Partial Identification of Economic Mobility
With an Application to the United States
Bibliographic Data
| ID | 19419366 |
|---|---|
| Authors | Daniel L Millimet (0000-0001-5178-5993, Southern Methodist University), Hao Li (0000-0001-5192-5458, Nanjing Audit University, corresponding author), Punarjit Roychowdhury (0000-0002-3623-9334, Indian Institute of Management Indore) |
| Year | 2020 |
| Volume | 38 |
| Issue | 4 |
| Pages | 732-753 |
| Publication date | 2020-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2019.1569527 |
| OpenAlex | W2913618313 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession
Econometrics · Economic mobility · Economics · Macroeconomics · Nonparametric statistics · Observational error · Panel data · Recession · Simplicity · Computer Science · Economic Growth and Productivity · Income, Poverty, and Inequality · Spatial and Panel Data Analysis
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0,14 |
| Citation span | 2019 - 2019 (1) |
| Citation velocity | historical |
| Highly cited | No |