arXiv 1 Apr 2021 · Econometrics
arXiv:2104.00473 · PDF · Extracted main text
An important class of structural models studies the determinants of skill formation and the optimal timing of interventions. In this paper, I provide new identification results for these models and investigate the effects of seemingly innocuous scale and location restrictions on parameters of interest. To do so, I first characterize the identified set of all parameters without these additional restrictions and show that important policy-relevant parameters are point identified under weaker assumptions than commonly used in the literature. The implications of imposing standard scale and location restrictions depend on how the model is specified, but they generally impact the interpretation of parameters and may affect counterfactuals. Importantly, with the popular CES production function, commonly used scale restrictions fix identified parameters and lead to misspecification. Consequently, simply changing the units of measurements of observed variables might yield ineffective investment strategies and misleading policy recommendations. I show how existing estimators can easily be adapted to solve these issues. As a byproduct, this paper also presents a general and formal definition of when restrictions are truly normalizations.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Attanasio, O., C. Meghir, and E. Nix (2020) Human Capital Development and Parental Investment in India | 0.980 | 34 | 5 | 94% |
| 2 | Agostinelli, F. and M. Wiswall (2024) Estimating the technology of children’s skill formation | 0.888 | 10 | 4 | 70% |
| 3 | Cunha, F., J. Heckman, and S. Schennach (2010) Estimating the technology of cognitive and noncognitive skill formation | 0.874 | 15 | 4 | 67% |
| 4 | Cunha, F. and J. Heckman (2008) Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation | 0.874 | 9 | 2 | 100% |
| 5 | Del Bono, E., J. Kinsler, and R. Pavan (2022) Identification of dynamic latent factor models of skill formation with translog production | 0.811 | 4 | 2 | 100% |
| 6 | Agostinelli, F. and M. Wiswall (2016) Estimating the technology of children’s skill formation | 0.644 | 2 | 2 | 100% |
| 7 | Agostinelli, F. and M. Wiswall (2016) Identification of dynamic latent factor models: The implications of re-normalization in a model of child development | 0.644 | 2 | 2 | 100% |
| 8 | Rubio-Ramírez, J. F., D. F. Waggoner, and T. Zha (2010) Structural vector autoregressions: Theory of identification and algorithms for inference | 0.644 | 2 | 2 | 100% |
| 9 | Evdokimov, K. and H. White (2012) Some extensions of a lemma of kotlarski | 0.511 | 2 | 2 | 50% |
| 10 | Attanasio, O., S. Cattan, and C. Meghir (2022) Early childhood development, human capital, and poverty | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2507.18995 | 1.000 | 15 | 6 |
| 2 | When “Normalization Without Loss of Generality” Loses Generality | 1.000 | 8 | 5 |
| 3 | Controlling for Latent Confounding with Triple Proxies | 0.874 | 5 | 2 |