rafmacalaba/datause-encoder-data huggingface dataset:
# 3. **Aggregate Governance** Inidicators

In this section, we organize a subset of the govemance indicators into six clusters corresponding to six basic aspects of governance, and describe how these indicators can be combined into aggregate governance indicators. In Appendix 2, we present six tables which identify the specific components of each governance cluster with reference to the tables describing sources in Appendix 1. The aggregate governance indicators described here are based on data for 1997 and 1998.8 We stress at the outset that the classification of indicators into clusters is not intended to be definitive. Rather, it reflects our own views of what constitutes a useful and interesting organization of the data that is consistent with prevailing notions of governance.<sup>9</sup>

# **Governance Clusters**

The first two governance clusters are intended to capture the first part of our definition of governance: the process by which those in authority and selected and replaced. We refer to the first of these as "Voice and Accountability", and include in it a number of indicators measuring various aspects of the political process, civil liberties and political rights. These indicators measure the extent to which citizens of a country are able to participate in the selection of governments. We also include in this category three indicators measuring the independence of the media, which serves an important role in holding monitoring those in authority and holding them accountable for their actions.

The second governance cluster is labelled "Political Instability and Violence". In this index we combine several indicators which measure perceptions of the likelihood that the government in power will be destabilized or overthrown by possibly unconstitutional and/or violent means. This index captures the idea that the quality of governance in a country is ccmpromised by the likelihood of wrenching changes in 8 As we expand the govemance database, we hope to construct comparable aggregates for other time periods, enabling comparisons over time as well as across countries.

9Users of the govemance database may question both the classification of individual indicators into these six clusters, and also the six categories themselves. Users are therefore welcomed to use individual indicators and construct aggregate indicators that are most useful to their own analytic purposes 7
# **D Normality of regression residuals**

In discussing the estimation results of section 4.1, we observed that regression residuals violated a skewness-kurtosis test for a normal distribution, but argued that this departure from normality would not have significant effects on coefficient estimates and inference. This appendix addresses that argument in more detail. First, we offer a general discussion of the importance of the normality assumption for the OLS and GLS estimators. Second, we present kernel density plots of regression residuals to illustrate the apparent departures from normality and discuss other model specifications that explicitly address this issue.

## **D.1 Significance of the normality assumption**

The assumption of normally distributed regression residuals is one of the pillars of the classical regression model since it allows the exact identification of the distribution of the least squares estimator and several test statistics. On the other hand, normal distribution of disturbances is only a special case of a much larger class of applications where normality is likely to be violated, such as panel data and time series models (Greene, 2000). Fortunately, most of the familiar properties of the LS estimator and relevant test statistics can be derived asymptotically, without relying on the normality assumption.<sup>45</sup> Before moving to large sample results, it is worth noting that even in finite samples, normality is not necessary to establish that least squares is a minimum variance linear unbiased estimator, which is a direct consequence of the Gauss-Markov theorem. However, the assumption of normal disturbances is needed to derive the exact distributions of _t_ and _F_ statistics, and normality or other specific distributional assumptions are required to extend the Gauss-Markov result to _all_ estimators rather than just the linear category.

It can be easily shown that the LS estimator is asymptotically consistent and normally distributed. These properties do not depend on the normality of residuals; they are a direct consequence of the Lindber-Feller central limit theorem and only require that the disturbances be independent and uncorrelated. In addition, the sample variance is an asymptotically consistent

> 45The following two paragraphs rely heavily on chapter 9 of Greene (2000).

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# 1. Introduction

Despite rapid increases in access to health services over the past two decades, maternal and child mortality remain high in many settings. Improving health outcomes requires not only that the subpopulations that most urgently need care get it, but also adequate quality of care (Das and Hammer 2014; Kruk et al. 2018). The Democratic Republic of Congo (DRC) almost perfectly illustrates the often weak link between health service utilization and health outcomes: although more than 80 percent of women receive antenatal care and deliver in facilities (Demographic and Health Survey 2014), the country remains among the ten with the highest maternal and infant mortality rates globally.<sup>4</sup> Equally striking is the steep socioeconomic gradient in health outcomes: under‐5 mortality in the bottom quintile is 54 percent higher than in the top quintile.<sup>5</sup> Although gaps in utilization between the poor and rich exist, they are substantially smaller in magnitude relative to the gaps in health outcomes.<sup>6</sup> This suggests that the poor do not receive the same quality of services or these services do not correspond as well to their needs. This paper focuses on the first hypothesis. We use recently collected data on care seeking and treatment quality to assess the relationship between wealth and quality of health services in the context of antenatal care in the DRC.

There are several advantages to focusing on antenatal care in studying clinical quality. First, antenatal care reduces maternal and neonatal mortality (Adam et al. 2005; Hollowell et al. 2011). Second, the content and quality of antenatal care varies widely (Hodgins and D’Agostino 2014) and can be assessed objectively based on the essential procedures list outlined in the World Health Organization (WHO) guidelines and officially followed by all providers in the country. Most components of an antenatal care consultation are not dependent on the condition or medical history of the patient, facilitating the construction of a relatively large sample of comparable patient‐provider interactions, something that tends to be difficult for more complex treatments or consultations. We focus on five main components of antenatal care: physical examinations, diagnostic testing, screening for danger signs, preventive treatment, and counseling. We measure quality as the proportion of these WHO‐recommended

> 4 Maternal mortality is 693 per 100,000 live births and infant mortality is 70 per 1,000 births

> 5 Under‐5 mortality was 117 per 1,000 in the bottom quintile and 76 per 1,000 in the top quintile. Gaps were even larger for undernutrition, with underweight (BMI < 18.5) rates of 18.2 percent in the lowest quintile and 7.4 percent in the top quintile.

> 6 For example, coverage of antenatal care is 78 percent in the lowest wealth quintile compared with 96 percent in the highest quintile.

2
|**Table**|**1. SDG indi**|**cators**|**related to gender (N=181 countries)**||||
|---|---|---|---|---|---|---|
||||||**Sex**||
|**Goal**|**Indicator**|**Tier**|**Description**|**Sub-Indicator (if any)**|**Disaggregation**<br>**Available**|**Any Data**<br>**Available**|
|5|5.1.1|2|Whether or not legal frameworks are in place<br>to promote, enforce and monitor equality and<br>non‐discrimination on the basis of sex|Any data: Legal frameworks that promote,<br>enforce and monitor gender equality<br>(percentage of achievement, 0 – 100) – Area 1:<br>overarching legal frameworks and public life;<br>AND/OR Legal frameworks that promote,<br>enforce and monitor gender equality<br>(percentage of achievement, 0 – 100) – Area 2:<br>violence against women; AND/OR Legal<br>frameworks that promote, enforce and monitor<br>gender equality (percentage of achievement, 0 –<br>100) – Area 3: AND/OR employment and<br>economic benefits; AND/OR Legal frameworks<br>that promote, enforce and monitor gender<br>equality (percentage of achievement, 0 – 100) –<br>Area 4: marriage and family|NA|52.5%|
||5.2.1|1|Proportion of ever-partnered women and girls<br>aged 15 years and older subjected to physical,<br>sexual or psychological violence by a current or<br>former intimate partner in the previous 12<br>months, by form of violence and by age||NA|81.8%|
||||5.2.2 Proportion of women and girls aged<br>15 years and older subjected to sexual violence||||
||5.2.2|2|by persons other than an intimate partner in the<br>previous 12 months, by age and place of<br>occurrence||NA|0.0%|
||5.3.1|1|Proportion of women aged 20-24 years who<br>were married or in a union before age 15 and<br>before age 18|Any data: Proportion of women aged 20-24<br>years who were married or in a union before<br>age 18 (%); AND/OR Proportion of women<br>aged 20-24 years who were married or in a<br>union before age 15 (%)|NA|47.0%|

16
indicators that—if superficially treated with casual thoughts—can interfere with monitoring progress toward the SDGs. We do not claim that we could offer answers to the all challenges that we identify, but we strive to make a contribution to refining the SDG indicators and advance their monitoring. We add to the nascent, but growing literature on measurement issues with the SDGs, by discussing jointly the various identification, interpretation, and data challenges with the SDGs.<sup>4</sup> In addition, we also offer a technical review of common approaches to measuring country progress, most of which have been applied to the MDGs.

Our findings suggest that several SDG indicators could be refined in terms of their wording, and their underlying objectives could be further clarified. We also point out potential pitfalls in interpreting progress on the SDGs, since different evaluation methods can lead to different conclusions. Furthermore, our review of the UN’s SDG database also illuminates the overwhelming challenge with missing data. Even when we consider the relatively more comprehensive data from the most recent five years, there are data only for just over half of all the SDG indicators, and just 19 percent of the data needed to thoroughly track the progress across countries and over time. We subsequently propose some simple but cost-effective solutions to these challenges.

This paper consists of five sections. We provide a brief introduction to the SDGs in the next section before discussing measurement challenges in Section III, which range from identification of appropriate indicators to interpretation of their progress, and various related data challenges. We subsequently offer in Section IV further reflections, as well as some cost-effective solutions; here we also briefly discuss how international organizations can help address these challenges. We

> 4 Earlier studies that discuss measurement issues include, for example, MacFeely (2018), Fukuda-Parr and McNeill (2019), and Ordaz (2019).

4
|First stage<br>(1)<br>Table 7: Alternative definition of dependent variable, IV estimates for swi|(2)<br>tchers|
|---|---|
|Dependent variable: Export (=1 if yes)||
|Industry-specific trade-weighted log real exchange rate<br>0.004***<br>(0.001)|0.004***<br>(0.001)|
|F-statistic<br>11.312|11.171|
|Second stage||
|A: Dependent variable: share of workers receiving technical upgrading||
|Export (=1 if yes)<br>0.080**<br>(0.036)|0.083**<br>(0.037)|
|B: Dependent variable: share of workers receving training in other categories||
|Export (=1 if yes)<br>0.021<br>(0.027)|0.025<br>(0.028)|
|Firm effects<br>Yes|Yes|
|Firm controls<br>No|Yes|
|Observations<br>29,347<br>Notes: Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05,<br>regressions include year effects. Firms controls are: log employment; average age <br>average tenure; male to female workers ratio; share of workers with less than <br>completed; share of workers with exactly high school completed; share of workers wit<br>high school completed; average log wage; share of white workers; and share of worke<br>skill levels 1 to 4.|29,347<br> * p<0.1. All<br> of workers;<br> high school<br>h more than<br>rs in each of|
One goal of SP is to assure some minimum standard of living in the society, as typically indicated by a poverty line. Naturally, national SP efforts depend on prevailing national poverty lines rather than international absolute lines, which aim to have common real value across countries (such as the World Bank’s $1.90 a day line). This motivates our next hypothesis:

**_Distribution 3: The relative poverty hypothesis._** _Higher national poverty lines in rich countries imply that more public SP spending is needed to reduce poverty._ In assessing the _a priori_ case for this hypothesis it must first be recognized that there are two, potentially offsetting, ways in which a higher average income will alter the cost of reducing poverty using SP spending. Poverty lines are typically relative in rich countries, meaning that they rise in proportion to the mean or median income; low- and middle-income countries, by contrast, tend to have absolute or only weakly relative poverty lines, meaning that they have an elasticity to the mean that is less than unity (Ravallion et al. 2009). On the other hand, the national poverty gap index (PG) using weakly relative poverty measures is likely to be a decreasing function of mean income.<sup>14</sup> It is then an empirical question whether the aggregate poverty gap per capita (PG times the national poverty line) is increasing or decreasing with mean income. If the (positive) elasticity of the national poverty line to mean income exceeds (in absolute value) the negative elasticity of the national poverty gap index with respect to mean income, then the cost of targeted efforts in using SP to reduce poverty will tend to be higher in rich countries.

To test this we can draw on estimates from the literature of these two elasticities. <u>Ravallion (2016b, Appendix) estimates the cross-country elasticity of the national poverty line to</u> the mean to be 0.52 (robust s.e.=0.04; n=598).<sup>15</sup> Using the estimates of the relative poverty gap index at the country level (calibrated to predicted national poverty lines) obtained by Ravallion <u>and Chen (2019), and also allowing for country fixed effects, we obtain an elasticity with respect</u> to the mean income or consumption of -0.56 (s.e.=0.08; n=144). Thus, the two effects of higher average income on the national poverty gap per capita (PG times the poverty line)—one on the — national poverty line and one on the PG itself essentially cancel out, suggesting that the income elasticity of the cost of eliminating poverty is likely to be close to zero.

> 14 PG is the aggregate proportionate distance below the poverty line (expressed as a proportion of the line and counted as zero for those above the line) per capita of the total population.

> 15 This is based on the compilation of national poverty lines across countries and over time from Jolliffe and Prydz <u>(2016)). Ravallion (2016b, Appendix) regressed the log of the national poverty line on the log of mean income</u> including country fixed effects.

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Figure F.1: Employment Weighted Size Distribution of Entrants: Model and Data <!-- Start of picture text --> 1<br>Calib<br>0.9 Data<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1−4 5−9 10−19 20−49 50−99 100−249 250−499<br><!-- End of picture text --> Note: The data corresponds to the employment-weighted distribution of rm sizes from the Business Dynamics Statistics database for the manufacturing sector in 2007. The unit of analysis are rms, and entrants are identied as rms with age equal to zero. Data points for rms with employment greater than 499 are undisclosed in the database.

The gure shows that while the Pareto distribution tracks closely the empirical distribution of entrants, it slightly under-predicts the shares towards the right tail of the distribution. There are two features of the equilibrium that are aected by the properties of the size distribution of entrants: the dynamics of employment over the life-cycle and the speed of convergence along transitional dynamics. Since large rms innovate more intensively, a smaller share at the top decreases the speed of employment growth over the life-cycle conditional on survival, and delays the speed of convergence. We experimented with a Log-Normal distribution, and found that aggregate and micro-level implications are largely unaected once parameter values are re-calibrated to satisfy the empirical targets, specially the ones referring to the average size of entrants relative to incumbents and the employment ratio between 21-25 and 1 year old rms.

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# **5 Results**

## **5.1 Refugee Presence Improves Local Public Goods**

<!-- Start of picture text --> Public Pri School Access Private Pri School Access<br>0.279<br>0.4<br>0.175 0.204<br>0.3<br>0.113<br>0.101<br>0.2<br>0.1 −0.036 0.02 −0.008<br>−0.097 −0.113<br>0.0<br>−0.1<br>−0.2<br>Baseline Presence x Presence x Presence x Presence x Baseline Presence x Presence x Presence x Presence x<br>Presence 2006 2011 2016 2020 Presence 2006 2011 2016 2020<br>Public Sec School Access Private Sec School Access<br>0.4<br>0.3<br>0.2<br>0.1 0.037 0.051<br>0.012 0.015 0.017<br>−0.017 −0.015 −0.037 −0.031 −0.037<br>0.0<br>−0.1<br>−0.2<br>Baseline Presence x Presence x Presence x Presence x Baseline Presence x Presence x Presence x Presence x<br>Presence 2006 2011 2016 2020 Presence 2006 2011 2016 2020<br>Health Access Health Utilization Road Density<br>0.113<br>0.15 0.093<br>0.075<br>0.081<br>0.10 0.046 0.052 0.043 0.053<br>0.05<br>−0.018<br>0.00<br>−0.05 −0.103<br>−0.10<br>Baseline Presence x Presence x Presence x Baseline Presence x Presence x Baseline Presence x Presence x<br>Presence 2011 2016 2020 Presence 2011 2016 Presence 2016 2020<br>Estimate<br>Estimate<br><!-- End of picture text --> Figure 5: Effect of refugee presence on local development and public goods provision. The figure illustrates that parishes with higher levels of refugee presence experience improvements in all four development outcomes. The results are from OLS DiD models for parishes within 150km of any refuge settlement, interacting Nearest + 20km Refugee Presence (time varying) with year, interacting controls with year, including year and regional fixed effects. Standard errors are clustered at the parish level. Estimates include 95% CIs.

19
**Figure 1. Share of Agricultural Income (excluding own consumption) for Urban.**

<!-- Start of picture text --> 20%<br>18%<br>16%<br>14%<br>12%<br>10% 1993<br>8% 1998<br>6%<br>4%<br>2%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br><!-- End of picture text --> Food items constitute a big share of expenditures for both rural (63%) and urban (58%) households in 1998, (67% and 60% in 1993 respectively), with more than onethird of it spent on rice. In the poorest decile for rural (urban) households this share goes up to 72% (67%) with rice making up 41% (32%) of all expenditures (Figure 2). Similarly in 1993, for the poorest decile, the share of food in total expenditure is 75% in rural and 76% in urban, with rice expenditure constituting the majority, 43 % and 45% respectively (Figure 3). Since rice is such a key part of Vietnamese households, any policy that affects the price of rice significantly will have an impact on the welfare of the households. The expenditure on rice is mainly from own consumption among the poor in rural areas, whereas in urban areas share of own consumption is low even among the poor (5%). Expenditure on education and health makes up a smaller share for poorer households than the richer ones. This difference is more pronounced in rural areas, and it might be due to the fact that the poor are less likely to afford health care, and also less likely to send their children to school.

10
# **Revising the Roads Investment Strategy in Rural Areas**

# **An Application for Uganda**

Gaël Raballand Patricia Macchi Dino Merotto Carly Petracco<sup></sup> .

**Keywords:** road planning, Uganda, agricultural potential.

>  Gael Raballand, Africa Transport Unit, The World Bank, J7-701, 701 18th Street, NW, Washington DC, 20433, Ph: 1 202 458 0586, Fax: 1 202 473 8326, email: graballand@worldbank.org. The authors would like to thank Ephrem Asebe for excellent data collection, Emily Schmidt and Liang You for the GIS and AEZ analyses, John Hine for its inputs and comments and Declan Magee and Harry Hagan (Dfid) for their contribution.
less than 1,000 per year in 2010-2012 to reach a peak of 12,927 migrants (about 0.60 percent of the country’s population) in 2016, and 8,522 in 2017. These rates in 2016/17 were the highest among all African countries on a per-capita basis. This surge in migration rates appears to be due to two main factors. The first was political events in The Gambia, where there was an attempted coup in 2014, and the autocratic President Jammeh had the country leave The Commonwealth and cracked down on political opposition, with political and economic instability acting as push factors for migration. Second, instability in Libya made it easier for people to transit through Libya on the backway to Italy, instead of facing more border enforcement when attempting to go through Morocco to Spain.

At the time of designing our intervention, the risks of traveling the backway were increasing for several reasons. In addition to a worsening political situation in Libya, changes in European migration policies made it more dangerous to attempt the crossing, and less likely to get asylum status when crossing successfully. As a consequence, the Washington Post reports that the share of migrants leaving Libya who reached Europe fell from 78 percent in January 2017 to 45 percent in June 2018 (Harlan, 2018). On top of this, deportation rates from Europe increased dramatically. Our prior was that the relatively recent increase in this risk, even if it is large enough to be somewhat transmitted through migrant networks, combined with the longer-term tendency of migrants to over-inflate how successful they are abroad, may result in many Gambian youth having overly optimistic expectations about both the ease of migrating to Europe, and the conditions they will face once they get there. This overall context suggests a potential role for information interventions in providing accurate information on the risks and outcomes of irregular migration.

Figure 1 also shows that irregular migration from The Gambia (as recorded in the Frontex data) fell dramatically during the period in which we designed and implemented our interventions, and particularly during our follow-up period. This drop is driven by several factors. First, after the end of the Jammeh government in 2016, and the country transitioning from a dictatorship to a democratic government, European governments became much less likely to grant asylum status to Gambians on the basis of political repression, and increased deportations (along with providing more aid to projects in The Gambia designed to increase local opportunities). Then, in 2020, border from our baseline survey to show trends in survey reports over the 2012-2018 period track well those in the Frontex data.

10