that approach? This paper studies a very stylized model of a supply chain, where we study how the decision of a multinational corporation changes in the presence of uncertainty. The two standard theories of supply chain are just-in-time and just-in-case. Just-in-time argues in favor of pursuing efficiency, while just-in-case studies how such decision changes when the firm faces idiosyncratic risk. We find that a robust supply chain is very different specially in the presence of systemic shocks. In this case, firms need to concentrate on the worst-case. This strategy implies a supply chain where the allocation of resources and capabilities does not correspond to the standard theories studied in economics, but follow a heuristic behavioral rule called “probability matching.” It has been found in nature and in experimental research that subjects appeal to probability matching when seeking survival. We find that a robust supply chain will reproduce this behavioral outcome. In fact, a multinational optimizing under uncertainty follows a probability matching which leads to an allocation that is suboptimal from the individual producer point of view, but rules out the possibility of supply disruptions.

Coronavirus disease 2019 (COVID-19) is exacerbating inequalities in the US. We build an agent-based model to elucidate the differential causal effects of nonpharmaceutical interventions on different communities and validate the results with US data. We simulate viral transmission and the consequent deterioration of economic conditions on socioeconomically disadvantaged and privileged populations. As found in data, our model shows that the trade-off between COVID-19 deaths and deaths of despair, dependent on the lockdown level, only exists in the socioeconomically disadvantaged population. Moreover, household overcrowding is a strong predictor of the infection rate. The model also yields new insights that fill in the gaps of our data analysis. While subsidisation narrows the socioeconomic gap in deaths of despair, the combination of testing and contact tracing alone is effective at reducing disparities in both types of death. Our results contribute to policy modelling and evaluation for reducing inequality during a pandemic.

Matched product data is collected from the leading online grocers in the U.S. The same exact products are identified in scanner data. The paper documents pricing strategies within and across online (and offline) retailers. First, online retailers exhibit substantially less uniform pricing than offline retailers. Second, online price differentiation across competing chains in narrow geographies is higher than offline retailers. Third, variation in offline elasticities, shipping distance, pricing frequency, and local demo- graphics are utilized to explain price differentiation. Surprisingly, pricing technology (across time) magnifies price differentiation (across locations). This evidence motivates a high-frequency study to unpack the patterns of algorithmic pricing. The data shows that algorithms: personalize prices at the delivery zipcode level, update prices very frequently and in tiny magnitudes, reduce price synchronization, exhibit lower menu costs, constantly explore the price grid, and often match competitors’ prices. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

How strongly does ESG (environmental, social and governance) performance affect stock returns? Answering this question is difficult because existing measures of performance, ESG ratings, are noisy. To tackle the bias, we propose a noise-correction procedure, in which we instrument ESG ratings with ratings of other ESG rating agencies, as in the classical errors-in-variables problem. The corrected estimates demonstrate that the effect of ESG performance on stock returns is stronger than previously estimated; the standard regression estimates of ESG ratings' impact on stock returns are biased downward by about 60%. Our dataset includes scores of eight ESG rating agencies for firms located in North America, Europe, and Japan. We determine which agencies’ scores are valid instruments (not all of them are) and estimate the noise-to-signal ratio for each ESG rating agency (some of which are very large). Overall, our results suggest that it is advantageous to rely on several complementary ratings. In our sample, stocks with higher ESG performance have higher expected returns. Our model provides several explanations for this finding.

In this paper, we develop a methodology to estimate hidden linear networks when only an aggregate outcome is observed. The aggregate observable variable is a linear mixture of the different networks and it is assumed that each network corresponds to the transmission mechanism of different shocks. We implement the methodology to estimate financial networks among US financial institutions. Credit Default Swap rates are the observable variable and we show that more than one network is needed to understand the dynamic behavior exhibited in the data.

New data-gathering techniques, often referred to as “Big Data” have the potential to improve statistics and empirical research in economics. In this paper we describe our work with online data at the Billion Prices Project at MIT and discuss key lessons for both inflation measurement and some fundamental research questions in macro and international economics. In particular, we show how online prices can be used to construct daily price indexes in multiple countries and to avoid measurement biases that distort evidence of price stickiness and international relative prices. We emphasize how Big Data technologies are providing macro and international economists with opportunities to stop treating the data as “given” and to get directly involved with data collection., Sloan School of Management

This paper investigates the divergence of environmental, social, and governance (ESG) ratings. Based on data from six prominent rating agencies - namely, KLD (MSCI Stats), Sustainalytics, Vigeo Eiris (Moody's), RobecoSAM (SP Global), Asset4 (Refinitiv), and MSCI IVA- we decompose the divergence into three sources: different scope of categories, different measurement of categories, and different weights of categories. We find that scope and measurement divergence are the main drivers, while weights divergence is less important. In addition, we detect a rater effect where a rater's overall view of a firm influences the assessment of specific categories.

© 2019, Brookings Institution Press. All rights reserved. This paper reviews the empirical literature on international spillovers and contagion. Theoretical models of spillover and contagion imply that the reduced-form observable variables suffer from two possible sources of bias: endogeneity and omitted variables. These econometric problems, in combination with the heteroskedasticity that plagues the data, produce time-varying biases. Several empirical methodologies are evaluated from this perspective: nonparametric techniques, such as correlations and principal components; and parametric methods, such as OLS, VAR, event studies, ARCH, and nonlinear regressions. The paper concludes that there is no single technique that can solve the full-fledged problem and discusses three methodologies that can partially address some of the questions in the literature.

Does membership in a currency union matter for a country’s international relative prices? The answer to this question is critical for thinking about the implications of joining (or exiting) a common currency area. This paper is the first to use high-frequency good-level data to provide evidence that the answer is yes, at least for an important subset of consumption goods. It considers the case of Latvia, which recently dropped its pegged exchange rate and joined the euro zone. The paper analyzes the prices of thousands of differentiated goods sold by Zara, the world’s largest clothing retailer. Price dispersion between Latvia and euro zone countries collapsed swiftly following entry to the euro. The percentage of goods with nearly identical prices in Latvia and Germany increased from 6 to 89 percent. The median size of price differentials declined from 7 percent to zero. If a large number of firms also behave this way, these results suggest that membership in a currency union has significant implications for a country’s real exchange rate.

Summary This manuscript proposes a new approach for unveiling existing linkages within the international oil market across multiple driving factors beyond production. A multilayer, multicountry network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network linkages than do traditional or static pairwise Granger-causal inference approaches. Building on previous work, the layers of the network include country- and region-specific oil production levels and rigs, both through simultaneous and lagged temporal dependences among key factors, while controlling for oil prices and a world economic activity index. The proposed approach extracts relationships across all variables through a dynamic, cross-regional network. This approach is highly scalable and adjusts for time-evolving linkages. The model outcome is a set of time-varying graphical networks that unveil both static representations of world oil linkages and variations in microeconomic relationships both within and between oil producers. An example is provided, illustrating the evolution of intra- and inter-regional relationships for two major interconnected oil producers: the United States, with a regional decomposition of its production and rig deployment, and the Arabian Peninsula and key Middle Eastern producers, with a country-based decomposition of production and rig deployment, while controlling for oil prices and global economic indices. Production is less affected by concurrent changes in oil prices and the overall economy than rigs. However, production is a lagged driver for prices, rather than rigs, which indicates that the linkage between rigs and production may not be fully accounted for in the markets.

Recent literature has argued that conventional measures of external sustainability – the trade balance and current account – are misleading because they omit capital gains on net foreign asset positions. We adjust the definition of the current account to include the capital gains and discuss how this may affect our thinking about external adjustment and sustainability. We do so in the context of a two-country macro-finance model of Pavlova and Rigobon (2008a) that allows exploration of the interconnections between equilibrium portfolios and external accounts' dynamics. We calibrate the model and find that it generates several testable implications, some of which have already been validated empirically. First, we establish dynamic properties of the capital-gains adjusted current account and show that they are fundamentally different from those of the conventional current account. Second, we find that capital gains have a stabilizing effect on the trade balance and the current account. Finally, we demonstrate that in response to a shock, the conventional and the capital-gains adjusted current accounts may move in opposite directions., Fondation Banque de France

This paper studies how portfolios with a global investment scope are actually allocated internationally using a unique micro dataset on U.S. equity mutual funds. While mutual funds have great flexibility to invest globally, they invest in a surprisingly limited number of stocks, around 100. The number of holdings in stocks and countries from a given region declines as the investment scope of funds broadens. This restrictive investment practice has costs. A mean-variance strategy shows unexploited gains from further international diversification. Mutual funds investing globally could achieve better risk-adjusted returns by broadening their asset allocation, including stocks held by more specialized funds within the same mutual fund family (company). This investment pattern is not explained by lack of information or instruments, transaction costs, or a better ability of global funds to minimize negative outcomes. Instead, industry practices related to organizational factors seem to play an important role.

In this paper, we study the supply–demand drivers of the price of oil over the last two decades. We address the problem of endogeneity using a novel SVAR approach, which allows us to incorporate technological restrictions that occur at the micro level in the production of crude oil to solve the identification problem in a reduced-form regression analysis that seeks to disentangle the drivers of oil prices. We explore the relationships between oil prices, rig counts, oil production and an index of world economic activity, and provide results for a heterogeneous set of countries. We find that when oil prices peaked in mid-2008—reaching almost US$150 compared to US$14 in 1998, a large proportion of the price move can be explained through a purely demand and supply factors.

The debt crisis in several European Union nations has resulted in a set of downgrades in sovereign ratings, sparking a lively debate whether these opinions actually matter. Ratings and bond spreads may both be considered as noisy signals of fundamentals. Ratings only add value if, controlling for spreads and observable country fundamentals, they help explain other market variables. We employed a unique dataset of over 75 000 daily observations on emerging countries around rating actions by the three major agencies. We found that ratings do indeed add information, and this finding is robust to a variety of different tests. Copyright © 2012 John Wiley & Sons, Ltd.

The U. S. government’s failure to provide adequate oversight and prudent regulation of the financial markets, together with excessive risk taking by some financial institutions, pushed the world financial system to the brink of systemic failure in 2008. As a consequence of this near catastrophe, both regulators and investors have become keenly interested in developing tools for monitoring systemic risk. But this is easier said than done. Securitization, private transacting, complexity, and “flexible accounting” prevent us from directly observing the many explicit linkages of financial institutions. As an alternative, the authors introduce a measure of implied systemic risk, the absorption ratio, which equals the fraction of the total variance of a set of asset returns explained or “absorbed” by a fixed number of eigenvectors. The absorption ratio captures the extent to which markets are unified or tightly coupled. When markets are tightly coupled, they are more fragile in the sense that negative shocks propagate more quickly and broadly than when markets are loosely linked.

We give semiparametric identification and estimation results for econometric models with a regressor that is endogenous, bound censored, and selected; it is called a Tobin regressor. First, we show that the true parameter value is set- identified and characterize the identification sets. Second, we propose novel es- timation and inference methods for this true value. These estimation and infer- ence methods are of independent interest and apply to any problem possessing the sensitivity structure, where the true parameter value is point-identified con- ditional on some nuisance parameter values that are set-identified. By fixing the nuisance parameter value in some suitable region, we can proceed with regular point and interval estimation. Then we take the union over nuisance parameter values of the point and interval estimates to form the final set estimates and con- fidence set estimates. The initial point or interval estimates can be frequentist or Bayesian. The final set estimates are set-consistent for the true parameter value, and confidence set estimates have frequentist validity in the sense of covering this value with at least a prespecified probability in large samples. Our procedure may be viewed as a formalization of the sensitivity analysis in the sense of Leamer (1985). We apply our identification, estimation, and inference procedures to study the effects of changes in housing wealth on household consumption. Our set es- timates fall in plausible ranges, significantly above low ordinary least squares es- timates and below high instrumental variables estimates that do not account for the Tobin regressor structure. Keywords. Partial identification, endogenous censoring, sample selection. JEL classification. C14, C24, C26.

A central assumption of open economy macro models with nominal rigidities relates to the currency in which goods are priced, whether there is so-called producer currency pricing or local currency pricing. This has important implications for exchange rate pass-through and optimal exchange rate policy. We show, using novel transaction level information on currency and prices for U.S. imports, that even conditional on a price change, there is a large difference in the pass-through of the average good priced in dollars (25%) versus non-dollars (95%). This finding is contrary to the assumption in a large class of models that the currency of pricing is exogenous and is evidence of an important selection effect that results from endogenous currency choice. We describe a model of optimal currency choice in an environment of staggered price setting and show that the empirical evidence strongly supports the model's predictions of the relation between currency choice and pass-through. We further document evidence of significant real rigidities, with the pass-through of dollar pricers increasing above 50% in the long-run. Lastly, we numerically illustrate the currency choice decision in both a Calvo and a menu-cost model with variable mark-ups and imported intermediate inputs and evaluate the ability of these models to match pass-through patterns documented in the data.

We use high frequency data and a new econometric approach to evaluate the effectiveness of controls on capital inflows. We focus on Chile's experience during the 1990s, and investigate whether controls on capital inflows reduced Chile's vulnerability to external shocks. We recognize that changes in the controls will affect the way in which different macro variables relate to each other. In particular, we consider the case where controls co-exist with an exchange rate band aimed at managing the nominal exchange rate. We develop a methodology to deal explicitly with the interaction between these two policies. The main findings may be summarized as follows: (a) a tightening of capital controls on inflows depreciates the exchange rate and (b), we find that a tightening of capital controls increases the unconditional volatility of the exchange rate, but makes it less sensitive to external shocks.

We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in ordinary least aquares (OLS) regression estimators with exogenous censoring and in instrumental variable (IV) estimators when the censored regressor is endogenous. Bound censoring such as top-coding results in expansion bias, or effects that are too large. Independent censoring results in bias that varies with the estimation method—attenuation bias in OLS estimators and expansion bias in IV estimators. Severe biases can result when there are several regressors and when a 0–1 variable is used in place of a continuous regressor.

We study the comovement among stock prices and exchange rates in a three-good, three-country, Centre-Periphery, dynamic equilibrium model in which the Centre's agents face portfolio constraints. We characterize equilibrium in closed form for a broad class of portfolio constraints, solving for stock prices, terms of trade, and portfolio holdings. We show that portfolio constraints generate wealth transfers between the Periphery countries and the Centre, which increase the comovement of the stock prices across the Periphery. We associate this excess comovement caused by portfolio constraints with the phenomenon known as contagion. The model generates predictions consistent with other important empirical results such as amplification and flight-to-quality effects. Copyright 2008, Wiley-Blackwell.

We study issues that arise for estimation of a linear model when a regressor is censored. We discuss the efficiency losses from dropping censored observations, and illustrate the losses for bound censoring. We show that the common practice of introducing a dummy variable to “correct for” censoring does not correct bias or improve estimation. We show how censored observations generally have zero semiparametric information, and we discuss implications for estimation. We derive the likelihood function for a parametric model of mixed bound-independent censoring, and apply that model to the estimation of wealth effects on consumption.

This paper measures the effects of the risks associated with the war in Iraq on various US financial variables using a heteroskedasticity-based estimation technique. The results indicate that increases in war risk caused declines in Treasury yields and equity prices, a widening of lower-grade corporate spreads, a fall in the dollar, and a rise in oil prices. This “war risk” factor accounted for a considerable portion of the variances of these financial variables over the three months leading up to the arrival of coalition forces in central Baghdad.

We estimate the interrelationships among economic institutions, political institutions, openness, and income levels, using identification through heteroskedasticity (IH). We split our cross-national dataset into two sub-samples: (i) colonies versus non-colonies; and (ii) continents aligned on an East–West versus those aligned on a North–South axis. We exploit the difference in the structural variances in these two sub-samples to gain identification. We find that democracy and the rule of law are both good for economic performance, but the latter has a much stronger impact on incomes. Openness (trade/GDP) has a negative impact on income levels and democracy, but a positive effect on rule of law. Higher income produces greater openness and better institutions, but these effects are not very strong. Rule of law and democracy tend to be mutually reinforcing.

Using a model incorporating the endogenous relationship between exchange rates and intervention, we show how a change in central bank policy can be used as an identification assumption. Estimating this model with simulated GMM for daily data from Australia and Japan, we find that a US$100 million purchase appreciates the Australian dollar by 1.3–1.8% but the yen by just 0.2%. Almost all of the impact of an intervention occurs during the day it is conducted and we confirm that central banks typically lean against the wind.

The empirical literature on ‘contagion’ focuses mainly on two questions: (1) what are the channels through which shocks are transmitted across countries, trade, macro similarities, financial weaknesses, or investor behavior? (2) Is there a shift in the transmission of shocks during crises? Are crises spread with higher intensity? If so, why? This paper concentrates on the econometric problems that arise in dealing with the second question. The data where most of these issues are raised are plagued with problems of simultaneous equations, omitted variables, and heteroskedasticity. The standard methodologies used in the literature are inappropriate if all three are present. This paper applies a new procedure that allows one to test for parameter stability, taking into account all three predicaments. The paper tests for the stability of the transmission mechanisms among 36 stock markets during the last three major international financial crises (Mexico 1994, Asia 1997, and Russia 1998).

This paper develops a method for solving the identification problem that arises in simultaneous-equation models. It is based on the heteroskedasticity of the structural shocks. For simplicity, I consider heteroskedasticity that can be described as a two-regime process and show that the system is just identified. I discuss identification under general conditions, such as more than two regimes, when common unobservable shocks exist, and situations in which the nature of the heteroskedasticity is misspecified. Finally, I use this methodology to measure the contemporaneous relationship between the returns on Argentinean, Brazilian, and Mexican sovereign bonds-a case in which standard identification methodologies do not apply. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

This paper analyzes the sovereign risk contagion using credit default swaps (CDS) and bond premiums for the major eurozone countries. By emphasizing several econometric approaches (nonlinear regression, quantile regression and Bayesian quantile regression with heteroskedasticity) we show that propagation of shocks in Europe's CDS has been remarkably constant for the period 2008-2011 even though a significant part of the sample periphery countries have been extremely affected by their sovereign debt and fiscal situations. Thus, the integration among the different eurozone countries is stable, and the risk spillover among these countries is not affected by the size of the shock, implying that so far contagion has remained subdue. Results for the CDS sample are conformed by examining bond spreads. However, the analysis of bond data shows that there is a change in the intensity of the propagation of shocks in the 2003-2006 pre-crisis period and the 2008-2011 post-Lehman one, but the coefficients actually go down, not up! All the increases in correlation we have witnessed over the last years come from larger shocks and the heteroskedasticity in the data, not from similar shocks propagated with higher intensity across Europe. This is the first paper, to our knowledge, where a Bayesian quantile regression approach is used to measure contagion. This methodology is particularly well-suited to deal with nonlinear and unstable transmission mechanisms.