PRI Discussion Paper Series (No.21A-08)

Discriminatory versus uniform auctions:
Evidence from JGB market

Corresponding author. Project Assistant Professor, University of Tokyo andVisiting Scholar, Policy Research Institute, Ministry of Finance, Japan
Takahiro Hattori
Visiting Scholar, Policy Research Institute, Ministry of Finance, Japan
Shogo Takahashi

November 2021

The views expressed in this paper are those of theauthors and not those of the Ministry of Finance or
the Policy Research Institute.

Research Department Policy Research Institute, MOF
3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo 100-8940, JapanTEL 03-3581-4111

Discriminatory versus uniform auctions:
Evidence from JGB market*
Takahiro Hattori† and Shogo Takahashi‡
Abstract
In 2007, the Japanese government changed the format of auctions for 30-year Japanese
government bonds (JGB) from uniform to discriminatory. We examine data before and after thischange to assess whether this has lowered the borrowing costs of the Japanese government, in thelargest government bond market in the world. As Ausubel et al. (2014) described, the generalrevenue ranking of uniform and discriminatory auctions is an empirical question. Our empiricalresult shows that this policy change lowered borrowing costs. We also show that a discriminatory
auction lowers the borrowing costs when the value of the bidders to JGB tends to be symmetric,
which is consistent with the prediction of Ausubel et al. (2014).
JEL codes: C57; D82; G18; G28
Keywords: Discriminatory auction; Uniform auction; Markup; When-issued markets; Japanese
government bond; government costs*

We would like to thank Peter Cramton, Ryo Ishida, Suresh Sundaresan, Hisayuki Yoshimoto, DanielMarszalec and the seminar participants in Kansai public economic seminar, Japanese Economic Association,
37th International Conference of the French Finance Association for their helpful comments. The views
expressed herein are those of the authors and do not necessarily reflect the opinions of the organizations theauthors belong to. Any remaining errors are the sole responsibility of the authors.
† Corresponding author. University of Tokyo. hattori0819@gmail.com.
‡
Ministry of Finance, Japan. shogo.takahashi.1112@gmail.com

1όϱτέωΩϩʀγϜϨʖʤNon Technical Summary in Japaneseʥ౨‫ښ‬୉ָ ޮ‫ڠ‬੕ࡨ୉ָӅ ಝ೜ߪࢥɼࡔແ૱߻੕ࡨ‫ٮ ॶڂݜ‬ҽ‫ڂݜ‬ҽ ෲ෨޻༺ࡔແ૱߻੕ࡨ‫ٮ ॶڂݜ‬ҽ‫ڂݜ‬ҽ ߶‫ڰ‬চ‫ޙ‬ը͗ࠅͲͺࠅ࠶͹൅ߨ͹ͪΌɼ೘ࡵʤΨʖέεϥϱʥΝࣰࢬ͢ͱ͏Ζɽ೧ؔͲ 100 ஻ԃΝ௔͓Ζࠅ࠶͗൅ߨ͠Ηͱ͏Ζ͞ͳ౵Νഐ‫ܢ‬Ͷɼ൅ߨରͲ͍Ζೖຌ੕ැͺࣁۜ௒ୣαηφΝఁ͚ཊ͓͵͗Δ
ࠅ࠶͹ভԿΝଇ͖ͤ͗॑གྷͳ͵Ζɽ֦ࠅͲ༽͏ΔΗͱ͏Ζ೘ࡵ๏ࣞͶͺ෵਼͹झྪ͍͗Ζ͗ɼकͶɼ֦ऀ͗Ԣࡵֻͪ͗ͨ͢͹ΉΉཚࡵՃ֪ͳ͵Ζʰαϱϗϱεϥψϩ๏ࣞʤdiscriminatory auctionʥʱͳɼ߶͏Ճ֪͖Δཚࡵ͢ͱ͏͘൅ߨֻͶୣͪ͢࠹ఁՃ֪ΝҲཱ͹ཚࡵՃ֪ͳͤΖʰξρο๏ࣞʤuniform
auctionʥʱ͗༽͏ΔΗͱ͏Ζɽը͗ࠅͲͺ෼Ճ࿊ಊࠅ࠶ͳ 40 ೧ࠅ࠶Νঈ͘ɼαϱϗϱεϥψϩ๏ࣞ͗‫͠༽׈‬Ηͱ͏Ζ͗ɼธࠅ͵ʹΝঈ͘ɼଡ͚͹ࠅͲ΍αϱϗϱεϥψϩ๏ࣞ͗༽͏ΔΗͱ͏Ζʤ֦ࠅ͹֕གྷͺ੶ీʀෲ෨ʤ2020ʥΝࢂল͠Ηͪ͏ʥɽ2020 ೧ͶϛʖϩʀϝϩήϫϞ‫گ‬दͳϫώʖφʀΤΡϩλϱ‫گ‬द͗ʰΨʖέεϥϱཀྵ࿨ʱͲόʖϗϩ‫ָࡃܨ‬ৈΝणৈͪ͢͞ͳ͖Δ΍Κ͖Ζͳ͕Εɼ‫Ͳָࡃܨ‬ͺΨʖέεϥϱͶͯ͏ͱ๴୉͵‫͗ڂݜ‬ட੷͠Ηͱ͏Ζɽࠅ࠶ࢤ৖Ͷͯ͏ͱͺɼࠅ࠶͹൅ߨ‫و‬ໝ͗‫ڌ‬୉Ͳ͍Ζ͞ͳ౵Νഐ‫ܢ‬Ͷɼʹ͹Γ͑͵೘ࡵ͹๏ࣞΝ༽͏Ζ͞ͳ͗൅ߨ౲‫ۃ‬͹ࣁۜ௒ୣαηφΝӀ͘Ծ͝Ζ͖ʹ͖͑Ͷͯ͏ͱ΍༹ʓ͵‫͗ڂݜ‬͵͠Ηͱ͏Ζɽ΍ͮͳ΍ɼۛ೧࠹΍॑གྷ͵‫͵ڂݜ‬ཀྵ࿨‫ڂݜ‬͹ҲͯͲ͍Ζ Ausubel et al. (2014)Ͳͺɼཀྵ࿨‫ڂݜ‬Ͷ͕͏ͱʹ͹Γ͑͵๏ࣞ͗൅ߨରͶͳͮͱ๮Ή͢͏͖Ͷͯ͏ͱ݃࿨෉͜Ζ͞ͳͺࠖೋͲ͍Ζ
ͳ͢ͱ͏Ζɽͨ͹қັͲͺɼΨʖέεϥϱ๏ࣞ͹༑྾ͺࣰৄ৏͹໲ୌͳ΍ߡ͓ΔΗɼࠅ࠶͹೘ࡵ͹ࣰৄ‫ڂݜ‬ͺ॑གྷ੓Ν߶Όͱ͏Ζɽࠅ࠶͹೘ࡵͶͯ͏ͱͺɼಝͶธࠅ͗αϱϗϱεϥψϩ๏͖ࣞΔξρο๏ࣞͶรߍ͢ͱҐ߳ɼ֦ࠅ͹υʖνΝ༽͏ࣰͪৄ͗‫׈‬൅ͶߨΚΗͱͪ͘ɽ΍ͮͳ΍ɼͨ͹಼༲ΝΊΖͳɼξρο๏ࣞ͹๏͗ྒྷ͏ͳ͏͑݃࿨Νड़͢ͱ͏Ζ‫͍͗ڂݜ‬ΖҲ๏ͲʤNyborg and Sundaresan 1996, Malvey and Archibald 1998͵ʹʥɼαϱϗϱεϥψϩ๏ࣞ͗ྒྷ͏ͳͤΖ‫݃ڂݜ‬ՎʤMarszalec 2017, Mariño and Marszalec 2020 ͵ʹʥɼ͠ΔͶɼͨ΍ͨ΍ྈंͲ͍ΉΕҩ͏͗͵͏ͳ͏͑࿨ชʤBarbosa et al. 2021ʥ΍ଚࡑͤΖɽ͞͹Γ͑Ͷɼࣰৄ෾ੵͲͺ‫Ͳ఼࣎ݳ‬ͺʹͬΔ͹๏ࣞ͗༑Ηͱ͏Ζ͖݃࿨Νͯ͜Ͷ͚͏য়‫ڱ‬Ͷ͍Ζ͞ͳ͖
Δɼը͗ࠅ΃͹ࣖࠨΝಚΖͪΌͶͺը͗ࠅ͹υʖνΝ༽͏ͪ‫͗ৄݗ‬චགྷͳ͏͓ΖɽຌߚͲͺɼ2007 ೧Ͷ 30 ೧࠶͹൅ߨ๏ࣞ͗ξρο๏͖ࣞΔαϱϗϱεϥψϩ๏ࣞͶรߍ͠Ηͪ͞ͳͶ஥໪͢ɼ͞͹੕ࡨรߍ͗೘ࡵ݃ՎͶ༫͓ΖӪ‫ڻ‬Ν෾ੵͤΖɽ۫ରదͶͺɼຌߚͲͺ೘ࡵ๏ࣞ͗ร
ߍͪ͢઴‫ޛ‬Ͷ͕͏ͱɼࠅ࠶͹೘ࡵ݃Վ༙͗қͶรԿ͖ͪ͢ʹ͖͑Ν‫ͤৄݗ‬Ζ΍͹Ͳ͍Εɼธࠅ͹υʖνΝ༽͏ͪ Nyborg and Sundaresanʤ1996ʥΏ஦ࠅ͹υʖνΝ༽͏ͪ Barbosa et al.ʤ2021ʥ͵ʹͳಋ༹ɼ‫ع‬Ͷཱི֮ͪ͢घ๑Ν༽͏ͱ͏Ζɽը͗ࠅͲͺɼ೘ࡵ઴ͶߨΚΗΖࠅ࠶͹༩༁खӀʤWI खӀʥ͍͗Ζ͗ɼWI ͹υʖνΝҲؑ͢ͱख
ಚͲ͘Ζ఼΍ɼը͗ࠅΝίʖηͳ͢ͱ༽͏Ζ‫ک‬Ίͳ͏͓Ζɽธࠅ͹कགྷ͵઎ߨ‫͍Ͳڂݜ‬Ζ Nyborg
2and Sundaresanʤ1996ʥͲͺɼ೘ࡵ๏ࣞ͹݃ՎΝण͜Ͷ͚͏ WI ͳ͹ηϕϪρχʤຌߚͲͺʰϜʖέΠρϕʱͳ‫΁ݼ‬ʥΝͳΖघ๑͗༽͏ΔΗͱ͏ΖɽWI ͺϓΧϭʖχखӀ͹ҲझͲ͍Ζ͗ɼ͞ΗΝ೘ࡵଲে͹ࠅ࠶ͶଲͤΖऩགྷͳմएͤΗͻɼϜʖέΠρϕͶͺ೘ࡵ๏ࣞ͹ࠫҡͶًҾͤΖӪ‫؜͗ڻ‬ΉΗͱ͏ΖͺͥͲ͍ΖɽຌߚͲͺɼϜʖέΠρϕΝཤ༽͢ͱɼ೘ࡵ͹รߍ͗͵͠Ηͪ 2007 ೧͹઴‫ ޛ‬3 ೧͹υʖνΝർֳ͢ͱɼϜʖέΠρϕ͗౹‫ܯ‬దͶΊͱ༙қͶรԿ͖ͪ͢Ν‫͏ͱ͢ৄݗ‬Ζɽຌߚ͹क͵൅‫ݡ‬ͺɼ30 ೧࠶೘ࡵ͗ξρο๏͖ࣞΔαϱϗϱεϥψϩ๏ࣞͶรߍ͠Ηͪ͞ͳͲɼϜʖέΠρϕ͗౹‫ܯ‬దͶ༙қͶఁԾͪ͢͞ͳͲ͍Ζɽ͞Ηͺɼαϱϗϱεϥψϩ๏ࣞͶรߍͪ͢͞ͳ
Ͳɼ೘ࡵཤյΕ͗ఁԾʤՃ֪ͺ৏তʥ͢ࣁۜ௒ୣαηφ͗ఁԾͪ͢Ն೵੓ΝࣖࠨͤΖ݃ՎͲ͍Ζɽ൅ߨ౲‫ۃ‬͹ࣁۜ௒ୣαηφΝӀ͘Ծͪ͝ͳ͏఼͑Ͳ౲࣎͹੕ࡨรߍΝγϛʖφͤΖ݃ՎͳմएͤΖ͞ͳ΍Ͳ͘ΖɽຌߚͲͺɼ৏‫ى‬͹݃Վ͗ϫώηφͲ͍Ζ͞ͳΝ༹ʓ͵‫ৄݗ‬Ν௪ͣͱ֮೟͢ͱ͏Ζɽྭ͓ͻɼ2007೧͹Ψʖέεϥϱ͹รߍͶࡏ͢ɼླྀಊ੓Ώ൅ߨྖ͗Ӫ‫ڻ‬Ν༫͓ͱ͏ΖՆ೵੓΍͍Ζɽͨ͹ͪΌɼຌߚͲͺࠅ࠶ࢤ৖͹ླྀಊ੓Ώ൅ߨྖɼͨ͹ଠ͹ร਼΍௧ՅΝ͢ͱ͕Εɼ৏फ़͹݃Վ͗ϫώηφͲ͍Ζ͞ͳΝ֮೟͢ͱ͏ΖɽΉͪɼ΍͢ 30 ೧೘ࡵ͹݃Վ͗ 30 ೧ࠅ࠶͹௒ୣαηφͶӪ‫ڻ‬Ν༫͓ͱ͏Ζ͹Ͳ͍Ηͻɼ೘ࡵ͹รߍ͗͵͏ 5 ೧͖Δ 20 ೧࠶͹݃ՎͶͺӪ‫ڻ‬Ν͍͓ͪͱ͏͵͏ͺͥͲ͍ΖɽජंΔͺ͞͹‫ৄݗ‬ΝϕϧιϚτηφͳΊ͵͢ 5ʷ20 ೧࠶͹೘ࡵͶ͕͏ͱಋ༹͹‫ৄݗ‬Νߨͮͱ͏Ζ͗ɼ͞ΗΔ͹೘ࡵ݃ՎͶͺӪ‫ڻ‬Ν‫݃͑͏ͳ͏͵͏ͱ͢·ٶ‬ՎΝಚͱ͏Ζɽ͞͹݃Վͺɼ30 ೧࠶͹ϜʖέΠρϕఁԾͺɼΨʖέεϥϱ๏ࣞ͹รߍͶًҾͤΖͳ͏͑ηφʖϨʖͳҲؑ੓͍͗Ζ݃ՎͲ͍ΖɽຌߚͲͺ Ausubel et al. (2014)Ͳ఑঑͠Ηͱ͏Ζʰ೘ࡵࢂՅं͹೘ࡵࡔͶଲͤΖՃ஍͗ଲসద
ʤsymmetricʥͲ͍Ηͻɼαϱϗϱεϥψϩ๏ࣞ͹Ά͑͗๮Ή͢͏ʱͳ͏͑ՀઈͶͯ͏ͱ΍ɼ‫ৄݗ‬Ν
ߨͮͱ͏Ζɽೖຌ͹ WI υʖνͲͺɼखӀ͹࠹߶Ճ֪ͳ࠹ఁՃ֪͹ࠫΝ೘घͤΖ͞ͳ͗Ͳ͘ΖͪΌɼ͞͹ཤյΕࠫΝɼඉଲস੓Νࣖͤେཀྵร਼ͳ͢ͱ༽͏Ζ͞ͳͲɼՀઈΝ‫ͤৄݗ‬Ζ͞ͳ͗Ͳ͘Ζɽຌߚͺ͞͹Հઈͳ੖߻ద͵݃࿨΍ಚͱ͏Ζɽͨ͹қັͲɼAusubel et al. (2014)͹ՀઈΝࣰৄͤΖॵΌͱ͹‫Ͳ఼͑͏ͳڂݜ‬΍ɼָढ़ద߫‫ݛ‬ʀ৿‫و‬੓͍͗Ζɽ͵͕ɼຌߚͺ 2007 ೧͹ 30 ೧࠶͹੏ౕรߍΝཤ༽ͪ͢‫͍Ͳৄݗ‬Εɼ‫ࡑݳ‬͹ۜ༧‫ͳڧ؂‬ͺҡ͵ΖՆ೵੓͍͗Ζ఼Ͷͺ஭қ͗චགྷͲ͍Ζɽຌߚͺ೘ࡵ๏ࣞ͹඲ՃͶ͕͏ͱɼජं͗எΖ‫ݸ‬Εɼը͗ࠅ͹υʖνΝ༽͏ͪॵΌͱ͹ࣰৄ‫͍Ͳڂݜ‬Εɼࣰࡏ͹඲ՃͶ౲ͪͮͱͺ෵਼͹෾ੵ͗චགྷͶ͵Θ͑ɽಝͶɼը͗ࠅͲͺྖద࣯ద؉࿪͵ʹ͹Ӫ‫͍͗ڻ‬ΖͪΌɼଡ໚ద͵඲Ճ͗චགྷͲ͍Ζ఼Ͷ஭қ͗චགྷͲ
͍Ζɽࢂߡช‫ݛ‬
੶ీྒྷʀෲ෨޻༺ʤ2020ʥʰೖຌࠅ࠶೘໵ʗξρο๏ࣞͳαϱϗϱεϥψϩ๏ࣞΝ஦ৼͳͪ͢೘ࡵ
ʤΨʖέεϥϱʥ੏ౕͳָढ़‫ڂݜ‬͹়ղʗʱPRI Discussion Paper Series (No.20A-06)

3

1. Introduction
In a government bond auction, the auction is announced, bids are submitted, the government
determines the market-clearing price and accepts all bids that exceed the clearing price. In a
discriminatory auction (units at different prices), bidders pay what they bid, while in a uniform
auction (units at the same price), bidders pay the market-clearing price for the accepted bids.
According to Brenner et al. (2009), many governments conduct discriminatory bond auctions
while some, such as the US and Switzerland, conduct uniform auctions. 1 A seminal paper by
Ausubel et al. (2014) finds that the general revenue ranking of uniform and discriminatory auctions
is ambiguous. In their conclusion, they emphasize that “determining the better pricing rule is,therefore, an empirical question” (p. 1391), suggesting that more empirical research is vital forevaluating this auction mechanism. They also indicate that the switch by the US Treasury from a
discriminatory to a uniform auction is one experiment that can provide such empirical evidence.
In this paper, we discuss this empirical question in the context of the Japanese government
bond (JGB) market. Japan started issuing 30-year JGBs in 1999. Initially, it conducted uniform
auctions, but decided to switch the auction format from uniform to discriminatory in April 2007.
The government also conducts discriminatory auctions for most 2-year to 20-year JGBs. Althoughmany studies have investigated the switch in US Treasury auctions, to date, no study has
investigated this switch in Japan’s case, despite it being one of the largest government bondmarkets in the world.
Irrespective of the auction mechanism, market participants can purchase the auctioned bond
before the actual auction. This forward contract in the primary market is called when-issued (WI)

1

Pycia and Woodward (2020) give a list of examples for auction shift.
4in the JGB market. Following the literature, such as Simon (1994), Nyborg and Sundaresan (1996),
Malvey and Archibald (1998), and Goldreich (2007), we examine the effect of auction formats on
the funding cost by comparing the results in terms of the auction outcomes and WI yields. The
strength of the Japanese data is the accessibility of WI data with a wide coverage of brokeragecompanies.2 Many papers have already compared discriminatory and uniform auctions, but the WIdata are not always available for most countries, except the US. In accordance with Simon (1994)
and Nyborg and Sundaresan (1996), we measure the markups, defined as the spread between thehighest winning yield at auctions over the WI yield, and examine whether the markups of theauction rate over the WI rate statistically decrease after the government switches from uniform todiscriminatory auctions. 3 We find that the markups are lower in the discriminatory auctions,suggesting that the Japanese government can reduce its cost of debt by changing its auction format.
Thus, our results show that the discriminatory auction lowers the cost of borrowing for theJapanese government.
Furthermore, we also investigate the important proposition by Ausubel et al. (2014) who state:2

The literature such as Lou et al. (2013) and Klingler and Sundaresan (2020) discusses the auction

underpricing. In our paper, we take a spread of the auction result over WI. WI is priced, reflecting the
expectation of the auction result by market participants; therefore, taking the spread of the auctioned yieldwith WI enables us to control the systematic underpricing or biases, stemming from JGB action sinceinvestors price WI reflecting these potential auction biases.
3

The previous studies, such as Simon (1994) and Nyborg and Sundaresan (1996), discuss whether theauction shift lowers (increases) the government funding cost (revenue) using the markups computed as
the spread of the winning yield at auctions over WI. For example, Nyborg and Sundaresan (1996) pointout that “The difference between the ‘true value’ of the security and the auction average is frequently
regarded as a measure of the winner’s curse” (p.73), and they use markups to examine whether theuniform auction provides a higher revenue than the discriminatory one.
5

“in settings with symmetric bidders, the pay-as-bid auction often outperforms” (p. 1366).4 As far
as we know, no literature empirically investigates this important prediction. To address this issue,
we use the proxy of the symmetric bidders in JGB auctions to test the prediction of Ausubel et al.
(2014). Our data contain the highest and lowest WI prices among bidders in JGB auctions. The
WI data is the observable bidder’s value before the auction, which reflects how symmetric thevalue of the bidders in the JGB auction are. By utilizing this proxy, we empirically show that themarkups are lower in a discriminatory auction when the value of the bidders tends to be symmetric.
This empirical result supports the prediction of Ausubel et al. (2014).
We perform additional tests to confirm that our results are robust. First, we control not only
the information about the auction (bid-cover ratios, issuance amount) but also the market condition(market liquidity, volatility, etc.). 5 In particular, since the Japanese government officiallyannounced that it had changed the auction system when the 30-year bond market had matured, we
include liquidity and the issue amount as proxies for the market maturity of the 30-year JGB.
Second, in our main analysis, we examine the sample period from April 2004 to March 2010,
covering the period before and after the policy change for the 30-year JGBs. Since the Japanese
government switched the format for 30-year JGBs from uniform to discriminatory auctions in
April 2007, the sample period from April 2004 to March 2010 compares three-year uniform withthree-year discriminatory auction results. For robustness, we use the data from April 2004 to April
2020, which covers data to the present.
We also conduct the placebo experiment by using the markup of 5-year, 10-year, and 20-year4

Hortaçsu et al. (2018) note that bids in US Treasury auction are typically “flat”.

5

Beetsma et al. (2018) discuss that more successful auctions of euro area public debt, as captured byhigher bid-to-cover ratios, lead to lower secondary-market yields.
6

JGBs. The Japanese government has continued to conduct the discriminatory auction for 5-year,
10-year, and 20-year JGBs during the sample periods, therefore a change to the auction format of
30-year JGB should not affect the markup of these JGB variables.6 By creating a dummy variable,
we regress the markup of these JGBs to the 30-year auction dummy and show that the shift of 30year JGB from uniform to discriminatory does not significantly affect the market of the other
JGBs. This result complements our empirical evidence of supporting the discriminatory auction.
We conduct a further robustness check. To provide additional evidence, we conduct
difference-in-differences analyses to detect how the auction shift of 30-year JGB affects themarkup. Using the 30-year JGB as a treatment group and the 5-, 10- and 20-year JGBs as control
groups, we can increase the number of observations for the estimation. Our result confirms thatthe auction shift significantly lowers the markup. Furthermore, our main analysis relies on WI data
since the existing literature using US data takes advantage of WI data for evaluating auction shifts.7
We also confirm that the auctioned yield of 30-year JGB has become significantly lowered after
the auction shift in the comparison of the other auctioned JGBs (5-, 10-, and 20-year JGBs).
Literature review: The theoretical literature is inconclusive in discussing which format,
uniform or discriminatory, reduces borrowing costs for issuing bonds. Historically, Friedman(1960) argues that the uniform auction is better than the discriminatory auction. 8 The extant

6

The Ministry of Finance, Japan started to issue 40-year JGB from 2007.

7

For example, Barbosa et al. (2021) use the China’s auction and evaluate the auction shift using changeof the winning yield instead of WI. This is because the data for WI market are not available in China’s
market.
8

In this paper, we focus on uniform and discriminatory auctions but there are other formats. For example,

Abbink et al. (2006) discuss the Spanish type auction, which is a hybrid of the uniform and discriminatory
auctions.
7

empirical literature comparing the uniform auction and the discriminatory one also reflects mixed
results. 9 Some studies, such as Simon (1994), Marszalec (2017), and Mariño and Marszalec(2020), support the discriminatory auction. List and Lucking-Reily (2000), Kagel and Levin(2001), and Engelbrecht-Wiggans et al. (2006), who conduct field and experimental studies, imply
there is less demand under the uniform auction. In contrast, Tenorio (1993), Umlauf (1993),
Malvey and Archibald (1998) and Goldreich (2007) support the value of the uniform auction.
Moreover, several papers, such as Nyborg and Sundaresan (1996), Hortaçsu and McAdams (2010),
Bonaldi et al. (2015), and Barbosa et al. (2021), report insignificant differences between theseauctions.
The closest literature to our paper includes Simon (1994), Nyborg and Sundaresan (1996),
Malvey and Archibald (1998), and Goldreich (2007), who explore the implications for the markupsof the winning yield at auctions over the WI yield. Although these papers highlight the US case,our paper focuses on Japan’s policy change from uniform to discriminatory auctions for only aspecific bond: the 30-year bond. There are empirical studies that discuss the auctions related to the
JGB, such as Hamao and Jegadeesh (1998), Tsuruoka (2018), and Inaba (2019), but no paper
investigates the change in the JGB auction from uniform to discriminatory, as far as we know.
Ausubel et al. (2014) assert that the revenue ranking of uniform and discriminatory auctions
is an empirical question. In this respect, using the case of the Japanese market, our results provide
empirical evidence that the discriminatory rule improves the performance of the auction throughlower financing costs for the government. Furthermore, our empirical results also confirm the9

Empirical research on auction types has also been conducted in different industries, such as the electricity

market, for example, in Bower and Bunn (2001).
8

policy implication in Ausubel et al. (2014) that the discriminatory auction often performs better
than the uniform auction in auctions with symmetric bidders, although literature has notinvestigated this yet. By using the WI data among bidders, we show that the symmetric value ofthe bidders reduces the borrowing cost of the government under discriminatory auction. As such,our results provide empirical evidence that supports the theoretical implications in Ausubel et al.
(2014).
Our paper is also related to Pycia and Woodward (2020). This paper discusses noncompetitive
auctions in Poland, China, France, Mexico, and the Philippines and mentions that if the share of anoncompetitive auction is higher, either format (discriminatory or uniform) can be revenue
dominant, but if the share is lower, both formats are equivalent, or the discriminatory format isrevenue dominant. In 30-year JGB, the share of noncompetitive action amounts to approximately
17%, and the ratio of the standard deviation over the mean is approximately 11%, which is thesecond-highest number among these countries. This prediction is consistent with our finding thatthe switch from uniform to discriminatory lowers the funding cost of the government.
The remainder of this paper is organized as follows. Section 2 explains fundamental
information about the JGB market and the WI market. Section 3 describes how to analyze theswitch of the 30-year bond’s auction format . Section 4 reports our empirical results including ourrobustness check and Section 5 concludes.

2. The JGB market
2.1 The JGB market overview10

10 The information and description in this section are based on the annual Debt Management Report, written by

9In Japan, the Ministry of Finance (MOF) announces the JGB Issuance Plan at the end of eachyear. The plan is determined in line with the government budget. In the past few decades, theissuance has been huge. For instance, the initial JGB Issuance Plan for FY 2020, announced in
December 2019, indicated an issuance amount of approximately 1.4 trillion dollars. In addition to
the initial issuance, the JGB issuance may change due to a supplementary budget need. In responseto the COVID-19 crisis, for example, the MOF altered the JGB Issuance Plan for FY 2020 fourtimes: on April 7, April 20, May 27, and December 15, 2020. After these alterations, the MOF
increased the issuance by 1.062 trillion dollars above the initial amount.11 The annual increases in
JGBs have led to an enormous amount of outstanding securities, reaching approximately 11.3
trillion dollars in December 2020.12
Due to this need to issue a huge amount of government bonds, the JGB market accounts fora large portion of the government bond market worldwide. Figure 1 shows the time series ofoutstanding government marketable securities from 1990 to 2009. In the 1990s, the US was thelargest government bond market worldwide. After 1999, however, the amount of outstanding JGBs
has risen rapidly and the JGB market surpassed the US as the largest market. This is an importantreason underscoring our focus on the JGB market and the need to examine policy alterations in thegovernment bond auction format at this point in time.

the MOF.
11

We calculated the issue amount in USD based on an exchange rate of 103.20 yen per dollar on January 1,

2021. For details on the issuance plans, see
https://www.mof.go.jp/english/jgbs/debt_management/plan/index.htmʤMinistry of Finance, Japan, date:
October 11 2021ʥ
12 Kameda (2014) analyzes the relationship between budget deficits and interest rates using JGB data.

10

The MOF in Japan has issued various kinds of bonds including the Treasury discount bill (2month, 3-month, 6-month, and 1-year), fixed-rate bonds (2-year, 5-year, 10-year, 20-year, 30-year,
and 40-year), 15-year floating rate notes (FRNs), and 10-year Treasury inflation-protected
securities (TIPS). Similar to the Treasury in the US, the MOF in Japan has used the scheme ofprimary dealers since October 2004 to ensure the smooth issuance of JGBs and improve liquidity
in the market. Currently, there are 21 primary dealers, including financial securities and banks. 13
While primary dealers have some specific obligations, such as bidding and purchasing
responsibilities, they can participate in some special auctions, such as buyback auctions andliquidity enhancement auctions. 1415 In the JGB primary market, however, other financial
institutions that are not primary dealers can participate in the JGB auctions, which helps preventthe short squeeze caused by too few participants at the auction.16 Additionally, since March 2001,13

For details about the primary dealers see the press release published by the Ministry of Finance, Japan.

https://www.mof.go.jp/about_mof/councils/meeting_of_jgbsp/160713pd.pdf ʤMinistry of Finance, Japan,
date: October 11 2021ʥ
14

For details about obligations and rights of primary dealers in Japan see the website released by the Ministryof Finance, Japan.
https://www.mof.go.jp/english/jgbs/debt_management/pd/index.htmlʤMinistry of Finance, Japan, date:
October 11 2021ʥ
15 Hattori (2019) examines the effect of the liquidity enhancement auctions in the JGB market. Díaz et al.

(2006) discuss Spanish Treasury bond market liquidity.
16 In January 2020, there were 233 financial institutions participating in the JGB auctions. For details about theparticipants see the press release published by the Ministry of Finance, Japan.
https://www.mof.go.jp/english/jgbs/topics/bond/List_of_auction_partcipants.pdfʤMinistry of Finance, Japan,
date: October 11 2021ʥ
11the Japan MOF has offered tap issues, where it auctions government securities issued previously
to improve market liquidity. The securities auctioned as tap issues have the same interest rates andmaturity dates as the original securities, except the issue dates. The Japan MOF usually announcesplans for tap issues in the previous fiscal year. Recently, the MOF has been offering tap issues
with from one to four issues annually in super long-term securities.

2.2 The JGB market Auction format17
The Japanese government conducts a competitive auction for issuing public debt. According
to Brenner et al. (2009), most countries use the discriminatory auction while some use the uniform
auction. In Japan’s auctions, both mechanisms are used. The Japan MOF generally uses thediscriminatory format for most auctions. However, 40-year bonds and 10-year TIPS are issued
through uniform auctions. Table 1 presents issued government securities and auction formats inJapan and other countries (the US, UK, Germany, and France). In Europe, many countries use thediscriminatory auction rather than the uniform auction. Although the UK also uses both auction
formats as well, all government securities except for TIPS are issued through discriminatory
auctions. Germany and France use discriminatory auctions for all their government bond auctions.
The US, however, uses the uniform auction for all government bond auctions.
The discriminatory auction differs from the uniform format in that the winning bidders pay
their winning bid price. In both formats, participants in the auctions are supposed to submit
multiple competitive bids that specify yield and quantity. The clearing price is determined at the17

The information and description in this section is based on the annual Debt Management Report, written by

the MOF.
12

point at which the aggregate demand function intersects the supply curve of the securities. 18 The
auctioneer allocates the securities to the highest competitive bidder and works down through thesubmitted bids until the bid price reaches the clearing price at the auction. Under the discriminatory
auction, winning bidders pay their bid price for all securities they are allocated. In the uniform
auction, however, each winning bidder pays the same price equal to the market-clearing price forallocated securities, regardless of their bid price.

2.3 The when-issued (WI) market
The description of when-issued market in Japan
The WI transaction occurs during the period between the auction announcement date and theissue date of the securities. The WI transactions before the auction date began in February 2004 in
Japan as a means to issue JGBs smoothly. In the WI market, dealers and investors can buy and sell
auctioned securities prior to the auction date. In other words, WI trading can be considered forward
contracting for buying an auctioned bond. Participants in the WI market trade on a yield basis. 19
After the auction date of the securities, trading in the market occurs on a price basis. Figure 2
shows WI transactions of the JGB market.
Since potential bidders at the auction will trade them in the WI market, WI yields will reflect
the demand for the auctioned securities. Hence, the WI market contributes to providing dealers
and investors with valuable information on the auctioned securities, such as yield and price, prior

18

The supply curve is inelastic at a quantity because the amount of sold securities is announced in advance anddoes not depend on the auction result.
19 The 15-year FRNs are traded on a spread (discount margin) basis because we cannot define their compound

yields due to floating interest rates.

13

to the auctions. Nyborg and Sundaresan (1996) examine auction data in the US and claim that theprice discovery function of the WI market might alleviate the winner's curse and short squeeze
because the market provides information on the auctioned securities and reduces uncertainty on
the securities before the auctions.
Previous literature examines the effect of switching auction formats on auctioned yields byutilizing the markup between the auctioned yield and the WI yield. The use of the WI yield as acommon reference for the auction result is justified because the WI index, as mentioned above,reflects the demand for the auctioned securities and can be considered to be the expected auction
prices. The use of the WI variables is also justified in our case as the WI trading format remained
unchanged before and after the JGB auction format switched from uniform to discriminatory inJapan. Moreover, the WI variables are determined prior to the auction and not affected by theformat of the JGB auction; therefore, the markup should include a premium for the risk of thewinner's curse or bid shading. For example, if the auction is conducted in the discriminatory format
and there is uncertainty in the market prior to the auction, the effect of the winner's curse may
increase and reduce the demand for the auctioned securities, expanding the markup. In contrast,
under symmetric information among market participants, the markup should also include the effectof demand reduction under the uniform auction, expanding the markup. Finally, a directcomparison of winning yields at auctions is inappropriate for this study because they are affectedby other variables that vary from auction to auction. Therefore, we need to use the WI yield or
spread as a common reference index and calculate the markup.

The advantage of the data availability
In the JGB market, we can obtain WI data before and after the auction changed from uniform14

to discriminatory auctions. Table 2 shows features of WI data used at the previous studies whichinvestigate the effect of the auction format on auction results. While Simon (1994) uses data froma daily report, Nyborg and Sundaresan (1996) and Goldreich (2007) use WI tick data provided byinterdealer brokers in the US. Although these tick data seem to be better than daily data such as
WI JGB data, their tick data does not cover the whole market since the information is provided byonly some parts of interdealer brokers. For example, Goldreich (2007) uses data from the GovPX,
a company that provides real time data and historical tick data based on information offered bymajor US brokers. Fleming and Mizrach (2009) states, however, GovPX have covered only twothirds of the whole market since one major broker has not provided information to the company.
In addition, data that Nyborg and Sundaresan (1996) use is provided by only one interdealer broker.
Although many empirical papers have investigated the auction format, WI data is notavailable for most countries. As we described in the introduction, Barbosa et al. (2021), who focus
on auctions in the Chinese government bond market, have a relatively similar motivation to ouranalysis, although WI data is not available in the Chinese market. Because of the data restriction,
Barbosa et al. (2021) use the “normalized auction yield rate” constructed as the weighted averagewinning rate at the auction minus the corresponding market bond yield one day before the auction,and exploit the effect of the experiment on the normalized yield. For Japan, on the other hand, we
have access to WI data that covers most interdealer brokers in Japan for a long period. The
Japanese Securities Dealers Association (JSDA) receives data from 18 major security companies
in Japan and has offered it every business day on its homepage since August 2002. In this data,
WI yield data have also been available since February 2004, when WI transactions started.
Moreover, we emphasize our WI data covers the highest and lowest price, which reflects thesymmetry of the bidder’s opinion toward the JGB market and makes it possible to examine the15hypothesis cast by Ausubel et al. (2014).

3. Analysis on the switch of the 30-year JGB auction format.
3.1 History of the JGB auction switch
As we have described, the Japanese government mainly uses discriminatory auctions forissuance of JGBs. However, the government has changed the auction format from the uniform
auction to the discriminatory auction. The government used a uniform auction when it started toissue 30-year bonds in 1999 but, in April 2007, the government switched from uniform to adiscriminatory auction format, after considering the opinions of JGB market participants. 20
However, some JGBs are still issued through uniform auctions. For instance, the government hascontinued to issue 40-year bonds by uniform auctions since Japan’s MOF started to issue suchsecurities in November 2007. In addition, 10-year TIPS have been issued mainly through uniform
auctions except in three cases.21 In the appendix, we explain the government scheme which allows
them to exchange opinions with market participants and the background of the auction formatchange for 30-year JGBs.

3.2 Data20

For details regarding opinions from market participants, see the minutes of the Meeting of the JGB Market

Special Participants (17th round) released by the Ministry of Finance, Japan.
https://warp.ndl.go.jp/info:ndljp/pid/1022127/www.mof.go.jp/english/bonds/minutes/17th070907.pdf
ʤMinistry of Finance, Japan, date: 11 October 2021ʥ
21

In auctions for 10-year TIPS, the Japan MOF used the discriminatory format for auctions on August 2007,

February 2008, and August 2008. We exclude TIPs because WI data are not available.
16We obtain auction data from the Japan MOF website. The MOF releases auction results
including an issuance amount, quantity of bids, the quantity of accepted bids, price, and yield, suchas the lowest price, the winning yield, and the spread at the auction (for 15-year FRNs). 22
The WI data are from the “Reference Statistical Prices [Yields] for OTC Bond Transactions”
posted by the JSDA. As we described in the section 2, the JSDA collects bond prices and coupons
daily from 18 main securities firms and provides securities-level data on its website. The statistics
are based on the quotations reflecting the execution prices on over-the-counter (OTC) securities
transactions and posted every business day. Data are based on quotations at 3:00 PM on the day.
23

We obtain the other data, such as bond volatilities and bid ask spread, from Bloomberg.
Based on previous literature, such as Simon (1994), Nyborg and Sundaresan (1996), Marlvey

and Archibald (1998), and Goldreich (2007), we use as a dependent variable the markup that is thewinning yield at auctions minus the WI yield. In our study, we use the highest winning yield at theauction (winning yield) to calculate the markup. As mentioned, the WI variables are not affectedby whether the auction is conducted as uniform or discriminatory. Hence, we can consider that themarkup includes a risk premium at the JGB auctions. If the switch of the auction format from
uniform to discriminatory reduces the markup, the switch would lower the MOF borrowing costs.
Our focus is on the 30-year bonds auctions where the MOF switched the auction format
from uniform to discriminatory. In addition, since the WI transaction data are available only after

22 Average price and yield are available if the auction format is discriminatory. However, yield is not available

if the securities are FRNs. The highest yield is not available if the securities are FRNs.
23

For details, see “System for Dissemination of Reference Statistical Prices (Yields) for OTC Bond

Transactions,” Japanese Securities Dealers Association,
http://www.jsda.or.jp/en/statistics/bonds/prices/files/baisanntiseidogaiyou.pdf ( Japanese Securities Dealers

Association, date: 11 December 2021)17

March 2004 in Japan and the MOF changed the auction format at 30-year bonds in April 2007, we
first focus on an approximate six-year.24 This period includes only 12 uniform auctions due to theavailability of WI data; therefore, we have to use a small sample. Figure 3 diagrams our empirical
study.

3.3 Methodology
Our objective is to examine whether the switch from uniform to discriminatory auctions
lowers the borrowing costs of the government. Following Simon (1994) and Goldreich (2007), we
regress the markups on the dummy variable for auction technique and the control variables asfollows:=

where
yield at time .

+

+

+

(1)

is the markup defined as the highest winning yield at auctions minus the WI
is a dummy variable that equals one if the auction is conducted

through the discriminatory format at timeand zero if the auction is conducted through theuniform format. A negative coefficient β indicates that the switch from the uniform to thediscriminatory auction lowers the borrowing costs for the MOF.
represent the control variables that include bond volatility and bid to cover ratio.
We compute the volatility as a standard deviation of the 30-year JGB. Because Simon (1994)
calculates volatilities from the observations at three business days prior to the auction date, we

24 The MOF did not conduct a 30-year bond auction in March 2004.

18

also compute a standard deviation using three business days. The bid to cover ratio is defined as
the amount of the bids received in the auction divided by the amount sold.
Some previous research has focused on auction choice and endogeneity. For example, Gupta
et al. (2020) examined India’s two-stage auction design and controlled for the endogeneity ofauction choice by switching regression models. However, unlike other governments previously
examined in the literature, the Japanese government only changed the auction format once, andafter the transition in April 2007, they did not change it in response to any market circumstances.
Therefore, we basically assume that the auction format is predetermined by the spread of WI andthe auctioned yield.
Ausubel et al. (2014) assert that “in settings with symmetric bidders, the pay-as-bid auction
often outperforms” (p. 1366). First, they show that discriminatory auction dominates uniform
auction under symmetric valuations and the flat demand for the auctioned good among investors.25
Second, they also show that discriminatory outperforms uniform under symmetric information
settings with decreasing linear marginal utility. For testing the effect of symmetric valuations on
the performance of the auction formats, we conduct the estimation as follows:

=
where

+

+

∙

+

+

(2)

is a proxy of the symmetric value of the bidder. As the proxy of the symmetricvalue of the bidder to the JGB auction, we use the spread between the highest WI price and the25

Ausubel et al. (2014) investigate the symmetric valuation among the bidder, but the other literature pursue

the different aspect of asymmetry in mutl-unit auctions. For example, Sade et al. (2006) argue asymmetry inbidders' capacity constraints plays an important role in inhibiting collusion and promoting competitive
outcomes in multiunit auctions.
19lowest one (WI price gap), which reflects the observable value of most broker-dealers before thegovernment conducts the auction. Our conjecture is that γ is positive because when the bidder’svalue tends to be symmetric, the outcome of the discretionary should be better (the markup tends
to be lower under the discriminatory auction).

4. Empirical results
4.1 The estimation based on Equation (1)The regression results are presented in Table 3. This reflects data on 30-year bonds in the
period of the first six years, from April 2004 to March 2010. Column (1) provides the result of theregression of markup on the auction dummy and shows that the coefficient of the auction dummyis negative and statistically significant at the 5% level. This implies that markups of the winningyield over WI yields are 1.78 basis points lower at the discriminatory auction than the uniform
auction.
Columns (2), (3), and (4) show the results of regressions with the control variables. In Column(2), bond volatility is included to control the effect of uncertainty about the value of the securities
on bidders’ behaviors. In Column (3), we add the bid to cover ratio at the auction, which captures
the extent of competition at the auction. In Column (4), we include the auction dummy, volatility,and bid to cover ratio. Although these specifications indicate lower significance than column (1),they show that the coefficient of the auction dummy is negative and statistically significant at the
10%. In terms of the control variables, the coefficients of the bid to cover ratio and the bondvolatility are not statistically significant.
These findings suggest that the switch from the uniform auction to the discriminatory auction
for the 30-year JGBs lowers the borrowing costs for the MOF. Since the winning yields do not20increase but rather lower due to the shift to the discriminatory auction, the revenue the MOF
receives from the auctions increase due to the change in auction format.

4.2 The estimation based on Equation. (2)Table 4 shows the results when we conduct the regression based on Equation (2). As in theprevious results, we use the data in the first 6 years (from April 2004 to March 2010) and estimate
four specifications: specification (1), without any other control variables, and specifications (2) (4) with control variables.
First, all the specifications show that coefficients of discriminatory auction dummy are
significantly negative as with the case of Equation (1), which implies that the effect of the auction
format switch is robust. They also show a lower significance level than the results under Equation(1). Second, we find that the coefficients of the interaction between discriminatory auction dummy
and WI price gap are positive and statistically significant at the 1% level. This result implies thatan asymmetricity among auction bidders makes discriminatory auction inefficient in terms ofrevenue, which is consistent with the argument by Ausubel et al. (2014). In addition, the resultson Equation (2) indicate relatively higher R-squared, which implies that these specifications are
better in terms of fitness of the models for the data.

4.3 Robustness check(i) Longer period
We conduct regressions with an expanded period: the period to date (April 2004 to a recent
auction, April 2020). Although we use the same specifications discussed in Section 3, we add atime-trend as a control variable as in Simon (1994), because the yield in the JGB market reflects a
21

decreasing time trend in the long run, especially after QQE (the 30-year yield was almost 0% in2016).
Table 5 provides the regression results in the period to date (April 2004 to April 2020). First,
we conduct the regression using Equation (1). In Columns (1), we regress the markup on theauction dummy and time-trend in the period. The coefficients of the auction dummy are negativeand statistically significant at the 10% level. In Columns (2), we add control variables (volatility
and bid to cover). Even if we control these variables, the coefficients of the auction dummy are
negative and significant. Overall, as in the baseline regression, these results indicate that thechange of auction format from uniform to discriminatory reduces the markup by approximately
1.4 basis point to 1.6 basis points and contributes to an increase in the MOF’s revenue.
Next, we regress under Equation (2) in the expanded period and check the heterogeneity ofthe effect of the auction switch with respect to asymmetricity of the JGBs valuation. Column (3)and (4) show the results. As with the previous regressions, we use the two kinds of specifications,and find that both specifications in the expanded period show that the coefficients of thediscriminatory auction dummy are significantly negative, which indicates the robustness of ourfinding described so far. With respect to the interaction term between the auction dummy and WI
price gap, we confirm that both specifications indicate a positive coefficient, and they arestatistically significant at the 5% or lower level, which is also consistent with the baseline result
and the argument by Ausubel et al. (2014).
For control variables, we have some references. The coefficients of the time-trend variable
are positive in all columns and statistically significant in all the specifications. With respect tobond volatility, both coefficients are positive, which is consistent with previous studies. The results
imply that uncertainty in the JGB market after 2010 causes bidders to demand higher yields.
22

(ii) Control for the amount and liquidity in the 30-year JGB market
Considering that the MOF switched the auction format because of the improvement of the
30-year JGB market, we controlled for the amount issued at each auction and liquidity (bid-ask
spread) as a robustness check.26 Table 6 shows the results with the two cases of the period: (i) from
April 2004 to March 2010 (columns (1) and (2)) and (ii) from April 2004 to April 2020 (columns(3) and (4)). Period (i) corresponds to the baseline case, and period (ii) corresponds to the expanded
period. All the specifications show that the coefficients of the auction dummy are negative andsignificant at the 10% or lower levels. In addition, the coefficients of the interaction term arepositive and statistically significant as with our previous results. With respect to the two additional
control variables, bid-ask spread and log of issue amount, none of the specifications had significant
coefficients. These findings imply that our result is robust even when we control for the proxyvariables concerning improvement of the market.

(iii) The placebo experiment (using 5- to 20-year JGBs)
Since the Japanese government changed the auction format of 30-year JGB, this shift
significantly reduced the markup of 30-year JGB. From a different point of view, the auction
dummy should not affect the markup of other JGBs. From April 2004 to the present, the Japanese
government has continued to conduct the discriminatory auction for 5, 10 and 20-year JGBs:
therefore, to regress the markup of these bonds on the auction dummy should be interpreted as the26

In terms of the amount issued, we use a logarithm of the issuance amount in the auction in the same way as

Barbosa et al. (2021). Although we also regress by using the exact value of the issuance amount rather than thelogarithm, we find that the results are same as the case of the regression using the logarithm.
23

placebo experiment to provide more convincing evidence of the auction shift.
In this placebo experiment, we use the two periods: (i) from April 2004 to March 2010 and(ii) from April 2004 to April 2020 based on Equation (1). Additionally, we also use all the control
variables, including issuance amount and bid ask spread. Table 7 shows the estimation result when
we use the data of 5, 10 and 20-year JGB. This table shows that the auction dummy has no
significant effect on the markup, which supports our key findings.

(iv) The Difference-in-Differences
For a further robustness check, we conduct the difference-in-differences analysis using datafrom 5-, 10-, 20- and 30-year JGBs. In this study, we can use not only the markup based on WI as
a dependent variable but also the winning yield. Because the MOF issues JGB on a monthly andquarterly basis, we can investigate how the winning yield of 30-year JGB changes after the auctionchange by comparing the auction results of 5-, 10-, 20- and 30-year JGBs. The existing research
on US data relies on WI data to evaluate auction shifts. However, some literature uses the winningyield without the WI price (see Barbosa et al. (2021)); therefore, our empirical result should evenbe convincing if we can confirm the robust result with the winning yield without the WI price.
Following Du et al. (2018), we conduct regression based on the following models:

=

+

30

∙

+

30
+

∙
30

30

2007 ++=

2007 +

2007 +++
30

2007 +24∙      (3)2007 ∙+(4)

where 30

is a dummy variable that takes value one if the bond is 30-year JGBs and zerootherwise, and2007 is a dummy variable that takes value one if the auction isconducted after the treatment (after April 2007) and zero otherwise. denotes the types of bonds(5-, 10-, 20- and 30-year JGBs), and our model includes fixed effects. 27 As with the case ofprevious specifications, we conjecture thatshould be negative andshould be positive.We

also use the winning yield as a dependent variable in this estimation to check whether our result
depends on WI data.
The results are shown at Table 8. Columns (1) to (4) are the regression where we use
markup as a dependent variable. Columns (1) and (2) are based on Equation (3), and Columns (3)and (4) are based on Equation (4). All specifications except for Column (2) show thatisnegative and statistically significant at the 10% or lower level. For the symmetricity effect,
Columns (3) and (4) show thatis positive and statistically significant at the 1% level. These

results show that the auction change reduced the markup by approximately 2 to 3 basis points andthat the effect becomes small if the WI price gap is large. This makes our baseline results morerobust.
When we use the winning yield as a dependent variable, the results are similar. Columns(5) to (8) show the results, Columns (5) and (6) are based on Equation (3), and Columns (7) and(8) are based on Equation (4). All specifications indicate that is negative andis positive,

and these results are statistically significant at the 10% or lower level. Thus, this implies that our
key findings are robust even if we use the winning yields rather than markups.

27

We drop fixed effects for 30-year JGBs due to the multicollinearity with the 30 JGB dummy.
25

5. Conclusion
We study whether switching the auction format from uniform to discriminatory contributes
to reduced borrowing costs by examining data on 30-year JGBs whose auction format changed in
April 2007. We find that the markups, defined as the spread between the winning yield at auctionsand WI yield, were lower in the discriminatory auction than the uniform one, suggesting that theJapanese government can reduce borrowing costs by switching the auction format from uniform
to discriminatory. We also show that the effect of the auction switch has a heterogeneity withrespect to asymmetric valuation among JGB’s traders. This finding is consistent with the argument
by Ausubel et al. (2014). Our result is robust when we conduct regressions in the case of a longer
period or with additional control variables. In addition, our placebo experiment shows that in theauction of other JGBs, the auction dummy variable does not affect the auction results, which makes
our baseline results more robust. Moreover, our difference-in-differences analysis using markup
and winning yields as dependent variable indicates the consistent results with our baseline
regression.
As Ausubel et al. (2014) emphasize, determining the better pricing rule is an empiricalquestion. We discuss the revenue ranking in the uniform auction and the discriminatory auction
by considering Japan’s case, one of the largest government securities’ markets in the world. Ourcontribution is to the ranking of the auction formats empirically and to show that discriminatory
auction dominates uniform auction in large and matured government securities markets withsymmetric valuation for the securities among investors and traders.
Since our paper is the first empirical paper that uses the Japanese data, therefore further
analysis is need for an actual application. Our analysis focuses on a one-shot change in 30-year
JGB in 2007. Especially, the BOJ has conducted an unprecedented monetary easing called QQE
26

right now. Our empirical results can provide the justification of past policy change, but more
empirical papers which utilize JGB data should be required for obtaining the policy implication.

27

References[1]. Abbink, K., Brandts, J., Pezanis-Christou, P. 2006. Auctions for government securities: A
laboratory comparison of uniform, discriminatory and Spanish designs. Journal of Economic
Behavior & Organization 61, 284–303.
>@ Ausubel, L. M., Cramton, P., Pycia, M., Rostek, M., Weretka, M. 2014. Demand reduction
and inefficiency in multi-unit auctions. Review of Economic Studies, 81 (4), 1366–1400.
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31

Figure 1. Time series of outstanding amounts of government marketable securities
Trillion USD
9
8

Japan

United States

7

Italy

Germany

United Kingdom

France

6
5
4
3
2
1
0

Source: OECD Statistics
Note: The data show central government debts.

.

32

Figure 2. When-issued transaction

33

Table 1. Government bond issuance in Japan and foreign countries

Source: “Debt Management Report 2020”, Financial Bureau, Ministry of Finance, Japan.
Note: Cash management bills are issued for a need for short-term finance.

34

Table 2. Features of WI data at the previous studies
Literature

Goldreich (2007)

SourceDetails

GovPX

GovPX provides real-time WI data and historical tick data based onmarket information from interdealer brokers. Offered data includes bid
and offer prices, quantity, trade prices, and its volumes.
In the 2000s, GovPX received market information from majorinterdealer brokers except for one, and therefore GovPX's information
covered about two-thirds of the whole market (Fleming and Mizrach,2009).
Goldreich (2007) use WI price data from a half-hour before theauction time (1:00 PM) to a half-hour after the auction time.

Garban. Inc. was one of the major interdealer brokers in the US
Treasury market in the 1990s. Offered data includes volume, theNyborg andmarket data from Garban. number of transactions, and yields.
Sundaresan (1996)
Inc.
Nyborg and Sundaresan (1996) examine the sample period from July
1992 to August 1993 and calculate markups by using WI transaction
data, focusing on from 12:00 PM to 3:00 PM.

Simon (1994)"Quotations on UnitedStates Government
Securities"

This is a daily report published by the Federal Reserve Bank of New
York.
Simon (1994) use WI yield measured at the close of business on
auction dates. Some of the WI yields are measured at the opening on
the day after the treasury auction.

Note: This figure describes features of WI data used in previous studies that investigate the effect of the auction
switch on auction results.

35

Figure 3. Image of our empirical study

36

Table 3. Results of OLS regression based on Equation (1) (First 6 years: Apr 2004 - Mar2010)

2004.4 - 2010.3
(2)
(3)

(1)
Auction dummy-0.0178 **(0.0077)

Volatility-0.0216 *(0.0111)-0.0232 *(0.0115)-0.3183(0.3497)

Bid to cover(4)-0.0248 *(0.0131)-0.2256(0.3400)

0.0095(0.0111)0.0076(0.0111)

Constant

0.0043(0.0038)

0.0136(0.0114)-0.0242(0.0338)-0.0120(0.0361)

R-squared

0.06840.08510.08990.0974

28

28

28

28

# Obs

Note: This table shows the regression result based on Equation (1). The period is from April 2004 to March
2010. The dependent variable is a markup, which is the spread of the winning yield at auctions over the WI rate
at auction days. The auction dummy is a dummy variable that takes value one if the auction is a discriminatory
price auction and zero if the auction is a uniform price auction. Volatility is the standard deviation of yields on
the previously issued 30-year bonds over the three business days prior to each auction. Bid to cover is the ratioof the quantity of tenders to supply by the MOF in each auction. Newey-West standard errors are in parentheses,
and note as follows: *, ** and *** stand for 10%, 5% and 1% significance, respectively.

37

Table 4. Results of OLS regression based on Equation (2) (First 6 years: Apr 2004 - Mar2010)
2004.4 - 2010.3
(2)
(3)

(1)
Auction dummy-0.0347 **(0.0128)-0.0422 ***(0.0151)-0.0429 **(0.0160)

Auction dummy ×
WI price gap

1.3233 *** 1.3055 *** 1.3730 ***(0.3964)(0.3868)(0.3987)

1.3598 ***(0.4013)Volatility-0.0377 **(0.0144)(4)-0.2620(0.2777)

Bid to cover-0.1268(0.2419)0.0120(0.0107)0.0110(0.0106)

Constant

0.0043(0.0039)0.0119(0.0096)-0.0317(0.0323)-0.0247(0.0330)

R-squared

0.25100.26220.28500.2873

28

28

28

28

# Obs

Note: This table shows the regression result based on Equation (2). The period is from April 2004 to March 2010.
The dependent variable is a markup, which is the spread of the winning yield at auctions over the WI rate at
auction days. The auction dummy is a dummy variable that takes value one if the auction is a discriminatory
price auction and zero if the auction is a uniform price auction. The WI price gap is the spread of the highest WI
price over the lowest WI price. Volatility is the standard deviation of yields on the previously issued 30-yearbonds over the three business days prior to each auction. Bid to cover is the ratio of the quantity of tenders to
supply by the MOF in each auction. Newey-West standard errors are in parentheses, and note as follows: *, **and *** stand for 10%, 5% and 1% significance, respectively.

38

Table 5. Results of OLS regression based on Equations (1) and (2) (Apr 2004-Apr 2020)
2004.4 - 2020.4
(2)
(3)

(1)
Auction dummy-0.0158 *(0.0085)-0.0144 *(0.0083)

Auction dummy ×
WI price gap
Trend

0.1474 **(0.0730)

0.1651 **(0.0637)(4)-0.0299 ***(0.0114)-0.0279 **(0.0123)0.9479 ***(0.3321)0.9007 **(0.3611)

0.2632 ***(0.0961)

0.2714 ***(0.0946)

Volatility

0.3010 *(0.1598)

0.2198(0.1402)

Bid to cover

0.0011(0.0036)0.0003(0.0032)

Constant-0.1450 *(0.0741)-0.1751 *** -0.2624 ***(0.0640)(0.0974)-0.2779 ***(0.0954)

R-squared

0.04250.06340.12400.1354

137

137

137

137

# Obs

Note:This table shows the regression result based on Equations (1) and (2). The period is from April 2004 to
April 2020. The dependent variable is a markup, which is the spread of the winning yield at auctions over the
WI rate at auction days. The auction dummy is a dummy variable that takes value one if the auction is adiscriminatory price auction and zero if the auction is a uniform price auction. The WI price gap is the spreadof the highest WI price over the lowest WI price. Trend is a time-trend. Volatility is the standard deviation ofyields on the previously issued 30-year bonds over the three business days prior to each auction. Bid to coveris the ratio of the quantity of tenders to supply by the MOF in each auction. Newey-West standard errors are inparentheses, and note as follows: *, ** and *** stand for 10%, 5% and 1% significance, respectively.

39

Table 6. Results of OLS regression (adding issuance amount and bid ask spread)
2004.4 - 2010.3

Auction dummy

(1)

(2)

-0.0284 **(0.0129)-0.0427 ***(0.0143)

Auction dummy × WI price gap

2004.4 - 2020.4(4)(3)-0.0158 *(0.0087)

1.3654 ***(0.4562)

Trend-0.0286 **(0.0125)0.8853 **(0.3687)

0.1238 **(0.0605)

0.2395 ***(0.0883)

Volatility-0.1528(0.3754)-0.1332(0.2809)

0.3251 **(0.1583)

0.2377 *(0.1388)

Bid to cover

0.0080(0.0112)0.0109(0.0109)

0.0022(0.0037)0.0009(0.0033)

0.1392(0.2694)-0.0348(0.2840)

Bid ask spreadLog of issue amount0.0414(0.0301)-0.0039(0.0460)

0.0197(0.0192)

0.0115(0.0168)

Constant-0.3696(0.2670)0.0086(0.3920)-0.3083 **(0.1494)-0.3462 **(0.1471)

R-squared

0.10510.28740.07000.1378

28

28

137

137

# Obs

Note:This table shows the regression result based on Equations (1) and (2). We use two periods: from April
2004 to March 2010 ((1) and (2)) and from April 2004 to April 2020 ((3) and (4)). The dependent variable is amarkup, which is the spread of the winning yield at auctions over the WI rate at auction days. The auction
dummy is a dummy variable that takes value one if the auction is a discriminatory price auction and zero if theauction is a uniform price auction. The WI price gap is the spread of the highest WI price over the lowest WI
price. Trend is a time-trend. Volatility is the standard deviation of yields on the previously issued 30-yearbonds over the three business days prior to each auction. Bid to cover is the ratio of the quantity of tenders to
supply by the MOF in each auction. Bid ask spread is the quoted bid-ask spread of the previously issued 30year bonds. The log of the issue amount is the logarithm of the issuance amount of each auction. We do notcontrol the bid ask spread in the first six years because of multicollinearity. Newey-West standard errors are inparentheses, and note as follows: *, ** and *** stand for 10%, 5% and 1% significance, respectively.

40

Table 7. Results of OLS regression based on Equation (1): 5 to 20-year JGBs
2004.4 - 2010.3
20-year(1)
Auction dummy

0.0064(0.0112)10-year(2)
0.0046(0.0076)

2004.4 - 2020.4
5-year(3)20-year(4)0.0050(0.0095)

0.0066(0.0093)-0.0001(0.0074)-0.0007(0.0107)-0.0018(0.0474)

0.2826 ***(0.0669)

0.1934 *(0.0996)Trend

10-year(5)5-year(6)

Volatility-0.0365(0.3017)-0.2179(0.2022)0.5546(0.5477)0.1580(0.1675)

0.2779 **(0.1267)

0.4577(0.3308)

Bid to cover-0.0083(0.0065)-0.0173 **(0.0074)-0.0009(0.0051)-0.0057 *(0.0029)-0.0124 ***(0.0038)-0.0029(0.0028)

Bid ask spread-9.6416(15.1396)

13.4081 *(7.9113)-0.2686(0.3555)

0.6742(0.4555)

1.0361 *(0.6154)

Log of issue amount0.0043(0.0258)-0.0423(0.0577)-0.0559(0.0447)0.0002(0.0166)-0.0015(0.0242)-0.0183(0.0180)

Constant

0.1800(0.4647)0.4743(0.5639)0.2772(0.5663)0.0174(0.1749)-0.2571(0.2356)-0.0350(0.2589)R-squared

0.07640.09890.09110.05540.14850.1275

72

48

72

193

169

193

# Obs

Note:This table shows the regression result based on Equation (1). We use two periods: from April 2004 to
March 2010 ((1) - (3)) and from April 2004 to April 2020 ((4) - (6)). The dependent variable is a markup,
which is the spread of the winning yield at auctions over the WI rate at auction days. The auction dummy is thesame dummy variable as we use in the previous regressions, which the value one in the period when theauction on 30-year JGB is discriminatory (until March 2007) and zero in the period when the auction is
uniform (after April 2007). Trend is a time-trend. Volatility is the standard deviation of yields on thepreviously issued bonds over the three business days prior to each auction. Bid to cover is the ratio of thequantity of tenders to supply by the MOF in each auction. Bid ask spread is the quoted bid-ask spread of thepreviously issued bonds. The log of the issue amount is the logarithm of the issuance amount of each auction.
Newey-West standard errors are in parentheses and are noted as follows: *, ** and *** stand for 10%, 5% and
1% significance, respectively.

41

Table 8. Results of OLS regression: Difference-in-difference using 5- to 30-year JGBs
Dependent variable

30-JGB dummy ×
Post Apr 2007

Markup: Winning yield minus WI yield
(1)

(2)

-0.0217 *(0.0121)-0.0174(0.0127)30-JGB dummy ×
Post Apr 2007 ×
WI price gap(3)

Winning yield(4)-0.0386 *** -0.0345 **(0.0147)(0.0156)

(5)

(6)

(7)

(8)

-0.1725 ***(0.0605)-0.1246 *(0.0666)-0.2190 ***(0.0745)-0.1563 *(0.0862)6.1052 **(2.5460)

6.4271 **(2.6160)

1.3233 ***1.3184 ***(0.4217)(0.4382)30-JGB dummy

0.0031(0.0059)-0.0017(0.0244)0.0031(0.0059)-0.0053(0.0241)

1.5434 ***(0.0480)

1.3516 ***(0.1432)

1.5278 ***(0.0491)

1.3362 ***(0.1532)

Post Apr 2007

0.0039(0.0044)0.0048(0.0058)

0.0039(0.0044)0.0052(0.0058)

0.0041(0.0307)0.0279(0.0353)-0.0275(0.0355)

0.0017(0.0405)

Volatility

0.1411(0.3001)

0.1443(0.3004)

4.7760 ***(0.9287)

4.7074 ***(1.0224)

Bid to cover-0.0057 *(0.0030)-0.0055 *(0.0029)-0.0008(0.0009)-0.0465 *(0.0265)

Bid ask spread-4.3906(13.2927)-4.5647(13.3888)-20.2434(63.4696)-15.2314(86.7192)

Log of issue amount-0.0022(0.0175)-0.0050(0.0173)-0.1293(0.1057)-0.1235(0.1142)

Constant

0.0013(0.0040)0.1255(0.3495)

0.0013(0.0040)0.1558(0.3496)0.9224 ***(0.0406)

2.4901(1.7908)

0.9380 ***(0.0419)2.4944(2.2748)

R-squared

0.03140.04940.05990.07750.85760.8736

0.86690.8840

222

222

222

222

247

247

222

222

# Obs

Note: This table shows the regression result based on Equations (3) and (4). The sample period is from March
2004 to March 2010. We use two dependent variables: markup, which is the spread of the winning yield at
auctions over the WI rate at auction days, and winning yield at auctions. The 30-JGB dummy is a variable thattakes the value one if the bond is 30-year JGB and zero otherwise. Post Apr 2007 is a variable that takes thevalue one if the auction was conducted after April 2007 and zero otherwise. The WI price gap is the spread of
the highest WI price over the lowest WI price. Volatility is the standard deviation of yields on the previously
issued bonds over the three business days prior to each auction. Bid to cover is the ratio of the quantity oftenders to supply by the MOF in each auction. Bid ask spread is the quoted bid-ask spread of the previously
issued 20-year bonds. The log of the issue amount is the logarithm of the issuance amount of each auction. We
also include 10-year JGB dummy and 20-year JGB dummy to control the fixed effects. Robust standard errors
are in parentheses and are noted as follows: *, ** and *** stand for 10%, 5% and 1% significance,
respectively.

42