Research Topics

Why Time Deficits Matter for Poverty

We cannot adequately assess how much or how little progress we have made in addressing the condition of the most vulnerable in our societies, or provide accurate guidance to policymakers intent on improving each individual’s and household’s ability to reach a basic standard of living, if we do not have a reliable means of measuring who is being left behind. With the support of the United Nations Development Programme and the International Labour Organization, Senior Scholars Rania Antonopoulos and Ajit Zacharias and Research Scholar Thomas Masterson have constructed an alternative measure of poverty that, when applied to the cases of Argentina, Chile, and Mexico, reveals significant blind spots in the official numbers.

The quality of match for each of four statistical matches used in the LIMEW estimates for France for 1989 and 2000 is described. The first match combines the 1992 Enquête sur les Actifs Financiers with the 1989–90 Enquête Budget de Famille (BDF). The second match combines the 1998 General Social Survey (EDT) with the 1989–90 BDF. The third match combines the 2003–04 Enquête Patrimoine with the 2000–01 BDF. The fourth match combines the 1999 EDT with the 2000 BDF. In each case, the alignment of the two datasets is examined, after which various aspects of the match quality are described. In each case, the matches are of high quality, given the nature of the source datasets.

The quality of match of four statistical matches used in the LIMEW estimates for Great Britain for 1995 and 2005 is described. The first match combines the fifth (1995) wave of the British Household Panel Survey (BHPS) with the 1995–96 Family Resources Survey (FRS). The second match combines the 1995 time-use module of the Office of Population Censuses and Surveys Omnibus Survey with the 1995–96 FRS. The third match combines the 15th wave (2005) of the BHPS with the 2005 FRS. The fourth match combines the 2000 United Kingdom Time Use Survey with the 2005 FRS. In each case, the alignment of the two datasets is examined, after which various aspects of the match quality are described. In each case, the matches are of high quality, given the nature of the source datasets.

The quality of match of four statistical matches used in the LIMEW estimates for the United States for 1992 and 2007 is described. The first match combines the 1992 Survey of Consumer Finances (SCF) with the 1993 March Supplement to the Current Population Survey, or Annual Demographic Supplement (ADS). The second match combines the 1985 American Use of Time Project survey (AUTP) with the 1993 ADS. The third match combines the 2007 SCF with the 2008 March Supplement to the CPS, now called the Annual Social and Economics Supplement (ASEC). The fourth match combines the 2007 American Time Use Survey with the 2008 ASEC. In each case, the alignment of the two datasets is examined, after which various aspects of the match quality are described. Also in each case, the matches are of high quality, given the nature of the source datasets.

The quality of match of four statistical matches used in the LIMEW estimates for Canada for 1999 and 2005 is described. The first match combines the 1999 Survey of Financial Security (SFS) with the 1999 Survey of Labour and Income Dynamics (SLID). The second match combines the 1998 General Social Survey (GSS) with the 1999 SLID. The third match combines the 2005 SFS with the 2005 SLID. The fourth match combines the 2005 GSS with the 2005 SLID. In each case, the alignment of the two datasets is examined, after which various aspects of the match quality are described. Also in each case, the matches are of high quality, given the nature of the source datasets.

In this paper, we conduct the novel exercise of analyzing the relationship between overall wealth inequality and caste divisions in India using nationally representative surveys on household wealth conducted during 1991–92 and 2002–03. According to our findings, the groups in India that are generally considered disadvantaged (known as Scheduled Castes or Scheduled Tribes) have, as one would expect, substantially lower wealth than the “forward” caste groups, while the Other Backward Classes and non-Hindus occupy positions in the middle. Using the ANOGI decomposition technique, we estimate that between-caste inequality accounted for about 13 percent of overall wealth inequality in 2002–03, in part due to the considerable heterogeneity within the broadly defined caste groups. The stratification parameters indicate that the forward caste Hindus overlap little with the other caste groups, while the latter have significantly higher degrees of overlap with one another and with the overall population. Using this method, we are also able to comment on the emergence and strengthening of a “creamy layer,” or relatively well-off group, among the disadvantaged castes, especially the Scheduled Tribes.