Summary of Duncan. et al., (2004) The Endogeneity Problem in Developmental Studies

In social science research, removing bias in inferring the casual relationship is always difficult matter. In this article, the authors focused on the endogeneity problem in developmental studies which is well-know but inadequately addressed in most empirical studies. The endogeneity problem occurs when the resulting correlations between the outcomes (dependent variable, Y) and the determined or influenced variables (independent variable, X) may in fact be the result of unmeasured characteristics of the individuals themselves or their parents (Z). In the experimental study, the endogeneity is not a problem because the subject of the research is randomly assigned, we can get an unbiased result. However, in most nonexperimental developmental studies, the potential endogeneity problem is unavoidable. This article describes the nature of the endogeneity problem in theory and practice, and how to solve it.

Although developmental theories inform the linkage between developmental outcomes and their family and contextual determinants, it often cannot explain processes by which family and contextual condition arise. As a result, omitted variables (unmeasured determinants) may bias estimates of coefficient of independent variable (family and contextual determinants). Therefore, to address endogenetiy bias problem and infer the causal relationship precisely, developmental studies often conduct the multivariate regression procedures to control all relevant covariates. If the problem of endogeneity can be thought of as one of unmeasured variables, the measure-the-unmeasured approach is attempted as an alternative. However, as the author of this article argues, there may be still serious bias left in this approach. One obvious problem of a measure-the-unmeasured approach is the question of what to measure because measuring all relevant would be impossible. Moreover, especially with cross-sectional data, some measures are themselves endogenous, so it could lead to over or underestimate the other coefficients in the regression model depending on the inter-correlation among all the explanatory variables and their separate correlations with the outcomes. Because of these limitations of non-experiments, the author of this article argues that the experimental study using random assignment is the “best practice” to overcome the endogeneity. The author also argues that there are possible ways to implement the randomized experiments in the area of human development excluding the practical or ethical issues. However, I wonder about these arguments. For example, in randomized experimental study to evaluate welfare-to-work program (page 71), once the control group realize the research situation, they could resist because they feel that they are deprived of the opportunities to participate in the program. On the other hand, the experimental group could act differently not as usual, and it could distort the result. And in this i-phone era, it is totally infeasible to control people from knowing the research situation.

Besides the randomized study, this article suggests various ways to remove endogeneity problem in developmental studies. First, with individual fixed-effects models using longitudinal data, unmeasured variables that are constant over time can be removed, and with sufficiently long panels, more elaborate methods may be used to control the unmeasured variables whose values change over time in specific ways. Another set of methods for reducing bias exploit within-family variation such as using difference of siblings. Key to the success of sibling models is an understanding of and statistical adjustments for the process by which children from the same family end up in different contexts of interest. However, it requires considerable time for researcher. Other developmental research has been able to take advantage of novel natural experiments involving family and extra-familiar contexts. However, finding out the analogous natural research design could be so difficult and seem to be too rare in practice. Moreover, even we can find it, we should be careful about using it. Because the result of the analogous natural experiment research could be turned out not to be the same one we really want to search for, and even the research that we believe as a natural experiment could be biased.

All in all, the author emphasizes the importance of implementing the natural experimental study to deal with endogeneity problem. I partly agree with this idea, but also worry about it because it could restrict from expanding researcher’s imagination for more affluent study. Therefore, I think it should be promoted discussions among social science researchers on strategies toward a more rigorous research with observational data such as propensity score or diference-in-differences estimates.


One response to “Summary of Duncan. et al., (2004) The Endogeneity Problem in Developmental Studies”

  1. Soyoon Weon says:

    In the end of my summary, my concern is that too much focused on the randomized data can make researchers give up testing a new and innovative hypothesis which has only unobservational data with “endogeneity problem”.
    For example, like Guo(2014)says, secondary analyses using national survey data sets is very useful, but only a few studies(16.4% of 189 studies) conduct secondary analyses. I guess it is partly due to the fact that some researchers are afraid of bias in the survey data set and they have no idea how to deal with that.
    Therefore, in my opinion, it is better to develop new strategies toward a rigorous research with unobservational data rather than being obsessed by feasibility of the experimental study.
    That’s why I use the expression “affluence study”.

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