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Poverty, and a lack of economic development, is probably the most important
international problems of our generation. While an average citizen of the United States enjoys
per capita consumption of over $40,000, an average Indian earns just about $1,000. Around a
billion people around the world live in absolute poverty, unable to afford enough food to sustain
them. This vast income disparity across the globe is at the root of a number of other major
problems, from mass migration and genocidal incidents, to being an important factor
encouraging terrorism and crime amongst discontented youths worldwide.
Over the past few decades however, some countries have had incredible success in
reducing poverty while others continue to have masses living with barely enough for subsistence.
In the 1950’s as the world decolonized there was a wide-spread belief that state-aided
development and a dominant public sector were key to creating wealth and reducing poverty.
Experts changed their opinions by the 70’s and 80’s as countries in East Asia used a more
capitalist pattern to create economic growth and succeeded in most cases. However, the debate
amongst experts continued with proponents of both extremes citing different country-specific
nuances as the source of economic growth in a country rather than more general policy and
political decisions.
Much of this debate was resolved in the 1990’s with the help of data mining of both timeseries and cross-country data. Regression analyses were carried out by a number of different
economists to find what variables had an impact on economic growth rates. Robert J. Barro
found that for 98 countries in the period from 1960-1985, that an initial low level of real per
capita income and a high level of human capital (schooling, health) encouraged growth and
investment, while a higher share of government in the economy and market distortion decreased
growth (Barro 1991). These results are hardly surprising. Barro also found that while democracy
is conducive to growth, “too much” democracy can lead to political instability and hamper
Much of the work on economic growth and variables that help determine it was done
prior to a time when data on was as easily available and vast as it is nowadays, in the internet
age. While many of its predictions have held true in a variety of cases, China’s growth can be
ascribed to its high investment in human capital, its open trade policies, its combination of
relative social freedom with tight political control (all variables Barro found positively for), there
are a number of cases where capitalist economic policies have failed (countries in Latin America
being the most notable). Some economists have used these findings to suggest that ascribing and
predicting growth based on a few variables is nonsense (Srinivasan 2001) and a more nuanced
country specific ‘expert’ knowledge is needed to be able to understand economic growth in a
country and its impact on poverty alleviation.
However, there is no conclusive evidence that it’s super-crunching that’s failed here and
not a lack of enough predictive variables. A more holistic data analysis could include – 1)
Separate measures of political, social and economic liberty within the country, 2) Economic
volatility measured by proxies like volatility within the countries stock market and its food
prices, 3) Communication infrastructure in terms of percentage of cell phone users and road
infrastructure in terms of paved lanes per square kilometer adjusted for population, 4) Education
in terms, not only of literacy but also teacher absenteeism, percentage of students taking school
matriculation exams, more proxies for trade and private industry freedom and more. Some other
variables that might not have a direct relation but could be of interest could include average age
of national ministers/secretaries and years of education etc. Of course, many of these variables
are inter-related, but the data is large enough for us to design controls for this data. Moreover,
further data analysis can be done on which of these factors, when accompanied by growth, make
the economic growth most effective in poverty reduction. While there is fair consensus on how
best to stimulate economic growth (both through expert theory and regression analysis), there is
data lacking on what variables influence how fast it trickles down. Variables that might be
important for this include, rural to urban population, rural to urban per capita GDP ratio, village
connectivity to urban centers, agricultural infrastructure, relative spread of manufacturing units
across the country etc. With further data analysis we might just beat the experts on how to spur
the economy, and how best to use its growth.
Barro, Robert J., Economic Growth in a Cross Section of Countries, The Quarterly Journal of
Economics, May 1991
Srinivasan, T.N., 2001, Growth and Poverty: Lessons from Development Experience,
April 2001, Asian Development Bank Working Paper 17