This paper undertakes a near real-time analysis of the income distribution effects of the COVID-19 crisis in Australia to understand the ongoing changes in the income distribution as well as the impact of policy responses. By semi-parametrically combining incomplete observed data from three different sources, namely, the Monthly Longitudinal Labour Force Survey, the Survey of Income and Housing and the administrative payroll data, we estimate the impact of COVID-19 and the associated policy responses on the Australian income distribution between February and June 2020, covering the immediate periods before and after the initial outbreak. Our results suggest that despite the growth in unemployment, the Gini of the equalised disposable income inequality dropped by nearly 0.03 point since February. The reduction is because of the additional wage subsidies and welfare supports offered as part of the policy response, offsetting a potential surge in income inequality. Additionally, the poverty rate, which could have been doubled in the absence of the government response, also reduced by 3 to 4 percentage points. The result shows the effectiveness of temporary policy measures in maintaining both the living standards and the level of income inequality. However, the heavy reliance on the support measures raises the possibility that the changes in the income distribution may be reversed and even substantially worsened off should the measures be withdrawn.
In this study, we extend the research on the dynamic poverty indexes, namely the dynamic Headcount ratio, the dynamic income-gap ratio, the dynamic Gini and the dynamic Sen, proposed in D'Amico and Regnault (2018). The contribution is twofold. First, we extend the computation of the dynamic Gini index, thus the Sen index accordingly, with the inclusion of the inequality within each class of poverty where people are classified according to their income. Second, for each poverty index, we establish a central limit theorem that gives us the possibility to determine the confidence sets. An applica…
This research attempts to explore the key research questions about what are the different microcredit programs in Haor area in Bangladesh? And do microcredit programs have a positive impact on livelihoods of the clients in terms of selected social indicators viz. income, consumption, assets, net worth, education, access to finance, social capacity, food security and handling socks etc. in Haor area in Bangladesh? Utilizing difference-in-difference and factor analysis, we explore the nature and terms of conditions of available formal and informal micro-creditss in Haor region of Bangladesh; and…
Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of wealth quality that the traditional wealth-based approach relied on, this novel approach makes use of only images posted on Dollar Street as input data on household wealth across 66 countries and predicts the consumption or income level of each household using the Convolutional Neural Network (CNN) method. The best result predicts the log of consumption level wit…
In this work, we explore the relationship between monetary poverty and production combining relatedness theory, graph theory, and regression analysis. We develop two measures at product level that capture short-run and long-run patterns of poverty, respectively. We use the network of related products (or product space) and both metrics to estimate the influence of the productive structure of a country in its current and future levels of poverty. We found that poverty is highly associated with poorly connected nodes in the PS, especially products based on natural resources. We perform a series …
University of California, Berkeley（加州大学伯克利分校）；Facebook, Inc.（脸书公司）
Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop the first micro-estimates of wealth and poverty that cover the populated surface of all 135 low and middle-income countries (LMICs) at 2.4km resolution. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregate…
Food insecurity is associated with increased risk for several health conditions and with increased national burden of chronic disease. Key determinants for household food insecurity are income and food costs. Forecasts show household disposable income for 2020 expected to fall and for 2021 to rise only slightly. Prices are forecast to rise. Thus, future increased food prices would be a significant driver of greater food insecurity. Structured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. In July 2020, each expert estimated the median, 5th p…
