• 「価格の実質硬直性：計測手法と応用例」
  Takayuki MizunoTsutomu WatanabeYukiko Umeno Saito

  Abstract

  本稿では，各企業が互いの価格設定行動を模倣することに伴って生じる価格の粘着性を自己相関係数により計測する方法を提案するとともに，オンライン市場のデータを用いてその度合いを計測する。Bils and Klenow (2004) 以降の研究では，価格改定から次の価格改定までの経過時間の平均値をもって価格粘着性の推計値としてきたが，本稿で分析対象とした液晶テレビではその値は 1.9 日である。これに対して自己相関係数を用いた計測によれば，価格改定イベントは最大 6 日間の過去依存性をもつ。つまり，価格調整の完了までに各店舗は平均 3 回の改定を行っている。店舗間の模倣行動の結果，1 回あたりの価格改定幅が小さくなり，そのため価格調整の完了に要する時間が長くなっていると考えられる。これまでの研究は，価格改定イベントの過去依存性を無視してきたため，価格粘着性を過小評価していた可能性がある。

  Introduction

  Bils and Klenow (2004) 以降，ミクロ価格データを用いて価格粘着性を計測する研究が活発に行われている。一連の研究では，価格が時々刻々，連続的に変化しているわけではなく，数週間あるいは数ヶ月に一度というように infrequent に変更されている点に注目し，そうした価格改定イベントの起こる頻度を調べるという手法が用いられている。そこでの主要な発見は，価格改定イベントはかなり頻繁に起きているということである。例えば，Bils and Klenow (2004) は，米国 CPIの原データを用いて改定頻度は 4.3ヶ月に一度と報告している。Nakamura and Steinsson (2008) は同じく米国 CPI の原データを用いて，特売を考慮すれば改定頻度は 8-11ヶ月に一度と推計している。欧州諸国に関する Dhyne et al (2006) の研究や，日本に関する Higoand Saita (2007) の研究でも，数ヶ月に一度程度の頻度で価格改定が行われるとの結果が報告されている。

  2009.09

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• The bursting of housing bubble as jamming phase transition
  Katsuhiro NishinariMitsuru IwamuraTsutomu WatanabeYukiko Umeno Saito

  Abstract

  Recently housing market bubble and its burst attracts much interest of researchers in various fields including economics and physics. Economists have been regarding bubble as a disorder in prices. However, this research strategy has overlooked an importance of the volume of transactions. In this paper, we have proposed a bubble burst model by focusing on transaction volume incorporating a traffic model that represents spontaneous traffic jam. We find that the phenomenon of bubble burst shares many similar properties with traffic jam formation on highway by comparing data taken from the U.S. housing market. Our result suggests that transaction volume could be a driving force of bursting phenomenon.

  Introduction

  Fluctuations in real estate prices have substantial impacts on economic activities. For example, land prices in Japan exhibited a sharp rise in the latter half of the 1980s, and its rapid reversal in the early 1990s. This large swing had led to a significant deterioration of the balance sheets of firms, especially those of financial firms, thereby causing a decade-long stagnation of the Japanese economy, which is called Japan’s “lost decade”. A more recent example is the U.S. housing market bubble, which started somewhere around 2000 and is now in the middle of collapsing. This has already caused substantial damages to financial systems in the U.S. and the Euro area, and it is expected that it may spread worldwide as in the case of the Great Depression in the 1920s and 30s.

  2009.05

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• Menu Costs and Price Change Distributions: Evidence from Japanese Scanner Data
  Tsutomu WatanabeYukiko Umeno Saito

  Abstract

  This paper investigates implications of the menu cost hypothesis about the distribution of price changes using daily scanner data covering all products sold at about 200 Japanese supermarkets in 1988 to 2005. First, we find that small price changes are indeed rare. The price change distribution for products with sticky prices has a dent at the vicinity of zero inflation, while no such dent is observed for products with flexible prices. Second, we find that the longer the time that has passed since the last price change, the higher is the probability that a large price change occurs. Combined with the fact that the price change probability is a decreasing function of price duration, this means that although the price change probability decreases as price duration increases, once a price adjustment occurs, the magnitude of such an adjustment is large. Third, while the price change distribution is symmetric on a short time scale, it is asymmetric on a long time scale, with the probability of a price decrease being significantly larger than the probability of a price increase. This asymmetry seems to be related to the deflation that the Japanese economy has experienced over the last five years.

  Introduction

  The menu cost hypothesis has several important implications: those relating to the probability of the occurrence of a price change; and those relating to the distribution of price changes conditional on the occurrence of a change. The purpose of this paper is to examine the latter implications using daily scanner data covering all products sold at about 200 Japanese supermarkets in 1988 to 2005.

  2007.11

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• Do Larger Firms Have More Interfirm Relationships?
  Mitsuru IwamuraTsutomu WatanabeYukiko Umeno Saito

  Abstract

  In this study, we investigate interfirm networks by employing a unique dataset containing information on more than 800,000 Japanese firms, about half of all corporate firms currently operating in Japan. First, we find that the number of relationships, measured by the indegree, has a fat tail distribution, implying that there exist “hub” firms with a large number of relationships. Moreover, the indegree distribution for those hub firms also exhibits a fat tail, suggesting the existence of “super-hub” firms. Second, we find that larger firms tend to have more counterparts, but the relationship between firms’ size and the number of their counterparts is not necessarily proportional; firms that already have a large number of counterparts tend to grow without proportionately expanding it.

  Introduction

  When examining interfirm networks, it comes as little surprise to find that larger firms tend to have more interfirm relatioships than smaller firms. For example, Toyota purchases intermediate products and raw materials from a large number of firms, located inside and outside the country, and sells final products to a large number of customers; it has close relationships with numerous commercial and investment banks; it also has a large number of affiliated firms. Somewhat surprisingly, however, we do not know much about the statistical relationship between the size of a firm and the number of its relationships. The main purpose of this paper is to take a closer look at the linkage between the two variables.

  2007.03

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