Silviase/jgov_v1.4_full_ann huggingface dataset:
## 不妊に悩む方への支援の強化

## ◆主な課題

妊娠や不妊に

関する知識の

普及啓発、

相談支援

安心・安全な

妊娠・出産に

向けた医療

機関の質の

確保

妊娠・出産に伴うリスクの少ない年齢、出産に至る可能性の高い時期の治療

## ◆対応の方向性

知識の普及啓発

・妊娠に関する正確な知識を持つことが第一歩であり、男性も含めた知識を広く啓発

◇相談体制の強化

・不妊治療の増加に伴う専門的な相談支援のニーズが高まっていることに対応

◇医療機関における良質な治療の確保

・治療の質を高める観点から、患者の利便性を配慮しつつ、医療機関の指定要件を厳格化

◇より安心・安全な妊娠・出産に資する公的支援

・妊娠・出産に伴うリスクが相対的に少ない年齢、治療により出産に至る確率が高い年齢での治療が必要

・長期間の治療による身体面・精神面の負担にも配慮が必要

・制度の見直しに関する周知

## ◆具体的施策

## O情報発信

・厚生労働科学研究班が作成したパンフレットの活用やホームページによる情報発信の強化を図る

女性健康支援センターの拡充

・利便性の強化 → 全国統一番号の新設や学習会の開催

・対応力の強化 → 相談員の研修会

○不妊専門相談センターの拡充

・利便性の強化 → 土日等の講習会等の実施

・対応力の強化 → 相談員の研修会や当事者を含めた関係機関の連携強化

## Ｏ医療機関の指定要件の見直し

・採卵件数が多い施設は、専門資格を有した医師、看護師の配置・安全確保の観点から、受精卵等を扱う場合のダブルチェックを義務化

O情報公開(医療機関に関する情報の透明性を確保)

関係学会や不妊に悩む方を支援する団体等の協力を得ながら、正確な理解を促すための取組

## ○助成事業の見直し

医学的知見を踏まえ、より安心・安全な妊娠・出産に資する観点から、対象範囲を見直し

①対象年齢 43歳未満

②通算回数 6回（40歳以降で開始した場合3回）

③年間回数・通算期間は制限なし

※対象年齢については、平成28年度より実施

※平成25年度までに既に助成を受けている方については、現行制度を引き続き適用

## ○周知の徹底

・制度の見直しについて、インターネット動画等により周知徹底を図る
## 不妊に悩む方への特定治療支援事業の見直し

## 《基本的考元方》

➢ 妊娠・出産に伴うリスクが相対的に少ない年齢

特定不妊治療により出産に至る確率がより高い年齢に必要な治療を受けられるようにする

長期間にわたる治療による身体面・精神面への負担にも配慮

## 《助成対象範囲の見直し》

現行	見直し案
年齢制限	年齢制限なし	43歳未満
年間助成回数	年間2回(初年度3回)	制限なし
通算助成回数	通算10回	40歳未満通算6回43歳未満通算3回
通算助成期間	通算5年	制限なし

【新しい日本のための優先課題推進枠】

○平成26年度から新規に助成を受ける

方を対象

※年齢制限については、平成28年度より実施

※平成25年度までに既に助成を受けている方は、現行制度を引き続き適用

※新制度への円滑な移行の観点から適切な移行措置を講ずる
参考 2

## ひとり親家庭への総合的な支援体制の強化 （推進枠 15億円）

## ひとり親家庭それぞれの様々な課題に対し、適切な支援メニューを組み合わせて総合的・包括的な支援を行う。

(1) 総合的な支援のための相談窓口の整備

自治体の規模、支援サービスの状況など地域の実情に応じたワンストップの相談窓口の構築を推進。好事例を全国展開。

(2)支援施策の充実強化

①転職・キャリアアップ支援等の就業支援関連事業等の充実強化　②子どもに対するピア・サポートを伴う学習支援の推進　※「日本再興戦略」において、女性が活躍できる環境整備を推進することとされ、「母子家庭の母等への就業支援」が位置づけられている。

※「子どもの貧困対策の推進に関する法律」が成立し、貧困率の高さが指摘されているひとり親家庭への支援施策の強化が求められている。

## ひとり親家庭の支援に関する主な課題

① 相談支援体制が不十分（多岐にわたる課題を把握・整理し、適切な支援メニューにつなげることができていない）

② 地域により支援メニューの実施状況にばらつきがある

③ 支援施策が知られておらず、利用が低調

④ 多くが非正規雇用で働いており、稼働所得が少ないため、個々の状況に応じた就業支援が必要

⑤ ひとりで仕事と子育てを両立するには、子育て・生活支援の充実が不可欠

⑥ 親との離別経験や将来への不安、親への気遣いなど特有の悩みを持つ子どもへの支援が必要

⑦ 貧困率の改善が求められている（子どもがいる現役世帯の「大人が一人」の相対的貧困率 50.8%）

## 具体的施策

### 総合的な支援の枠組みの構築【新規】6.6億円

○総合的な支援のための相談窓口の整備

母子自立支援員に加え新たに就業支援専門員を配置し、ワンストップの相談窓口で適切な支援メニューを組み合わせることにより総合的・包括的な支援を実施（200か所）

○支援施策に関する広報啓発活動の強化

○国による地方自治体への支援

地方自治体の支援体制の検証、好事例の全国展開 等

### 就業支援関連事業等の充実強化 5.2億円

支援施策の充実強化

○身近な地域での事業の充実強化

（１）就業支援講習会の拡充、相談関係職員の研修等の充実

（２）個々の状況に即した自立支援プログラムの策定の拡充

（３）就職活動等の際の生活援助や保育サービスの提供等の拡充

### 子どもへの支援の推進(ピア・サポート、学習支援) 2.8億円

○子どもが気軽に相談できる児童訪問援助員（ホームフレンド）の派遣の拡充

子どもの心に寄り添うピア・サポートも行いつつ、学習意欲の喚起や教科指導等を行う学習支援ボランティア事業の拡充
## ひとり親家庭への総合的な支援

## 就業支援

○就業支援講習会の拡充

○相談関係職員の資質向上

○自立支援プログラムの策定

○ハローワーク等との定期的な連絡調整や同行支援など

## 子どもへの支援

《特有の悩みを持つ子どもへの支援の充実》

○ピア・サポート、学習支援

・児童訪問援助員(ホ-ムルルド)の派遣 【拡充】

・学習支援ボランティア事業 【拡充】

## 養育費の確保、経済的支援

○養育費相談支援センター等による養育費相談

○児童扶養手当の支給

○母子寡婦福祉資金の貸付　など
# Inflation Expectations of Japanese Households: Micro Evidence from a Consumer Confidence Survey

Masahiro Hori, Masaaki Kawagoe

November 2011

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan

The views expressed in “ESRI Discussion Papers” are those of the authors and not those of the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
# Inflation Expectations of Japanese Households: Micro Evidence from a Consumer Confidence Survey $ ^{*} $

Masahiro Hori $ ^{\dagger} $

Hitotsubashi University, and Economic and Social Research Institute, Cabinet Office

Masaaki Kawagoe

Cabinet Office

## Abstract

Economists unanimously agree that economic agents’ expectations are crucially important in determining macroeconomic outcomes. However, mainstream macroeconomists usually simply assume that expectations are rational, leaving unexamined the fundamental question whether individual agents’ actual expectations are rational or not. Against this background, this study examines the properties of Japanese households’ inflation expectations using micro-based inflation expectations data from the Monthly Consumer Confidence Survey Covering All of Japan. Our analyses show that actual inflation expectations by Japanese households are not rational in the sense that they are upward biased, at least ex post, and individual households appear not to instantaneously incorporate into their expectations information that is freely available from news reports on the views of professional forecasters. Our findings, moreover, suggest that while the sticky information model appears to better explain inflation expectations dynamics (than rational expectations models), we encounter a handful of facts that look inconsistent with the simple model.

Keywords: inflation expectations, consumer survey

JEL classification codes: D84, E31

1. Introduction

2. Empirical Model Derivation

3. Data Sources

4. Empirical Analysis

5. Conclusion
## References

### 1. Introduction

Economists unanimously agree that the expectations of economic agents are crucially important in determining macroeconomic outcomes. Yet, to a large extent, the assumption by mainstream economists that the expectations of a “representative” agent are rational is simply that, an assumption, and fundamental questions such as whether expectations are really rational or not, whether it is harmless to ignore the fact that not everyone has the same expectations, and many related issues have not been empirically examined.

While there is large body of literature testing the rationality of macroeconomic expectations, $ ^{1} $ until recently there had been essentially no work testing alternative models of expectations using actual empirical data on expectations. Only in the past decade or so have there been efforts to provide testable alternatives which incorporate a more realistic account of expectations into mainstream economic theory. One of the first attempts in this direction was the study by Mankiw and Reis (2002), who introduced the costs of information processing into their model of “sticky information.” The model suggests that if there are any costs involved in collecting and processing information, agents may choose to update their expectations less frequently, creating staggered changes in expectations.

Sticky information models provide a handful of empirically testable implications, including the fact that there should be disagreement among economic agents about inflation expectations (Mankiw, Reis, and Wolfers 2004). In the United States, there is a long tradition of collecting data on inflation expectations, $ ^{2} $ and based on such data, a considerable number of empirical studies have been conducted to test the hypotheses derived from these models (for an
overview of such studies, see Curtin 2005). Inspired by models of disease spread from the epidemiology literature, Carroll (2003), for example, provides micro foundations for the sticky information theory and derives a simple equation suitable for empirical analysis.

Turning to Japan, there has been almost no serious research on households' inflation expectations, primarily due to the lack of data on inflation expectations. A rare exception is the study by Hori and Shimizutani (2004) examining survey data from the Kokumin Seikatsu Monitors (Monitor Survey on National Life in Japan). Only following the experience of deflation in the late 1990s and early 2000s did the Japanese government and a government-affiliated institution, in April 2004, launch two independent surveys on inflation expectations. The first is the Monthly Consumer Confidence Survey Covering All of Japan (MCCS), which collects information on households' expectations about inflation; the second is the Monthly Survey of Japanese Economic Forecasts (ESPF) covering economic forecasts produced by professional economists in Japan.

This paper takes advantage of micro level data from the two monthly surveys to estimate inflation expectation dynamics. To the best of our knowledge, this is the first study on Japan of this kind. $ ^{3} $ Based on the sticky information literature in the United States, and especially the study by Carroll (2003), we propose a test of alternative models of inflation expectations. While Carroll’s study used aggregated macro data to produce interesting findings, here we use the same setting to analyze the rich information contained in the micro data from the survey on inflation expectations by individual households. Although we are of course interested in the macroeconomic implications of inflation expectation dynamics, our main purpose here is to examine the micro foundations of inflation expectations modeling. And for this reason, it is more natural to use micro data rather than macro data in our empirical analysis.
Our analysis shows that actual inflation expectations of households in Japan are far from rational in the sense that they are biased upward, at least ex post, and that households do not instantaneously utilize information that is available almost for free from news reports on professional forecasts. We also find that although sticky information models appear to better explain the observed dynamics of inflation expectations (than rational expectations models), they can only explain a relatively small part of the disagreements in households' expectations, suggesting that there must be other factors present that are not accounted for by the existing simple models.

The remainder of the paper is organized as follows. Section 2 briefly describes the settings of the sticky information model proposed by Carroll (2003) and derives our empirical specification to test the model using our micro data. Next, Section 3 provides an outline of the two sets of survey data on inflation expectations in Japan, the MCCS and the ESPF, and discusses interesting features of the derived series for inflation expectations in Japan. We then confirm the fact that professional forecasts are “more rational” than household expectations, and that therefore households can use the consensus professional forecast as an anchorage to form reasonable inflation expectations. Section 4 then presents the results of several regressions to test whether the sticky information model, as well as the rational expectations model, can well represent expectation dynamics among Japanese households. Section 5 concludes the paper.

### 2. Empirical Model Derivation

We base our inflation expectation analysis below on the type of sticky information model proposed by Carroll (2003). The Carroll model assumes that in any given period each individual faces a probability $ \lambda $ of reading the latest news article on inflation. Individuals who do not read an article simply continue to believe the last forecast they read about. Thus, individuals
change their inflation expectations with a probability of $ \lambda $ . Let $ \pi_{t,t+12} $ be the inflation rate between month t and month $ t+12 $ , i.e., $ \pi_{t,t+12} = \log(p_{t+12}) - \log(p_t) $ , where $ p_t $ is the aggregate consumer price index in month t. If we denote the Newspaper forecast printed in month s for inflation between month $ t_1 $ and month $ t_2 $ ( $ \geq t_1 $ ) as $ N_s[\pi_{t_1,t_2}] $ , the inflation expectation of an individual household (i) as of date t is given by $ E_{i,t}[\pi_{t,t+12}] = N_t[\pi_{t,t+12}] $ with probability $ \lambda $ and $ E_{i,t}[\pi_{t,t+12}] = E_{i,t-1}[\pi_{t,t+12}] $ with probability $ 1 - \lambda $ .

Defining $ M_{t}[\bullet] $ as the operator that yields the population-mean value at time t, we can express the mean inflation expectation as a function of the Newspaper forecasts:

$$ \begin{align*}M_{_{t}}[\pi_{_{t,t+12}}]=&\lambda N_{_{t}}[\pi_{_{t,t+12}}]+(1-\lambda)M_{_{t-1}}[\pi_{_{t,t+12}}]\\=&\lambda N_{_{t}}[\pi_{_{t,t+12}}]+(1-\lambda)\{\lambda N_{_{t-1}}[\pi_{_{t,t+12}}]+(1-\lambda)(\lambda N_{_{t-2}}[\pi_{_{t,t+12}}]+\cdots\}.\end{align*} $$

This expression for the mean inflation expectation is identical to the equation in Mankiw and Reis (2002), except that they assume updating agents that compute their own rational forecasts rather than forming their expectations based on Newspaper forecasts. Carroll presents his model with information processing costs as the micro foundations for the Mankiw and Reis equation. With a few additional assumptions on consumers' beliefs about the information process, he further derives the following equation:

$$ M_{_{t}}[\pi_{_{t,t+12}}]=\lambda N_{_{t}}[\pi_{_{t,t+12}}]+(1-\lambda)M_{_{t-1}}[\pi_{_{t-1,t+11}}]{\cdot} $$

That is, mean inflation expectations for the next year should be a weighted average of the current ‘rational’ (or Newspaper) forecast and last period’s mean inflation expectations. Carroll used this directly testable time series equation $ ^{6} $ to estimate the evolution of inflation expectations and to find a plausible middle ground between rational expectations and adaptive expectations.
While time series analyses based on aggregated data have produced interesting findings, here we use the same setting to derive a different specification that we use to examine inflation expectations by individual households $ (i) $ . Although we are of course interested in the macroeconomic implications of inflation expectation dynamics, our main interest is in the micro foundations of the model, so that it is more appropriate to use micro data in our empirical analysis. Moreover, given the nature of the data available for Japan – a panel that covers only a relatively short period but contains a large cross-section of agents – a micro data based analysis is the only efficient way to examine the validity of the model.

By focusing on the changes in inflation expectations, we can derive the following equations for inflation expectations by individual households:

$$ E_{i,t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t,t+12}]=N_{t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t,t+12}] $$

when the household’s expectation is revised in the month, and

$$ E_{i,t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t,t+12}]=E_{i,t-1}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t,t+12}]=0 $$

when the household’s expectation is unchanged in the month.

After a few steps of simple mathematical manipulation, we can rewrite the equations as

$$ E_{i,t}[\pi_{_{t,t+12}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}]=(N_{_{t-1}}[\pi_{_{t-1,t+11}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}])+(N_{_{t}}[\pi_{_{t,t+12}}]-N_{_{t-1}}[\pi_{_{t-1,t+11}}]) $$

and

$$ E_{i,t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t-1,t+11}]=(N_{t-1}[\pi_{t-1,t+11}]-E_{i,t-1}[\pi_{t-1,t+11}])+(E_{i,t-1}[\pi_{t-1,t+11}]-N_{t-1}[\pi_{t-1,t+11}])+(E_{i,t-1}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t-1,t+11}]). $$

And as Carroll’s assumptions on households’ belief about the inflation process imply $ E_{i,t-1}[\pi_{t,t+12}]=E_{i,t-1}[\pi_{t-1,t+1}] $ , we obtain

$$ E_{_{i,t}}[\pi_{_{t,t+12}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}]=(N_{_{t-1}}[\pi_{_{t-1,t+11}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}])+(N_{_{t}}[\pi_{_{t,t+12}}]-N_{_{t-1}}[\pi_{_{t-1,t+11}}]) $$

and

$$ E_{_{i,t}}[\pi_{_{t,t+12}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}]=(N_{_{t-1}}[\pi_{_{t-1,t+11}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}])+(E_{_{i,t-1}}[\pi_{_{t-1,t+11}}]-N_{_{t-1}}[\pi_{_{t-1,t+11}}]) $$

That is, when a household revises its inflation expectation from month t-1 to month t, the size of
the adjustment should be the gap between its inflation expectation and the Newspaper forecast in the previous month $ (t-1) $ plus the size of the change in the Newspaper forecast from t-1 to t. When the household chooses not to revise its expectation, the size of adjustment equals zero by definition.

Since all variables in (5) and (6) are directly observable, we can run the regression below to assess the validity of the sticky information model of inflation expectations:

$$ E_{i,t}[\pi_{_{t,t+12}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}]=\beta_{_{1}}(N_{_{t-1}}[\pi_{_{t-1,t+11}}]-E_{_{i,t-1}}[\pi_{_{t-1,t+11}}])+\beta_{_{2}}(F_{_{t}}[\pi_{_{t,t+12}}]-N_{_{t-1}}[\pi_{_{t-1,t+11}}])+\varepsilon_{_{i,t}}\quad(7), $$

where $ F_{t}[\pi_{t,t+12}]=N_{t}[\pi_{t,t+12}] $ if $ E_{i,t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t-1,t+11}]\neq0 $ , and $ F_{t}[\pi_{t,t+12}]=E_{i,t-1}[\pi_{t-1,t+11}] $ if $ E_{i,t}[\pi_{t,t+12}]-E_{i,t-1}[\pi_{t-1,t+11}]=0 $ . Comparing this to (5) and (6) provides the testable restriction that $ \beta_{1}=\beta_{2}=1 $ , which implies that the sticky information model describes inflation expectation dynamics well. Another testable restriction, namely that $ \beta_{1}=0 $ , is also of interest, since it is a necessary condition for rational expectations. It is obvious that $ \beta_{1}\neq0 $ violates rationality, because it means that the expectation revision is correlated with information that could have been known at the time of the preceding forecast. $ ^{7} $ The necessary and sufficient conditions for inflation expectations of individual households to be rational are $ (\beta_{1},\beta_{2})=(0,1) $ , and that the Newspaper forecast $ (N_{t}[\pi_{t,t+12}]) $ is rational.

### 3. Data Sources

Estimating equation (7) requires us to identify data sources for inflation expectations and for Newspaper forecasts of inflation over the next year. Here we explain our data sources.

3.1 Monthly Consumer Confidence Survey (MCCS)
In order to obtain the micro based annual inflation expectation data $ (E_{i,t}[\pi_{t,t+12}]) $ , we take advantage of the household level data from the Monthly Consumer Confidence Survey Covering All of Japan (MCCS) from April 2004 to February 2009 collected by Shin Joho Center, Inc., on behalf of the Cabinet Office. (See Appendix A for more details on the MCCS.) One component of the survey asks households to think about the inflation rate over the next year. The exact wording of the question on price expectations is as follows, with allowed the responses shown in brackets:

(Price Expectation Question) During the next 12 months, do you think that prices of goods and services that you frequently purchase will go down, up, or remain the same? [(1) down by more than 5 percent, (2) down by 2-5 percent, (3) down by less than 2 percent, (4) remain the same, (5) up by less than 2 percent, (6) up by 2 to 5 percent, (7) up by more than 5 percent, or (8) don’t know.]

Unfortunately, the survey does not ask households to answer the question in actual percentage figures. Therefore, when we are forced to use numerical values of inflation expectations in our analysis, we will use the median value of the multiple choice percentage intervals, excluding answers (1) and (7), for which we arbitrarily assigned -7.5 percent and +7.5 percent, respectively. To allow for the possible distortion caused by our imprecise measures, we also tried our regression analyses using the original discrete choice variables, as reported in the tables below for reference, $ ^{8} $ and found that the results of the regressions are almost the same as those based on the median value.

To compare the multiple choice percentage intervals with actual numerical inflation rates, the reverse operation, that is, transforming the actual numerical inflation data into interval based data, is also necessary. This means that it becomes necessary to set an interval for the response that prices would “remain the same.” We try out three intervals for the “remain the same”
same" response, namely, (-0.1, 0.1), (-0.3, 0.3), and (-0.5, 0.5) in our analysis.

### 3.2 Monthly Survey of Japanese Economic Forecasts (ESPF)

Our strategy to identify the Newspaper forecast for annual inflation exactly follows that employed by Carroll (2003) and uses the mean annual inflation forecast from the Monthly Survey of Japanese Economic Forecasts (known as ESP Forecast, or ESPF). The ESPF, conducted by the Economic Planning Association, has collected forecasts from leading private economic forecasters in Japan since April 2004. $ ^{9} $ The survey questionnaire is distributed to forecasters around the $ 25^{th} $ of each month, and the survey result is published around the $ 10^{th} $ of the following month. The survey asks participants for quarter-by-quarter forecasts for the current and next fiscal year for a variety of economic variables, including CPI inflation (excluding fresh food). We calculate the annual inflation rate $ (N_{t}[\pi_{t,t+12}]) $ as the weighted average of quarterly expectations. For more details on the ESPF, see Appendix B.

### 3.3 Preliminary Overview of the Data

We can examine the characteristics of the survey responses by comparing them with the realized inflation rate, i.e., the CPI inflation rate (excluding fresh food) over the next 12 months, as shown in Table 1, where we classified the realized inflation rate into the multiple choice percentage intervals (from (1) to (7) in the MCCS). Regardless of our choice of the “remain the same” interval, the upper triangle regions always show higher probabilities than the lower regions, implying that inflation expectations were upwardly biased.

Transformation of the responses into numerical values as explained in Section 3.1 allows us to compare mean expected inflation rates from various sources. Figure 1 graphs the
calculated mean annual inflation expectations based on the two surveys (where the horizontal axis refers to expectations at the endpoint of the relevant forecast horizon rather than at the time the forecast was made), as well as the development of actual (realized) inflation. We include two more inflation expectation series for reference: the mean annual inflation expectations from the Kokumin Seikatsu Monitors (between the $ 2^{nd} $ quarter of 2001 and the $ 1^{st} $ quarter of 2004), and the mean annual inflation expectations from the Opinion Survey on the General Public's Views and Behavior (OSGP) (between March 2004 and March 2009) by the Bank of Japan. $ ^{10} $

These simple figures allow several observations. First, Japanese consumers were in the grip of deflationary expectations until 2004 (see Hori and Shimizutani 2005) but have since shaken them off. Second, average inflation expectations by households have a tendency to be biased upward (by roughly 1 percentage point or more) over the entire observation period. $ ^{11} $ And third, all the forecasts seem to move in parallel with current price developments rather than their target, i.e., future price developments.

The latter two features are striking, because a number of studies using inflation expectations data for the United States report that the mean (or median) of inflation expectations yields relatively accurate inflation forecasts, and that household surveys and professional surveys are equally accurate (see, e.g., Mankiw et al. 2004). The short time span of our dataset – five years compared with 50 years for the U.S. data – prevents us from verifying whether the bias results from idiosyncrasies of Japanese forecasters. Another possible source of the upward bias is low inflation bias. Kamada (2008) showed that corrected forecasts from the OSGP using Kahn’s (1997) method remain below zero until 2005. However, this correction cannot be applied to the MCCS where only qualitative responses are available. Therefore, we will keep