Silviase/jgov_v1.4_full_ann huggingface dataset:
## ① 女性職員の登用の実態やその阻害要因の把握

職員数の男女比と管理職を含む各役職段階に登用されている者の男女比を比較し、大きな差がある場合にはその理由を把握・分析し、改善に向けて必要な対応を行う。

## ② 女性職員の職域拡大、人事管理の柔軟化等を通じた計画的育成

ア 採用時の人事配置について、毎年の新規採用者の配置状況を勘案しつつ、男女に偏りがないよう努める。

イ 女性職員の積極的な登用を図るため、職員の意欲と能力の把握に努めつつ、男女で偏りのない職務経験の付与に配意する。

ウ 特定の業務に女性職員が多く配置されている、男性職員のみが配置される業務があるなど、職域が固定化していないか確認し、固定化の解消に努める。

エ 男女共に、極めて優れた能力を有すると認められる職員については、管理職への登用も視野に入れつつ、速やかに昇任させるよう努める。

オ 管理職となるために必要な職務の経験については、例えば、出産・子育て期等を迎える前又は出産・子育て期等を終えてから、重要なポストを経験させるほか、必要な研修の機会を付与するなど、柔軟な人事管理を行う。

カ 管理職の候補となり得るような女性職員については、女性職員の登用目標の達成に向け計画的な育成を図る。その際、Ⅱ・Ⅲ種試験・一般職試験採用の女性職員のうち優秀と見込まれる者については、採用後１０年以内に多様な職務経験を付与するなど、必要な職務機会の付与や研修等の支援を通じて積極的な育成に努める。

## （５）管理職員の意識改革

① 事務局長は、管理職員等に対し、女性の登用を図る観点から、従来の意識や慣行の改革、キャリア形成支援等、女性職員の意欲を向上させる取組の重要性についての明確なメッセージを電子メール、職員掲示板等により定期的に発出するものとする。

② 女性職員の登用の拡大に関する重要事項については、適宜周知徹底するほか、研修等において女性職員の登用拡大に向けた体制・考え方等について周知する。

## (6) 女性職員の研修等を通じたキャリア形成支援

① 業務に必要な知識等の習得や省庁横断的な人的ネットワークの形成を促進するため、研修の対象となり得る職員に占める女性職員の割合に留意しつつ、人事院等が実施する業務研修等に女性職員を積極的に参加させるよう努めるとともに、女性職員向けの各種研修への参加機会の確保に努める。

② 女性職員の各種研修への積極的な参加を促すため、各種研修のスケジュールを随時、職員掲示板等に掲示する。

③ 女性職員の幹部候補育成課程への参加を促すとともに、主体的キャリア形成支援プログラムや政策分析専門家育成プログラム等を通じて、キャリ
アイメージの形成を促す。

④ 仕事と家庭の両立を図りながら活躍し、いわゆるロールモデルとなり得る女性職員の育成に努めるほか、内閣人事局が作成する女性職員の活躍事例集を毎年度職員に周知する。

⑤ 仕事と家庭の両立を図る女性職員の上司や人事担当者は、人事評価面談やその他の機会において、必要に応じて今後のキャリア形成に関する助言等を行い、女性職員の意欲の向上を図る。

⑥ 仕事と家庭の両立等に悩む女性職員が気軽に相談できる体制を整えるため、育児休業取得経験者等（性別を問わない。）のうち協力を得られる者を「子育て・キャリア相談員」として登録し、女性職員がより一層相談しやすくなるような取組を行う。また、女性職員に助言、指導するメンターの導入等の取組を推進する。

## (7) 目標

以上のような取組を通じて、女性職員の登用に関し平成32年度末において以下の目標の達成を図る。

目標事項	目標値
課長補佐相当職以上に占める女性の割合	10%程度

## 第 3 推進体制等

### 1. 推進体制

委員会事務局に事務局長をトップとする個人情報保護委員会女性職員活躍とワークライフバランス等推進会議を開催し、女性職員活躍と職員のワークライフバランス推進の必要性を繰り返し委員会内に周知徹底することで、種々の取組を着実に実行する。

## 2 ．女性職員活躍・ワークライフバランス推進担当官の設置

委員会事務局総務課に女性職員活躍・ワークライフバランス推進担当官を置き、取組指針の推進に係る業務及び同指針の推進に関する内閣人事局との連携業務等を処理させる。

## 3 ．フォローアップの実施

個人情報保護委員会女性職員の活躍とワークライフバランス等推進会議は、毎年1回、本計画に基づく取組状況のフォローアップを行い、結果を公表する。
### Chapter 10. Technological Research & Development Supporting the 21st Century

## [System for promoting technological research & development]

In conducting technological R&D, we are trying to improve cooperation among relevant ministries and agencies, universities, independent administrative corporations and private enterprise in the various bureaus of MLIT, research institutions, local development offices, and the Hokkaido Development Bureau. As we gain their cooperation, we are promoting integrated technological R&D across field boundaries, and are aggressively attempting to activate and diffuse the results in new technology public works and in the construction and transportation industries.

### 145. (Development of infrastructure technology for coexistence with nature)

Restore and reduce the burden on the natural cleaning cycle

## [ Improving construction management techniques ]

We are trying to employ the ISO management system in conjunction with improving integrating techniques in public works.

We are also promoting upgrading of construction equipment and environmental and safety measures to improve the efficiency of the construction industry.
# 平成27年度継続課題に係る継続評価書（平成25年度以降採択課題向け）

：富士通(株)、エヌ・アール・アイ・セキュアテクノロジーズ(株)、

研究機関

名古屋大学

研究開発課題：サイバー攻撃の解析・検知に関する研究開発

研究開発期間 ： 平成 25 ～ 27 年度

代表研究責任者：塩崎 哲夫

☑ 総合評価：適

(評価点 19 点／ 25 点中)

(総論)

研究開発課題全体が順調に進捗しており、来年度も引き続き研究開発を精力的に実施することが望ましい。

研究開発成果の社会展開に向けた取組が十分に行われており、製品化等の具体的な実績が得られている。

(コメント)

● 本研究開発課題の特色である「行動特性分析に基づく不正検知技術」について、来年度以降も積極的に研究開発を推進することが望ましい。

● 外部有識者及びビジネスプロデューサーとの密接な連携が認められ、研究開発成果の社会展開を強力に後押しする体制が提案されている。
(1) 当該年度における研究開発の目標達成(見込み)状況及びアウトカム目標の達成に向けた取組みの実施状況

\[\left(5\sim1\right.\\ \left.105\right.\\ \left.\left(5\sim1\right.\right.\\ \left.\left.\left.\left.\

(総論)

研究開発目標を概ね達成しており、研究開発課題全体が予定より早く進捗している。アウトカム目標達成に向けた取組の実施状況も妥当である。

(コメント)

研究開発目標に関して、既に達成しているか、もしくは今年度中に達成見込みという状況であり、研究開発が順調に進捗している。

● 計画を前倒しして成果の一部を既に製品化しており、研究開発成果の社会展開に関して目標以上の実績を上げている。

## (2) 当該年度における研究資金使用状況

$$ \left(5\sim1\right. 的 5 段階評価 )\quad:\quad 評価 4\left. 評価点 \right) $$

(総論)

効率的に研究資金が使用されており、適切な使用状況である。

(コメント)

● クラウド環境を利用するなど、必要経費を削減するための努力が認められる。
# (3) 研究開発実施計画及びアウトカム目標の達成に向けた取組み

(5～1の5段階評価）：評価4（評価点）

## (総論)

当初の予定を前倒ししての研究開発実施を計画しており、評価できる。

適切なリソース配分の下、早期の成果展開を目指す研究開発実施計画となっており、評価できる。

(コメント)

● 本研究開発課題の特色である「行動特性分析に基づく不正検知」等の重要な研究開発テーマについて重点的に研究開発の実施を予定するなど、研究テーマの選択と集中が適切に行われており、限られたリソースを効率的に活用できる計画となっている。

● 研究開発成果の製品化を含め、アウトカム目標の達成に向けた具体的な計画が示されている。

● 社会情勢や技術動向の変化が早い分野であるので、来年度もこれらの変化に対応して研究開発を実施することが望ましい。

● 各研究テーマについて、査読付き論文等の学術的な成果に結び付けるための取組を、より一層推進するべき。

## (4) 予算計画

（5～1の5段階評価）：評価3（評価点）

## (総論)

研究開発計画に照らして適切な予算計画である。

(コメント)

● 研究開発計画に照らして、適切な予算計画が組まれており、効率的な研究資金の利用が期待できる。
## (5) 实施体制

（5～1の5段階評価）：評価4（評価点）

## (総論)

研究開発計画に照らして適正な人員配置となっている。

外部有識者、ビジネスプロデューサとの十分な連携体制が構築されている。

## (コメント)

● 外部有識者から構成されるアドバイザリーボードや、アウトカム目標の達成に向けた取組を推進するビジネスプロデューサからの知見を研究開発に生かすための十分な連携体制が構築されている。

各研究テーマ間において、「利用者の行動特性」という視点を軸に、最終目標である「総合的セキュリティ基盤の実現」に向けてさらなる連携の推進が期待される。
Cabinet Office

# ESRI Discussion Paper Series No.331

# Effects of Commodity Price Shocks on Inflation: A Cross-Country Analysis

Atsushi Sekine and Takayuki Tsuruga

July 2016

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. (Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)
# Effects of Commodity Price Shocks on Inflation: A Cross-Country Analysis $ ^{*} $

Atsushi Sekine $ ^{\dagger} $ and Takayuki Tsuruga $ ^{\ddagger} $

July 14, 2016

## Abstract

Since the 2000s, large fluctuations in commodity prices have become a concern among policymakers regarding price stability. This paper investigates the effects of commodity price shocks on headline inflation with a monthly panel consisting of 144 countries. We show that the effects of commodity price shocks on inflation are transitory. While the effect on the level of consumer prices varies across countries, the transitory effects are fairly robust, suggesting a low risk of the so-called second-round effect on inflation. Employing the smooth transition autoregressive models that use past inflation as the transition variable, we also explore the possibility that the effect of commodity price shocks could be persistent, depending on inflation regimes. In this specification, commodity price shocks may not have transitory effects when a country's currency is pegged to the U.S. dollar. However, the effect remains transitory in countries with exchange rate flexibility.

JEL Classification: E31; E37; Q43

Keywords: Commodity prices, inflation, pass-through, local projections, smooth transition
## 1 Introduction

Fluctuations in commodity prices since the early 2000s have renewed policymakers' attention to their effects on inflation. One of the concerns for policymakers is that commodity price shocks could spill over into prices of other goods and services. Commodity price shocks affect consumer prices through changes in the commodity prices themselves (the first-round effect), but they may also spill over into prices of other goods and services (the second-round effect). $ ^{1} $ Because prices of other goods and services move more slowly than commodity prices, fluctuations in commodity prices followed by the second-round effect may lead to price instability for long periods. This concern raises the issue of how monetary policy should respond to the commodity price shocks. Janet Yellen, then Vice Chair of the Federal Reserve, argued that commodity price shocks have only modest and transitory effects on U.S. inflation and that fluctuations in commodity prices during the 2000s did not “warrant any substantial shift in the stance of monetary policy.” $ ^{2} $ By contrast, Mario Draghi, the President of the European Central Bank, expressed a concern that fluctuations in commodity markets may have a “lasting impact on the outlook for prices.” $ ^{3} $

This paper investigates the effects of commodity price shocks on inflation in the 2000s by estimating impulse responses (IRs). In particular, we ask whether recent fluctuations in commodity prices have a persistent or transitory effect on inflation. If commodity price shocks spill over widely into prices of other goods and services, then the effect of commodity price shocks would be persistent due to slow price movement of other goods and services. But if commodity price shocks have only a weak second-round effect, then a first-round effect is dominating and the effect of commodity price shocks would be only transitory. In this case, as Yellen argued regarding U.S. inflation, further monetary policy responses to changes in commodity prices may not be required. To focus on the extent to which the effect of commodity price shocks is persistent, we rely on IR analysis rather than the estimation of the commodity price pass-through to inflation. $ ^{4} $

We estimate IRs to commodity price shocks using panel local projections. The local projections
developed by Jordà (2005) have some practical advantages, compared to the standard approach such as vector autoregressions (VAR). First, local projections can be made using the least squares of the single equation. Therefore, we can easily apply regressions to the cross-country panel and/or extend regressions with interaction terms. Second, local projections are relatively robust to misspecification. Because the direct estimation of IRs based on local projections does not constrain the shape of IR functions, we could mitigate biases arising from the misspecification such as the lag structure of regressions. Third, the nonlinearity of the estimation equation can be addressed with less computational burden than the standard nonlinear estimation. In this paper, we extend the model to the smooth transition autoregressive (STAR) model. As explained in Auerbach and Gorodnichenko (2013), estimating IRs using local nonlinear projections does not need to keep track of the transition variable's changes over time.
We estimate the IRs from the panel data, because we are interested in inflation in the 2000s. In our analysis, we evaluate IRs averaged across countries. The panel estimation imposes parameter restrictions on the stochastic process of an individual country’s inflation. However, we avoid evaluations of individual countries’ IRs because the IRs might be imprecisely estimated with a small number of time-series observations.
We find that the effects of commodity price shocks on inflation are transitory. The benchmark estimation suggests that, on average, the Consumer Price Index (CPI) increases by 1.87 percent in 12 months in response to a 10 percent increase in commodity prices. But in the subsequent year, changes in consumer prices become substantially small, implying that commodity price shocks have only a transitory effect on inflation. This suggests that risks of a second-round effect are low on average. We further show that the transitory effect on inflation is fairly robust, even if we control for cross-country differences that may affect IRs and even if we consider a variety of measures of commodity prices such as energy and non-energy commodity price indexes.
Using the STAR model, we further explore the possibility that responses of consumer prices to commodity price shocks depend on the lagged inflation rate in each economy. The dependence of price responses on inflation reflects Taylor’s (2000) argument that higher inflation leads to more frequent price changes, resulting in the increased sensitivity of prices to shocks. Our approach is similar to Auerbach and Gorodnichenko (2012, 2013), who use smooth transition models and
focus on government multipliers varying between recessions and expansions. $ ^{5} $ Instead of looking at recessions and expansions, our focus lies on the IRs varying between high- and low-inflation regimes.

The estimation results suggest that the IRs differ between the high- and low-inflation regimes, when a country’s currency is pegged to the U.S. dollar. In particular, when the country is experiencing high inflation relative to its average inflation, commodity price shocks tend to increase inflation substantially, and to decrease afterward. Under a low-inflation regime, the effect of commodity price shocks on inflation persists, although inflation per se is not substantially high. However, such differences are not observed when countries have exchange rate flexibility. IRs in developed countries with exchange rate flexibility exhibit very similar patterns, regardless of inflation regimes. We thus conclude that, even if we allow for the dependence on inflation regimes, there would be a low risk of a second-round effect at least in countries with exchange rate flexibility.

This paper is organized as follows. Section 2 describes the methodology and the data. Section 3 shows the main results along with robustness analysis. Section 4 introduces nonlinearity to the estimation equation based on the STAR model. Section 5 concludes.

## 2 Estimating IRs from local projections

### 2.1 Benchmark regressions

To introduce the benchmark regression in this paper, consider a panel $ \operatorname{AR}(q) $ process:

$$ p_{j,t}-p_{j,t-1}=\alpha_{j}+\sum_{i=1}^{q}\beta_{i}(p_{j,t-i}-p_{j,t-i-1})+\gamma u_{c,t}+u_{j,t}, $$

where $ p_{j,t} $ represents the logarithm of the CPI for country j in period t and $ p_{j,t}-p_{j,t-1}\left(=\pi_{j,t}\right) $ in the left-hand-side variable is country j's inflation. In this equation, we decompose shocks to inflation into the international commodity price shock $ u_{c,t}(\sim\mathrm{i.i.d.}(0,\sigma_{c}^{2})) $ and a linear combination of other shocks to inflation $ u_{j,t} $ . We assume that these shocks are serially uncorrelated and $ u_{c,t} $ is orthogonal to $ u_{j,t} $ . For now, we leave the description of how unobservable $ u_{c,t} $ is estimated to the
subsequent section and assume that it is observable. The parameter $ \alpha_{j} $ includes the country fixed effects, $ \beta_{i} $ captures persistence of inflation, and $ \gamma $ represents the sensitivity of inflation to $ u_{c,t} $ . Note that (1) can also be understood as an approximation of the general ARMA process.

The purpose of our analysis is to estimate IRs of CPIs and inflation to a commodity price shock. Rather than estimating the IRs from (1), the local projections directly estimate IRs from

$$ p_{j,t+k}-p_{j,t-1}=\alpha_{j,k}+\sum_{i=1}^{q}\beta_{i,k}(p_{j,t-i}-p_{j,t-i-1})+\gamma_{k}u_{c,t}+u_{j,t+k}^{k},\quad\mathrm{f o r}k=0,1,...,K, $$

where k denotes the forecast horizons. $ ^{6} $ Here, we estimate $ \alpha_{j,k} $ , $ \beta_{i,k} $ , and $ \gamma_{k} $ for each horizon k with $ K+1 $ separate regressions. In (2), shocks to inflation are again decomposed into $ u_{c,t} $ and $ u_{j,t+k}^{k} $ , but the latter typically includes $ u_{j,t+i} $ for $ i=0,1,\ldots,k $ and $ u_{c,t+i} $ for $ i=1,2,\ldots,k $ . This implies that $ u_{j,t+k}^{k} $ follows the MA(k) process. In the estimation, we set K at 24 months and the maximum number of lags q is determined by the Bayesian Information Criterion (BIC).

The IR of consumer prices for the k-th period after a one percent increase in commodity price shocks can be written as

$$ IR(k)=\gamma_{k},\mathrm{~for~}k=0,1,...,K. $$

The coefficient $ \gamma_{k} $ represents the response of k-period-ahead consumer prices to a current commodity price shock. Another interpretation of $ \gamma_{k} $ is the cumulative IR of inflation over k periods because $ p_{j,t+k} - p_{j,t-1} = \sum_{i=0}^{k} \pi_{j,t+i} $ . Note that, if we estimate IRs from (1), the estimated IR function is a nonlinear function of $ \hat{\beta}_{i} $ and $ \hat{\gamma} $ , where the hatted parameters denote the estimates of coefficients. By contrast, the local projections directly estimate IRs and the estimated IRs do not depend on $ \hat{\beta}_{i,k} $ . Hence, the lagged inflation is introduced into (2) only to control for the inflation persistence rather than to estimate the dynamic effects of commodity price shocks. This direct estimation of IRs can mitigate biases arising from misspecification of the stochastic process. (See Jordà 2005 and Teulings and Zubanov 2014.) While the local projections have advantages, they also have some limitations. Our approach may not perform better than the standard approach in terms of the mean squared forecast error. Marcellino, Stock, and Watson (2006) argue that, if the model is correctly specified, forecasts of the standard approach tend to be more efficient than direct forecasts.
of the local projections. Kilian and Kim (2011) also report that the confidence intervals of the IRs from local projections may be less accurate than the bootstrap VAR confidence interval, based on Monte Carlo simulations. Hence, we note that there is a trade-off between bias and estimation variance.

We estimate (2) using the least squares dummy variable (LSDV) estimator with Newey-West heteroskedasticity and autocorrelation consistent covariance matrix, because $ u_{j,t+k}^{k} $ for k > 0 follows a moving average process. It is well known that the presence of lagged dependent variables in panel estimations may lead to a severe bias when the serial correlation of the dependent variables is high and the time-series dimension of the data is short (Nickell 1981). Although the serial correlation of the dependent variables may be high, the sample period used for our estimation is not extremely short (T = 95) compared to the cross-sectional dimension (N = 144 or 120). We thus proceed with the LSDV estimator.

### 2.2 Commodity price shocks

We can estimate the IRs of consumer prices only when commodity price shocks $ u_{c,t} $ are observable. To obtain a proxy of $ u_{c,t} $ , we take the commodity prices as given and assume that commodity price shocks $ u_{c,t} $ can be represented by the residuals in the following forecasting equation used by Chen, Rogoff, and Rossi (2010, hereafter CRR):

$$ \pi_{c,t}=a+b\pi_{c,t-1}+c_{A U S}\Delta s_{t-1}^{A U S}+c_{C A N}\Delta s_{t-1}^{C A N}+c_{N Z}\Delta s_{t-1}^{N Z}+\varepsilon_{c,t}, $$

where $ \pi_{c,t} $ denotes commodity price inflation and $ \Delta s_{t}^{j} $ is the nominal exchange rate growth in country j vis-à-vis the U.S. for j = AUS, CAN, NZ. Here, a, b, and $ c_{j} $ are the parameters estimated and $ \varepsilon_{c,t} $ is the error term. CRR show that the nominal exchange rate growth of resource-rich countries such as Australia (AUS), Canada (CAN), and New Zealand (NZ) has strong forecasting power for commodity price inflation. $ ^{7} $ In (4), the lag length of explanatory variables is determined by the BIC, but the main results in this paper are robust to the length of the lags.
We assume that commodity price shocks $ u_{c,t} $ can be represented by $ \varepsilon_{c,t} $ . We thus estimate $ u_{c,t} $ using the residual of (4). While there might be some other important variables that are helpful in forecasting $ \pi_{c,t} $ , the literature suggests that finding such variables is not an easy task. Groen and Pesenti (2011) find that the factor augmented regressions which replace $ \Delta s_{t}^{j} $ with the principal components from the factor model often perform poorly compared to CRR's forecasting models.

The use of the residual $ \hat{\varepsilon}_{c,t} $ leads to a generated regressor problem in our estimation. In this case, the usual standard error estimation for the coefficients in (2) is downward biased (Murphy and Topel 1985). To address this generated regressor problem, we use the heteroskedasticity-robust version of Murphy and Topel's (1985) standard error correction. (See Hardin 2002).

### 2.3 Data

We construct a balanced panel for the headline CPIs taken from the International Financial Statistics (IFS) of the International Monetary Fund (IMF) from January 2000 to December 2010. $ ^{8} $ Because the raw data of the CPIs are seasonally unadjusted, we use the X-12-ARIMA procedure to make a seasonal adjustment. We use 144 countries for the benchmark estimation. $ ^{9} $ The countries used in our analysis are summarized in Table 1.

To estimate aggregate commodity price shocks, we use the non-energy commodity price index published by the World Bank (WB) for the benchmark estimation. While we report results based on the non-energy commodity prices, the main results in this paper are robust to the choice of alternative commodity price indexes such as the energy commodity price index of the WB; the All Commodity Price, Fuel Price, and Non-Fuel Price Indexes published by the IMF; and the spot index of the S&P Goldman Sachs Commodity Index (S&P GSCI). $ ^{10} $ We note that all of these
indexes are denominated in the nominal U.S. dollars. The estimation of commodity price shocks requires nominal exchange rate data. We take the data from Datastream.

As a preparatory analysis, Figure 1 plots the commodity price inflation and our forecasts from (4) in the upper panel and the resulting forecast error $ \hat{\varepsilon}_{c,t} $ in the lower panel. All series in the figure are expressed at an annual rate, and commodity price shocks are estimated based on the sample period from February 2001 to December 2008. $ ^{11} $ Both commodity price inflation and its forecast errors are very volatile and show large declines particularly during the global financial crisis (September and October 2008). We confirm that the nominal exchange rate growth in (4) Granger-causes commodity price inflation, consistent with CRR. In addition, the standard Ljung-Box statistics for $ \hat{\varepsilon}_{c,t} $ for lags 1 to 12 are all insignificant at conventional significance levels, suggesting that serial correlation would not exist in the estimated commodity price shocks.

## 3 Main results

### 3.1 IRs from the benchmark estimation

Figure 2 plots the estimated IRs of the CPIs to a 10 percent increase in a commodity price shock in period t = 0. It also reports the 95 percent confidence interval bands represented by the shaded area. Panel (a) of the figure refers to the benchmark estimations. The IRs indicate that the CPIs increase by 1.87 percent (at an annual rate) in period 12 and the estimated responses after this period range from 1.80 to 2.13 percent. The 95 percent confidence intervals are narrow for all forecast horizons, suggesting that the IRs are estimated quite precisely.

The shape of the IRs suggests that the effects of commodity price shocks on inflation are transitory, as discussed by Yellen. Because the estimated IRs are defined for the price level, we can measure inflation responses using the slope of the IR function. In Figure 2, the slope is substantially steep during the first year after the shock but becomes nearly flat afterward. This implies that the effect of commodity price shocks on inflation virtually disappears in the second year after the