rafmacalaba/data-use-mentions-extended huggingface dataset:
9 households with access to a source of drinking water other than surface water, in households with some sanitation facility, particularly those with an improved sanitation facility. Children born in a health facility and children living in urban areas show significantly lower postneonatal mortality rates than their counterparts.<sup>7</sup> Neonatal mortality patterns show few significant differences using bivariate comparisons. Surprisingly, neonatal mortality rates are higher if the child was born in a health facility, and this difference is significant at the 1 percent level. This could reflect a causal effect, perhaps because of higher risk of infection due to exposure of the infant to patients in the facility. It is possible, however, that this represents at least in part a selection effect: women with higher risk pregnancies for which the newborn will face greater mortality risk may be more likely to seek birth in a health facility, and it is also possible that neonatal deaths are more likely to be reported if they take place within a facility.<sup>8</sup> With available data it is not possible to distinguish between these two hypotheses.

Next we analyze the correlates of postneonatal and infant mortality in a multivariate framework using a linear probability model. The dependent variable is a measure of mortality at the individual level, which takes value 1 if the child died during the postneonatal (or infant) period and value 0 if the child survived. Explanatory variables include variables measured at district, household, mother, and individual level. Most of the variables are measured at household level: urban/rural location, province, location in malaria risk zone, ITN ownership, access to drinking water and sanitation, and socioeconomic status as measured by a wealth index. Antenatal care variables (intake of iron supplement, intake of anti-malarial drugs, tetanus vaccination) and mother‘s educational level are measured for the mother.<sup>9</sup> Whether the child was born in a health facility is measured at individual level. Because vaccination records are
]|[0.393]||
|Girls|7.151|6.483|0.668|4.358|4.463|-0.105|10.000|8.538|1.462**|
||[0.349]|[0.268]||[0.450]|[0.361]||[0.550]|[0.421]||
|**Hours**|**Spent on**|**Household**|**Chores**|||||||
|All|11.526|11.447|0.079|11.733|11.428|0.305|11.315|11.467|-0.152|
||[0.365]|[0.248]||[0.525]|[0.353]||[0.435]|[0.328]||
|Boys|9.550|9.375|0.175|9.802|9.547|0.255|9.290|9.205|0.085|
||[0.407]|[0.297]||[0.590]|[0.447]||[0.482]|[0.400]||
|Girls|13.515|13.447|0.067|13.695|13.216|0.478|13.332|13.683|-0.351|
||[0.527]|[0.335]||[0.727]|[0.460]||[0.619]|[0.433]||

**Source:** Authors’ analysis based on an agricultural household survey conducted in 2016
rents tend to be higher in locations closer to the city core. More specifically, housing rents become on average 11 percent lower every 1 km away from the city core. This negative correlation between the distance from the city core and housing rents remains statistically significant even after controlling for the quality of dwelling characteristics and access to services (4 percent lower rents with every 1 km away from the CBD). This suggests that given the characteristics of dwelling and access to services, it still costs extra to live closer to the city core, where economic opportunities and urban amenities are concentrated.

Moreover, it is less expensive to live in neighborhoods that are precarious and/or are prone to flooding (even after controlling for dwelling and service quality), therefore attracting low-income households. As already seen, housing rents are clearly lower in precarious areas. This is partly because those housing units tend to be structured with low-quality materials and lack access to services. Even after discounting such low-quality aspects, housing rents are on average 20 percent lower in precarious areas, compared to non-precarious areas. There may be some other negative characteristics (that is, disamenities) in precarious neighborhoods. Nevertheless, such disamenities lower the rents in precarious areas and thereby attract low-income households. Similar discount in rents is found in neighborhoods that are prone to flooding (11 percent lower rents).

# **5. Conclusion**

Drawing on recently collected household survey data, this paper examines living conditions in Kinshasa, by focusing on its spatial variations. The key findings are summarized as follows. First, worse living conditions are endured by poor and/or less educated households in precarious and/or peri-urban neighborhoods. Second, inequality in access to water is driven by locational factors, while sanitation access depends on household characteristics, such as the education levels of household heads. Third, while there is a concern about negative externalities from Kinshasa’s extremely high population and built-up density, poor households benefit from density to some extent, as indicated by a higher
share, with an acceleration until the mid-1980s followed by a fall. Unlike in other nations, however, in the Brazilian case, this was accompanied by a U-shaped path of the share of services. The reason for this fall in the services share till the mid-1980s and later recovery was the unusual strength of the agriculture sector, whose share remained more of less stable around 11.6% until 1985, and then declined steadily reaching 5.5% in 2007.

Another important component of the model, the size of firms, has also changed substantially during our sample period in Brazil. The number of occupied people per establishment at the municipal level can be constructed from data on occupied individuals and number of establishments in each sector collected by IBGE and published by IPEA for the years 1970, 1975, 1980, 1985 and 1995. We added average numbers at the national level for 2006 in agriculture and for 2007 in the other two sectors, reflecting information obtained from the agricultural census and different surveys in manufacturing ( _Pesquisa Anual de Indústria_ ) and services ( _Pesquisa Anual de Serviços_ and the _Pesquisa Anual de Comércio_ ). These data sources include formal as well as informal employment.

Figure 2 plots the evolution of the mean number of employees per establishment in each sector. Starting from the late 1970s the agriculture sector becomes the one with the smallest average firm, followed by the services sector; the largest average establishment is located in the manufacturing sector. More specifically, the average for the period is 3.9 employees per plant in agriculture, 21.8 in industry, and 5.0 in services. That is, the average size of a firm in the services and the manufacturing sectors is 1.3 and 5.6 times larger than the one in agriculture, respectively. A closer examination of the chart also tells that over the period 1970 - 2007 the average size seems to evolve cyclically in the manufacturing sector, decline in agriculture and increase in services.

# **3.2 Econometric Analysis**
(1)-(4) are from baseline surveys conducted prior to the assignment of treatment status in each phase. Baseline covariates are missing for initial enrollees in Phase 1: any missing values are coded as 0, and a dummy indicating which observations have missing values as well as the interaction between that dummy and Private are included in the regressions. The bottom of the table reports the share of non-missing observations for which the covariate equals 1, as well as the p-value of the F-test for Private + PrivateXCovariate=0. Regressions build on the specification in Table 3, Col. (1). All regressions include day and worker fixed effects. Standard errors clustered by worker.

Due to the short length of the baseline phone surveys, we could not ask extensive questions about redistributive pressures or transfers.<sup>30</sup> However, we supplement this with another proxy for the severity of redistributive pressure: whether the individual reports making transfers to “acquaintances”, defined as individuals who the worker does not consider close family members or friends; such transfers are especially likely to reflect a social tax rather than altruism. In qualitative work, workers expressed particular frustration about such transfers. Among such individuals, gaining access to the Private accounts raises earnings by a striking 313 FCFA or 17.5% (Col. 2, _p_ =0.014). Among workers who do not report such extreme social taxation, the treatment effects are substantively smaller and not statistically significant at

> 30Specifically, among the questions in Figure 3, at baseline in both Phase 1 and Phase 2, we only asked the question in Panel D; this is the source of heterogeneity in Table 4, Col. (1).

28
providers, if fewer than 10 providers were available). Only staff on duty that day were included in the absenteeism measure. Staff who were away from the facility due to outreach or fieldwork were counted as present. Country-level averages for each assessment were calculated using country-specific sampling weights. This gives us a mean of the outcome in question that is representative of the country population.

# **_Estimates of care readiness_**

Combining data from the clinical vignettes (to measure knowledge) with data from the unannounced visit (to measure the extent to which providers are present in the facility at regular opening hours), and visual inspections (to quantify the availability of drugs and equipment), we estimate the probability that a facility is ready to provide care that meets minimum quality standards. A facility is defined as being ready to provide care that meets minimum quality standards for a given condition if there is at least one provider present in the clinic at a given day who can correctly diagnose and treat that condition, and the facility has a minimum set of drugs to treat it. (See Section S9 in the appendix for further details of the calculation.) The probability of care readiness that does not meet minimum quality standards represents the sum of two important quality gaps: the gap between what health workers are supposed to know and what health workers actually know, and the gap between what health workers know and what health workers can do (either due to absenteeism or a lack of essential drugs). By varying the underlying parameters used to construct the measure, we can assess the relative importance of these two gaps. This research does not examine an additional important gap, between what workers can do and what they actually do in practice. We report three estimates for care readiness for each condition. Our main estimate is based solely on the survey data. We then consider a hypothetical scenario in which all health workers are present: we replace the survey-based estimate of absenteeism at the facility level for a type of worker 8
# **I. Introduction**<sup>**2**</sup>

Investment plays a critical role for growth and social development. Investment in human capital and high-quality, sustainable infrastructure lays the foundation for output and productivity growth, provides basic services to households and market access for firms; enables sustainable urban development; and opens corridors of trade to link into the global economy (Global Infrastructure Facility 2015). Competitiveness rests on strong human capital and quality infrastructure (World Economic Forum 2016). Infrastructure mitigates the challenges to economic activity imposed by distance, helps integrate domestic markets, and established critical connections to international markets. Strong human capital increases productivity and earnings.

Investment growth in emerging market and developing economies (EMDEs) has slowed sharply since 2010. However, investment growth performance has varied significantly across different regions (Figure 1). The objective of this paper is to examine the regional dimensions of recent weakness in investment.<sup>3</sup> Specifically, the paper addresses the following three questions. First, how has investment growth in the six EMDE regions evolved? Second, what are the remaining investment needs across these regions? Third, which policies can help address investment needs?

Investment growth in EMDEs slowed from 10.2 percent in 2010 to 3.4 percent in 2015 and, based on partial data, likely slowed further in 2016. This slowdown exceeded 5 percentage points in almost half of EMDEs. In 2015, investment growth was below its long-term average in more than 60 percent of EMDEs and negative in about 30 percent of EMDEs.

Developments have varied widely across regions, however, reflecting the presence of commodity importers or exporters, the degree of political stability, and spillovers from key trading partners and investors. In the EMDE regions with a substantial number of commodity-importing economies—East Asia and Pacific (EAP), which accounted for one-quarter of global investment during 2010-15 and South Asia (SAR), which accounted for 4 percent—investment growth has been stronger
―use‖ of bank accounts (Danske Bank, 2011). Similarly, accounts might be used only to withdraw government transfers. In our sample, 33.8% of respondents report using a checking account in 2008 and 35% in 2009, with only 13 individuals (1.2%) adding an account in 2009 and no individuals closing an account (changes in financial usage between the two years are shown in Table 2).

We also have information on consumer credit received from a bank or other formal financial institution, including consumer debt, credit card debt, and mortgages.<sup>10</sup> In 2008, 18.1% of our sample received bank credit and in 2009, 17.7% did so. Table 2 shows that 12.2% of the sample (131 individuals) used bank credit in 2008 but not in 2009, while 11.8% of the sample (127 individuals) who did not have bank credit in 2008 did have it in 2009.

We measure the use of informal debt by defining a dummy that equals one when individuals respond ―yes‖ to the question ― _Do you currently have debt?”_ but do not report having any bank credit. In 2008, 17% of individuals in the sample report using informal sources of borrowing, while 12.9% of individuals used informal borrowing in 2009; 13% of the sample used informal borrowing only in 2008, and 9% used informal borrowing only in 2009. Note that informal borrowing typically involves shorter repayment periods than formal borrowing, along with higher interest rates, penalties, and other fees.

# **4.3** **_Capacity to Spend and Save_**

The next set of variables assesses respondent spending and saving capacity. The survey includes a self-reported measure of spending capacity. This is a categorical

> 10 Less than 5% of individuals have a mortgage or credit card.

13
annual production of crop _k_ (in kg).<sup>64</sup> The resulting estimates are displayed on Table III. The remaining Cobb-Douglas coefficients ( _γk_<sup>_l_,</sup><sup>_γ_</sup> _k_<sup>_n_) must add to (1</sup><sup>_−γ_</sup> _k_<sup>_f_) for each crop</sup><sup>_k_. We apportion this residual</sup> value to land and labor using data from Sri Lanka’s IO matrix on the labor share of value added: where _CEMPs_ is total employee compensation in sector _s_ , _V As_ is total value added (at factor cost prices) in sector _s_ , and _s_ ( _k_ ) is the sector to which crop _k_ belongs. The land coefficient _γk_<sup>_l_is then</sup> computed as: _γk_<sup>_l_= 1</sup><sup>_−γ_</sup> _k_<sup>_f−γ_</sup> _k_<sup>_n_.</sup>

# **VI.A.4 Land Distribution Parameters**

The model’s parameters that govern land distributions ( _{μi_ , _σLi_<sup>2</sup><sup>_}i_) are estimated by leveraging the</sup> properties of the log-normal. We use 2016 HIES data to estimate _σ_ ˆ _Li_ as the (weighted) standard

> 63 _<u>pRoW,k</u>_ As ratios _pColombo,k_<sup>and</sup><sup>_<u>pColombo</u>_</sup> _pRoW,k_<sup>_<u>,k</u>_</sup> are each other’s reciprocals, only one of them can be larger than unity.

> 64Fertilizer use _Mk_<sup>_f
mark>In the past decades, many studies have focused on understanding the economic impact of flooding on urban transportation networks using various types of methods, such as widely used input-output (I-O) models (</mark> T. J. Kim et al., 2002; Okuyama, 2007 <mark>), the dynamic REMI TranSight model by</mark> <u>Stewart (2006</u> <u><mark>), or the spatial</mark></u> <mark>computable general equilibrium (SCGE) model by T</mark> atano & Tsuchiya (2008 <mark>). Many of these models require high-quality traffic data, abundant travel supply and demand information and accurate hazard mapping, making it difficult to apply these methods in developing country cities where such information is scarce. This study overcomes these challenges and conducts a comprehensive assessment of the effect of flooding on transportation networks in Kinshasa, Democratic Republic of the Congo (DRC). This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers’ socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods. This study provides insights into the following research questions: 1) How does flooding impact multiple modes of transportation in Kinshasa in terms of headways, travel speed, travel itinerary, public transit service frequency, etc.? 2) How does flooding impact job accessibility and commuting times for different income groups and what are the corresponding opportunity costs? 3) Which road segments should be prioritized for flood protection and mitigation plans in terms of network criticality and vulnerability to pluvial and fluvial flooding?<
Policy Research Working Paper 10591

# **Abstract**

This paper reports the views and perceptions of randomly selected education policy makers in the East Asia Pacific region, based on surveys of 651 senior public officials in 14 middle-income countries. The findings show that officials tend to prioritize increasing secondary school completion over improving learning quality, and they severely underestimate learning poverty and do so by a larger margin than officials in other countries. Officials were most likely to cite system capacity as the primary constraint to improving learning. The findings show that officials’ support for gender equality and disability inclusion is high. Interviewed officials tend to oppose violence against students and prefer to invest in in-service teacher training or early-grade reading compared to other options, such as EdTech or inclusion for students with disabilities. This mix of alignment and misalignment between policy makers’ goals and the stated goals of development partners can inform future engagement in policy dialogue, analysis, and information campaigns.

This paper is a product of the Education Global Practice. 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 at nyarrow@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_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
|**GRACE**|no|The supply and demand is simulated by<br>nested CES functions.|GHGs include CO2, methane (CH4),<br>nitrous oxide (N2O), fluorinated gases<br>(FGAS)|
|---|---|---|---|
|**Ifs**|The capital/output (capital/production)<br>ratios for all fuel types decline over<br>time due to technological<br>improvements at rates determined by<br>two user controllableparameters.|Energy system cost minimization|Fossil fuel consumption generates carbon<br>emissions specific to coal, oil, and natural<br>gas use. Net deforestation generates<br>additional carbon emissions or removal.|
|**PHOENIX**|Electricity-generating technologies are<br>placed into three nests: base load,<br>intermediate load, and peak load|Energy system cost minimization|GHG and other pollutants|
|**WorldScan2**||Nested CES (constant elasticity of<br>substitution) function|GHG and other pollutants|

**Table 5.b Representation of renewable energy, substitutions and emissions for Partial Equilibrium**

|**Model**|**Renewables and Intermittency**|**Functional Structure for Energy**<br>**System Substitution**|**Emissions**|
|---|---|---|---|
|**DNE21+**|Electricity demand and supply are formulated<br>for 4 time periods; Useful capacity of wind and<br>solar power depends on time period and share<br>of wind and solar power is limited; Electricity<br>storage options can mitigate limitations|Bottom-up energy system model with<br>substitution calculated endogenously by<br>minimizing total energy system cost|GHG and other pollutants
- _Ic,_ is the index of complexity introduced byBlanchard and Kremer and defined as follows:

with _zy_ is the share of inputjin the production of good i"'; _- ds,p_ and _db,p_ are the distance of potential suppliers and buyers, respectively, for fins in industry Ilocated in province p, and are defined as follows": with:

- _S,1_<sup>is the share of input/used in the production of commnodity</sup> I;

- _Bj1_ is the share of output Ipurchase byindustryj;

- _Cj,_ is the concentration of industryjin province _h_ in 1990;

- _DPh_ is a dummy variable which takes value

- Opif and 13 are the same province;

- 1 ifp and _h_ are neighboring provinces;

- 2 otherwise.

16 This index was construtedusing the 1990 Russian a1l0-sectors" in,ut-output tables. 17 These indices were calculated using the 1990 Soviet input-output Tables.

13
, Malaysia, Sri Lanka, Uganda, and Vietnam. And three PASEC countries in Africa for which microdata have not yet been made available—the Democratic Republic of Congo, Madagascar, and Mali—were not included in the simulations. The main reason for not including NLAs in these simulations is the unavailability of public use microdata files or even grouped data required to run the simulation models.

The simulation results were rescaled to match our baseline values, given the countries listed above.<sup>47</sup> On average, our simulation baseline result was 1.4 percentage points higher, with the reported baseline of

> 43 World Bank 2019.

> 44 Quality is assessed in this context in terms of design, implementation, comparability, frequency, timeliness, documentation, and data access.

> 45 The number of countries with adequate learning assessments for the learning poverty indicator is considerably lower than this population-weighted figure.

> 46 This level of data scarcity is not unprecedented.  In 1981, when global monetary poverty was first reported at a global scale, only 55 percent of the global population was covered by household surveys, with several regions not even reaching the 40 percent reporting benchmark. For most of the 2000s, thanks to the impetus generated by the Millennium Development Goals process, this number has increased, remaining close to 90 percent. For more information see <u>http://iresearch.worldbank.org/PovcalNet/home.aspx.</u>

> 47 World Bank 2019.

22
production. There are five main types of cultivable land in Sierra Leone: (i) The Uplands account for 80 percent of arable lands, highly leached with low fertility status, suitable for a variety of food and cash crops; (ii) Inland Valley Swamps (IVS) account for nearly 12 percent of arable lands and are highly fertile from hillside runoff; (iii) Mangrove Swamps, which accounts for

> 10 (2015 Population and Housing Census)

10