Volume 21, Number 2 (June 2012)
The Journal of Income Distribution, Volume 21, Number 2 (June 2012) is now finally out. We apologise to our readers for the continuing delay. The Editorial Office has faced the difficulties of having been quite understaffed for a stretch and is still a bit behind in its manuscript preparation.
Volume 21, Number 2 is, however, here and of timely interest! It brings together several articles related through gender and/or general welfare themes. In the first and second articles, two different approaches to measuring the potential for a gender effect in a family’s income and the labour market are presented. Welfare is obviously touched on in all areas of income distribution, but here it is a fundamental topic in the last three articles. It is once again addressed through its manifestation in small but intense ways, whether in terms of assessing its association to the distribution of earnings or its measurement in relation to specific periods or to data errors and the handling of missing values. Using the data of national and international collections, the articles link the world through their perspectives on income distribution.
The application of new perspectives on sources of information is key to two of this issue's five articles. In the first, the distribution of family income is analysed in its connection to the distribution of personal income and the composition of families. The authors, Deborah Reed and Maria Cancian, found, by employing 36 years’ worth of data in the Annual Social and Economic Supplement (the March files) of the Current Population Survey, that an argument has to be made for going beyond labor earnings and other income sources to examining the composition and work behavior of families in order to provide researchers with a unique awareness of to just what degree changes in family income relationships affect the distribution of family income. Their research in non-parametric measures of changes in family income relationships invites subsequent studies to account further for the increase in family income inequality in the United States between 1968 and 2003.
The principle behind the last article is also to question the results of reading survey data. Despite fielding information through an identically appropriate questionnaire given to all those within the Individual Development [Savings] Accounts (IDA) pilot program, Mark Schreiner and Michael Sherraden argue that there were probable data errors and the handling of missing values in an 18-month and a 48-month survey carried out by enumerators of 840 caseloads in total, all in Tulsa County, Oklahoma, USA, which asked basic questions about assets, liabilities and net worth. Observed, in sum, was that some extrapolations of the estimated impacts of the programs on net worth are remarkably large, both economically and statistically. Schreiner and Sherraden argue that the results obtained simply using the statistics collected yielded inconsistencies as compared to those computed using micro data revised upon individual call backs to participating households. As, therefore, the revised IDA measures were not fully re-confirming of the most positive analyses, the authors conclude that the jury is still out on the impacts of IDAs on net worth and that data quality and modeling assumptions are crucial even (or especially) for such randomized experiments.
Interaction between tried and true methods for employing income inequality data (Gini indices and group modelling) and the asking of distributional questions is tested in the other three articles of this issue. In “Welfare Rankings of The Distribution of Earnings in Italy”, Paolo Liberati and Shlomo Yitzhaki have been able to show, using the Analysis of Gini (ANOGI), that differences among regional distributions accentuate over time, which confirms the persistence of dualism within Italian geographical areas. They first probe the use of dominance techniques and find that, while such are standardly used income inequality tools, when they are applied to the distribution of earnings in Italy, they reveal hardly any inequities. The authors note that these techniques are admittedly extensively used to minimize disagreement about the set of social values introduced into the analysis, but as such, dominance conditions simply do not allow extensive ranking. The introduction of more specific indicators is needed to allow for ranking on the basis of specific value judgments. Liberati and Yitzhaki maintain nonetheless that there is no reason to expect that similar analyses repeated in other countries or at an international level will remove uncertainty in welfare rankings, mainly, however, because when moving toward loading on social values and increasing the dimensions of analysis, unanimous welfare prescriptions will be quite unlikely.
Vulnerability in measurement – both relative and absolute – is demonstrated by the Gini index, when different Income Reference Periods (IRP) are adopted, as addressed in the fourth article in this issue, by Carsten Schröder, in connection with the predictability of the employment histories of German residents. Derived distributional indices are put indirectly through a series of validation exercises to reveal that length of the IRP affects the shape of the income distribution and indices, such as the Gini. Changing the period of income measured proves to make it so much less predictive an element that a uniform measurement period of income seems to be required for the validity of distributional analyses. The Gini index thus again plays a part in this as in the third article.
For all but one article in this issue reference has been largely the interpretation of empirical data available in different regions of the world. In the second article, by Wei-Bin Zhang, discussion of empirical studies is a small subset of the overriding 3-group model, established as a theoretical construct. In the article, methodology is applied to develop a dynamic model. The goal is to assess economic growth when proposing a growth model with gender-heterogeneous households and an elastic labor supply. With it, Zhang demonstrates that either a rise in female education or the propensity to work of any one group will increase the female labor participation within that group, but that such a change has weak effects on the female labor participation within all other groups. The dynamic model differs from traditional growth and distribution related studies’ models, as it describes interaction among economic growth, time, gender, income, and wealth distribution. Again within the devised general comparative dynamic analysis, an alternative approach to consumer behavior is presented. Through modelling this one-sector growth multi-group model with gender and endogenous labor supply, Zhang is avowedly contributing to a joint effort to assess the dynamics of female labor participation, wealth, and income distribution in a competitive economy with capital accumulation as the main engine of economic growth.
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