Estimating firm-level production functions with spatial dependence

This paper proposes a three-stage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (201...

Full description

Saved in:
Bibliographic Details
Main Authors: CHANG, Pao-li, MAKIOKA, Ryo, NG, Bo Lin, YANG, Zhenlin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Online Access:https://ink.library.smu.edu.sg/soe_research/2769
https://ink.library.smu.edu.sg/context/soe_research/article/3768/viewcontent/Estimating_Firm_Level_Production_Functions_with_spatial_dependence.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soe_research-3768
record_format dspace
spelling sg-smu-ink.soe_research-37682024-11-06T06:40:24Z Estimating firm-level production functions with spatial dependence CHANG, Pao-li MAKIOKA, Ryo NG, Bo Lin YANG, Zhenlin This paper proposes a three-stage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (2015) and Wooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and lagged input usages of related firms, and spatially correlated productivity shocks across firms, where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to validate these properties. The proposed estimator is consistent under DGPs with or without spatial dependence, and with strong/weak or positive/negative spatial dependence. In contrast, the conventional estimators lead to biased estimates of the production function parameters if the underlying DGPs have spatial dependence structure, and the magnitudes of the bias increase with the strength of spatial dependence in the underlying DGPs. We apply the proposed estimation algorithm to a Japanese firm-to-firm dataset of 14,178 firms during the period 2009–2018. We find significant and positive spatial coefficients in the Japanese firm-level productivity process via all three channels proposed above. 2024-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2769 https://ink.library.smu.edu.sg/context/soe_research/article/3768/viewcontent/Estimating_Firm_Level_Production_Functions_with_spatial_dependence.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Productivity estimation spatial dependence supplier-customer network factor market pooling knowledge spillover Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Productivity estimation
spatial dependence
supplier-customer network
factor market pooling
knowledge spillover
Econometrics
spellingShingle Productivity estimation
spatial dependence
supplier-customer network
factor market pooling
knowledge spillover
Econometrics
CHANG, Pao-li
MAKIOKA, Ryo
NG, Bo Lin
YANG, Zhenlin
Estimating firm-level production functions with spatial dependence
description This paper proposes a three-stage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (2015) and Wooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and lagged input usages of related firms, and spatially correlated productivity shocks across firms, where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to validate these properties. The proposed estimator is consistent under DGPs with or without spatial dependence, and with strong/weak or positive/negative spatial dependence. In contrast, the conventional estimators lead to biased estimates of the production function parameters if the underlying DGPs have spatial dependence structure, and the magnitudes of the bias increase with the strength of spatial dependence in the underlying DGPs. We apply the proposed estimation algorithm to a Japanese firm-to-firm dataset of 14,178 firms during the period 2009–2018. We find significant and positive spatial coefficients in the Japanese firm-level productivity process via all three channels proposed above.
format text
author CHANG, Pao-li
MAKIOKA, Ryo
NG, Bo Lin
YANG, Zhenlin
author_facet CHANG, Pao-li
MAKIOKA, Ryo
NG, Bo Lin
YANG, Zhenlin
author_sort CHANG, Pao-li
title Estimating firm-level production functions with spatial dependence
title_short Estimating firm-level production functions with spatial dependence
title_full Estimating firm-level production functions with spatial dependence
title_fullStr Estimating firm-level production functions with spatial dependence
title_full_unstemmed Estimating firm-level production functions with spatial dependence
title_sort estimating firm-level production functions with spatial dependence
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/soe_research/2769
https://ink.library.smu.edu.sg/context/soe_research/article/3768/viewcontent/Estimating_Firm_Level_Production_Functions_with_spatial_dependence.pdf
_version_ 1816859100075524096