In incomplete data models the EM principle implies the moments the Information Matrix test assesses are the expectation given the observations of the moments it would assess were the underlying components observed. This principle also leads to interpretable expressions for their asymptotic covariance matrix adjusted for sampling variability in the parameter estimators under correct specification. Monte Carlo simulations for finite Gaussian mixtures indicate that the parametric bootstrap provides reliable finite sample sizes and good power against various misspecification alternatives. We confirm that 3-component Gaussian mixtures accurately describe cross-sectional distributions of per capita income in the 1960-2000 Penn World Tables.

The demographic transition -the move from a high fertility/high mortality regime into a low fertility/low mortality regime- is one of the most fundamental transformations that countries undertake. To study demographic transitions across time and space, we compile a data set of birth and death rates for 186 countries spanning more than 250 years. We document that (i) a demographic transition has been completed or is ongoing in nearly every country; (ii) the speed of transition has increased over time; and (iii) having more neighbors that have started the transition is associated with a higher probability of a country beginning its own transition. To account for these observations, we build a quantitative model in which parents choose child quantity and educational quality. Countries differ in geographic location, and improved production and medical technologies diffuse outward from Great Britain, the technological leader. Our framework replicates well the timing and increasing speed of transitions. It also produces a strong correlation between the speeds of fertility transition and increases in schooling similar to the one in the data.

The role of friends in the US opioid epidemic is examined. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), adults aged 25-34 and their high school best friends are focused on. An instrumental variable technique is employed to estimate peer effects in opioid misuse. Severe injuries in the previous year are used as an instrument for opioid misuse in order to estimate the causal impact of someone misusing opioids on the probability that their best friends also misuse. The estimated peer effects are significant: Having a best friend with a reported serious injury in the previous year increases the probability of own opioid misuse by around 7 percentage points in a population where 17 percent ever misuses opioids. The effect is driven by individuals without a college degree and those who live in the same county as their best friends.

How do property taxes affect house prices, homeownership, and welfare? I focus on Italy, a country with high homeownership, an outdated property tax system, and failed reform attempts. As in many other countries, owner-occupied houses are exempt from property taxes in Italy. Additionally, property taxes are calculated using outdated cadastral values. I show that using cadastral values creates a regressive property tax since cadastral values are relatively lower for more expensive housing units. I develop a life-cycle model with endogenous homeownership to assess the effects of reforming the current system. My findings show that removing the owner-occupied exemption and adjusting cadastral values to market values increases government property tax revenues as a percentage of GDP by over 0.8 percentage points but also increases homeownership rates by 1.2 percentage points. The increase in homeownership results from lower property tax rates on smaller houses. Finally, I show that in the short run, the reform increases the welfare of young households but lowers the welfare of older ones. In the long run, welfare increases for new generations. Higher welfare is mainly due to the decrease in house prices in equilibrium.

Japan ranks 116th out of 146 countries in the World Economic Forum’s Global Gender Gap Index 2022, well below many developed countries, and has one of the largest gender pay gaps among high-income countries. On the other hand, women’s labor force participation is high in Japan. However, women are much more likely to work in non-regular jobs, which are associated with lower wages and fewer hours. Men, in contrast, have regular, higher-paid jobs with long-hours requirements. In this paper, I build and estimate a model where couples jointly decide their occupations and working hours. Occupations differ in their flexibility. Regular jobs require long working hours, and hourly wages are a convex function of hours worked. Non-regular occupations have a linear mapping between hours worked and hourly wages. The model also allows for social norms that penalize women who earn more than their husbands. Given the inflexibility of regular jobs and social norms, women are more likely to choose non-regular jobs or not to work, and allocate a larger share of their hours for home production. The model can account for all of the observed gender gaps in labor force participation, 33% in occupational choices, 74% in labor hours, and 34% in wages. Through the lens of the model, the inflexibility of regular jobs explains almost all the gaps in occupational choices and wages, while social norms that penalize women who earn more than their husbands account for all of the gap in the participation rate and half of the gap in hours worked.

We show that the influence functions of the information matrix test for the multinomial logit model are the Kronecker product of the outer product of the generalised residuals minus their covariance matrix conditional on the explanatory variables times the outer product of those variables. Thus, it resembles a multivariate heteroskedasticity test à la White (1980), which confirms Chesher’s (1984) unobserved heterogeneity interpretation. Our simulation experiments indicate that using theoretical expressions for the conditional covariance matrices involved substantially reduces size distortions, while the parametric bootstrap practically eliminates them. We also show that the test has good power against several relevant alternatives.

Among high-income countries, fertility rates differ significantly, with some experiencing total fertility rates as low as 1 to 1.3 children per woman. However, the reasons behind low fertility rates are not well understood. We show that uncertainty created by dual labor markets, the coexistence of temporary and open-ended contracts, and the inflexibility of work schedules are crucial to understanding low fertility. Using rich administrative data from the Spanish Social Security records, we document that temporary contracts are associated with a lower probability of first birth. With Time Use data, we also show that women with children are less likely to work in jobs with split-shift schedules. Such jobs have a long break in the middle of the day, and present a concrete example of inflexible work arrangements and fixed time cost of work. We then build a life-cycle model in which married women decide whether to work, how many children to have, and when to have them. Reforms that eliminate duality or split-shift schedules increase women’s labor force participation and reduce the employment gap between mothers and non-mothers. They also increase fertility for women who are employed. Reforming these labor market institutions and providing childcare subsidies would increase the completed fertility of married women to 1.8 children.

This paper studies the impact of financial frictions on the allocation of cash flow using administrative data of Spanish non-financial corporations from 2003 to 2019. Employing an analytical framework based on the uses and sources of funds identity, I estimate regression models to examine the allocation of cash flow across its competing uses. My findings reveal that larger financial frictions are, on average, associated with a higher proportion of marginal cash flow allocated to debt repayment and lower proportions allocated to cash savings, investment, and dividend distribution. The analysis also highlights that the effect of financial frictions on the allocation of marginal cash flow varies significantly with variables capturing the economic and financial situation of each firm such as leverage, cash holdings, capital, and the availability of investment opportunities.

This paper investigates the impact of supervisory resolution tools, specifically bail-ins versus bailouts, on the ex-ante banks’ portfolio composition and resulting ex-post default probabilities in the presence of both idiosyncratic and systematic shocks. Banks make decisions regarding short-term versus long-term risky investments while considering the expected resolution policy. I find that both types of shocks can generate financial instability, which the two resolution tools address through distinct channels. With only idiosyncratic shocks, creditor bailouts, acting as debt insurance, eliminate the equilibrium with bank defaults, while bail-ins induce banks to invest less in the risky short-term asset, which may also prevent defaults. In the presence of both shocks, creditor bailouts can prevent systemic defaults, while bail-ins are less effective in preventing them and could even contribute to systemic risk.

The purpose of this survey is to summarize the academic literature that studies some of the ways in which portfolio management has been affected in recent years by the availability of big datasets: many assets, many characteristics for each of them, many macro predictors, and various sources of unstructured data. Thus, we deliberately focus on applications rather than methods. We also include brief reviews of the financial theories underlying asset management, which provide the relevant background to assess the plethora of recent contributions to such an active research field.

We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of signal-to-noise of macro shocks.

We develop a methodology for modeling household income processes when subjective probabilistic assessments of future income are available. This allows us to flexibly estimate conditional cdfs directly using elicited individual subjective probabilities, and to obtain empirical measurements of subjective risk and persistence. We then use two longitudinal surveys collected in rural India and rural Colombia to explore the nature of income dynamics in those contexts. Our results suggest linear income processes are rejected in favor of more flexible versions in both cases; subjective income distributions feature heteroskedasticity, conditional skewness and nonlinear persistence.