rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In late November 2008, the United States Federal Reserve announced an hitherto unprecedented policy of unconventional monetary intervention, involving a $600 billion purchase of mortgagebacked securities. This policy—which has come to be known as quantitative easing (QE)—was designed with the explicit purpose of bolstering weak asset markets, as well as stimulating real activity, given the perceived constraints of the zero lower bound on short-term interest rates. Over the course of the succeeding five years, the Fed engaged in a total of three separate QE episodes, so that by the beginning of the Fed’s gradual unwinding in January 2014, its balance sheet had more than doubled, to $4 trillion.

Although QE was meant to be an expansionary monetary policy for the U.S. economy, the program had profound implications for developing countries. Faced with near-zero returns in the U.S. and other high-income countries—many of which were implementing unconventional monetary policies of their own—financial capital began to seek alternative sources of yield. Emerging economies, which had enjoyed heady growth rates and stable political-economic environments over the past decade, appeared to be an ideal investment alternative.

However, cross-border financial flows respond to a host of standard “fundamental” factors— such as interest rates and growth differentials—and although inflows did recover from crisis lows during the period when QE was in place, that fact alone is insufficient for establishing whether the postcrisis recovery can be attributed to QE, since global economic and monetary conditions were also changing during that time. Moreover, since unconventional monetary policies were _designed_ to operate along the same fundamental variables associated with capital flows, it is difficult to definitively answer the question of precisely what effect QE may have had on financial flows between 2009 and 2013.

Our approach to answering this question is therefore modest. Rather than ascribe a specific, quantitative estimate to the total effect of QE—which would require us to first ascertain the impact of QE on a host of secondary variables—our strategy is to begin by accounting for potential QE spillover effects through standard transmission channels—namely via liquidity, portfolio balancing, and confidence—and then seek to establish whether QE episodes saw any additional effects on financial inflows attributable to _unobservables_ . Although such an estimate is likely to underestimate the total effects of QE (since the _total_ effect of QE has to account for its effect via standard channels as well), it sidesteps the challenging problem of disentangling the relative contributions of QE versus other factors (such as overall economic activity) on fundamental measures, such as the interest rate. Nevertheless, our approach is biased toward a negative outcome, so if indeed we _do_ find an independent effect during QE episodes, we can be fairly certain that QE played some role in influencing the movement of cross-border financial flows.

We rely on data for gross capital inflows across as many as 60 developing countries, using 2
banks are able to fund themselves at lower rates. However, they also generate less interest income per unit of asset which is surprising, especially considering that they finance the government to a lesser extent. At the same time, listed banks generate much larger income from fees and commissions, which helps explain their higher profitability.

Listed banks have higher cost ratios vis-à-vis non-listed banks. Since average wages in the two groups are similar, this is due essentially to a higher ratio of employees to assets. This result may be partly explained by their smaller size, but could also be explained by the stricter corporate governance, disclosure and compliance requirements imposed on listed banks, which may translate into more staff requirements and higher costs. At the same time, these requirements probably also explain why these banks are able to maintain better quality portfolios and lower levels of provisioning, and are ultimately able to generate higher profits despite their higher operating costs.

# **_5.2 Regression Results_**

In this section we use regression analysis on a sample of annual bank observations in nine nonGCC MENA countries for the period 2001-08 to elaborate on our two-group comparison findings. Table 6 reports the mains results while Appendix C reports additional results involving weighted regressions. Our weights are inversely proportional to the number of banks in a given country to account for differences in the number of banks per country which could skew the results in favor of countries with more banks.

Our main independent variables of interest are a set of dummies: public, foreign, and listed. By including these dummies simultaneously, their regression coefficients need to be interpreted relative to the reference group of private, domestic, and non-listed banks. To account for confounding factors we also include the following bank-level, time-varying controls. As a measure of bank size we use the 1-period lag of the log of total assets. To distinguish between the different strategies on income-generating activities of banks, we also include the non-interest income to total assets. In line with other empirical studies, to control for different asset and funding management approaches, we use the deposit to assets and loan to assets ratios. In addition to bank-level controls we also include time and countries dummies to capture the general regional trends and country-specific time-invariant conditions.

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Most of the data sets used in this study contain information to construct a diversity score for the child and the minimum acceptable diet measure, but lack information relevant for the other dimensions of food security. The diversity scores measures the consumption from different food groups. In general there are seven groups (grains, roots and tubers; legumes and nuts; dairy products; flesh foods including organ meats; eggs; vitamin A rich fruits and vegetables including orange and yellow vegetables; and other fruits) of which an individual needs daily consume four.<sup>9</sup> The minimum acceptable diet combines dietary diversity, breastfeeding and meal frequencies.<sup>10</sup> The dietary diversity score depends on the age of the child. For example, those under six months of age should be exclusively breastfed whereas older infants should receive complementary feedings as well as milk.<sup>11</sup> For the Helen Keller data from Bangladesh, we are able to construct a household level food insecurity measure, but not a measure of minimum acceptable diet. A child has to meet both the dietary diversity and the minimum acceptable diet (or food security) definitions be considered adequate in food.

# **2.2 Measurement of Child Care**

## **2.2.1 Ideal Indicators for Child Care**

Adequate care measures the capacity of the child’s caregiver to provide a healthy environment for the child to grow up in. Ideally, the measure is based on information on (1) the caregiver’s education, knowledge, and beliefs; (2) the health and nutritional status of the caregiver; (3) the mental health, lack of stress, and self‐confidence of the caregiver; (4) the caregiver's autonomy and control of resources; (5) the workload and time constraints of the caregiver; and (6) the social support received by the caregiver from family members and the community. Below we expand on the specific components of an ideal adequate care measure.

##  **_Caring Behavior: Breast‐feeding and Complementary Feeding, Health Seeking, Hygiene‐ Related_**

- Caregiver feeding behavior (observation of one or more eating episodes

- Caregiver responsiveness during feeding episodes

- Frequency of behavior such as feeding, number of spoonfuls, number of touches

- Breastfeeding practices (e.g., exclusive breastfeeding up to 6 months, early initiation of breastfeeding, breastfeeding at 2 years)

- Introduction of solid/semi‐solid/soft foods 6‐8 months

- Child feeding index (constructed from DHS data using the following yes/no questions: current breastfeeding; use of bottles; dietary diversity; feeding/meal frequency)

- Taking a child to a health clinic for treatment of illness

- Maternal hand‐washing with soap

- **_Maternal Education, knowledge and beliefs_**

> 9 For Indonesia the dietary diversity measures is only available for the household, not specific to each child.

> 10 Meal frequency information is not available in the Helen Keller Bangladesh survey or in the Indonesia RKD survey. 11 For milk intake we include breast milk, formula, and cow’s milk.

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quantity shifts can be entirely explained by a sharp downward shift in the demand for public schooling and an increase in the demand for private schooling after FPE.

We take this apparent shift in demand as _prima facie_ evidence that the (perceived) quality of public primary education declined under FPE. To corroborate this evidence, in the following sections we explore possible mechanisms that might link FPE to reduced school quality.

# **III Funding**

The first potential mechanism linking FPE to school quality noted in the introduction was a change in physical, financial or human resources within schools. A reduction in per-pupil resources in public primary schools after FPE could produce a decline in perceived school quality and, in turn, explain the failure of net enrollment to respond to a price decrease. This section attempts a reckoning of the net changes in school resources under FPE, combining household data sources on parental contributions with official government data on capitation grants and pupil-teacher ratios.

## **Financial resources**

The fundamental shift in school finance under the FPE reform was the replacement of school fees with a central government grant. Did these grants fully compensate for the lost fee revenue?

The answer is likely to differ by socio-economic group. In Section II we saw that wealthier households (as proxied by the education level of the household head) paid higher fees for public primary schools prior to FPE, and that this inequality was attenuated with the abolition of fees. Figure 2a shows this same result graphically. Fees, and hence school resources, fell by $2.5 per pupil for households whose head had no schooling, while fees fell by more than $25 per pupil for the small fraction of households whose head had some tertiary education.

Unlike the reduction in fees, the FPE grant intended to compensate for this lost fee revenue was uniform across all schools. In principle, the equivalent of approximately $14 USD per child per annum was deposited in local bank accounts for each school (Gene Sperling 2008). The accounts were to be administered by “school management committees”, chaired by the head teacher and in22
asset recovery and thereby encourage and facilitate more systematic and timely return of stolen assets. Luxembourg has started directing recovered funds from illicit activities to bilateral ODA.

Further information: <u>http://www.worldbank.org/star</u> Reading: World Bank 2007

# **Supplemental Reserve Facility (SRF)**

The supplemental reserve facility is a nonconcessional lending facility to respond to very shortterm financing on a large scale due to macroeconomic shocks. It was introduced in 1997 by the IMF in response to the Asian financial crisis.

Further information: <u>http://www.imf.org/external/np/exr/facts/howlend.htm</u>

# **Swaps**

A swap is a derivative in which two parties agree to exchange one stream of cash flows today against another stream in the future. Swaps can be used to hedge risks including interest rate, currency, and commodity price risks. In development financing, for example, the World Bank offers currency, interest rate, and commodity price swaps.

Further information: <u>http://treasury.worldbank.org/</u>

# **UNITAID**

“UNITAID is an international drug purchase facility, established to provide long-term, sustainable and predictable funding to increase access and reduce prices of quality drugs and diagnostics for the treatment of HIV/AIDS, malaria and tuberculosis in developing countries.” “Long term commitment and the purchasing of high volume of drugs and diagnostics allows UNITAID to get lower prices as manufacturers are encouraged to increase their production which results in economies of scale.”<sup>121</sup> UNITAID receives funding predominantly from the Solidarity Levy on Airline Tickets.

Further information: <u>http://www.unitaid.eu/</u> See also: Solidarity Levy on Airline Tickets

# **Voluntary Solidarity Contribution (VSC)**

A voluntary solidarity contribution is a voluntary copayment by consumers when purchasing goods or services. The contribution is collected with the sales process. Examples are a VSC on airline tickets and on mobile phones. VSCs are similar to blended value products but the consumer has the choice to contribute.

See also: Blended Value Products Voluntary Solidarity Contribution (VSC) on Airline Tickets

> 121 http://www.unitaid.eu/.

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# WPS3613

## **Scrutinizing Public Expenditures**

**Assessing the Performance of Public Accounts Committees**

### **By**

**Rick Stapenhurst**<sup>**1**</sup>

**Vinod Sahgal**<sup>**2**</sup> **William Woodley**<sup>**3**</sup>

**and**

**Riccardo Pelizzo**<sup>**4**</sup>

### World Bank Policy Research Working Paper 3613, May 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Senior Public Sector Management Specialist, World Bank Institute 2 Principal Financial Management Specialist, World Bank

> 3 Senior Consultant, World Bank Institute

> 4

> Assistant Professor, Singapore Management University
# Figure 19. SAR: Potential output growth

A. Potential output growth B. Potential output growth by different estimates <!-- Start of picture text --> Percent 2000-10 2011-21<br>8 2022-30 2000-21<br>6<br>4<br>2<br>0<br>EMDEs SAR<br><!-- End of picture text --> <!-- Start of picture text --> Percent 2000-21 2011-21<br>8<br>7<br>6<br>5<br>4<br>PFA Exp.<br>D. School closures<br><!-- End of picture text --> <!-- Start of picture text --> C. Within- and between-sector D. School closures<br>contributions to productivity growth<br>Percentage points Weeks<br>8 Within sector 40 Partial Full World<br>Between sector<br>6 35<br>30<br>4<br>25<br>2 20<br>0 15<br>10<br>5<br>0<br>SAR EMDEs Mar-Aug 2020 Sep 20 - Aug 21 Sep 21 - Mar 22<br>Sources: Asian Productivity Organization database; Groningen Growth Development Center database; ILOSTAT<br>database; OECD Structural Analysis database; Penn World Tables; UN Population Prospects; World Bank; World<br>Bank, Development Indicators database.<br>1990-99 2000-10 2011-16 1990-99 2000-10 2011-16<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21. A.B. Period averages of annual GDP-weighted averages.

A. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 3 economies are from SAR (Bangladesh, India, and Pakistan).

B. PFA = production function approach; Expectations-based estimates (“Exp.”) are potential growth proxied by five-year-ahead IMF World Economic Outlook growth forecasts Expectations sample includes Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, and Pakistan.

C. Productivity is defined as real GDP per worker (at 2010 market prices and exchange rates). Sample includes 3 EMDE economies from SAR (India, Pakistan, and Sri Lanka) and 19 other EMDEs. Growth “within sector” effects show the contribution of initial real value added-weighted productivity growth rate of each sector, holding employment shares fixed. Growth “between sector” effects show the contribution arising from changes in sectoral employment shares. Median of country-specific contributions.

D. Unweighted averages. Data up to March 2022.
**Figure 3.  Declining crude oil production and exports**

<!-- Start of picture text --> 600<br>Exports<br>Product'n less exports<br>Imports<br>500 Cons ' n less imports<br>400<br>300<br>200<br>100<br>0<br>1995 1997 1999 2001 2003 2005<br>Source: Statistik Ekonomi Energi Indonesia 2006 and 2007<br>Millions of barrels<br><!-- End of picture text --> Proven oil reserves have stabilized over the past two years and now approach 13 years of 2007 production levels (Figure 4). Meanwhile, proven gas reserves have trended upwards since significant restatements in the late 1980s. Reserves have grown faster than production over the past 15 years, and would now cover 45 years of production at 2007 levels (Figure 5). These two pictures together suggest that gas production is likely to drive future growth in Indonesia’s energy exports.

**Figure 4.  Oil reserves have stabilized**

**Figure 5.  Gas reserves trending higher**

<!-- Start of picture text --> Years25 15 Bl. Years80 Tril.4<br>barrels Proven reserves m 3<br>Proven reserves (years of current production; LHS)<br>20 (years of current 12<br>production; LHS) Proven reserves<br>60 3<br>(trillions m 3 ; RHS)<br>15 9<br>Proven reserves 40 2<br>(billions of barrels; RHS)<br>10 6<br>20 1<br>5 3<br>0 0 0 0<br>1980 1984 1988 1992 1996 2000 2004 1980 1984 1988 1992 1996 2000 2004<br>Source: BP Statistical Review of World Energy; World Bank staff calc'ns Source: BP Statistical Review of World Energy; World Bank staff calc'ns<br><!-- End of picture text --> The Indonesian Crude Price (ICP) is the oil price referred to in the government budget documents and related discussions. However, this single reference price masks the diversity of the various crude oils produced in Indonesia and the shift in the country’s production towards lower-grade ‘sour’ crude oils. The margin between the price of Indonesia’s overall crude oil exports and the international benchmark of West Texas Intermediate (WTI) has widened marginally this decade. However, prices of Malaysian Tapis crudes have risen significantly above WTI, while the equivalent Indonesian crudes have followed this trend of an increasing premium (Figure 6). These developments coincide with declining output and 6