The Role of Brazil in U.S.-Latin American Relations

The Gini can be calculated as a ratio of areas on the Lorenz curve diagram, fam. OMG, so in this diagram, the peeps are ranked from poor to rich on the horizontal axis, while the vertical axis shows how much moolah they be raking in. Like, total income vibes, ya know? The curve that comes from this is the Lorenz curve, fam. Next to this curve a perf equality line is drawn, fam. The space between this lit equality line and the Lorenz curve is A and the area under the Lorenz curve is B. The Gini coefficient is, like, totally defined as If xi is, like, a point on the horizontal axis, and yi is, like, a point on the vertical axis then the area B can be approximated with trapezoids and stuff:

There are like, mad pros and cons for using the Gini, ya know? 

The main flexes are that it's a mad basic single variable that's hella easy to vibe with and can be compared over time, ya feel? Because of these advantages, it's been like the most used variable to measure inequality, which makes it even more lit to compare, ya know? However, like, with most convenient single variable measures, there's like important info about inequality that one can't get from the Gini. Is the tea for a Yo G, is it like the poor be hella broke or the rich be flexin' hard? Two economies can have similar incomes and Gini's but still have hella different income distributions, ya know?Another point of influence according to Barros et al. (2010) is spatial segmentation, like, urban/rural and regional stuff has, like, a major impact on how much money you make, ya know? There's, like, this whole vibe of more integration happening, you know? Like, between federal states and also between urban and rural areas in Brazil. It's, like, totally trending. The earnings gap between urban and rural areas is like, hella big, but it's been lowkey shrinking in the past years. Bourguignon et al. (2007) be like, they came up with a way to measure how much unequal opportunities be adding to the whole earnings inequality situation. They like, totally find that parental education is, like, the most important thingy affecting earnings, but the occupation of the father and race also, like, have an impact. 

The vibes variable OMG, like, getting educated totally boosts your earnings, ya know? 

And, like, moving to a new place also has a major impact on your earnings, for real! Hailu and Soares (2009) find that the decline in Brazil's inequality can be explained by fam size getting smaller, easier access to education, and cash transfers to the poor. OMG, outta the 8 determinants in table 2.1, like 7 of 'em are used in the decomposition analyses in section five. Lit! The choice of variables follows Ferreira et al. (2006) who have done the decomposition for Brazil for earlier years, ya feel me? Like, you can totally compare stuff over time and analyze how things that make inequality happen have changed. This section be all about peepin' into different ways inequality be showin' up. Firstly, here are five lit axioms for inequality measures and I'm about to spill the tea on each one. Get ready, fam! OMG, like secondly, we gotta talk about these five types of measures and how they're totally subjected to these axioms. Like, tbh, all the ways we measure inequality are usually hella correlated and the results all point in the same vibe. But like, not all types of measures are like suitable for every type of research, ya know? Moreover, using various measures can lowkey flex trends and peep possible measurement errors or changes in data collection. The Gini-Coef is like the OG way to flex how unequal things are. It's like, all about how the cash flow and bling is spread out in a specific spot or nation. The outcomes go from 0 to 1 (or 0 to 100 when used as an index) where a value of zero is like, total equality and a value of 1 is like, total inequality. Like, when there's tea, you know? omg so like if there's 2 peeps in a spot and the Gini is 1, the rich homie be flexin' with all the stuff while the poor homie be havin' nada.

Desired vibes of inequality measures

The desired vibes that a lit inequality measure should satisfy are listed below (Cowell 2011). If a measure doesn't meet all these axioms, it doesn't mean it can't be useful, ya know? It's like, it's not gonna work for all the tests they usually do in inequality research, ya know? Anon: it don't matter who's got the dough. Whether person A has 40% and B has 60% or the other way around should not affect the outcome of the measure, ya know?Sis, it's all about that scale invariance, like it don't even matter how big the pie is, you feel me? The measured inequality should not flex if everyone's income changes with the same percentage, ya feel? Pop rep invariance: it don't matter how many peeps the pie is shared with. If two countries with an identical income distribution are merged, the measured inequality should be the same, fam. Pigou-Dalton transfer principle: it lowkey measures inequality. Transferring that cash from the rich to the poor should totally decrease the level of inequality, ya know? Additive Decomposability: total inequality is, like, a vibe of inequality in subgroups. There should be, like, a legit connection between the total inequality in a country and the inequality in its subgroups, you know? Lorenz curves and Gini-coefficients, like, they're all about income inequality and stuff, you know?