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Hardly any evidence exists on the effects of mental illness on refugee labor outcomes. This paper offers the first study on this topic in the context of Australia, one of the host countries with the largest number of refugees per capita in the world. Analyzing the Building a New Life in Australia longitudinal survey, the paper exploits the variations in traumatic experiences of refugees interacted with post-resettlement time periods to causally identify the impacts of refugee mental health. The findings show that worse mental health, as measured by a one-standard-deviation increase in the Kessler mental health score, reduces the probability of employment by 14.1 percent and labor income by 26.8 percent. There is also evidence of adverse impacts of refugees’ mental illness on their children’s mental health and educational performance. These effects appear to be more pronounced for newly arriving refugees and those without social networks, but they may be ameliorated with government support.

The paper discusses the main issues related to negative and zero incomes that are relevant for the measurement of poverty. It shows the prevalence of non-positive incomes in high- and middle-income countries, provides an analysis of the sources and structure of these incomes, outlines the various approaches proposed by scholars and statistical agencies to treat non-positive incomes, and explains how non-positive incomes and alternative correction methods impact the measurement of standard poverty indexes. It is argued that negative and zero incomes cannot be treated equally in terms of household well-being and that standard methods used by practitioners fail to recognize this fact likely resulting in overestimations of poverty.

We introduce a simple methodology to estimate the total number of infected with SARS-CoV-2 based on the number of deaths in households with at least one confirmed case of COVID-19. If we are willing to assume that a single member of a household with $n$ members will infect the remaining members with probability 1, then the number of deaths in a household follows a binomial distribution with parameters $(n-1,p)$ where $p$ is the CFR. Although the method may be affected by classification errors, its simplicity will allow to reduce the error of the estimates by increasing the sample size, since it requires minimal laboratory testing capabilities. We illustrate our methodology with data from Mexico and estimate the CFR in 0.34 \%, that is, we estimate that the total number of infections is about $300$ times larger than the number of deaths. We specify some dataset limitations. In comparison, using the number of deaths to date and a recently published results from random tests in Iceland, we calculated the ratio estimated infections/deaths in about $200$ for that country.

Incomes in surveys suffer from various measurement problems, most notably in the tails of their distributions. We study the prevalence of negative and zero incomes, and their implications for inequality and poverty measurement relying on 57 harmonized surveys covering 12 countries over the period 1995-2016. The paper explains the composition and sources of negative and zero incomes and assesses the distributional impacts of alternative correction methods on poverty and inequality measures. It finds that the main source of negative disposable incomes is negative self-employment income, and that high tax, social security withholdings and high self-paid social-security contributions account for negative incomes in some countries. Using detailed information on expenditure, we conclude that households with negative incomes are typically as well off as, or even better, than other households in terms of material wellbeing. By contrast, zero-income households are found to be materially deprived. Adjusting poverty and inequality measures for these findings can alter these measures significantly.

OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey impu- tations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds that the choice of an optimal model largely depends on the distribution of incomes and the poverty line. Comparing the performance of different econometric and machine learning models is therefore an important step in the process of opti- mizing poverty predictions and targeting ratios.

The paper provides initial evidence that excess mortality rates by locality can be used as a statistically reliable predictor of looming mortality crises. Using recently published daily deaths figures for 7,357 Italian municipalities, we estimate the growth in daily mortality rates between the period 2015-2019 and 2020 by province. All provinces that experienced a major mortality shock in mid-March 2020 had increases in mortality rates of 100% or above already in mid-February 2020. This increase was particularly strong for males and older people, two recognizable features of COVID-19. Using panel data models, we find a strong positive and significant association between overall deaths and COVID-19 related deaths, and between early increases in mortality rates in February 2020 for any cause and the March 2020 outbreak in COVID-19 deaths. We conclude that the growth in mortality rates can potentially be used as a statistically reliable predictor of mortality crises, including COVID- 19 crises.

The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper offers a first attempt to measure poverty among refugees using cross-survey imputations and administrative and survey data collected by the United Nations High Commissioner for Refugees in Jordan. Employing a small number of predictors currently available in the United Nations High Commissioner for Refugees registration system, the proposed methodology offers out-of-sample predicted poverty rates. These estimates are not statistically different from the actual poverty rates. The estimates are robust to different poverty lines, they are more accurate than those based on asset indexes or proxy means tests, and they perform well according to targeting indicators. They can also be obtained with relatively small samples. Despite these preliminary encouraging results, it is essential to replicate this experiment across countries using different data sets and welfare aggregates before validating the proposed method.