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Instrumental Variable Network Difference-in-Differences (IV-NDID) estimator: model and application

Author

Listed:
  • Sandy Dall erba
  • Andre Chagas
  • William Ridley
  • Yilan Xu
  • Lilin Yuan

Abstract

The difference-in-difference (DID) framework is now a well-accepted method in quasi-experimental research. However, DID does not consider treatment-induced changes to a network linking treated and control units. Our instrumental variable network DID methodology controls first for the endogeneity of the network to the treatment and, second, for the direct and indirect role of the treatment on any network member. Monte Carlo simulations and an estimation of the drought impact on global wheat trade and production demonstrate the performance of our new estimator. Results show that DID disregarding the network and its changes leads to significant underestimates of overall treatment effects.

Suggested Citation

  • Sandy Dall erba & Andre Chagas & William Ridley & Yilan Xu & Lilin Yuan, 2021. "Instrumental Variable Network Difference-in-Differences (IV-NDID) estimator: model and application," Working Papers, Department of Economics 2021_17, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2021wpecon17
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    Keywords

    International Trade; Climate Change; Crop Yield;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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