rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table 5. Family planning and reproductive outcomes</u>**

||<br>Ever had<br>terminate<br>d<br>pregnancy|<br>Ever used<br>prenatal<br>help|<br>Received<br>assistance<br>with the<br>delivery|<br>Number of<br>antenatal<br>care visits|<br>Unmet need<br>for family<br>planning|Wife reports<br>use of any type<br>of modern<br>contraceptive|Husbands<br>reports use of<br>any type of<br>modern<br>contraceptive|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(5)|(7)|(8)|(9)|
|W gives herself more power<br>|0.018***|0.044***|0.006*|0.242***|-0.019***|0.028***|0.025***|
|than H gives her [A]|(0.00)|(0.00)|(0.00)|(0.07)|(0.01)|(0.00)|(0.00)|
|H gives W more power than she<br>|0.009**|0.036***|0.001|0.111|-0.015***|0.020***|0.027***|
|gives herself [B]|(0.00)|(0.00)|(0.00)|(0.07)|(0.01)|(0.00)|(0.00)|
|W and H agree she is main/joint|0.004|0.054***|0.007*|0.361***|-0.040***|0.036***|0.048***|
|DM [C]|(0.00)|(0.00)|(0.00)|(0.07)|(0.01)|(0.01)|(0.01)|
|Constant|0.060***|0.849***|0.984***|4.393***|0.674***|0.092***|0.058***|
||(0.01)|(0.01)|(0.01)|(0.10)|(0.01)|(0.01)|(0.01)|
|_Controls_||||||||
|Individual/Household-level<br>characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|66,735|52,736|52,788|45,487|71,274|62,114|58,543|
|Adjusted R-squared|0.02|0.35|0.24|0.14|0.04|0.17|0.18|
|Mean of excluded category|0.14|0.65|0.82|4.35|0.62|0.17|0.15|
|P-value A=B|0.03|0.05|0.15|0.04|0.45|0.10|0.60|
|P-value A=C|0.00|0.00|0.75|0.03|0.00|0.09|0.00|
|P-value B=C<br>Notes:|0.22|0.00|0.06|0.00|0.00|0.00|0.00|

(1) Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 (2) W=wife, H=husband, DM=decision-making In Table 6, we examine welfare outcomes for children. Within health, outcomes were measured for children in the household younger than five years old. In these cases, when significant, agreement is associated with the best outcomes for boys’ stunting and wasting as well as for the probability of girls receiving all four recommended vaccinations: a BCG vaccination against tuberculosis, the DPT vaccine to prevent diphtheria, pertussis, and tetanus, the polio vaccine and a measles vaccination.

23
Figure 1 exhibits substantial variation over time in the share of aid disbursed by policy-selective donors, mainly driven by the fact that starting in the 1990s many more donor became policy-selective in their aid allocation. However, from a recipient’s perspective the question then arises whether improving policies “pays off” in terms of receiving more aid. Table 2 runs such a test from the recipient perspective. Despite the presence of policy selective donors, improving policies may not pay off because of the presence of non-policy selective donors.

Table 2: Policy Selectivity (1990–2016)

|Dependent Variable:|Log of To|tal Aid|||||
|---|---|---|---|---|---|---|
|||OLS|||FE||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged CPIA|0.12<sup>_∗∗∗_</sup>|0.40<sup>_∗∗∗_</sup>|0.40<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|0.56<sup>_∗∗∗_</sup>|0.56<sup>_∗∗∗_</sup>|
||(0.04)|(0.13)|(0.13)|(0.07)|(0.19)|(0.19)|
|Lagged CPIA, squared||-0.15<sup>_∗∗_</sup>|-0.15<sup>_∗∗_</sup>||-0.19<sup>_∗_</sup>|-0.19<sup>_∗_</sup>|
|||(0.07)|(0.07)||(0.10)|(0.10)|
|Lagged Total Aid|0.89<sup>_∗∗∗_</sup>|0.89<sup>_∗∗∗_</sup>|0.89<sup>_∗∗∗_</sup>|0.64<sup>_∗∗∗_</sup>|0.64<sup>_∗∗∗_</sup>|0.63<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|
|Lagged GDP per capita|-0.06<sup>_∗∗∗_</sup>|-0.05<sup>_∗∗∗_</sup>|-0.05<sup>_∗∗∗_</sup>|-0.07|-0.07|-0.07|
||(0.01)|(0.01)|(0.01)|(0.05)|(0.05)|(0.05)|
|Lagged Population|0.06<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.29<sup>_∗∗_</sup>|0.24<sup>_∗_</sup>|0.22<sup>_∗_</sup>|
||(0.01)|(0.01)|(0.01)|(0.13)|(0.13)|(0.13)|
|Lagged Democracy (ANRR)|||0.01<br>(0.01)|||0.06<sup>_∗∗_</sup><br>(0.03)|
|Constant|0.07|-0.11|-0.11|-2.14|-1.56|-1.33|
||(0.09)|(0.12)|(0.12)|(2.27)|(2.25)|(2.25)|
|N|3195|3195|3195|3195|3195|3195|
|R-squared|0.94|0.94|0.94|0.49|0.49|0.50|
|F statistic|2459.33|2470.87|2546.14|59.40|61.08|59.48|

Dependent variable is the log of total aid disbursements measured in constant USD excluding emergency aid, food aid, and debt relief (OECD Table 2a). All covariates are in logs, except for democracy. Democracy measure taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. GDP and population data is taken from the WDI database. All regressions use recipient-year observations and include a constant term and period dummy variables, which are not reported. Recipient level cluster-robust standard error reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent Table 2 shows that there is a strong relationship between the lagged CPIA measure and current aid after controlling for the lagged aid level, population, and income per capita.<sup>4</sup> The table shows OLS

> 4Our aid data are from the OECD, Table 2a, that provides aid data in the donor-recipient-year dimensions. We adjust our aid measure and focus on development aid ignoring emergency and food aid and debt forgiveness by following the procedure described in Annen and Kosempel (2009).

14
not give the number of employees but rather the number of persons engaged. Since 1971 we thus obtain from the surveys average paid remuneration per person engaged, a rather uninteresting average, and only for large enterprises. For 1968 and earlier the surveys report number of employees in November and average remuneration per employee obtained on that basis may be biased if seasonality is important. We have moreover the problem that the surveys from 1971 define large establishments as those with 10 and more persons engaged and lump together all public enterprises (in any case mostly large) with large private establishments thus defined, while the surveys up to and including 1968 as large classify all establishments, private and public, with 10 and more employees. The averages for 1970, presented under surveys, were in effect obtained from the 1970 census which enumerated both employees and persons engaged and was presented with breakdowns on large and small establishments on both definitions. For 1960-62, finally, large establishments are defined as having 10 or more employees and 10 and more installed hp.

- 51 -
Figure 1. Concept 1 and Concept 2 (international) inequalities, 1952-2006 <!-- Start of picture text --> Concept 2<br>Concept 2 without China<br>Concept 1<br>1950 1960 1970 1980 1990 2000 2010<br>year<br>.65<br>.6<br>.55<br>Gini coefficient in percent<br>.5<br>.45<br><!-- End of picture text --> Note: Before 1960, there are between 80 and 90 countries included. After 1960, the number varies between 130 and 150. .

The data start in 1952, mostly because it is the first year for which GDP for China is available, and the calculation of Concept 2 international inequality without China would be meaningless.<sup>12</sup> The sample composition is practically fixed (the number of countries does not vary much) since 1960, when after decolonization the data for most African countries became available.<sup>13</sup> Let us focus first on Concept 1 inequality. Its _level_ is between 4 and 5 Gini points higher than with the earlier PPP data (not shown here). Between 1960 and early 1980s, Concept 1 inequality was stable at the Gini value of about 53. After the early 1980s, and not just coincidentally with the increase in real interest

> 12 For more details on the data see Milanovic (2005, Chapter 4) although the calculations there were done with the “old” PPPs.

> 13 For the countries that emerged after the break-up of the Soviet Union, Yugoslavia and  Czechoslovakia we use, when available (and they generally are), their republican GDPs per capita for all years prior to independence. We do this in order to keep the composition and the number of the countries in the sample as fixed as possible. The same approach is applied to Pakistan and Bangladesh, and Ethiopia and Eritrea.

9
countries like Malawi where domestic truckers were protected by restrictive entry regulations, ended up essentially penalizing farmers—a common policy outcome in Africa. They also highlighted the deleterious effects of cartels and regulations through “freight bureaus” on Central African corridors where freight rates per ton/km were about 80% more and truck-utilization rates 40% less than on East African corridors.<sup>13</sup> Throughout West Africa, they found that bilateral agreements, queuing systems and quotas stifled competition. Even on the most competitive trucking corridors of East Africa, anticompetitive regulations abounded, with e.g. Kenya prohibiting international transit trucks on the Mombasa-Kigali corridor from taking domestic freight on the return trip, forcing them to cover 1,700km empty.<sup>14</sup> In fact, a new cross-country database on services policy reveals a perverse pattern: many landlocked countries restrict trade in the very services that connect them with the rest of the world (Borchert et al., 2012). In particular, air-transport policies are significantly more restrictive on average in landlocked countries than elsewhere. The phenomenon is most starkly visible in Sub-Saharan Africa and is associated with lower levels of political accountability. This paper finds evidence that these policies lead to more concentrated market structures and more limited access to services than these countries would otherwise have, even after taking into account the influence of geography and incomes, and the possibility that policy is endogenous. In the aviation sectors, moving from an intermediate level of restrictiveness to an open regime could lead to a 25 percent increase in flight connections per airline.

> 13 Interestingly, when regressing transport prices on road condition, they found negative and significant effects in East Africa, but insignificant or positive effects in West and Central Africa (Table 4.3 p. 42), suggesting pricing formulas based on anticompetitive arrangements rather than marginal costs in those regions.

> 14 They collected data on costs (Vehicle Operating Costs (VOCs), transport costs incurred by transport providers) and prices paid by end users from a sample of trucking companies operating across the continent. They then simulated the effects of a reduction in: (i) fuel price; (ii) informal payments; (iii) reduction in border crossing time; (iv) rehabilitation of corridors. Their simulations showed that for West Africa (and to a lesser extent Central Africa), a reduction in fuel prices and a rehabilitation of roads would have no effect on prices paid by end users because of barriers to entry. By contrast in Eastern Africa, the same policies would reduce prices paid by end users.

15
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 13. Evolution of hidden costs associated with SONABEL**

||Energy||Implicit||Average|Total hidden|Total hidden|
|---|---|---|---|---|---|---|---|
||produced/purchased<br>(GWh/year)|System<br>losses (%)|collection ratio<br>(%)|Average total<br>cost ($/kWh)t|effective tariff<br>($/kWh)|costs ($ million/year)|costs (%<br>revenues)|
|2007|736|18|91|0.35|0.29|70|46|
|2008|755|16|91|0.35|0.31|57|37|

_Source:_ Derived from Eberhard and others (2008) and Briceno-Garmendia and Shkratan (2010). _Note:_ GWh = gigawatt-hour; kWh = kilowatt-hour.

While high by continental standards, SONABEL’s hidden costs look modest in the West African context, which says more about Burkina’s neighbors’ operational inefficiencies than about SONABEL’s efficiency. In any case, what is extremely relevant is that no expansion in access will succeed if measures are not taken beforehand to reduce these economic losses (figure 18).

**Figure 18. Hidden costs of power utilities in selected West African countries, as percentage of the revenues**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Liberia Niger Mali Ghana Cote d'Ivoire Benin Burkina Faso<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

Going forward, the high cost of power in Burkina is one of the country’s most salient challenges. The cost of power in Burkina—around $35 cents per kWh—is significantly higher than in similar countries (figure 19). This is because Burkina’s power costs are pegged to international oil prices, given its predominantly thermal-based generation capacity, and are prone to additional markups related to transport. Burkina has no known reserves of hydrocarbons (oil, gas, or coal) and relies on distant coastal ports (more than 1,000 km away) for fuel imports to meet both its transport and power generation needs, which are currently in excess of half a million tonnes annually.

This poses a long-term challenge, and fostering power trade in the medium run is the most practical solution to lowering costs. Yet, Burkina needs to eliminate distortions created by the existing subsidy mechanism to promote more efficient consumption patterns, harmonize with the WAPP, achieve regional integration, and thus increasingly add cheaper imported electricity to the consumed electricity mix. Unfortunately, even with the existing subsidies, power prices in Burkina are among the highest in Africa (figure 20).

30
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# Appendix A   Externality parameter

## **Externality elasticities by sector**

|Sector||Externality el<br>|asticities<br>||
|---|---|---|---|---|
||Nonproductive|Road|Electricity|Telecom|
|Crop agriculture|0.000|0.050|0.001|0.020|
|Export agriculture|0.000|0.075|0.015|0.025|
|Mining and gas|0.000|0.085|0.020|0.015|
|Industries|0.000|0.035|0.100|0.015|
|Construction|0.000|0.055|0.075|0.045|
|Private services|0.000|0.025|0.055|0.035|
|Public services|0.000|0.050|0.050|0.050|

27
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- 93 -

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Planning resource management in indigenous territories of Pacific tropical America: experiences of the binational Awa project in Ecuador and Columbia Plan Binacional Awa, Quito, Unpubl. 9p

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# Marshall, John 1984

- Death blow to the bushmen

Cult. Surv. Quart. 8 (1) 13-17

- Martin, Gary J 1988 Ethnobotanical studies, Oaxaca, Mexico Proj. Rep. WWF-US Unpubl. 16p

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- McNeely, Jeffrey and David C Pitt 1985 Culture and Conservation

- The Human Dimmension in Environmental Planning London; New York

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- Peru: Manu National Park and indigenous people endangered IWGIA Newsletter 37 126-135

# Nature Conservancy International 1988

- A cooperative project to establish the Mbaracayu Wildlands Area in Paraguay

Nature Cons. Washington Unpubl 6p

# Old Crow 1986

Old Crow, Yukon, a case study World Cons. Strat. Conf. Ottawa
# **Figure 11: Total number and percentage of ninis not searching for work in LAC by gender and age, 1992-2010**

Panel A: Percentage of ninis not searching for work in LAC by gender (15-18 years) Panel B: Number of ninis not searching for work in LAC by gender (15-18 years) <!-- Start of picture text --> 87.6 87.4<br>83.2 85.1 84.8 4.5 4.5 4.4 4.3 4.4<br>81.1<br>79.7<br>76.9 77.5<br>74.0 3.5 3.5 3.3<br>69.7 3.1 3.0<br>63.3 63.5<br>60.6<br>55.0 1.4<br>1.0 1.0 1.1 1.2<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Panel C: Percentage of ninis not searching Panel D: Number of ninis not searching for<br>for work in LAC by gender (19-24 years) work in LAC by gender (19-24 years)<br>86.8 8.2 8.0 8.3 8.0 8.2<br>78.4 81.2 80.1 79.0 80.2 7.2 7.1 7.1 6.7 6.6<br>71.7 69.6 69.3 70.7<br>48.5 44.8 1.0 0.9 1.3 1.3 1.5<br>38.7 40.4 41.2<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>90.0 5.0<br>80.0 4.0<br>70.0 3.0<br>(Millions)<br>60.0 2.0<br>Ninis not searching for work (% relevant Ninis population) Ninis not searching for work<br>50.0 1.0<br>90.0<br>8.0<br>80.0<br>6.0<br>70.0<br>4.0<br>60.0 (Millions)<br>50.0 2.0<br>Ninis not searching for work (% relevant Ninis population) Ninis not searching for work<br>40.0 0.0<br><!-- End of picture text --> Panel D: Number of ninis not searching for work in LAC by gender (19-24 years) Source: authors’ own computation with data from CEDLAS and The World Bank (2015) _Household status:_ Figure 12 and Figure 13 present the breakdown of ninis who decide to start their own households (defined as those ninis who are either a household head, partner of a household head, or married or in a civil partnership), both overall and with children. In both cases, the patterns are similar for both age groups, with a much greater percentage of female ninis starting households and starting households with children. While for 19 to 24 year-olds the percentage of ninis—be they male or female—starting their own households with or without children has risen over the past 18 19
**Table 2: Comparing the SPI and the SCI**

||**SCIs**|**SPI**|
|---|---|---|
|_Coverage_|||
|Country coverage|146|174+|
|Time covered|2004-2019|2016 onwards|
|_Selected indicators_|||
|Numberof indicators|25|51|
|Pillars covered|Methodology; Source Data; Periodicity and<br>Timeliness|Data Use, Data Services, Data Products,<br>Data Sources, and Data Infrastructure|
|_Aggregation method_|Simple arithmetic average|Revised weighted average|
|_Operational relevance_|1) Track the strengths and weaknesses of<br>country statistical capacity overtime in a<br>cost-effective manner;<br>2) Track the progress and sustainability of<br>Bank-financed projects in statistical<br>capacity building;<br>3) With a focus on MDGs in Periodicity<br>section, the old SCIs provided a<br>monitoring tool for country MDGs data<br>production capacity.|1) Provide an objective, justifiable<br>assessment of country statistical<br>capacity over time with comprehensive,<br>up-to-date information;<br>2) Provide guidance to the WB teams in<br>assessing the progress and sustainability<br>of the Bank supported projects;<br>3) Inform Systematic Country Diagnostics;<br>4) Provide a monitoring tool for countries’<br>SDGs data production capacity.|
|_Limitations/Identified weaknesses_|1) Output focused, failing to address the<br>infrastructure/resource part of the NSOs;<br>2) Relative narrow scope of pillars and<br>indicators;<br>3) Unable to reflect the change of data<br>landscape and new data requirements<br>brought up by the SDGs.|1) Selection of indicators under the pillars<br>of data use and data infrastructure are at<br>an early stage of development.<br>2) In the data sources pillar, indicators on<br>administrative data and geospatial data<br>availability are not comprehensive.<br>3) Indicators under the data products Pillar<br>are constrained by the development<br>stage of SDGs indicators.<br>This leaves room for future<br>development ofthe SPIs structure.|

**Sources** : Adapted with modifications from Cameron et al. ( _2021_ ).

25
# d)

<!-- Start of picture text --> Livestock (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>Range of all sample paths<br>1.5 average<br>10% quantile<br>200 50% quantile<br>90% quantile<br>1.4 Stochastic-optimistic<br>Stochastic-pessimistic<br>100<br>1.3<br>0<br>1.2<br>1.1 -100<br>1 -200<br>Deterministic-baseline<br>0.9 Deterministic-optimisticDeterministic-pessimistic -300<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion tons million tons<br><!-- End of picture text -->

# e)

<!-- Start of picture text --> Timber (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>3.2<br>40<br>3<br>30<br>2.8 20<br>2.6 10<br>0<br>2.4<br>-10<br>2.2<br>-20<br>2 Range of all sample paths<br>-30 average<br>10% quantile<br>1.8 Deterministic-baseline -40 50% quantile90% quantile<br>Deterministic-optimistic Stochastic-optimistic<br>1.6 Deterministic-pessimistic -50 Stochastic-pessimistic<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>f)<br>Output of Ecosystem Services (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>3 Deterministic-baseline 20<br>Deterministic-optimistic<br>2.9 Deterministic-pessimistic 15<br>10<br>2.8<br>5<br>2.7 0<br>-5<br>2.6<br>-10<br>2.5 -15<br>-20 Range of all sample paths<br>2.4 average10% quantile<br>-25 50% quantile<br>90% quantile<br>2.3 -30 Stochastic-optimistic<br>Stochastic-pessimistic<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion tons million tons<br>billion USD<br>million of 2004 US dollars<br><!-- End of picture text --> Figure 4: Optimal Paths of Land-Based Goods and Services 22
**Table 1: Health indicators for Uganda and selected other African countries**

||**Infant**<br>**mortality**|<br>**Diarrhoea**<br>**prevalence**|**Nutrition**|**al status**|**Antenatal**<br>**care**|**Vaccina**|**tion coverage**<br>**For children**|**during first**<br>**aged 1 – 2 y**|**year of life**<br>**rs**|
|---|---|---|---|---|---|---|---|---|---|
|**Country/year**<br>|**< 1 year**|**< 6 months**|**HAZ**|**WHZ**||**BCG**|**DPT3**|**Polio3**|**Measles**|
|**Uganda**||||||||||
|Uganda 1988|98.3|26|39.4|1.2|76.1|41.4|20.6|21.3|24.4|
|Uganda 1995|81.3|17.7|35.8|5.4|81.6|79.4|54.5|52.6|45.2|
|Uganda 2000/01|88.3|17.8|34|5.1|83.8|75.0|42.0|49.6|42.3|
|**Other countries**||||||||||
|Burkina Faso 1992/93|93.7|15.7|30.6|16.7|57.1|80.2|32.6|32.8|37.1|
|Burkina Faso 1998/99|105.3|14.4|34.9|16.7|60.7|69.9|34.8|36.0|32.1|
|Cote d'Ivoire 1994|88.5|11.6|24.4|8.3|79.1|72.8|41.0|45.1|37.7|
|Cote d'Ivoire 98/99|112.2|10.3|21.3|9.6|78.5|82.0|54.9|54.6|51.3|
|Ghana 1988|77.2|16.3|31.1|7.2|55.7|33.5|19.1|18.4|19.4|
|Ghana 1993|66.4|14.2|25.9|11.3|59.2|80.8|57.1|57.3|50.6|
|Ghana 1998|56.7|13.6|19.2|13|62.8|85.9|67.6|67.1|60.9|
|Kenya 1989|60.7|17.1|-|-|48.9|58.0|52.7|54.3|41.7|
|Kenya 1993|61.7|13.5|29.0|4.6|71.0|94.7|85.4|84.1|76.3|
|Kenya 1998|73.7|11.9|33|6.1|64.1|94.0|76.3|71.6|70.7|
|Nigeria 1990|87.4|10.9|37.9|8.7|23.5|50.1|28.5|28.6|30.4|
|Nigeria 1999|75|8.2|45.5|12.4|38.9|52.0|24.8|23.0|32.1|
|Zimbabwe 1988|49.1|15.1|27.4|1.6|72.9|74.9|65.3|65.2|62.7|
|Zimbabwe 1994|52.8|13.8|21.4|5.5|72.2|94.6|80.4|80.3|73.9|
|Zimbabwe 1999|65|6.1|26.4|7.1|80.5|87.4|77.5|65.5|81.4|
|Sources: Own computa|tion from|various DHS|Surveys(w|ww.measu|redhs.com/da|ta/indicato|rs<br>).|||

Note: Infant mortality is measured in terms of deaths per 1,000 live births For nutritional status, HAZ refers to height-for-age (stunting) and WAZ to weight-for-age (wasting). In the different vaccinations, BCG implies vaccination against tuberculosis and DPT against three Diphtheria, Pertussis (whooping cough) and Tetanus. As for the latter as well as for Polio, three doses are required to ensure full coverage, we report only the share of children who received all three doses.

20