BIG ideas come from BIG data.

Among the many policies which have been proven to reduce relative inequality, it is is has been generally been agreed upon the fact that the current policy of corporation tax. Where the vast majority of shareholders of any kind of firm tend to be among the highest of income earners in a country, by reducing the profits through taxation, it would thus reduce dividends of high income earners. The role of tax policies fueling wealth disparity has also been an age-old debate between the politicians and economists. Economists such as Krugman and Orszag argued appropriate progressive taxation leading to social spending is the key to minimize inequality. On the other hand, regressive taxation, which naturally favours the rich, is often the incentives taken by politicians to gain votes and attract investments.

Romer and Romer Laffer curve study suggested a 33% on income tax is the ideal rate of income tax for the US. This estimated that an additional increase in a percentile income taxation, would reduce GDP by 3.042%. This study poses an inconvenient consequence to taxation as, it possesses the question to whether or not we should sacrifice the small benefit of the poor for the growth of the economy. In reference to the United States, where their constitution idolises capitalism and the rule of the free market. The use of taxation does not pose as a good idea. The 19th century American theologian and author, James Freeman Clarke, once said: “A politician thinks of the next election. A statesman, works of the next generations.” The current conventional systems such as monetary policies and fiscal policies are insufficient in tackling inequality, as these policies keep in mind of politics first, economics second. Davos 2017 will undoubtedly go down in history as the cornerstone that marked the beginning of change. Whether the emergence of protectionism is like “locking oneself in a dark room, keeping out the light and air” described by Xi, or light at the end of the tunnel that is “Making America Great again” by putting millions of American back to work, redistribute wealth and reducing economic inequality, remains to be judged in due course.

Trade liberalization or better known as globalization, has been implicated for shifting the inequality from a global arena to a domestic platform. Innately, when a rich country trade with the poor, the low-skilled laborers will see employment opportunities and wage increase, while similar workforce in the rich nations bear the consequences of wage stagnation or reductions. In addition, globalization is believed to reward firms in particular niche providing more opportunities for the elites, resulting in increasing international influence and decreasing local effects.

One of the rising revolutionary theories in modern economics is the role of big data. The famous example of this science is “Google Flu Trends (GFT)”, which uses search outcomes of Google to predict flu outbreaks. This has been proven to be extremely quick to produce predictions and is also very cost-effective. The increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.

Encouraging Pro-equality Culture- Indeed the evidence consistently supports Shiller’s views. Empirical economists support the evidence of a negative correlation of about 0.5-0.8 percentage points between long-term growth rates and sustained economic inequality. Researchers in both sides of Atlantic's have also found inequality generated higher health and social problems including drug abuse, teenage pregnancies, incarceration, obesity and suicide. The same studies also highlighted negative correlations with life expectancy, educational performance and social mobility. Moreover, multiple studies have concluded inequality to be “the single factor most closely linked and consistently related to crime.” Unsurprisingly, the studies also supported inverse link between income inequality and social cohesion.

Indeed, the widening income gap has become an issue that generated clouds of uncertainty globally. At the eve of this year’s World Economic Forum in Davos, Oxfam again published the report on “An economy for the 99 percent”. The rich poor divide is far greater than had been feared, as the headlines read “Eight richest men in the world own the same wealth as the 3.6 billion people who make up the poorest half of humanity”. The charity detailed how the elites are fueling the economic inequality by influencing politics, tax avoidance and driving down the wages. As usual, this raised the condemnations that the elites who are advocating changes in Davos are “all talks and no actions”, as they only cements the power of bankers, industrialists, technocrats and politicians serving the common interests: their own! That really raised the fundamental questions of the roles of the world leaders attending Davos in the past decades in solving or fueling Economic Inequality?

Social scientists could use big data to inform citizens, policy-makers and debates more widely about the inequalities that mark our world. The most effective kinds of social science now are ‘data-rich’. However, they are also theoretically sophisticated and offer an alternative form of data analysis to technocratic models derived from computation and information sciences.

The authors highlighted above are also deeply ‘objective’ in that they carefully report their data sources, their analysis of them, and thus where their findings come from. Yet all three also take a passionate, even politicized, view of their purposes. Putnam makes it clear that he wants to halt the decline of social capital. Wilkinson and Pickett are deeply perturbed by how socially damaging inequality is, while Piketty’s concern to document the dynamics of wealth and income inequality throughout the past century and to reform capitalism is also clear. Data, theory and politics are richly and fruitfully combined in these three works. As we look to the future, it is important for social scientists to demonstrate their effectiveness in knowing how best to present and select data, in contrast to the data driven and empiricist models often used in more technocratic visions of the ‘big data’ world, which are poorly placed to analyse inequality. Social scientists should not feel threatened by ‘big data’. They should embrace their skills and sophistication in knowing how to deploy it to best effect.

Whether “Big Data” methods work or not comes down to whether the data reflect the distribution of populations of interest or not. Analysis using a database of customers, which is often massive, will be extremely useful for a retailer because they are mostly likely the retailer’s population of interest, i.e., future customers. But, “Big Data” collected through internet or compiled in data clouds might not be particularly useful for poverty estimation and analysis because the data providers might not include the poor in whom we are most interested. Even if the data include the poor, the relative frequency of observations describing the rich will likely be higher than for the poor, and thus simple averages of this kind of data will be biased towards the rich.

The determination of wages by free market capitalism is an unachievable Utopia. The competition, skill and opportunity distribution are never equal, which inevitably results in market failures. With such discrepancy, the market concentrates wealth, pass the environmental costs to the society, and take advantages of the highly abundance low skill workers. The Neoclassic economic theory often implicates inequality in wages and salary is the most obvious culprit for economic inequality. The Marxian economics theory also attributes inequality to job automation and increase in capital intensity within capitalism aiming to minimize cost and maximize profits. In modern era of computerization and technological advancement replacing human labour, inevitably exerts reduction in employment opportunities, which invariably widening of the rich-poor divide.

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