rafmacalaba/data-use-mentions-extended huggingface dataset:
<u>Data Sources</u> The macroeconomic series for annual GDP (in constant prices), labor productivity (defined as the ratio of GDP to employment), the unemployment rate, real effective exchange rates (period average, deflated by CPI) and the trade balance (period average, deflated by GDP) are taken from IMF WEO and World Bank WDI databases. Data on the Gini coefficient, a measure of inequality, come from the Standardized World Income Inequality Database (SWIID). Table S1.2 provides a summary of our data sources. Our tariff series, T, is based on trade tariff rate data at the product level and aggregated to country level, with weights given by the import share of each product, measured as a fraction of value. The main source is the reform dataset compiled by the IMF (Ostry, Prati, and Spilimbergo 2009; Prati, Onorato, and Papageorgiou 2013; Giuliano, Mishra, and Spilimbergo 2013) which cover an unbalanced sample of 151 countries from 1964 to 2004. We extend the data to 2014 using tariff data from the World Integrated Trade Solution (WITS) and World Development Indicators (WDI). Appendix S2 provides a description of the tariff data (including data sources) and presents key descriptive statistics for tariffs and tariff changes. The appendix also includes time series plots of tariffs for a number of individual countries as well as aggregated grouped by income.

# B.   Industry-level analysis

The empirical specification for industries is analogous to the macro specification: 9
25 district). All of these variables were derived from information in the hospital inventory. The name of the hospital gives its location and specialization. The number of general and private hospitals reported in the inventory was used to derive an estimate of private and general hospitals in the same district, although private hospitals in earlier years may be underrepresented.

# _Household data_

Our individual-level analysis is based on data from the 2004, 2006 and 2008 Vietnam Household Living Standards Survey (VHLSS), merged with district-level hospital autonomy data constructed from the hospital inventory. We originally planned to use data from the 2002 VHLSS, but we concluded that the health data had been collected in too different a fashion from the later surveys for them to be comparable. Thus in contrast to the hospital-level analysis, in the individual-level analysis we do not have data starting before the initial wave of hospital autonomization in 2003. The VHLSS sample covers all Vietnam‘s provinces but not all its districts.

# Autonomy

We look to the hospital (or hospitals) in the household‘s district of residence when constructing our measure of hospital autonomy. Where there is just one hospital, we define the household‘s local hospital ―market‖ as autonomized from the date the single hospital gained autonomy status. This single hospital will typically be a district hospital, but need not be – it could be a provincial hospital, for example. Where there are several hospitals in the household‘s district, we assume that the individual will be referred upwards: where there is a district hospital, we use its autonomy status to determine the district‘s autonomy status; where there is no district hospital but a provincial hospital, we use the provincial hospital‘s
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The quality of Angola’s regional road corridors is also poor, making the country’s regional connectivity with the broader SADC economic area difficult. This situation both prevents Angola from developing regional trade with surrounding countries and limits surrounding countries from making greater use of the Port of Luanda (see figure 13).

Transportation represents an important bottleneck for Angola’s economy. Poor road infrastructure and transport logistics slow Angola’s overall economic development. Angola is one of the worst performers in the world according to the 2010 Logistics Performance Index (LPI). Its manufacturing sector depends on imports for as much as 40 percent of inputs, which can be difficult and expensive to procure given the poor quality of the roads. Moreover, around 2.1 percent of the production of manufacturing firms is lost in transit, more than in any other African country (World Bank 2007). It takes 49–53<sup>1</sup> days to export or import goods, among the longest times in Africa. Road access is particularly problematic for firms outside Luanda.

For all of these reasons, Angola urgently needs to establish an operational road fund. The country’s current extraordinary spending on the road sector is entirely budget-financed. Such high levels of public funding are not sustainable in the long term, and there is the risk that today’s heavy investments may not be adequately maintained in the future. It is therefore critical for Angola to work toward the establishment of a road fund resourced from a fuel levy and other road-user charges. The optimal fuel levy needed for long-term road-network sustainability in Angola would be on the order of $0.15 per liter of fuel, which is toward the middle of the range for African countries (figure 12). But currently, gasoline and diesel prices are heavily subsidized by the state and there is no mechanism for fuel levy collection.

**Figure 12. Angola’s public contribution exceeds the maintenance and rehabilitation needs of
# **Appendix A.**

**Comparing Trends in School Safety:** The items measuring school safety were not identical in the TIMSS 2007, 2011, and 2015 questionnaire, which may result in concerns about the reliability of the trends generated for school safety.<sup>11</sup> To check the robustness of the findings, factor decomposition was used to analyze two distinct indexes of school safety, one for 2007 and one for 2011 using all the variables available. After averaging these two new indexes by country, the trends in these indexes were assessed by country within and across the region. Results revealed that the school safety index in 2011 was lower than the school safety index of 2007 for MENA countries, while the opposite was true for countries outside the region. Computing a difference-in-difference indicates that school safety dramatically dropped in the MENA region before and after the start of the Arab Spring, regardless of the methodology chosen ( **_Table 5_** ). The average drop is about half a standard deviation.

**Table 5. Comparing Trends in School Safety Between 2007 and 2011 in MENA and Elsewhere**

||Safety<br>index of<br>2007|Safety<br>index of<br>2011|Difference|
|---|---|---|---|
|Countries outside MENA participating in 2007 and||||
|2011|0.08|0.23|0.14|
|MENA countries participating in 2007 and 2011<br>(treatment group)|0.03|-0.34|-0.37|
|**Difference-in-difference**|||**-0.51**|

Source: Analysis using TIMSS 8<sup>th</sup> grade data, 2007 and 2011.

**Table 6: Pairwise correlations of the PISA disciplinary climate by country and by wave**

|2000|2003|2009|
obtain geographically disaggregated estimates of droughts, heavy rainfall, river floods and storms with hurricane force. Section three reviews the empirical methodology to estimate the welfare effects. Section four reviews the data used and section five presents the empirical findings regarding the welfare effects of different natural disasters. Section six concludes.

# **2 Mapping Natural Hazards**

Vietnam’s 2007 “National Strategy for Natural Disaster Prevention, Response and Mitigation to 2020” identifies droughts, floods, and cyclones as its most important natural hazards. Landslides, which often happen following flash floods or cyclones, are mentioned as well. One common approach to determine whether a household has been affected by an extreme weather event is to ask the household directly. In contrast to external measures of extreme weather events derived from spatially scattered weather stations or satellite data, self‐reported shock measures have the advantage of being locally more accurate—households know whether they experienced an extreme weather event or not. They also circumvent the challenge of identifying the cut‐off beyond which a particular meteorological variable or combination of meteorological variables constitutes an extreme weather event. Indeed, deciding when rainfall is too much or too little, when winds are too strong, flood levels too high, or temperatures too high or too low across a series of settings and groups is demanding.
13 where i is the index for the n individuals in the economy. As is well known, this measure is decomposable across mutually exclusive and exhaustive groups indexed by j with population weight xj: Pα = ∑ xj Pα, j Consider now dividing the economy up into three locations‐‐cities, towns and rural. Further, if we have a panel data set at time t and t+1, we can divide the population up into nine mutually exclusive and exhaustive groups of movers and stayers in each location, and these are the nine groups in the poverty decomposition. Change in national poverty is then simply given by: ∆Pα = ∑ xj ∆Pα, j Thus national change in poverty is a weighted sum of the change in poverty of those who were and stayed in rural areas, those who moved from rural areas to cites, those who moved from rural areas to towns, those who were and stayed in towns, those who moved from towns to rural areas, those who moved from towns to cities, and the corresponding three categories for cities, making nine groups in total.

The contribution of each category to national poverty reduction is: cj = [xj ∆Pα, j]/[ ∆Pα] Consider now a division of the population into stayers (3 groups) and movers (6 groups). The relative contribution of each group to poverty reduction depends both on xj and on ∆Pα, j. As is the case in the Kagera sample, even if ∆Pα, j is relatively small for a particular group of movers (rural to town compared to rural to city), xj can be relatively so large (number of rural to town moves compared to number of rural to city moves), that xj ∆Pα, j for this group can be larger than for another group of movers.

In a purely accounting sense, then, national poverty reduction is greatest when (i) poverty reduction is greatest in that group where the stayers are relatively large in number
Table 1: Categories and dimensions of the Colombian multidimensional poverty index.

|**Category**|**Dimension**|**Deprived if**|
|---|---|---|
|**Education**|Educational achievement|Any person older than 15 years<br>has less than 9 years of schooling.|
||Literacy|Any person older than 15 years<br>and illiterate.|
|**Childhood and youth**|School attendance|Any child 6 to 16 years old does<br>not attend school.|
||Children behind grade|Any child 7 to 17 years old is<br>behind the normal grade for his<br>age.|
||Access to child care services|Any child 0 to 5 years old doesn’t<br>have access to health, nutrition or<br>education.|
||Child labour|Any child 12 to 17 years old<br>works.|
|**Employment**|Long term unemployment|Any economically active member<br>has been unemployed for 12<br>months or more.|
||Formal employment|Any employed household<br>members is not affliated to a<br>|
|||pension fund.|
|**Health**|Health insurance|Any person older than 5 years<br>does not have health insurance.|
||Health services|Any person who fell sick or ill in<br>the last 30 days did not look for<br>|
|||specialized services.|
|**Standard of living**|Water system|<br>Urban: Household not connected<br>to public water system. Rural:<br>Household obtains water used for<br>cooking from wells, rainwater,<br>spring source, water tanks, water<br>carriers or other sources
fanfare, having an independent fiscal institution monitor and report on compliance can raise the quality of public debate on the subject.

**Second, the rules in place should ease accession to the EU.** National fiscal rules should be consistent with EU rules. But the rules do not need to be identical to the EU’s to be consistent with them. Debt limits should be less than 60 percent of GDP, and the total set of rules should be simpler than those of the EU. The focus should also be on changes that will not be regretted if accession to the EU is delayed.

**Third, the systems of public financial management that support fiscal rules should continue**

**to be improved** . These improvements are among those most likely to pass the no-regrets test. Being able to measure the government’s debt and deficit according to the rules of ESA 2010, for example, is necessary if the countries are to join the EU, but it will be valuable even if they do not. Being able to monitor debts and deficits according to these rules will, for example, make it easier to see if fiscal rules are being followed only at the expense of a build-up of arrears or the transfer of spending responsibilities to noncommercial state-owned enterprises. Once ESA data can be measured with sufficient reliability, they can be used for setting fiscal rules or targets. Ensuring that forecasts are unbiased is also valuable, and it will be useful to evaluate the success of attempts to get unbiased forecasts by establishing fiscal councils. Solving the problem of biased forecasts will also increase the prospects for making medium-term spending plans credible. Spending reviews of the kind used in many EU countries can ensure that across-theboard spending restraint is supplemented by periodic, in-depth examination of whether spending is efficient and well-targeted.

**Fourth, limits should not be mistaken for targets** . To adopt a debt limit of, say, 60 percent of GDP is not to say that government should aim to keep debt at
of a population threshold. As with the OECD algorithm for identifying FUAs, the AI typically relies on gridded population data as input.<sup>20</sup> To see more formally how the AI measures a country’s level of urbanization, let � index a cell in a population grid for that country, and �� and �� the population and population density respectively associated with that cell. Furthermore, let ��1, ... , � index a settlement point within the country that has an associated population of ��, where �� ��<sup>�</sup> and �<sup>�</sup> denotes the threshold population level that defines a “sizeable” settlement. Given this, associated with each of these � settlement points there will be an agglomeration �� and grid cell � will be classified as belonging to agglomeration � _iff_ �� ��<sup>�</sup> and ��� ��<sup>�</sup> , where �<sup>�</sup> is a threshold population density level, ��,� is the estimated travel time from grid cell _i_ to the settlement point _j_ , and �<sup>�</sup> is a threshold travel time level.<sup>21</sup> Based on this, a country’s level of urbanization, as measured by the share of its population living in urban areas, is given by: From this, it is clear that there are three key thresholds to which values need to be assigned in the implementation of the AI methodology: namely, �<sup>�</sup> , �<sup>�</sup> , and �<sup>�</sup> . Following Uchida and Nelson (2010) and World Bank (2008), it has become standard to set �<sup>�</sup> �50,000
|**1,503**<br>_All hosts_|**814 (622)**<br>_EMDE hosts only (same region)_|
|---|---|
|**Share ofgroup assets in host countries**||
|**8.4%**|**4.1% (3.2%)**|
|_All hosts_|_EMDE hosts only (same region)_|
|**Share ofgroup revenuegenerated in host co**|**untries**|
|**10.1%**|**7.4% (5.7%)**|
|_All hosts_|_EMDE hosts only (same region)_|

Source: Orbis Bank Focus; team analysis. Figures cover financial and real sector of majority-owned subsidiaries only. Caveats include significant missing financial information for subsidiaries. Direct ownership information was used in case total ownership was missing. The highest holder in the hierarchy of the corporate group is designated as the parent. Also, consolidation practices differ across countries, including within groups.

A regression analysis using the data for the 51 EMDE banks show the relationship between the international activities of these EMDE groups and various group-level banking outcomes (profitability, market-to-book values, cost-to-income ratio, and bank risk -proxied by NPL over loans) and funding strategies (deposit, short-term funding, and equity funding and well as banks’ positions in terms of liquidity and market capitalization). The analysis is built on the following econometric model: B B o o o o B i B + ii,jj , rr ii ii = αα+ θθ + ββB iiBBo iiB ooiiooB iiBBooiiooBB + γγB CCooBBooiiooiioo jj ii where δδCCo BBooiiCC CCooBBooiiooiiooB B o o o + εεo is the dependent variable for bank _i_ from country _j_ capturing profitability (return on average assets and net interest margin), market performance (market ii,jj value of equity over book value of equity) and
Third, BVAR models had significantly poorer forecast performance than all other approaches, in terms of bias, precision, and direction of change, for all commodities and all horizons. The strength of BVAR models lies not so much in forecast performance, but in scenario analysis: among the approaches considered here, BVAR models allow the most straightforward translation of global output growth forecasts into industrial commodity price forecasts ( **Table 13** ).

There are some caveats on the comparison. Broadly speaking, these caveats bias the comparison against the BVAR approach. First, future prices, consensus forecasts, and the machine learning approach directly predict nominal price levels while bivariate correlations forecast percent changes of nominal prices. BVAR models predict real prices, then inflate nominal prices using actual inflation rates. Second, all the models except for the BVAR approach generate monthly price forecasts that are aggregated into quarterly forecasts for comparison; in contrast, the BVAR uses the quarterly average of monthly data as input to generate quarterly price forecasts since GDP data is only available at the quarterly frequency. Third and most important, forecasts were done without any judgement. None of the forecasting approaches here have pre-designed scenarios or priors. However, in reality, it is often of interest to condition the forecasts on different scenarios. The OEM is particularly useful to conduct scenario exercises that take into account changes in policy variables, global growth, inflation, and structural variables. Another purpose of conditional forecasts is to incorporate information from higher frequency data or judgment into the model (Karlsson 2013). This underscores the main advantage of the BVAR model, which allows the forecaster to simulate scenarios or test priors while maintaining the statistical properties of model. The forecaster can then make inferences about the posterior forecast conditional on the prior. The version of the BVAR model used in this exercise leads to a larger forecast bias than other models for oil prices. However, in practice, the forecaster could subsequently adjust the parameters to reflect changes in priors to arrive at more informed forecasts.

# *
# Appendix A4 Confidence Intervals for Beta Estimates

95% confidence intervals for asset beta estimates for BT calculated using the FTSE 100 index.

# Table A4.1: Confidence intervals for five-year beta estimates calculated from data with different interval lengths

||**Daily data**|**Weekly data**|**Monthly data**|
|---|---|---|---|
|Asset beta estimate|0.87|0.91|0.82|
|Upper bound|0.92|1.02|1.03|
|Lower bound|0.81|0.79|0.61|
|Number of observations|1,422|294|65|

Table A4.2: Confidence intervals for daily beta estimates for BT calculated over periods of different lengths

||**Five year (1990-95)**|**One year (1994)**|
|---|---|---|
|Asset beta estimate|0.87|1.02|
|Upper bound|0.92|1.13|
|Lower bound|0.81|0.90|
|Number of observations|1,422|252|

60
Policy Research Working Paper 5869

# **Abstract**

Using Indonesian manufacturing census data (1991– 2001), this paper rejects the hypothesis that the East Asian crisis unequivocally improved the reallocative process. The correlation between productivity and employment growth did not strengthen and the crisis induced the exit of relatively productive firms. The attenuation of the relationship between productivity and survival was stronger in provinces with comparatively lower reductions in minimum wages, but not due to reduced entry, changing loan conditions, or firms connected to the Suharto regime suffering disproportionately. On the bright side, firms that entered during the crisis were relatively more productive, which helped mitigate the reduction in aggregate productivity.

This paper is a product of the Development Research Group and the Development Economics Vice Presidency. 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://econ.worldbank.org. The author may be contacted at mhallward@worldbank.org.

_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._ Produced by the Research Support Team
Table 8: Treatment Effect for Target Child on Literacy and Numeracy Test Scores: Pre- and PostCovid Samples

|||_Literacy_|_Skills_||_Numer_|_acy Skills_|
|---|---|---|---|---|---|---|
||Prop.|Zero|Z-s|core|Z-s|core|
||(1)|(2)|(3)|(4)|(5)|(6)|
||_March 20_|_Sam_<br>_20 & Sept-_|_ple 2 Pre-_<br>_Dec with_|_and Post-C_<br>_less than 1_|_ovid_<br>_0% of scho_|_ol closures_|
|Any Treatment (T1 or T2)|-0.094*<br>(0.053)||0.274**<br>(0.133)||0.439**<br>(0.178)||
|Videos (T1)||0.121*||-0.129||-0.328|
|||(0.071)||(0.127)||(0.207)|
|Videos + Mobile apps (T2)||-0.218***<br>(0.048)||0.507***<br>(0.160)||0.880***<br>(0.191)|
|Observations|5,077|5,077|5,077|5,077|5,077|5,077|
|R-squared|0.200|0.243|0.296|0.302|0.285|0.312|
|Covariates|YES|YES|YES|YES|YES|YES|
|Enumerator FE|YES|YES|YES|
# 3. UNHCR Data on Syrian Refugee Households in Jordan

<mark>The analysis that follows uses information about individuals and households who arrived in Jordan between 2011 and 2014. Almost all these households (98.8 percent) are not living in camps but living with host communities in different places—mostly in Amman (31.7 percent), Irbid (28.3 percent), Mafraq (14.4 percent), and Zarqa (11.8 percent). The analysis is based on two UNHCR datasets: the ProGres database and JD‐HV dataset. The ProGres dataset records the Syrians arriving in Jordan who register as refugees with UNHCR. It contains demographic and other information on the principal applicant and all the other individuals registered under the principal applicant.</mark><sup>18</sup> <mark>We refer to the principle applicant and the individuals registered with them as a household. The JD‐HV dataset contains more information on the socioeconomic characteristics of households and individuals and data on household expenditure, which are used to measure poverty. The ProGres database has information for a total of 54,408 Syrian refugee</mark> households, comprising 208,014 individuals, for whom JD‐HV data are also available.<sup>19</sup> <mark>Our analysis proceeds as follows. First, using the ProGres data we track the relationships between the different members of the household and the principal applicant. Next, we categorize all households into eight types that reflect some of the indicators of vulnerability used by UNHCR and capture some of the household characteristics that result from forced displacement:</mark><sup>20</sup>

- **Couple with children** : Married couple (at least one of whom is at least 18 years old) with children of their and/or the children of others (classified by UNHCR as separated children).

- **Single person** : One adult (person 18