Discussion Question: How do you accurately account for economic inequality in order to better assess living standards across and within countries? Discuss the factors affecting economic inequality and the implications on a local and global scale. Before even getting into the debate of assessing living standards, inequality and its measurement, let us first define income and consumption and how these measures are used to assess living standards in mainstream economics. According to Folbre, income encompasses all resource inflows to the household while resource outflows make up for the consumption variable (Folbre, 2009). In today’s world, living standards are assessed by looking at either of the two measures; however, most people agree that looking at living standards from the consumption angle gives us a better, more accurate feel for disparities. That being said, we recognize that these two measures do not provide a full picture: we look at resource inflows and outflows to and from households; however, we do not take into account what actually happens within the household. In recent years, more and more economists have shifted their attention to those non-market factors that directly impact standard of living among the population. In the following paragraphs, we will first examine how economic inequality is currently measured and the limitations underlying those methods. We will then elaborate on the additional parameters (household production and leisure) and their importance in better accounting for household wealth. Last, we will discuss the differences between cross-country and within-country inequalities and the role of globalization on economic inequality. When trying to assess household living standards and subsequent utility, economists currently look at what goes into the household or what goes out of it in order to carry their economic distribution analyses. With the income method, it is easier to capture the monetary value of resources that go into a household. The downside of it is that wealth appreciation and capital gains are not accounted for in that process. These exclusions make it easier to come up with inequality quantifications; but that also tells us that there are a few things that are missing from those estimations. With the consumption method, we capture household expenditures in order to measure overall annual consumption. The limitation of this technique is that long-lived consumer durables generating consumption services flow are left out (Foldre, 2009). With both income and consumption methods, we rely on market transactions to determine standard of living and subsequent well-being. Nowadays, more and more household surveys are designed to capture more subtle metrics in order to better assess inequality. That way, not only economists will get a better grasp of the relationship between income and consumption, but also, it will allow them to study those two metrics together, as complimentary tools rather than substitutes. The downside of using surveys is the potential lack of data accuracy because this type of data collection is error prone and misreports can easily occur. In order to improve inequality reporting on local and global levels, the new focus is on household production and leisure valuation. Shedding light in those two parameters will allow a better grasp of the big picture since we include non-market transactions that way. The hardest part remains the measurement of those transactions. That type o data is more accessible for developed countries; the further away we move from development, the harder it is to capture that type of data and then, the quality of the data collected becomes questionable. Household production encompasses activities that you can hire someone to do (cooking, child and elderly care, etc…). According to the United Nation Human Development Report of 2007-2008, women spend two thirds of their time engaging in non-market work while men spend a third of their time on non-market work. These ratios are quite sizeable to not account for them in the overall measurement of economic inequality. Leisure, on the other hand, currently accounts for activities that display recreational attributes (socializing, surfing the net, etc…). The challenge lies within categorizing an activity as non-market work or not. This may skew results when attempting to come up with an objective assessment of activities to account for in order to generate a comprehensive measure of living standards. In her paper entitled “Inequality and Time Use in Households,” Nancy Folbre notes that measuring inequality should involve capturing full income and full consumption estimates. By full income and consumption, it is implied that there is an imminent need to assess both variables by incorporating market and non-market work into the equation (Jenkins and Kerm, 2009). Using the opportunity cost method, one can assess the value of non-market work by computing how much he/she would have been paid if “on-the-clock”. Another way to estimate non-market work is to find out the market wage one would pay someone to get that same job done. To account for full consumption, non-market goods need to be valued at their current market price. It is indeed a lot harder to measure the non-market component of full income than full consumption because of the fuzziness associated with classifying a household activity as non-market or not. In recent years, with more and more women participating in the labor force, there is a shift from household production to market production naturally. When looking at the consumption side, this means that the underlying economies of scale coming from household production withers away and inequality measurements shift consequently. Another observation that increased inequality among households is the rise in women wages. According to Folbre, richer women tend to marry richer men and the end-result is a widening income gap among households and thereby an increased difference when it comes to living standards. How did economic inequality and poverty evolve over the past years on a global and on a local scale? In his paper entitled “Functional Distribution and Inequality,” Glyn describes the evolution of global inequality in the last decades, while emphasizing the different trends of inequality within and between countries. The research found negative correlations between both poverty and inequality indices, on the one hand, and mean income per capita on the other. Based on observed movements in Gini coefficients (the most widely used summary measure of inequality), inequality has risen in all but the low-income country aggregates over the past two decades, although there are significant regional and country differences. For instance, in emerging Europe, Latin America, developed nations and developing Asia, inequality has increased; however, it has declined in sub-Saharan Africa. According to the IMF report on Globalization and Inequality, a couple of things regarding income distribution are worth being pointed out. First of all, , income growth has been positive over the past two decades in virtually all regions and all income groups during the recent period of globalization(IMF, 2007). What ought to be noted about that is that income inequality has increased mainly in middle and high-income countries, and less so in low-income countries. This recent experience seems to be a clear change in course from the general decline in inequality in the first half of the twentieth century, and the perception that East Asia’s rapid growth during the 1960s and 1970s was achieved while maintaining inequality at relatively low levels.

The limitations of these methods of assessing inequality are that comparison of inequality data across decades is carried out with difficulty, because of the various warnings about data accuracy and methodological comparability. Therefore, one should proceed with care when it comes to data interpretation for those earlier decades. In many of the developing nations, data collection was a huge issue back then; however, data collection is getting better and better as years go by and methods of collection improve, we are better equipped to engage in data analysis to assess regional and global inequality.

Globalization has played an essential role when it comes to income distribution and inequality levels. According to O’Rourke, micro-data showed that in developed nations, high-skilled workers are more in favor of globalization than lower-skilled workers (O’Rourke, 2003). In advanced countries, low-skilled jobs have a higher chance of being outsourced; therefore, the workers in that area fear job loss more greatly and subsequently are apprehensive about the global trade issue. Also, immigration becomes an issue to these lower-skilled workers as they fear being replaced by cheaper labor. According to Ferreira and Ravallion, in their paper entitled “Poverty and Inequality: The Global Context,” evidence shows that global trade has contributed to increase in within-country inequality over the past years(Ferreira & Ravallion, 2009). However, the impact of global trade on inequality is different from country to country and the evidence varies depending on the demographics of the country. In sum, it is true that global trade impacts inequality is some ways; nevertheless, some other off-spring of globalization need not to be ignored. The globalization of knowledge, education, training and transfer of technology are quite important in the long-run as they ultimately affect economic inequality for those countries experience globalization.

References
Ferreira, F.G.H., & Ravallion, M. (2009). Poverty and inequality: The global context. In W. Salverda, B. Nolan, & T.M. Smeeding (Eds.). The Oxford Handbook of Economic Inequality. Oxford: Oxford University Press.
Folbre, N. (2009). Inequality and time use in the household. In W. Salverda, B. Nolan, & T.M. Smeeding (Eds.). The Oxford Handbook of Economic Inequality. Oxford: Oxford University Press.
Glyn, A. (2009). Functional distribution and inequality. In W. Salverda, B. Nolan, & T.M. Smeeding (Eds.). The Oxford Handbook of Economic Inequality. Oxford: Oxford University Press.
Jenkins, S.P., & Van Kerm, P. (2009). The measurement of economic inequality. In W. Salverda, B. Nolan, & T.M. Smeeding (Eds.). The Oxford Handbook of Economic Inequality. Oxford: Oxford University Press.
Wolff, E.N. (2007). Recent Trends in Household Wealth in the United States: Rising Debt and the Middle-Class Squeeze
UNDP (2007-2008). Human Development Report 2010. New York: United Nations Development Programme.
International Monetary Fund (2007). “Reaping the Benefits of Financial Globalization” (Washington: International Monetary Fund)