rafmacalaba/datause-encoder-data huggingface dataset:
# lViii INTRODUCTION

Table **15** Long-Run and Stationary Population,<sup>Momentum, Doubling Yqar, and Year When Net</sup> Reproduction Rate Reaches 1 by World<sup>Bank Region and Country Department</sup>

|||Populatio|n (milli|ons)<br>|Station-||Year<br>||
|---|---|---|---|---|---|---|---|---|
||||||ary over|Popu-|when||
|Region and<br>country<br>department|1985|2050|2100|Station-<br>ary|1985<br>popula-<br>tion|lation<br> <br>mo-<br>mentun|popula-<br>tion<br>doubles|<br>Year<br>when<br>NRR=1|
|World|4,844|10,035|11,330|11,514|<br>2.4|1.4|2044|2060|
|Borrowers and non-borrowers|||||||||
|Sub-Saharan Africa|457|1,986|2,634|2,716|<br>5.9|1.6|2008|2060|
|Asia and Oceania|2,574|4,715|5,001|<br>5,058|<br>2.0|1.5|(a)|2055..|
|Europe, Middle East,<br>and North Africa|1,147|2,187|2,502|2,539|2.2|1.3|2059|2055|
|America|667|1,146|1,192|<br>1,201|<br>1.8|1.4|(a)|2030|
|Borrowers only|||||||||
|Africa Region|415|1,858|2,480|<br>2,559|<br>6.2|1.6|2008<br>|2060<br>|
|1 occidental and Cer.tral|40|185|250|<br>258|6.5|1.5|2008|2050|
|2 Eastern|106|486|654|<br>674|6.4|1.6|2008|2050|
|3South-Central<br>and<br>Indian Ocean|52|245|333|<br>343|6.5|1.5|2008<br>|2055<br>|
|4 Western|119|472|592|<br>608|5.1|1.6|2009|2050|
|5 Sahelian|36|175|252|<br>263|7.3|1.5|2009|2060|
|6 Southern|62|295|400|<br>412|6.7|1.6|2007|2055|
|Asia Region|2,384|4,483|4,771|<br>4,827|<br>2.0|1.5|2099|2040|
|1Other South|134|351|401|<br>412|3.1|1.6|2021|2030|
|2 Southeast|274|568|609|616|2.2|1.6|2042<br>|2030<br>|
|3China|1,040|<br>1,626|1,672|<br>1,681|<br>1.6|1.5|(a)|2000|
|4India|765|1,602|1,732|1,759|2.3|1.4|2041|2015<br>|
|5 Indonesia and Pacific|170|335|356|360|2.1|1.5|2055|2040|
|EMN Region|474|1,451|1,766|1,800|3.8|1.5|2017|2055|
|1 Pakistanand Turkey|146|502|607|618|4.2|1.6|2012|2040|
|2 North Africa|55|170|197|199|3.6|1.6|2014|2050|
|3Middle East|168|<br>657|839|<br>859|<br>5.1|1.6|2010|2055|
|4 Europe|105|<br>121|123|<br>124|<br>1.2|1.2|(a)|2030|
|LAC Region|387|<br>800|850|<br>857|<br>2.2|1.6|2043|2030|
|1 Brazil|136|<br>265|278|<br>280|<br>2.1|1.6|2061|2005|
|2 Central America|104|<br>237|254|<br>256|<br>2.5|1.7|2029|2030|
|3Carlbbean and Other|70|150|<br>162|<br>164|<br>2.3|1.6|2037|2030|
|4 Temperate and Tropical|78|148|<br>156|<br>157|<br>2.0|1.5|2101|2025|

Note: World Bank regions and<sup>country departments</sup> are defined in Exhibit 2. a. Population will not double<sup>with projected rates.</sup>
examined states are similar to the other states in their respective regions in terms of population, population density, GDP per capita, and poverty levels, which are some of the key control variables used in this analysis (Table 2). However, it should be noted that Minas Gerais falls below the regional average in terms of GDP, and Rio de Janeiro has a higher-than-average population density.

**Table 2: Selected States and Regional Comparators, 2002–2003**

||**State**|**Other States in**<br>**the Region**|**Difference**|
|---|---|---|---|
|**Pernambuco (Northeast)**||||
|Population (millions), 2002–2003|7.92|4.98|2.94|
|Population Density (per sq. km), 2002–2003|80.37|49.83|30.54|
|GDP per Capita (constant 2000), 2002–2003|3,538.35|3,062.30|476.05|
|Poverty,2002–2003|60.80|59.21|1.59|
|**Minas Gerais (Southeast)**||||
|Population (millions), 2002–2003|17.90|18.20|‐0.28|
|Population Density (per sq. km), 2002–2003|30.50|181.69|‐151.19|
|GDP per Capita (constant 2000), 2002–2003|5,762.00|9,041.67|‐3279.67**|
|Poverty,2002‐2003|26.08|23.20|2.88|
|**Rio de Janeiro (Southeast)**||||
|Population (millions), 2002–2003|14.40|19.30|‐4.95|
|Population Density (per sq. km), 2002–2003|328.59|82.33|246.26***|
|GDP per Capita (constant 2000), 2002–2003|9,357.82|7,843.06|1,514.76|
|Poverty,2002‐2003|22.89|24.27|‐1.38|

_Source_ : Authors’ calculations, based on data from IBGE. _Note_ : *** Significant at the 0.01 level (2-tailed); ** Significant at the 0.05 level (2-tailed).

**_Health sector_ .** Child mortality has declined rapidly across the country, with the national average rate dropping 34 percent from 24.9 deaths per 1,000 born alive in 2002 to 16.4 in 2011.  The largest reductions have been achieved in the northern states and Alagoas in the northeast, which initially had rates above 40.  On the other extreme, Amapá was the state in which the reduction in child mortality was the smallest.

Regarding the states of interest, both Pernambuco and Ceará are among the states that have observed the largest reductions between 2007 and 2011 in child mortality relative to their initial levels (Figure 1). These states also have lower child mortality rates than the predicted value for their GDP per capita (Figure 2). On the other hand, Minas Gerais falls on the average rate predicted for its GDP per capita while Rio is above it.

Consistent with the trend for the Northeast region, Pernambuco and Ceará have rapidly and steadily reduced child mortality rates since 2002. Child mortality in Pernambuco was higher than the regional average and the national rate in 2002. By 2011, the state had surpassed the regional reduction average and was below the national average. Figure 3 shows that from 2006 to 2007 the slope abruptly changes but then reverts to a similar rate of decline as before. This change coincides with the introduction of the _Pernambuco para Todos_ policy. Ceará displays a 16
Various factors contribute to the EMDE cross-border banking phenomenon, including: (i) the retrenchment of AE banks due to balance sheet repair and changing business models (including de-risking)<sup>3</sup> after the global crisis, (ii) unfavorable economic and regulatory conditions, and institutional constraints in the home country which impede domestic expansion or make it less attractive, and (iii) increasing cross-border corporate banking, trade, and other economic ties -- supported by financial deepening in host countries -- which provide new opportunities for growth and diversification, typically based on cultural and geographical proximity.

Cross-border banking growth prospects between EMDEs differ by region and even country. Besides (bilateral) macroeconomic and trade factors, an important issue is the risk of uneven implementation of financial regulations and national protective measures (e.g., “ring-fencing” requirements on local subsidiaries of foreign banks so host supervisors can exert more control) which can create an unlevel playing field for EMDE groups and deter them from expanding and deepening their international operations.

In this context, the mode of operation of an EMDE group in the host country is an essential factor (i.e., through a subsidiary or a branch; also see Fiechter et al., 2011). While branches can be costefficient for EMDE groups to expand rapidly, allow for the flow of intra-group capital and liquidity, and benefit from the strength of the parent group, host regulators also have less control over them -- particularly during a crisis when the EMDE group comes under stress -- compared to a locally incorporated and legally independent subsidiary which is required to hold capital and source liquidity in the host country. In the wake of the global financial crisis when the pernicious impacts of a foreign bank branch model supported by wholesale parent funding became apparent, some host regulators have pushed towards the “subsidiary model” with an emphasis on local funding, particularly for EMDE groups with systemically relevant (retail) operations in

> 3 Globally, correspondent banking relationships, predominantly with AE banks, have declined, leaving a gap in some cases that impedes cross-border activity such as trade and remittances. A recent survey from the International Finance Corporation (IFC, 2017) found that over one-quarter of bank respondents in Europe and Central Asia and Latin America and the Caribbean reported decreases, and over one-third in Sub-Saharan Africa. 72 percent of global respondents point to external factors such as compliance costs with home and host regulation, in Sub-Saharan Africa in particular. These costs pertain to, inter alia, software or system upgrades; limitations in customer information; a lack of harmonization in global, regional and local regulatory requirements; and lack of well-trained staff.

10
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**(LOG-)** CPI IN THE RAND **ZONE COUNTRIES 1973-88**

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||production and jobs in Argentina.” They urged sectors that import|
|---|---|
||products covered by the import licensing system to invest in job creation<br>in Argentina, pointing out that the sectors would be monitored as to the<br>quantity, the quality, and the prices of the products they supplied to the<br>Argentine market.<br>|
||_Source:_Ministry of Industry 2011b.|
|March 10,<br>2011|The Ministry of Industry announced that imports of cars and inputs used<br>by local car producers will be limited to the value of local production that<br>they export. The restriction is aimed at reducing the increasing trade<br>deficit in the sector. Imports originating in MERCOSUR and Mexico will<br>be exempt from the restriction.<br>The measure was announced during a meeting of government officers<br>with top representatives of the automotive industry and will not be laid<br>down in any regulation or written instrument other than the meeting’s<br>press release.<br>To obtain the necessary import permits, car producers will have to submit<br>a sworn statement in which they set forth their exports plans and get the<br>approval of the Ministry of Industry.<br>By April 2011, most car producers, including Volkswagen, Mercedes-<br>Benz, Porsche, PSA Peugeot-Citröen, and Alfa Romeo had concluded<br>agreements with the government in which they committed to reduce their<br>trade deficits by balancing exports and imports. Firms that do not have<br>local car production undertook to export goods other than cars or auto<br>parts. Such was the case of Alfa Romeo and Porsche, which pledged to<br>export biofuel and wines, respectively.<br>_Source:_Global Trade Alert 2011a.|
|May 3,<br>2011|On March 10, 2011, the Ministry of Industry adopted a verbal measure<br>whereby imports of cars and inputs used by local car producers would be<br>limited to the value of local production that they export. Starting on May<br>3, 2011, local media reported that similar restrictions were being extended<br>to the pharmaceutical industry. As in the case of the automotive industry,<br>local suppliers of pharmaceutical products were required to submit deficit<br>reduction plans as a condition to obtaining the necessary import permits.<br>To prevent supply shortages in the local market, only locally produced<br>medicines are subject to the measure.<br>|
||_Source:_Global Trade Alert 2011b.|
|June 22,<br>2011|In opening the forum for the value chain of textiles and apparel (part of<br>Argentina’s Strategic Plan 2020), the Minister of Industry, declared that<br>“we will continue to have a strong domestic market, protected against<br>unfair competition.” She elaborated that restrictions to dampen imports<br>have boosted local textile production, and the challenge now is to support<br>higher investment to further increased local production and reduction of<br>the sectoral trade deficit.|
||_Source:_Secretariat of Public Communication 2011.|
|July 30,<br>2011|On March 10, 2011, the government started to adopt a series of verbal<br>measures aimed at balancing imports and exports in certain sectors of the|

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**Table 8: Determinants of energy intensity (CRS, Pooled Effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indust**|**ries**|**Pulp and P**<br>**industry**|**aper**|**Cement ind**|**ustry**|**Iron and St**<br>**industry**|**eel**|**Aluminum i**|**ndustry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|-0.3196|0.001|-0.8013|0|0.143|0.045|-0.5174|0.052|-1.226|0|
|**Ln(price of energy/price of**<br>**output)**|-0.5391|0|-0.4112|0|-0.2297|0|-0.7303|0|-0.129|0.48|
|**Ln (technology development)**|-0.0396|0|-3.9E-05|0.996|-0.0108|0|-0.0114|0.445|-0.011|0.501|
|**Ln(price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|-0.0199|0|0.0125|0.202|-0.0081|0.003|-0.0018|0.906|-0.009|0.603|
|<br>**Foreign capital intensity**|0.1264|0.638|-0.5138|0.279|-0.0048|0.984|6.3715|0.013|3.4486|0.022|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**development)**|-0.0166|0.242|0.0215|0.539|-0.0066|0.597|-0.6037|0.011|0.0152|0.88|
|**Collectives**|-0.6109|0|0.0435|0.785|-0.417|0|-1.3253|0|-0.137|0.48|
|**Foreign**|-0.5531|0.002|0.3844|0.095|-0.3889|0.039|-1.1923|0.002|-2.932|0|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.46|0|-0.1459|0.274|-0.0634|0.358|-0.3965|0.106|-1.055|0|
|<br>**Shareholding**|-0.1981|0|0.1447|0.17|-0.2003|0|-0.3153|0.025|-0.598|0|
|**Private**|-0.0724|0.551|0.145|0.625|-0.1534|0.026|-1.1842|0.058|-0.774|0.021|
|**Other**|0.3063|0.618|(omitted)||0.0748|0.806|(omitted)||(omitted)||
|**North**|-0.6237|0|-0.9509|0|-0.264|0|-0.3122|0.188|-0.351|0.193|
|**Northeast**|(omitted)||(omitted)||(omitted)||(omitted)||(omitted)||
|**East**|-0.5403|0|-0.8415|0|-0.4445|0|-0.2796|0.193|-0.841|0.001|
|**South**|-0.4563|0|-0.4094|0.012|-0.3897|0|-0.518|0.031|-0.677|0.006|
|**Southwest**|-1.2901|0|(omitted)||-0.1151|0.528|-0.6779|0.082|-0.937|0.001|
|**Year 2000**|0.0863|0.258|0.0033|0.979|0.0492|0.382|-0.0163|0.928|0.1242|0.451|
|**Year 2001**|0.0948|0.215|-0.012|0.924|0.0588|0.296|-0.0543|0.765|0.1212|0.471|
|**Year 2002**|0.0222|0.773|0.011|0.931|0.0238|0.675|-0.1498|0.412|0.0799|0.644|
|**Year 2003**|-0.0735|0.344|-0.0288|0.823|-0.0171|0.764|-0.3569|0.053|-0.08|0.651|
|**Year 2004**|-0.0719|0.362|-0.03|0.818|0.0223|0.703|-0.4386|0.018|-0.12|0.516|
|**R**<sup>**2**</sup>**(obs.)**|0.4022 (15|28)|0.2469 (29|0)|0.3144 (677|)|0.4149 (41|8)|0.5962 (143|)|

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Table 9: Summary Statistics for Satisfactory Projects in First Half

|**Variable**|**Mean**|**(Std. Dev.)**|**N**|
|---|---|---|---|
|Dummy for FCS Country|0.362|(0.481)|724|
|CPIA Rating|3.292|(0.392)|684|
|GDP per capita growth|2.292|(3.643)|679|
|Dummy for DPO|0.109|(0.312)|724|
|Share of project in largest sector|0.644|(0.242)|722|
|Project Size - $ m|39.87|(53.924)|724|
|Project length (yrs)|5.572|(2.237)|724|
|Dummy FPD project|0.083|(0.276)|724|
|Dummy HDN project|0.298|(0.458)|724|
|Dummy PREM project|0.173|(0.378)|724|
|Dummy SDN project|0.443|(0.497)|724|
|Supervision Costs per Yr - $’000s|104.424|(115.974)|561|
|Preparation Costs - $’000s|469.115|(407.175)|658|
|Preparation time - mths|16.657|(14.869)|724|
|Total Staff Weeks per Yr|30.227|(31.248)|539|
|No. of Mission Days per Yr|64.295|(62.011)|305|
|No. of Consultant Days per Yr|41.972|(75.571)|539|
|Staff + Consultant Days per Yr|193.106|(184.887)|539|
|Size of Country Offce|8.949|(9.444)|559|
|IRS Ratio of Country Offce|0.437|(0.3)|541|
|No. of Risk Flags per Yr|0.662|(0.523)|724|
|Approval-Effectiveness time (mths)|5.270|(3.364)|723|
|TTL Experience (yrs)|6.528|(6.399)|724|
|TTL Quality|0.682|(0.296)|634|
|Dummy for TTL onsite|0.102|(0.263)|724|
|No. of words in PDO|57.658|(49.232)|623|
|No. of PDO Indicators|1.681|(3.111)|623|
|No. of Intermediate Indicators|4.658|(8.137)|623|

40
the start of conflict episodes. The Freedom House and Political Terror Scale variables are both oriented so that higher values correspond to worse outcomes, and we also observe on average higher values of both measures prior to conflict episodes. In the shocks category, growth is lower prior to conflict events, and natural disasters are neighbors in conflict are both higher before conflict. In contrast, positive terms of trade shocks tend to precede conflict events in our sample.

# **4. Results**

Table 3 provides a first look at the univariate predictive power for the various correlates of conflict. The rows of the table correspond to the correlates of conflict discussed in Section 3, grouped into the three categories of latent conflict, institutions, and shocks. The first three columns of the table report the Type 1 and Type 2 error rates, as well as the prediction loss function, for predictions of conflict based on each variable individually. Since the classifiers in this table generate predictions using a single variable, all four classification rules collapse to the same form – setting a threshold value for the single explanatory variable above which the classifier signals conflict.<sup>16</sup> In this table, in all cases the threshold is set to minimize the prediction loss function – and therefore all four classifiers generate the same threshold and predictions.

Table 3 shows substantial heterogeneity across the individual variables in terms of their ability to predict conflict. Type 1 errors or missed conflicts range from 4% to 88% of all conflict episodes, while Type 2 errors or false alarms range from 6% to 83% of all non-conflict episodes. Variables such as the spatial inequality and the young male population share stand out as generating predictions with very low Type 1 error rates, implying that they are consistently high in conflict episodes. In contrast, variables such as the CPIA and natural disasters stand out in terms of having very low Type 2 error rates, i.e. they consistently have good values in episodes with no conflict, but not necessary poor values in conflict episodes. The balance of Type 1 and Type 2 errors reflected in the objective function also shows significant heterogeneity, ranging from 33% to 47%. To put these values of the prediction loss function in context, note that had we simply predicted conflict at random with a probability equal to the unconditional probability observed in the sample, the prediction loss function would be 50%.<sup>17</sup> The two

> 16 For the purposes of this table we simplify the random forest to a single classification tree with a single explanatory variable, which corresponds to a single partition of the data.

> 17 To see this, note that if the unconditional probability of conflict is � , the probability of correctly predicting a conflict is �<sup>�</sup> , while the probability of generating a missed conflict is �(1 −�) , implying a Type 1 error rate of

22
**Table 6 – Tobacco-Related Events, by Gender, 2016**

|_Event_|_All_|_Women_|_Men_|
|---|---|---|---|
|Ischemic heart disease|673,152|269,411|403,741|
|Tuberculosis|622,909|244,059|378,849|
|Chronic obstructive pulmonary disease|500,616|166,757|333,859|
|Cerebrovascular disease|334,295|158,159|176,136|
|Cervical cancer|25,192|25,192||
|Colon and rectum cancer|16,695|8,260|8,435|
|Tracheal, bronchus, and lung cancer|13,227|3,372|9,854|
|Stomach cancer|12,642|5,616|7,025|
|Lip and oral cavity cancer|6,429|3,395|3,034|
|Bladder cancer|6,177|1,750|4,426|
|Leukemia|4,675|2,034|2,641|
|Nasopharynx cancer|3,771|919|2,852|
|Liver cancer due to other causes|3,386|370|3,016|
|Larynx cancer|2,694|478|2,216|
|Esophageal cancer|1,729|539|1,190|
|Total tobacco-related events|2,227,589|890,313|1,337,275|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

_Note_ : Incidence is defined as the number of new cases of a given disease during a given period in a specified population. It is also used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases, new or old, in the population at a given time.

**Table 7 – Years of Life Lost, by Gender, 2016**

|_Years_|_All_|_Women_|_Men_|
|---|---|---|---|
|30–34|220,583|8,060|212,523|
|35–39|302,657|14,839|287,819|
|40–44|400,801|29,242|371,559|
|45–49|495,617|40,812|454,805|
|50–54|576,637|63,748|512,889|
|55–59|609,999|49,992|560,007|
|60–64|546,819|55,091|491,729|
|65–69|404,476|52,486|351,991|
|70–74|308,666|42,070|266,596|
|75–79|178,786|29,140|149,645|
|80 plus|110,610|22,656|87,954|
|Total|4,155,652|408,136|3,747,516|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

# **Tobacco-related medical costs**

The most recent study estimating the economic cost of smoking in Indonesia was conducted in 2015. The study estimates the treatment costs of 19 chronic diseases attributable to smoking. Annual direct health care costs attributable to tobacco use amount to US$2.2 billion (equivalent to 2.5 percent of GDP in 2015). The treatment costs of hypertension, chronic obstructive pulmonary diseases, and ischemic heart disease showed the highest cost burden (Kristina et al. 2018). Similarly, a 2008 study estimated the economic loss attributable to tobacco use at US$18.5 billion (NIHRD 2009). Kristina et al. (2015) estimate the indirect mortality costs of premature cancer deaths and years of potential life lost attributable to smoking in Indonesia. They estimate smokingattributable cancer mortality at nearly a third of total cancer deaths (30.6 percent) and the years of life lost at 1.2 million. Furthermore, cancer mortality costs related to smoking amounted to US$1.3 million in 2013.

10