rafmacalaba/data-use-mentions-extended huggingface dataset:
. The ELEP was used to target four different prefectures among the poorest ones in the country, and to determine the cash transfer amounts. The selection of the four different prefectures was due to the will to cover distinct climates and seasons, as is shown in Figure 2.

The four prefectures selected were Mali, Telimele, Siguiri and Kerouane, and are shown in Figure 2. The distribution of households between the four different prefectures is presented in Table 1. In each of these prefectures, four sous-prefectures were chosen. In each sous-prefecture, between 9 and 10 villages were randomly selected to be either control or treatment.

# 5. Timing of the cash transfers and the Ebola epidemic

The Ebola epidemic started in March 2014 in Guinea. Figure 3 presents the timing of the epidemic, the school closures and the cash transfers. As can be seen, in 2014, the Ebola epidemic was ongoing and the schools were closed, not only in Guinea but in most of the neighboring countries as well. The baseline survey was conducted in this context of the ongoing Ebola crisis. Schools could not open as expected in September 2014, so the school year started instead in 6
associated with different economic environments.<sup>16</sup>

> 13The data sets vary slightly in which of these variables are available. The IHDS data do not include information on homeownership. Only the IHDS data include the occupation of the household head and the distance to the closest fuel source.

> 14These variables are measured at the district level, reported separately for urban areas of the district and rural areas of the district.

> 15Urbanization impacts the returns to hygienic behaviors since communicable disease is more easily transmitted in dense areas. By contrast, low population density might reduce the risk of infectious disease. A more agriculturally oriented area might also value the fertilizer content of human waste.

> 16Muslims live disproportionately in more urban areas, and the presence of other minority groups (Jain, Sikh, Christian, Buddhist) can also be associated with different economic environments. Note that in controlling for the Muslim share of districts, the analysis still compares Hindu and Muslim households but within locations that have similar demographic shares. Also included is the sex ratio, which varies

9
inflation targeting. This has been done in some cases by testing whether the output gap enters significantly in an empirically-estimated interest rate rule. Leiderman and Bar-Or (2000), for instance, estimated the policy reaction function of the Central Bank of Israel using monthly data for the period 1994-99 and found that interest rates are positively related to current inflation, lagged changes in output, and the previous period's interest rate. It is important to note, however, that the fact that there is a significant coefficient attached to output in an estimated interest rate reaction function does not necessarily imply that in practice monetary policy is guided in part by an output stabilization objective. To the extent that current inflation is only an imperfect proxy for future inflation, and to the extent that the latter does depend on the level of economic activity, it is possible for output to appear in the reaction function with a significant coefficient-without, by itself being indicative of a departure from "strict" inflation targeting. Indeed, in the derivation of the optimal interest rate rule in Svensson's model given above, it was shown that even when the output gap does not enter the policy loss function (A = 0), it will (together with current inflation) affect the value of the instrument; the reason is that both variables help predict future inflation.

The second issue relates to the role of forward-looking components in actual policy reaction functions. This is important because simple forecastbased rules have some clear similarities with the type of optimal flexible inflation-forecast targeting rules derived earlier; monetary policy under both types of rules seeks to offset deviations between expected inflation and the inflation target at some horizon.<sup>40</sup> The Reserve Bank of New Zealand's policy projections, for instance, are based on an explicit, forward-looking policy reaction function that takes a relatively simple form (Archer (2000)). Some econometric studies (see Clarida, Gall and Gertler (1998a, 1998b)) have shown that a reaction function that includes a
.334|
|Transportation|0.063|0.000|0.000|0.142|
|Wood andpaperproducts|0.047|0.194|0.136|0.702|
|Total|1|0.051|0.026|0.512|
|**Panel B. By Broad Source**|||||
||Share of total<br>import value|IHC firms’ share<br>of source region<br>import value|Share of total<br>import value<br>cleared under IHC|IHC share of<br>IHC firm<br>imports|
|European Union|0.518|0.076|0.039|0.514|
|Other|0.482|0.025|0.013|0.506|
|Total|1|0.051|0.026|0.512|

Source: Authors’ calculations based on Serbian customs data.

We now turn to an analysis of 2010 data in order to show that even before the IHC program was undertaken, IHC firms and non-IHC firms differed in the distribution of customs clearance times, in their inspection rates, and in the scale of their imports. Table 3 shows summary statistics on customs clearance times in 2010 for firms that do and do not adopt the IHC program by the end of 2013. The striking pattern that emerges is that the distribution of clearance times for firms that adopt the IHC program by 2013 is well below that of firms that do not adopt, at all locations in the distribution. In particular the firms that will later adopt the program exhibit substantially faster clearance times at the 90<sup>th</sup> percentile - 3.6 hours versus almost 3 days (70.4 hours) - which indicates that they have far fewer outlier import declarations that take an abnormally long time to clear customs than do other firms.

26
# **2.3 NTM data**

We use an updated version of a novel comprehensive NTM database for Indonesia<sup>3</sup> built by the World Bank. The data is hand collected through extensive regulatory checks. It has the most disaggregated NTM classification available, i.e., 3-digit MAST level, and the highest frequency available, monthly series. This classification is the most appropriate for policy analysis as it identifies individual measures introduced or modified by each agency. There are over 600 regulations spanning 13 government ministries and agencies. A total of over 60 3-digit NTMs are available in the data set, each with values coded between 0 and 1 to signify when they were in effect on the corresponding products, that is, the data varies at the month-product pair. The starting point of this database is data collection by the Economic Research Institute for ASEAN and East Asia (ERIA) in collaboration with UNCTAD. Then the regulations are traced forward and backward from 2015 and coded accordingly, using various other sources, primarily through government repositories. This data is the most up-to-date data on NTMs and at a high-level of frequency, both of which make the data most relevant for the aim of this paper.

As non-tariff measures usually serve a public policy objective, extensive evaluation needs to be made to identify those that may require reform. We focus on four measures in this analysis that previous research has found to be more restrictive to trade than necessary and requiring elimination or reform (see Cal`ı et al. (2022) and Cal`ı and Montfaucon (2021) based on data up to 2018). These measures are pre-shipment inspections (PSI, MAST code C1), mandatory certification with Indonesian product standards or _Standar Nasional Indonesia_ (SNI, MAST code B7), port of entry restrictions (PoE, MAST code C3), and import approvals (IA, MAST code B14). Additionally, three of these measures have been found to have higher tariff ad valorem equivalents compared to
the conditions that (a) the real output shock ut affects all the variables in the long run; (b) the real exchange rate shock vt affects both the real exchange rate and price inflation, but not real output, in the long run; and (c) the nominal shock wt affects only the rate of inflation in the long run (Clarida and Gali 1994). In view of the preliminary information based on AIC and other criteria (Kawai and Okumura 1996), lag length is set at one. For xt, we use the real oil prices, which are given by the US dollar prices of oil adjusted for the US producer price index. Data frequency is annual and the sample period is 1970-98 (1979-98 for China).

# 2. **Impulse Response Analyses**

The above model can be used to analyze the patterns of impulse response of relative real GDP growth (simply real GDP hereafter) and relative price inflation (simply the price hereafter) when there is a shock in the rate of change in real effective exchange rates (simply the real effective exchange rate hereafter). In what follows, we will use the impulse responses of the two key macroeconomic variables as a way of assessing the impact of fluctuations in major currency exchange rates on the East Asian economies, namely, China, Hong Kong SAR, Indonesia, Korea, Malaysia, the Philippines, Singapore, Taiwan POC and Thailand.

Panels in Figure 2 depict, for the respective economies, the impulse responses of real GDP and the price to a one-standard deviation shock to the real effective exchange rate; they also show how the real effective exchange rate itself will respond over time. It appears that the initial responses of real GDP and prices are asymmetric between the crisis and non-crisis countries. First, in response to a unit depreciation of the real effective exchange rate, real GDP initially increases in the non-crisis countries, namely, China, Hong Kong SAR, Singapore and Taiwan POC. In contrast, real GDP initially declines
mainly serves to investigate to what extent results from an activity perspective will differ from results based on a product perspective.

**Figure 5: Economic growth and degree of path-defying specialization**

<!-- Start of picture text --> 4 IRL<br>MNG CHN<br>LTU<br>TUR LKA TWN MLT<br>KOR<br>POL<br>ROULVA<br>2 EST INDSVK VNM<br>IDNLUX<br>RUS BGR SWD E U THA USA<br>CZE KHM AUS<br>NOR HUNJPN GBRAUT BELDNKCANNLDBGDPHL<br>FRA<br>FIN SVN FJI<br>0 ESP<br>NPL KGZPRT MEXBRA<br>ITA<br>CYP<br>PAK<br>-2<br>GRC<br>.2 .3 .4 .5 .6<br>Average Path Defying Share<br>Orthogonal Component of Real GDP per Capita Growth<br><!-- End of picture text --> _Notes_ : Figure plots the orthogonal component of average real GDP per capita growth rate against the average share of path defying entries (as defined in equation 11) across the eight overlapping periods for each country. Regression control for initial (log) GDP per capita. Slope (standard error) of the linear fit is 3.73 (2.18). Size of circles represent country size measured as average real GDP in 2017 US$. _Sources_ : Authors’ calculations based on Penn World Table version 10.0, ADB MRIOTs and Occupations Database.

Additional data sources and regression results are given in Appendix B. The dependent variable is the average growth rate of GDP per capita in the period [t, t + 5]. The key variable of interest is the share of path-defying entries which appears to be strongly significant and positively related to growth. Interestingly, the positive impact remains even after controlling for factor endowments in terms of human capital levels and financial development.
|265.96|165.22|-100.74|
|N|645|399||396|245||
|Early difusion period: 1985-1994|162.15|149.74|-12.41|159.56|143.26|-16.30|
|N|1,816|1,651||1,407|1,250||
|Late difusion period: 1995-1999|191.71|162.80|-28.91|214.36|148.81|-65.55|
|N|769|733||683|553||
|**E. Total number of children**|||||||
|Pre-ultrasound: 1973-1984||2.91|||3.12||
|N||14,095|||10,690||
|Early difusion period: 1985-1994||2.13|||2.22||
|N||25,857|||22,169||
|Late difusion period: 1995-2005||1.85|||1.96||
|N||23,480|||22,372||

NOTES: This table shows pre- versus post-ultrasound trends in unadjusted (raw) outcomes among secondand higher-order children, by firstborn sex and child gender. Panel E shows the total number of children per mother for the sample of mothers whose first and last birth took place within each of the three periods. Data: NFHS in Panels A and E. REDS in Panels B, C, D.

39
by the World Bank have data on the most commonly reported indicator on education: government education spending as a proportion of gross domestic product (GDP) (Figure 1). Also, of the 124 countries that reported this information in 2000, only 75 reported the same information in 2015. This limited coverage stands in stark contrast to the data available for the health sector. Since 2000, 186 countries have compiled detailed health-financing data each year. We also found discrepancies between statistics provided by UNESCO’s Institute of Statistics (UIS) and the International Monetary Fund (IMF) on the estimated value of countries’ public education spending as a share of GDP, with the IMF’s numbers averaging 0.7 percentage point higher. Similarly, we found an average 0.8 percentage point difference between UIS data for this measure and those reported in the World Bank’s public expenditure reviews (PERs), since 2013 (Figure 1).<sup>2</sup>

# **Box 1: Indicator Definitions and Sources**<sup>**a**</sup>

We primarily used data from UNESCO’s Institute of Statistics (UIS) to document country-level changes in education spending between 1999 and 2015 (Table 1). Where there were gaps in the UIS database, we used data from the International Monetary Fund’s (IMF) Government Finance Statistics (GFS) database, with these comprising about 12 percent of all observations. We used data from the same sources to break down spending by level of education (primary, secondary, and tertiary).<sup>b</sup> Our data on overall government spending and GDP came from the World Bank’s World Development Indicators (WDI), supplemented by data from the Organisation for Economic Co-operation and Development (OECD).<sup>c</sup>

|**Indicator**|**Source**|
|---|---|
|1.
**_<u>________________________________________________________</u>_**

# **Endnotes**

> **_i_** Gausch and Kogan (2001) found that inventory holdings in the manufacturing sector in many developing countries are two to five times higher than in the United States. Their estimates further show that developing countries could reduce the unit cost of production by as much as 20 percent by reducing inventory holdings by half.

> **_ii_** - Maskus, Wilson, and Otsuki (2001) address several of the more important empirical methods and approaches to quantifying the gains of trade facilitation related to harmonized regulations.

- In addition, the Australian Department of Foreign Affairs and Trade and Chinese Ministry of Foreign Trade and Economic Cooperation (2001) suggest that moving to electronic documentation for trade would yield a cost savings of some “1.5 to 15 percent of the landed cost of an imported item.”

- UNCTAD (2001) considers trade facilitation in the broader context of creating an environment conducive to developing e-commerce usage and applications. The results show that a reduction of one percentage point in the cost of maritime and air transport services could increase Asian GDP by $3.3 billion.

- The Asia Pacific Economic Cooperation group (APEC 1999) found that the shock-derived reduction in trade costs ranged from 1 percent of import prices for industrial countries and Korea, Taiwan, and Singapore, to 2 percent for developing countries.<sup>**_ii_**</sup> The study estimated that APEC merchandise exports would increase by 3.3 percent from trade-facilitation efforts.

- Francois, van Meil and van tongeren (2003) found that a uniform 1.5% reduction in TTCs leads to benefits from trade facilitation of $72 billion

> _iii_ Many of the variables used in constructing the Networked Readiness Index of 2002-2003 of the Global Information Technology Report, are relevant to trade facilitation as well.

70 __________________________________________________________________________________________________________
sup>2</sup> .

Using data over 10 years, this study examines the efficiency and quality impacts across three provider payment systems in Croatia: (1) the original fee-for-service (FFS); (2) broad case-based groupings under the PPTP system, and (3) DRG case-based groupings under DTS, which are in effect today.<sup>3</sup> The main objective is to examine the impact of introducing case-based payment systems, both broad (PPTP) and more detailed (DTS). Since

> 1 Like Croatia, several other European countries (e.g., Ireland, Romania, Germany and Slovenia) also have decided to import and modify the AR-DRG system, and not to develop their own. There are different versions of the DRG system in the world, namely: NorDRG (Denmark, Finland, Iceland, Norway, Sweden); AR-DRG (Australia); DPC (Japan); CMG (Canada); HCFA/CMS (USA); HBCs (Hungary); DBC (Netherlands); HRG (UK); G-DRG (Germany); T-DRG (Turkey); GHM (France); LDF (Austria) 2 Steinbusch PJ, Oostenbrink JB, Zuurbier JJ, Schaepkens FJ. The risk of upcoding in casemix systems: A comparative study. Health Policy 2007; 81(2-3):289-99

> 3 Through the rest of the paper, the term DRG will be used to refer to detailed case-based groupings such as DTS or other DRG systems in the world. The term case-based payment systems will refer to both PPTP and DTS.

3
Policy Research Working Paper 10877

# **Abstract**

This paper introduces a novel index to measure public investment quality, utilizing the World Bank’s investment project performance data from 120 countries over 2000– 21. After detailing the construction of the index, the paper examines how public investment quality influences the relationship between the level of public investment and sovereign risk. The findings show that high levels of public investment are linked to lower sovereign risk in countries with high investment quality and, conversely, to higher sovereign risk in countries with low investment quality. This relationship is especially pronounced in sub-investment grade countries. These results are corroborated by showing that when public investment quality is high, scaling up public investment enhances fiscal sustainability by reducing the ratio of debt to gross domestic product in the long run: high-quality public investment is self-financing. However, the opposite is true when public investment quality is low, where increased public investment results in a deterioration of fiscal fundamentals.

This paper is a product of the Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted ataadarov@worldbank.org and ugo.panizza@graduateinstitute.ch.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
all Project implementation activities are on track and are properly monitored and evaluated. Given this intervention for youth is something new in PNG, great effort was put into establishing mechanisms, systems and capacity to deliver program services efficiently to targeted youth.

Youth are accepted into the program through continuous intakes, which occur once every 3 months. The enrollment of each intake into the Project consists of several steps:

- (i) First, the Project Management Unit (PMU) of the NCDC initiated a communication and awareness building campaign in selected districts inviting youth that meet the described eligibility criteria to apply. This process usually took 2‐4 days in a given neighborhood/district of the city. The PMU also worked with community leaders to reach out to potentially interested youth.

- (ii) Second, interested applicants showed up at a screening site on a given date to undertake the Eligibility Screening Survey (ESS), which was administered by an independent survey firm. The ESS collected basic information, including the socio‐economic characteristics of youth.

- (iii) Third, the survey firm collected and processed the ESS data to establish eligibility. To be eligible for selection into the Project, youth were required to be of eligible age (16‐35),<sup>8</sup> have

> 8 It is worth noting that while initially the upper age limit was set at 25 years, it was subsequently (starting from intake 4) increased to 35 years.

6
.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|1,238|2,429|1,238|2,429|
|Numberof individuals|684|915|684|915|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Columns (1)-(2) and IV-FE model in Columns (3)-(4). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.
year.<sup>6</sup> Because data on tenure are sparse before the mid-1990s, our sample is based on data for 1995– 2020. We restrict it to workers between the ages of 20 and 65, as often done in the literature on job tenure (see, for example, <u>Gregg and Wadsworth 2002</u> and <u>Burgess and Rees 1998). We group</u> countries into four regions: Western Europe, Northern Europe, Central Europe and the Baltic countries, and Southern Europe.<sup>7</sup>

> 6 Our analysis does not include German data because Eurostat provides no data on Germany to non-EU researchers (Eurostat 2022).

> 7 See <u>table A.1, in the appendix, for a detailed description of the sample and the classification of countries. These groups</u> correspond to the standard categorization used in the World Bank (World Bank 2022b).

8
disclaimer in most of the papers reviewed here.

While we do not directly observe return migration, we proxy for the extent of returns by following a given arrival cohort of immigrants over different waves of the survey or the census. Figure 1 focuses on the different immigrant groups who arrived to the United States in 1998 and 1999 as they are observed in the 2009 and 2019 American Community Surveys (ACS). Those are expressed as a percentage of the initial flow of immigrants to the US in 1998-1999, estimated from the 2000 US Census. The orange and green bars present data from the 2009 and 2019 ACS, respectively. The first two bars on the very left of Panel A look at male migrants who were between the ages of 20-25 at the time of their arrival in 1998 and 1999. These bars imply that, in 2009, the number of immigrants in this group dropped to 92% of its number recorded in the 2000 census. Their number further declined to 75% according to the 2019 ACS. Since the decline in the number of people in the census or the ACS might be due to mortality, we calculate the gender and age specific mortality rates and 4