On the test of exclusion restriction in linear instrumental variable regressions

2019 ◽  
Vol 38 (15) ◽  
pp. 2897-2900
Author(s):  
Ai Deng
Keyword(s):  
Instrumental Variable ◽  
Exclusion Restriction

scholarly journals Rain, Rain, Go Away: 176 potential exclusion-restriction violations for studies using weather as an instrumental variable

2021 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Sensitivity Analysis ◽  
Social Science ◽  
Instrumental Variable ◽  
Science Studies ◽  
Prima Facie ◽  
Exclusion Restriction ◽  
Independent Variables ◽  
Independent Variable

Instrumental variable (IV) analysis assumes that the instrument only affects the dependent variable via its relationship with the independent variable. Other possible causal routes from the IV to the dependent variable are exclusion-restriction violations and make the instrument invalid. Weather has been widely used as an instrumental variable in social science to predict many different variables. The use of weather to instrument different independent variables represents strong prima facie evidence of exclusion violations for all studies using weather as an IV. A review of 217 social science studies reveals 176 variables which have been linked to weather, all of which represent potential exclusion violations. I conclude with practical steps to systematically review existing literature to identify possible exclusion violations when using IV designs. I demonstrate how sensitivity analysis can quantify the vulnerability of a particular IV estimate to exclusion restriction violations in the literature.

scholarly journals Coarsening Bias: How Coarse Treatment Measurement Upwardly Biases Instrumental Variable Estimates

2016 ◽  
Vol 24 (2) ◽  
pp. 157-171 ◽  
Author(s):  
John Marshall
Keyword(s):  
High School ◽  
Great Britain ◽  
Instrumental Variable ◽  
Data Availability ◽  
High School Education ◽  
Continuous Treatment ◽  
Treatment Variable ◽  
Exclusion Restriction ◽  
Fine Grained ◽  
Long Run

Political scientists increasingly use instrumental variable (IV) methods, and must often choose between operationalizing their endogenous treatment variable as discrete or continuous. For theoretical and data availability reasons, researchers frequently coarsen treatments with multiple intensities (e.g., treating a continuous treatment as binary). I show how such coarsening can substantially upwardly bias IV estimates by subtly violating the exclusion restriction assumption, and demonstrate that the extent of this bias depends upon the first stage and underlying causal response function. However, standard IV methods using a treatment where multiple intensities are affected by the instrument–even when fine-grained measurement at every intensity is not possible–recover a consistent causal estimate without requiring a stronger exclusion restriction assumption. These analytical insights are illustrated in the context of identifying the long-run effect of high school education on voting Conservative in Great Britain. I demonstrate that coarsening years of schooling into an indicator for completing high school upwardly biases the IV estimate by a factor of three.

scholarly journals Effects of Rural Electrification on Employment: A Comment on Dinkelman (2011)

2020 ◽  
Author(s):  
Gunther Bensch ◽  
Gunnar Gotz ◽  
Jörg Peters
Keyword(s):  
Instrumental Variable ◽  
Female Employment ◽  
Outcome Variable ◽  
Seminal Paper ◽  
Exclusion Restriction ◽  
Academic Literature ◽  
General Terms ◽  
Point Estimates ◽  
Cast Doubt ◽  
Identification Strategy

This paper replicates and extends the seminal paper by Dinkelman (2011) on the impacts of electrification on female employment. We revisit the validity of the identification strategy that uses the land gradient as an instrumental variable (IV). Our robustness checks cast doubt on the exclusion restriction as the IV drives the outcome variable in non-electrified regions. We also demonstrate that it is more difficult to disentangle the effects of electricity and road infrastructure than the original paper claims, because the IV affects both. We additionally highlight that the IV is weak, consequently preventing interpretation of the point estimates that are used throughout the original paper. The concomitance of a questionable exclusion restriction and a weak IV is particularly problematic. We conclude by arguing that the take-aways of the original paper for policy and the academic literature need to be reconsidered. In general terms, our comment shows the difficulties of using geographical variation as a natural experiment for infrastructure evaluation.

International Remittances and Private Healthcare in Kerala, India

2020 ◽  
Vol 17 (3) ◽  
pp. 445-460
Author(s):  
Mohd Imran Khan ◽  
Valatheeswaran C.
Keyword(s):  
Instrumental Variable ◽  
Capital Investment ◽  
Healthcare Expenditure ◽  
Healthcare Services ◽  
Private Healthcare ◽  
International Remittances ◽  
Variable Approach ◽  
Using Data ◽  
The Impact ◽  
Instrumental Variable Approach

The inflow of international remittances to Kerala has been increasing over the last three decades. It has increased the income of recipient households and enabled them to spend more on human capital investment. Using data from the Kerala Migration Survey-2010, this study analyses the impact of remittance receipts on the households’ healthcare expenditure and access to private healthcare in Kerala. This study employs an instrumental variable approach to account for the endogeneity of remittances receipts. The empirical results show that remittance income has a positive and significant impact on households’ healthcare expenditure and access to private healthcare services. After disaggregating the sample into different heterogeneous groups, this study found that remittances have a greater effect on lower-income households and Other Backward Class (OBC) households but not Scheduled Caste (SC) and Scheduled Tribe (ST) households, which remain excluded from reaping the benefit of international migration and remittances.

An instrumental-variable method for bearing-only localization under small noise

2020 ◽  
Author(s):  
Yiqun Zou ◽  
Bilu Gao ◽  
Xiafei Tang ◽  
Lingli Yu
Keyword(s):  
Instrumental Variable ◽  
Variable Method ◽  
Small Noise ◽  
Instrumental Variable Method
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