Publications

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung-Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate hidden dependence and non-stationarities involving heteroskedasticity, thereby uncoupling these tests from limiting assumptions that reduce their applicability in empirical work. To enhance the Ljung-Box test for non-i.i.d. data a new cumulative test is introduced. Asymptotic size of these tests is unaffected by hidden dependence and heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series. Tests for the i.i.d. property of a time series are also developed. An extensive Monte Carlo study confirms good performance in both size and power for the new tests. Applications to real data reveal that standard tests frequently produce spurious evidence of serial correlation.

We investigate the role of training in reducing the gender wage gap using the UK-BHPS. Based on a lifecycle model and using tax and welfare bene t reforms as a source of exogenous variation we evaluate the role of formal training and experience in defining the evolution of wages and employment careers, conditional on education. Training is potentially important in compensating for the effects of children, especially for women who left education after completing high school, but does not fundamentally change the wage gap resulting from labor market interruptions following child birth.

We use matched employer-employee data from Sweden to study the role of the firm in affecting the stochastic properties of wages. Our model accounts for endogenous participation and mobility decisions. We find that firm-specific permanent productivity shocks transmit to individual wages, but the effect is mostly concentrated among the high-skilled workers; firm-specific temporary shocks mostly affect the low-skilled. The updates to worker-firm specific match effects over the life of a firm-worker relationship are small. Substantial growth in earnings variance over the life cycle for high-skilled workers is driven by firms accounting for 44% of cross-sectional variance by age 55.

Large internet platforms collect data from individual users in almost every interaction on the internet. Whenever an individual browses a news website, searches for a medical term or for a travel recommendation, or simply checks the weather forecast on an app, that individual generates data. A central feature of the data collected from the individuals is its social aspect. Namely, the data captured from an individual user is not only informative about this specific individual, but also about users in some metric similar to the individual. Thus, the individual data is really social data. The social nature of the data generates an informational externality that we investigate in this note.

Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, nonlottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores, grades, and interviews to rank applicants to screened schools, combined with lottery tie-breaking at unscreened schools. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity designs to allow for multiple treatments and multiple running variables, some of which are randomly assigned. Lotteries generate assignment risk at screened as well as unscreened schools. Centralized assignment also identifies screened school effects away from screened school cutoffs. These features of centralized assignment are used to assess the predictive value of New York City’s school report cards. Grade A schools improve SAT math scores and increase the likelihood of graduating, though by less than OLS estimates suggest. Selection bias in OLS estimates is egregious for Grade A screened schools.

Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over-subscribed schools using randomly assigned lottery numbers, non-lottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at \screened” schools, combined with lottery tie-breaking at unscreened \lottery” schools. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity methods to allow for multiple treatments and multiple running variables, some of which are randomly assigned. The key to this generalization is a local propensity score that quantifies the school assignment probabilities induced by lottery and non-lottery tie-breakers. The local propensity score is applied in an empirical assessment of the predictive value of New York City’s school report cards. Schools that receive a high grade indeed improve SAT math scores and increase graduation rates, though by much less than OLS estimates suggest. Selection bias in OLS estimates is egregious for screened schools.

Large internet platforms collect data from individual users in almost every interaction on the internet. Whenever an individual browses a news website, searches for a medical term or for a travel recommendation, or simply checks the weather forecast on an app, that individual generates data. A central feature of the data collected from the individuals is its social aspect. Namely, the data captured from an individual user is not only informative about this specific individual, but also about users in some metric similar to the individual. Thus, the individual data is really social data. The social nature of the data generates an informational externality that we investigate in this note.

Carbon budgets are a useful way to frame the climate mitigation challenge and much easier to agree upon than the allocation of emissions. We propose a mechanism with countries agreeing on the global carbon budget, while the decision to emit is decentralized at the country level. The revenue is collected in a global fund and allocated according to endogenously defined weights proportional to the marginal cost of climate change. The proposal features a unanimous agreement of the national citizenries of the world and global Pareto efficiency. We run a simulation in the spirit of the Paris Agreement, with zero emissions after 2055. At the Global Unanimity Equilibrium, permits are priced at 90$/tC, yielding 1.3 trillion dollars annually. Africa, India and the less developed countries in Asia are the only net recipients, while the US and China are the largest net contributors.