rafmacalaba/datause-encoder-data huggingface dataset:
countries would not change classification immediately, but would only be placed in the new category for two or three years. But the biggest obstacle would appear to be that which affects all rankings: when one country becomes high income (for example), another country must become middle income. Some current users of the classification set policy on the basis of gaining a higher classification status; a ranking based on quartiles may be less fit for this particular purpose than the current methodology – though it can, of course, be argued that this use is not the intended analytic purpose of the income classification.

Another approach that would avoid these problems would be to rank countries by quartiles at a point in time (say for data relating to 2014, i.e. FY16), and set the upper bound of each group as the new graduation thresholds. Thresholds recalibrated in this way to the current period would initially include around 50 countries in each income category, and could then be updated for inflation every year. Changes in country classification would continue from 2016 as in the current system. Based on current data for the 2014 calendar year, the thresholds would need to be set around $2,000, $7,000 and $21,000.

As well as the analytical classification, the World Bank also classifies countries for operational lending purposes; this classification is published as part of Operational Policy 3.10,<sup>17</sup> and categorizes borrowing countries according to their lending eligibility: IDA, IBRD, and Blend. A further operational classification used by the World Bank is the list of countries in Fragile and Conflict Affected Situations.<sup>18</sup> Aggregates and groupings for these categories are already available in the _World Development Indicators_ database for analytic purposes and may be more appropriate for analyzing policy questions of interest, such as the multiple facets of fragility and resilience of countries. One option is to align the _income_ thresholds for low income and IDA graduation (US$1,045 and US$1,215 GNI per capita, as of July 1, 2015). At present, many within and outside the World Bank mistakenly assume them to be synonymous and this confusion can be avoided by linking them. It can also lend clarity to the Bank’s operational cutoff.

> 17 http://go.worldbank.org/2DXSXPUD80.

> 18 http://siteresources.worldbank.org/EXTLICUS/Resources/511777‐1269623894864/FY15FragileSituationList.pdf.

29
By including household fixed effects, we effectively purge from our estimates time-invariant differences between treated and non-treated groups that could confound PEP’s fertility effects. In addition, by flexibly accounting for non-parametric temporal changes in a rich set of pre-migration household characteristics, we address dynamic differences between eligible and ineligible migrants. As such, _β_ 1 measures fertility changes among treated migrant households relative to non-treated migrant households, from before to after PEP’s rollout.<sup>9</sup> Specifically, we gauge the impact of regularization on the probability of having children less than one, one, two, or three years old in 2020 and 2021. Since the amnesty was announced in July 2018 and registration did not open until one month later, changes in fertility behaviors induced by the policy would only be observed during or after 2019. In 2020 and 2021, we should be able to observe changes in the likelihood of having children less than one, one, and two years old. However, we should not be able to observe changes in the likelihood of having children three years old. We will consider the likelihood of such an event to be a falsification test.

# **VI PEP’S FERTILITY IMPACTS**

Table 2 illustrates the results of estimating equation (1) in three panels. Panel A shows results using the data from baseline and 2020 (the first wave of VenRePS). Panel B presents results using the data from baseline and 2021 (the second wave of VenRePS). Finally, panel C shows results stacking the three periods of data: (i) baseline data from before PEP, which relies on recall questions; (ii) the first survey wave (2020); and (iii) the second survey wave (2021). Each column corresponds to a different regression evaluating the effects of PEP eligibility on the probability of having children less than one year old (column 1), one year old (column 2), two years old (column 3), and three years old (column 4).

We find consistent evidence that PEP eligibility lowered the probability of having children in all panels. Our preferred results are those in panel C, as they include all data waves.

9Since PEP take-up rates were close to 94 percent, the derived Intent-to-Treat (ITT) estimates should not be very different from the Average Treatment Effects (ATE).

11
# SECTOR ORGANIZATION, GOVERNANCE, AND THE INEFFICIENCY OF AFRICAN WATER UTILITIES<sup>1</sup>

Antonio Estache* _The World Bank Institute,_ and ECARES, Universite Libre de Bruxelles Eugene Kouassi

> _Universite de Codody,_ and _Resource Economics -_ West Virginia University

Keywords: Water utilities, unbalanced panel data, efficiency analysis, stochastic frontier models.

JEL Classification: C22, E23, E27 'We are grateful to I. Alikhani, A. Locussol, S. Perelman, L. Trujillo and all the participants to various seminars on regulation in West Africa for helpful discussions and suggestions.

> * Direct correspondence to: Antonio Estache, the World Bank Institute, WBI, 1818 H St NW, Washington, DC 20433., USA. Tel. (202) 458-1442 Fax. (202) 676-9874 Email: Aestache@worldbank.org.
# Discriminatory Laws Against Women: A Survey of the Literature<sup>1</sup>

Sanchari Roy

## **JEL classification** : J13, J16, J31, K36, K38, P48

**Key words** : gender discrimination; women’s legal rights; mobility, assets, labour market; entrepreneurship, marriage, childcare, pensions

> 1 I thanks Asif Mohammed Islam for helpful feedback during the preparation of this paper. Sanchari Roy: email: <u>sanchari.roy@kcl.ac.uk; address: Department of International Development, King’s College London, Strand,</u> London WC2R 2LS, United Kingdom.
Table S.3: Selective Attrition - Fitzgerald et al (1998) method with OLS

|_Dependent_|_variable: _|_Probability_|_to be trac_|_ed in 2010_|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log total consumption|0.001|||||-0.073|
||(0.015)|||||(0.072)|
|Log consumption expenditures||-0.009||||0.006|
|||(0.014)||||(0.057)|
|Log consumption from stocks|||0.017|||0.040|
||||(0.011)|||(0.030)|
|Log nonfood spending||||0.010||-0.001|
|||||(0.011)||(0.013)|
|Log tropical livestock units|||||0.113<sup>∗</sup>|0.061|
||||||(0.059)|(0.062)|
|CNDD+factions (_S _<sup>_CNDD_+</sup><br>_i_<br>)||||||-0.005<sup>∗∗</sup>|
|||||||(0.002)|
|FNL factions (_S _<sup>_FNL_</sup><br>_i_<br>)||||||0.002|
|||||||(0.002)|
|Violence||||||-0.005|
|||||||(0.011)|
|Sex Head||||||-0.032|
|||||||(0.048)|
|Age Head||||||0.000|
|||||||(0.001)|
|Married||||||0.149|
|||||||(0.112)|
|Divorced||||||0.010|
|||||||(0.186)|
|Widow||||||0.071|
|||||||(0.123)|
|Primary School||||||-0.021|
|||||||(0.029)|
|Secondary School||||||0.015|
|||||||(0.036)|
|Small business||||||0.045|
|||||||(0.039)|
|Construction||||||0.023|
|||||||(0.051)|
|Extractive activity||||||0.049|
|||||||(0.103)|
|Public Sector||||||0.053|
|||||||(0.058)|
|Constant|0.825<sup>∗∗∗</sup>|0.912<sup>∗∗∗</sup>|0.708<sup>∗∗∗</sup>|0.749<sup>∗∗∗</sup>|0.825<sup>∗∗∗</sup>|0.737<sup>∗∗∗</sup>|
||(0.142)|(0.123)|(0.098)|(0.105)|(0.015)|(0.219)|
|Province FE|No|No|No|No|No|Yes|
|Observations|1227|1227|1123|1102|1232|1007|
|_R_<sup>2</sup>|0.000|0.001|0.003|0.001|0.004|0.030|

_Notes:_ Authors’ analysis based on data described in the text. The table reports OLS estimates. Selective attrition has been tested for the log of dependent variables in 2006. Consumption and spendings are per adult equivalent. Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 36
# **External loan disbusements as percent of GDP and the implied path of the NPV**

**of PPG external debt to exports; 2005-2016**

<!-- Start of picture text --> 18%<br>180% 16%<br>14%<br>150%<br>12%<br>120%<br>NPV of Debt to Exports - LHS 10%<br>Loan Disbursem ents to GDP - RHS<br>90% 8%<br>6%<br>60%<br>4%<br>30%<br>2%<br>0% 0%<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>NPV o f Debt to Exports<br>Loan Disbursements as % of GDP<br><!-- End of picture text --> Chart 2 presents the profile of external loan disbursements and the NPV of debt to exports from 2005 to 2016. As can be seen from the chart, once the projected increase in external loan disbursements during 2007-09 has been absorbed, there is very little scope left for any further increase in external borrowing without breaching the debt sustainability ceilings. The sustainable level of gross public external borrowing is only about 5% of GDP, and even this level of borrowing would be unsustainable if the country were to suffer a major shock to GDP growth or export growth. This is one of the key policy messages of this paper: the government cannot solve its budgetary resource constraints through higher external borrowing on a sustainable basis, even if external lenders are willing to extend it more credit in the medium term, because that will jeopardize long-term debt sustainability.

# _Summary of resource envelope projections_

The projected budgetary resources for primary expenditures are shown in table 3. We have not included privatization receipts, because these resources are too uncertain and are also not sustainable in the long term. Compared to 2006, when primary 25
to resources. To design the questionnaire, we built on the fact that consumptions common to various cells in the household appeared clearly defined, as well as the responsibilities for paying for those consumptions, while cells’ own resources turned out to be not entirely pooled within the households. We conceived the survey so as to capture this detailed information.

Consumption data are collected in four distinct parts: food taken at home; household common consumption expenditures, including consumption of home-produced food; cell specific expenditures (such as expenses for clothing of the cell members, mobile phone, transportation, or food taken outside the home); and finally expenses shared between several cells (but not common to the whole household). From this data, per capita consumption is computed at the cell level. Common consumption expenditures are attributed to cells in proportion to the share of household members they include. Food expenditures are compiled based on a detailed account of who shares which meal and how much money is specifically used to prepare the meal.<sup>7</sup> Each cell is ascribed its share in the food expenditures for the meals it joined in. For those meals, we have to assume food is shared evenly among participants. In fact, meals are often taken collectively from one common dish and individual food intakes are not observable. Though food expenditures for meals at home are often shared by the whole household, in 17% of households, subgroups emerge that take some or all of their meals separately, making room for unequal food consumption among household members. In addition, some members take parts of their meals outside the home. In any event, non-food expenditures naturally offer wider possibilities of divergence within households.

All the analyses presented in this paper exclude housing expenses. In this survey, only a very small share of the sample declares paying a rent for their dwelling. In fact, when everyone lives in adobe houses they built, as is the case in many parts of the country, the market price for the rental of such a dwelling simply doesn’t exist. In such a situation, it is hardly possible to use the data to impute rents to home owners. We also exclude health expenditures, as inter 9
# _4.2 What is the purpose of the GGIT Index?_

The index aims to assess the environmentally responsible use of computers and other ICT resources (Green Computing) by the IT departments of government institutions and other organizations. The index can facilitate comparisons between different organizations in their pursuit of environmental ITS sustainability, identify areas for potential improvement, set up the baselines and help interpret the trends within an organization over time and across organizations or sectors.

# _4.3 What are the desired characteristics of the index?_

Constructing a measure of Green Government ITC entails many choices that can appear to be subjective and unrelated to one another.<sup>3</sup> A set of desired characteristics for such an index provides the guiding principles that help make these choices explicit and organize them to obtain a relevant and practical measurement tool. Literature on measurement offers the standard set of such characteristics that we modify for the purposes of GGIT (e.g., Alkire et al. 2015).

_Simplicity_ : this characteristic speaks to the simplicity of a measurement approach and the ease with which it might be communicated. Unclear or overly complicated measures discourage IT management and staff participation in constructive policy discussions. Barriers to understanding an index can lead stakeholders to question its usefulness as a policy-relevant tool. On the other hand, a well-understood index allows stakeholders to evaluate for themselves its qualities. This index property also requires a clear link between the indicators used in an index and the underlying data. Transparency of the Green Government IT index and its components will facilitate its takeup by organizations, promote independent confirmation of its findings, and support the index’s credibility.

_Coherence_ : speaks to the authenticity of the index in capturing the phenomenon it aims to measure. One should be able to describe in plain language the underlying concept of what is being measured by the index and articulate why this concept is a credible version of what needs to be measured. In our case, we measure the capability of government IT departments to use technologies and practices to reduce their resource consumption and GHG emissions.

> 3 This section draws on a discussion in Cameron et al. (2021).

10
# **Appendices**

_Table A1: Population share by displacement status and marital status of female headship (%)_

||IDPs/Re<br>De facto|fugees<br>De jure|Hosts/no<br>De facto|n-IDPs<br>De jure|
|---|---|---|---|---|
|Ethiopia|72.73|22.25|1.97|3.0%|
|N.E Nigeria|17.65|12.51|57.71|12.13|
|Somalia|29.71|7.75|49.00|13.55|
|South Sudan|9.39|6.66|56.91|27.05|
|Sudan|34.87|8.13|37.77|19.23|

_Table A2: Censored headcounts of each indicators in the MPI, by the gender of the household head in Ethiopia (2017)_

|Ethiopia|Male-heade|d HH<br>F|emale-heade|d HH|Difference|
|---|---|---|---|---|---|
||h0|SE|h0|SE||
|Years of Schooling|14.0|0.013|33.3|0.02|-19.2***|
|School attendance|10.9|0.011|12.7|0.01|-1.8|
|Food Security|14.2|0.013|49.4|0.04|-35.2***|
|Pregnancy care|1.8|0.006|5.2|0.01|-3.4***|
|Physical safety|4.3|0.008|41.8|0.04|-37.5***|
|Early marriage|4.1|0.008|47.6|0.02|-43.5***|
|Waste Disposal|8.1|0.011|47.4|0.03|-39.3***|
|Drinking water|2.0|0.004|7.2|0.01|-5.2***|
|Electricity|14.4|0.013|46.3|0.04|-31.9***|
|Cooking fuel|17.0|0.014|55.7|0.03|-38.7***|
|Housing|14.0|0.013|53.3|0.04|-39.3***|
|Sanitation|13.5|0.013|29.6|0.04|-16.1***|
|Unemployment|5.9|0.010|15.4|0.03|-9.5***|
|Legal id|4.6|0.008|40.4|0.05|-35.8***|
|Bank account|16.8|0.014|55.6|0.03|-38.7***|

Notes: h0 is the proportion of male- and female-headed households deprived in various indicators, SE- standard errors. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

_Table A3: Censored headcounts of each indicators in the MPI, by the gender of the household head in Nigeria (2018)_

|Nigeria|Male-heade|dHH|Female-head|edHH|Difference|
|---|---|---|---|---|---|
||h0|SE|h0|SE||
|Years of Schooling|17.7|0.042|12.5|0.016|5.2|
|School attendance|19.7|0.055|13.6|0.021|6.1|
|Food Security|20.0|0.053|13.9|0.023|6.1|
|<br>Pregnancy care|9.1|0.024|5.5|0.028|3.7|
|<br>Physical safety|1.1|0.004|1.1|0.006|0.0|
|<br>Early marriage|7.8|0.036|10.9|0.051|-3.2|

34
4efil 4 en~ U~e reita onslnp be~e~ii kxb **i** ecig. **L** Os . **o,i** ond outcomes in the health sector are XsM, ; ,sgL¢s, ~xielu4g.xi~ in l,'g i ;wctlv1ty~ or--n technology development in **[er tj,ked** qear~tiuxe~ ,TC-c2"i _tvo years_ of chooling for ruraJ .farmers; SiS1htbk Rr~6~ X$ rc 1 < ~ .~ amaong trn exen}tfitry Ion vny f~s concgr rs [ of .i chie.nA & .a'.'09W i# i Iantbiology' Tecinolo were correlated ciffisior<sup>t6</sup> and'use<sup>more</sup> yu armqm mma M~tAfnGWeCg~an rtiand'ueelo tkdUe fi.'ig,isli4 ~litiftcY 0e pn'PORWlace as it 3 I., bd a-a contrys educatioa system, by ec4 am -- kno'wlbdgZ. d by ot t li hthinaun elenlent of caltural and fifnot allfctors at; u*nnbie tr inmprvement ugoa government 9>XtioSaS$l,g, ite,fitc,,omeA cv pJ,dc,idhpt $, u,it i-f }<xGwlOecg, anid a GNCr-cal level of _Energy:_ Despite its importance to economic growth and<sup>povertyalleviation,</sup> energy continues to be exploited in a short-sighted and unsustainable<sup>way.Unfortunately</sup> many countries are promoting fossil fuel energy policies<sup>andpracticesthatarecausing</sup> environmental degradation at the local (particulates and smog),<sup>regional (aciddeposition)</sup> and global (climate change) scales, leading to significant<sup>lossofhumanlifeand</sup> ecological damage. Currently, two billion people in the world<sup>are withoutelectricity.A</sup> central question to be answered in addressing the energy needs<sup>of thisone third of earth's</sup> population is-how do we adequately address the growing<sup>energy needsof theworld's</sup> population without exploiting the natural resource base<sup>andcompromisingthe</sup> environment? Modem clean, renewable energy technologies (e.g.,<sup>solar,wind,modem</sup> biomass) need to be developed further and there needs to be<sup>an increaseinthe efficiency</sup> and sustainability of energy use in transportation, industry,<sup>andhousing.</sup> Depending upon projections of population and economic growth, technology<sup>change and the growth</sup> rate of different information markets and governance structures,<sup>globalprimaryenergy</sup> use by 2100 is predicted to range from a low of 514 EJ to<sup>ahigh of 2,683 EJ,whileCO</sup> 2 emissions are predicted to increase to between 2.7 Gt and 36.7<sup>Gt [IPCC2000]</sup><sup>**8**</sup> <!-- Start of picture text -->
| Figuire 3: lProjectedl C2 Ems2Bs!ons<br>30 - | | W Umts meAIFI<br>20 35- - AlT<br>15 -.- _ - A2<br>10 - Bl<br>5 ~ ~ ~ ~ ~ ~ ~ ~ -=B2<br>0 Units measured<br>1990 2020 2050 2100 in GtC/yr.<br>Source. IPCC<br>[2000]<br><!-- End of picture text -->

8 Emissions scenarios coded in Figure 3 correlate with the six illustrative scenarios used<sup>bythe IPCC in</sup> their 2000 publication _Emissions Scenarios_ See publication for description of<sup>scenanocharacteristics.</sup> 8
What do these results mean? The magnitude of the coefficient seems to be large if we take into consideration the elasticity of growth per capita with respect to changes in the share of the working age population. However, change in age structure is a low frequency process. For example, between 1950 and 2010 the average per capita growth in Brazil was about 2.77, while the share of working age population increased by 0.2 percentage point. Assuming a coefficient of 1.5, changes in the working-age population share would have contributed to about 0.3 percentage point, which is about 11 percent of the average growth observed over this period.

Another issue regarding the share of working age population is that it might be driven by changes in the share of children or elderly in the total population. The descriptive statistics suggest that the rising share of the working age population is driven by a decrease in the share of children in most of countries. So, using a similar specification with share of children instead of working-age population, negative and significant coefficients should be expected, as presented in Table 7.

**Table 7 Effects of changes on the share of children (0-14) on the population on real GDP per capita growth**

||Fist-Differ<br>|ence<br>||Panel Fixe<br>|d-effects<br>||Generalize<br>|d Method o<br>|f Moments<br>|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of CHD|-0.384***|-0.351***|-0.387***|-0.304***|-0.253***|-0.338***|-0.506**|-0.431***|-0.428***|
||(0.108)|(0.0963)|(0.0814)|(0.100)|(0.0892)|(0.0727)|(0.197)|(0.159)|(0.123)|
|Initial GDP-pc||-0.506***|-0.536**||-2.342***|-2.260***||-0.0973|-0.705|
|||(0.177)|(0.217)||(0.401)|(0.506)||(0.536)|(0.576)|
|Schooling (years)|||0.569**|||-0.201|||0.932*|
||||(0.257)|||(0.546)|||(0.550)|
|British colony|||0.349||||||1.035|
||||(0.306)||||||(1.483)|
|French colony|||-0.0618||||||-1.737|
||||(0.312)||||||(1.521)|
|Non-colony|||-0.234||||||-0.333|
||||(0.308)||||||(0.577)|
|Landlocked|||0.328||||||-0.778|
||||(0.315)||||||(1.376)|
|Latitude|||-0.000910||||||0.00459|
||||(0.0122)||||||(0.0168)|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Authors’ estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

17
Differences in the level of labor productivity between SMEs and large firms widen the gap significantly at the mean and/or some of the quantiles in SSA (by 14.5 to 49.4 percent of the total gap) and LAC (by 20 to 236.5 percent), but not in ECA and MENA. “Structurally”, labor productivity significantly widens the gap in all the regions at one or more quantiles. While the impact varies substantially across the quantiles within a region, when significant, it is quite large as a proportion of the total gap. For instance, in SSA, the significant “structural” effect occurs at the 40<sup>th</sup> to 70<sup>th</sup> quantiles, which widen the gap by 261 to 355 percent, and in MENA by 635 to 2,826 percent between the 60<sup>th</sup> and 90<sup>th</sup> quantiles.

Recall that in the full sample, the skills obstacle significantly narrowed the gap “structurally” by at most 26 percent of the total gap at some of the quantiles. This overall impact is somewhat muted likely because of contrasting effects at the regional level. Focusing on the statistically significant “structural” effects at the various quantiles, the skills obstacle narrows the gap in SSA by 33 to 34 percent of the total gap, in LAC by 41 percent or more, and in ECA by 20 to 136 percent. In contrast, in MENA, it significantly widens the gap by 685 percent at the 90<sup>th</sup> quantile.

In the full sample, we found that government support did not reduce the gap between SMEs and large firms. In fact, government support widened the gap significantly at some quantiles (by 14.3 percent “structurally” at the 30<sup>th</sup> quantile). A qualitatively similar result obtains in ECA, LAC, and SSA. However, in MENA, government support significantly narrows the gap “structurally” and via the “endowment” effect. For example, “structurally”, the gap is smaller at the mean by 21.3 percent of the total gap and at several quantiles by 40.5 to 67.1 percent. Thus, MENA stands out as the only region where government support helps SMEs more than large firms. This is an 35