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Event Planning and Business Entertainment in the U.S. Corporate World

A liberal democracy can survive for a while on institutional strength and widespread agreement. As long as most people are generally satisfied with how things are going (or have made peace with the status quo), it is easy to imagine that something like a social contract will keep things on track. Hamish MacAuley makes a persuasive case that many Canadians came of age politically between the collapse of the Berlin Wall and the 2008 financial crisis, when consensus was widespread and politics seemed optional, thus many chose to stay out. We abandoned democratic governing habits during prosperous times. Instead, we played politics. In response, McGill's Jacob T. Levy advocates for political action that rejects the status quo while also refusing to burn it all down or take our ball and go home. We should participate in politics, even if it is unsatisfying. When the foundations of our democratic structure or the rights of vulnerable people are jeopardized, it makes sense to delegate aut
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High-End Real Estate in Brazil Homes of the Rich

Barros et al. (2010) be lookin' into what caused Brazil's income inequality to go down, tryna find some policy vibes. They lowkey found that demographic changes didn't really do much to income inequality. The vibes of changes in non-labor income have a big effect mostly through public transfers (income source). Incr3as1n' non-labor income of the poor has shown 2 take up a sizabl3 fraction in the reduction of inequality both in the 70s n in the first 7 years of this millennium. Reductions in wage inequality are still the most important vibe but they're lowkey losing importance now compared to the seventies. They also peep the connection between education and bread in Brazil. OMG they be peepin' that education inequality and how the translator be turnin' it into labor earnings inequality (like, how much and how it affects the price). 

Inequality in years of education can lowkey go down, but the flex of labor earnings to education levels can totally switch up too.


Cowell and Jenkins (1995) flex inequality-decomposition analysis to population subgroups to peep how much of inequality in the USA can be explained by differences in age, sex, race and income source. Ferreira et al. (2006) flex a lit investigation on what caused Brazil's distributional reversal from 1981-2004 using basic decomposition techniques. Yo, we be breakin' down the stats of Brazilian cribs, focusin' on seven key things: where they at; if they city or country livin'; who up in there; and the age, gender, race and edumacational flex of the household head, fam. By peeping decomps for three years: 1981, 1993 and 2004, we uncover the tea on the basic trends in what's causing inequality. Elbers et al. (2008) suggest a different way to peep inequality between groups by peeping at education, race, rural/urban, and regional differences. They lowkey be like, checking out their normalized measure to make it easier to compare different settings with mad different parameters. They do tho look at the same type of determinants and find hella explanatory value for them, but like in a diff gradation than other measures do. The factors they look into are: education, race, whether you're in the city or the sticks, and where you're from. Yo, peep this: the way welfare gets handed out ain't the same as how income gets spread, ya feel me? Social policy programs might be all like, provided by the government with social services instead of just giving out cash. Velez et al. (2004) flex that public policy in Brazil is lowkey reducing inequality in welfare compared to income inequality. The regressive effects of indirect taxation are like, totally offset by the major progressive effects from direct taxation and social public expenditure (excluding pensions).

Determinants of inequality within a country, fam


Inequality is like, how spread out stuff is in a distribution, ya know? The vibes that determine that distribution are therefore also the vibes that determine the inequality. Like, to figure out the real deal on income inequality in a country, you gotta create and test a full-on general equilibrium model of the economy, ya know? This is like, hella challenging and historically empirical researchers have used mad shortcuts to find the determinants of inequality, ya know? One way to find the determinants of inequality is by splitting the population into different groups and breaking down the inequality measures accordingly, then giving credit to those variables that contribute a lot to the inequality between groups. This methodology was like totally created by Bourguignon (1979), Cowell (1980) and Shorrocks (1980) and was like reviewed by Cowell and Jenkins (1995). NAnother way to find the determinants of inequality is by flexing on income sources and breaking down inequality by income source to see which source is the biggest culprit and figure out why. This method was developed by Shorrocks (1982), fam.
Yet another way is by flexing on how equality be breaking down into changes in group squad, group average and group inequality. Mookherjee and Shorrocks (1982) developed this methodology using mad scalar decompositions, fam.

OMG as data availability and computational power skyrocket, mad researches are dropping on whole distributions. 


OMG, like from Oaxaca-Blinder decompositions (Oaxaca, 1973 and Blinder, 1973) to some progress with partial equilibrium approaches, we're making moves towards general equilibrium approaches (which still get so much hate).
In section five, like, there's this one number that's used to break down the inequality in Brazil. It's pretty wild, tbh. Even tho it has obvi limitations, as discussed above, this method can lowkey flex on the relevance of variables and the development of this relevance thru time. Table 2.1 spills the tea on the factors that contribute to inequality within a country, as discussed in some old-school papers. Several papers be investigating different determinants and using different methods to derive or assess these variables, ya know? At the end of this section, we pick some variables that we'll use in the breakdown in section 5 of this thesis. It's hella convenient to work with just one number, fam. Scalar indices can like totally spill the tea on the structure of inequality, the relevant variables that influence it and the importance of covariates. Decomps of changes in scalar measures do, like, suffer from serious shortcomings tho. Info on a whole distribution is like all crammed into one number, which is like so not efficient, you know? The decompz do not control for each otha and it's impossible to separate asset redistribution from changes in returns.

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