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Government reviewing study alleging civil servants disproportionately bought homes near unannounced MRT stations

The Public Service Division says it is reviewing the data and methodology of the working paper by the US-based National Bureau of Economic Research and will refer the matter to the Corrupt Practices Investigation Bureau if necessary.

CNA SingaporeRenee Kuek查看原文 ↗
政府正在审查一项研究,该研究指控公务员不成比例地在未公布的地铁站附近购买房屋。

The Public Service Division says it is reviewing the data and methodology of the working paper by the US-based National Bureau of Economic Research and will refer the matter to the Corrupt Practices Investigation Bureau if necessary.

An MRT train passes a public housing estate in Singapore. (File photo: CNA/Lan Yu)

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SINGAPORE: The Public Service Division (PSD) said on Monday (Sep 14) that it was reviewing information after a working paper by a US-based research organisation reported that civil servants disproportionately purchased homes near planned but not yet publicly announced MRT stations.

In response to media queries, PSD said that it was “aware of an academic working paper comparing home purchase rates near planned MRT stations”.

The paper reported that “civil servants, as classified by the authors, disproportionately purchased homes near planned but not yet publicly announced MRT stations”, PSD said.

“We take any integrity concerns extremely seriously. We are currently reviewing the data and methodology within the paper and will refer to CPIB if there is a material basis to do so.”

The working paper was published by the National Bureau of Economic Research (NBER) and titled "Do Social Norms Substitute for Enforcement? Evidence from Public Officials' Home Purchases in Singapore".

“NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications,” according to the first page of the paper.

The paper was authored by Tomasz Piskorski from Columbia University, Jian Zhang from the University of Hong Kong, Amit Seru from Stanford University and Chun Zhao from Stanford University.

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