Role of Time and Space in Social Science Research

1. Role of Time and Space in Social Science Research Yu Xie University of Michigan

2. Third Principle • Patterns of population variability vary with social context, which is often defined by time and space. • “Social Context Principle”

3. Different “Regimes” of Variability • Social contexts are different from social groups in that the former are self-contained social systems with natural boundaries, for example by time and space. • Patterns of individual variability may be governed by “relationships” between individuals that are not reducible to individuals’ attributes. • Patterns of individual variability may be governed by macro-level conditions such as “social structure,” “political structure,” or “culture,” which may be discontinuous and fixed. • Collective action may lead to changes of macro-level conditions and human relationships –major sources of social change. [Premise of Marxism.]

4. Accounting for Heterogeneous Responses • More difficult to handle, a degree of freedom problem. • Possible with nested data, assuming that patterns of relationships are homogeneous (or following a distribution) within social contexts (by time or space). • dk is allowed to vary across k (k=1,…K), social context, but is fixed within k. For example: • Yik = ak + dkDik + eikak = l+fzk+mk dk = g1+szk+nk • Application of the Social Context Principle.

5. Comments • Comment 1: It is possible to impose a parametric assumption on individual-level di, but the results are dependent on the assumption. (Bayesian approach.) • Comment 2: Nested (or hierarchical) structure could be used for time-variation or spatial variation. A key assumption: there are common features that are shared by different elements in a common social context. • Comment 3: If variations across social contexts are systematic, they can be modeled (i.e., multi-level models, or hierarchical linear models, random-coefficient models, and growth-curve models). If such variations are left as observed (or saturated), we have the fixed-effects model.

6. Two Important Features of Time and Space • 1. Commonality: sameness or similarity across different units of analysis within a common domain of time/space. This is the basis for multi-level modeling. • 2. Continuity/contiguity: social contexts are more different if they are further apart in time/space. This is the basis for temporal/spatial autocorrelation.

7. Why Study Variation by Time and Space? • Accounting for temporal and spatial variation is of intrinsic scientific value. • Temporal variation allows us to study dynamic processes and test theoretical hypotheses. • If we cannot observe dynamic processes, under strong assumptions, spatial variation provides an alternative (“reading history sideways”) • This is the basis for comparative sociology.

8. Research Designs for the Study of Temporal Variation • Time series study: N=1, t =1, …T. Almost always the aggregate for a population. • Trend study: it=1…Nt, t=1,..T, not same i’s observed over time. • Panel study, i=1…N, t=1,..T, N >>T.Same i’s observed over time. • Event history study, n=1…N, t=1,..T,T >>N Same i’s observed over time.

9. Spatial Analysis • Is similar to trend study. • Cannot observe the same persons in multiple times.

10. Focus of the Classical Chicago School of Sociology • Importance of residential location “locational attainment”. • Effects of neighborhood on socioeconomic attainment. • Earlier work on assimilation experiences of European immigrants.

11. Controlling Heterogeneity Via Fixed Effects Model • Is done at the individual level in a panel study == assuming that there are unobserved, fixed characteristics at the individual level. • Is done at the contextual level in a multi-level or trend study == assuming that there are unobserved, fixed characteristics at the contextual level.

12. Time versus Space regarding Commonality and Continuity • Commonality is usually a stronger assumption for a spatial entity than for a unit observed over time. • Boundary could be arbitrary • Assumed commonality may not be actually true • Temporal continuity is not symmetrical. Past affects future. • “1st Law of geography”  distance decay function in space. • Space could be social rather than geographic.

13. Two Primary Demographic Sources of Social Change • 1. Cohort replacement. • Cohort is no more than one way to define social grouping. Significance depends on social consequence. • There are within-cohort differences: between and within other forms of social grouping. • It intersects with period: If there is a non-repeatable significant juncture early in life (such as education), it’s social consequences are likely to be cohort effects, • 2. Life-course. • Within-cohort change. • Primarily a function of age. • Serves also as a principle of social grouping. There is variability between and within other forms of social grouping.

14. Two Sources of “Autocorrelation” • 1. For different individuals (i=1,..N) in a common environment (k), they may share a common error (variance component model). E.g, Yik = a + dDik + mk + eik • 2. Individuals in adjacent social contexts (say k and k+1) are more similar to each other than individuals located in distant social contexts (social contagion effects). That is,mk andmk+1 may be correlated. We usually only consider autocorrelation of order 1 across contexts (immediate neighbor effect).

15. Problem of Age, Period, and Cohort • Linear dependency: Cohort + age = period. Cohort = period - age • Various devices have been made. They all require untestable assumptions and thus debatable. • Problem is often a substantive one. Requires knowledge of the subject matter.

16. Reading History Sideways? • Arland Thornton’s 2001 PAA presidential address and 2005 book. • Spatial variation should not be mistaken as temporal variation. • Major reason for the failure of social Darwinism. • Reasons: contextual variability, such as culture, and path-dependency.

17. Example 1 (Xie and Hannum 1996, AJS) • Motivated by Nee’s market transition theory. • Research question 1: are earnings returns to education higher in Chinese cities that have experienced more rapid economic growth than in other cities? • Research question 2: are earnings returns to political capital (CCP) lower in Chinese cities that have experienced more rapid economic growth than in other cities?

18. Xie and Hannum’s Results • The study is based on a multi-level model. • Two main findings: • No regional variation in the influences of political capital and gender on earnings. • Education returns are lower, not higher, in cities that have experienced more economic development (but with higher intercepts) than in other cities.

19. Example 2 (Hauser and Xie 2005, SSR) • An extension of Xie and Hannum, using trend data between 1988 and 1995 in 35 Chinese cities. • Temporal-spatial design. • Capitalizing on differential rates of economic growth by region.

20. Hauser and Xie’s Findings • Net returns to schooling almost doubled for both men and women during this period. Returns to party membership, net of other factors, more than doubled. • These increases in returns to human capital and political capital do not account for the remarkable rise in the overall level of inequality. • Increases in returns to schooling were depressed in cities experiencing greater levels of economic growth in the intervening period.

21. Example 3 (Raymo and Xie 2000, ASR) • Research Questions: • How does educational homogamy change in response to economic development? • Is there a unique East Asian regime in educational homogamy? • The study is again temporal-spatial, although relying only on data from four geographic units.

22. Raymo and Xie’s Findings • There has been declines in the strength of educational assortative mating. • But there appears a convergence across US. China, and Japan. • Thus, no unique Confucian pattern of very high levels of educational assortative mating.