rafmacalaba/datause-encoder-data huggingface dataset:
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Niger already spends a sizable amount ($225 million per year) to meet its infrastructure needs (table 18). About 77 percent of the total is allocated to capital expenditure and 23 percent to operating expenditures. The largest share of infrastructure spending in Niger goes to water and sanitation (30 percent), followed by the transport (28 percent), power (25 percent), ICT (14 percent), and irrigation (5 percent) sectors.

Niger’s existing spending amounts to around 6.7 percent of its GDP (figure 20). This is less than

**Figure 19. Niger’s infrastructure spending needs in the regional context**

As percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC-NoFragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Liberia<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Sierra Leone<br>Niger<br>Kenya<br>Mali<br>Ghana<br>Benin<br>Uganda<br>Cote d'Ivoire<br>Nigeria<br>South Africa<br>Cape Verde<br>0 10 20 30 40 50 60 70 80<br>Capex O&M % of GDP<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

the average for nonfragile, low-income comparator states. Relative to this peer group of low-income, nonfragile states, Niger spends less on power and ICT.

**Table 18. Financial flows to Niger’s infrastructure**

<u>$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|C<br>ODA|apital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|0|0|0|3|27|31|31|
|Irrigation|2|8|0|0|0|8|10|
|Power|30|11|0|14|0|25|56|
|Transport|8|16|30|9|0|54|62|
|WSS|11|28|23|1|4|56|66|
|Total|51|63|53|27|31|174|225|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology.

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Table 12: Medium-term impacts of _Mais Educação_ on educational outcomes for schools opting for the Portuguese activity

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.157|–4.370**|–2.955*|
||(0.829)|(1.764)|(1.666)|
|_R_ <sup>2</sup>|0.716|0.829|0.825|
|Observations|1,494|1,494|1,494|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|–0.200|–1.022|0.107|
||(1.350)|(2.223)|(2.461)|
|_R_ <sup>2</sup>|0.766|0.786|0.77|
|Observations|1,200|1,200|1,200|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined Portuguese activity in 2008 and remained in the activity until 2011. Pedagogical monitoring must be chosen by schools as one of the fields of knowledge, but schools can choose for which subjects they offer pedagogical support (Portuguese, mathematics, sciences, history, etc.) This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
**Table 1: Comparison of Fiscal and Business Cycle States**

||**Full Sample**|**Advanced**|**Developing**|
|---|---|---|---|
|**Relative frequency**<sup>**a**</sup>||||
|Strong fiscal and recessionary state|2.2|2.4|1.8|
|Weak fiscal and recessionarystate|2.1|2.4|3.0|
|**Test of differences**||||
|In means<sup>b</sup>|[52.3, 54.0]|[57.3, 57.9]|[43.4, 44.6]|
||<br>0.25|<br>0.76|<br>0.55|

_Note:_ The table shows the association (or lack thereof) between different fiscal positions and the recessionary state. a The top panel shows the relative frequency (percent of observations) of the strong fiscal position and the recessionary state, and that of weak fiscal position and the recessionary state. The frequencies are reported for the full sample and also for specific country groups: advanced and developing economies. The strong (weak) fiscal position corresponds to the 10<sup>th</sup> (90<sup>th</sup> ) percentile of debt-to-GDP ratio in each sample. The bottom panel reports results that show the statistical significance of the difference of those relative frequencies. The recessionary state is determined by the Harding-Pagan (2002) business cycle dating algorithm.

b The top entry shows the average debt-to-GDP ratio (in percent) during expansions (left) and recessions (right). The bottom entry shows the p-values of two-group _t-test_ of difference in means with unequal variances.

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**Figure 4: Employment transition matrices for men by employment status, formality and sectors between 2012 and 2018, by level of education**

|||Men||
|---|---|---|---|
||Private informal|**84**<br>5<br>2<br>4<br>5|75% (75%)|
||Private formal|57<br>**28**<br>10<br>3<br>2|9% (7%)|
|No formal<br>|Public formal|15<br>8<br>**73**<br>4|10% (10%)|
|education|Unemployed|76<br>2<br>6<br>**2**<br>14|2% (3%)|
||Out of labor force|74<br>1<br>5<br>1<br>**19**|5% (5%)|
||Private infor|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**75**<br>13<br>2<br>5<br>5|64% (62%)|
||Private formal|51<br>**36**<br>4<br>2<br>7|13% (15%)|
|Primary|Public formal|7<br>11<br>**78**<br>1<br>3|13% (13%)|
||Unemployed|72<br>4<br>5<br>**9**<br>10|2% (4%)|
||Out of labor force|61<br>13<br>7<br>2<br>**17**|8% (6%)|
||Private infor|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**75**<br>11<br>6<br>4<br>4|49% (55%)|
||Private formal|45<br>**38**<br>12<br>3<br>2|15% (16%)|
|Secondary|Public formal|8<br>8<br>**81**<br>3|19% (22%)|
||Unemployed|60<br>15<br>10<br>**7**<br>8|4% (3%)|
||Out of labor force|54<br>17<br>9<br>6<br>**14**|13% (5%)|
||Private infor<br>|mal<br>Private formal<br>Public formal<br>Un-employed|OLF|
||Private informal|**57**<br>20<br>14<br>4<br>5|26% (28%)|
||Private formal|31<br>**48**<br>16<br>3<br>2|21% (22%)|
|Post-|Public formal|6<br>7<br>**84**<br>0<br>3|41% (44%)|
|secondary|Unemployed|40<br>31<br>14<br>**8**<br>7|7% (3%)|
||Out of labor force|36<br>22<br>22<br>10<br>**10**|4% (4%)|
||Private info|rmal<br>Private formal<br>Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to men who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by educational levels. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

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Figure 2. Incidence of poverty in Vietnam in Rural-Urban areas <!-- Start of picture text --> 70<br>60<br>50 “ @ 1993<br>. 01998<br>40<br>29<br>30 25<br>20 18<br>9<br>a<br>0 a<br>Rural Urban Rural Urban<br>Total Poverty Line Food Poverty Line<br><!-- End of picture text --> Source: World Bank et al, 1999 Progress in social outcomes - Social indicators also improved markedly over the period, with literacy rates reaching 92 percent, and infant mortality rates falling to below 40 per thousand (Table 1). Much of this success is due to past commitment to the development of human capital backed up by investment in basic social services. A considerable part of section three examines past performance in a range of social indicators and assesses the challenges to future progress.

Geographical inequality has increased — During the years between 1993-1998 the Gini index remained basically unchanged. More disaggregate analyses show that the poor did better than the rich, in relative terms (Paul Glewwe and Hai Anh Dang Hoang, 2002). However, these national level findings mask rising inequalities associated with geography and ethnicity. The gap between rural and urban areas has grown during the 1990s, and inequality between regions has widened, which is related to the uneven pattern of growth across regions. Analysis presented in section three will frequently refer to regional, ethnic and gender-based inequality in a range of important social and poverty outcomes.

2
like malnutrition). With a binary outcome, like the variables being used in the present paper, the bounds of the concentration index are μ -1 and 1- μ , where μ is the mean of the outcome variable (Wagstaff 2005); thus as the mean increases, the range of possible values shrinks, tending to zero as the mean goes to one. Figure 6 shows the population mean and the concentration index for the earliest and latest surveys for all countries for all MDG indicators; see also column 6 of Table 5.<sup>8</sup> Four points are worth noting in Figures 5 and 6. First, reinforcing earlier studies, we find in both charts inequalities on all but one indicator to the disadvantage of the poor: the poor, on average, have higher rates than the better off on all the MDG health status indicators except HIV, and lower rates on the MDG intervention indicators. Second, for the intervention indicators, we see the ratios in Figure 5 getting closer to one and the concentration indices in Figure 6 approaching zero as the population average gets closer to one. All the charts in Figure 6 have something of a half-arrowhead appearance, reflecting the fact that the bounds shrink as the population mean rises towards one, and that inequality is almost everywhere pro-rich (so we see only the left or right of the arrowhead). Third, in the case of the intervention indicators the markers in both charts seem to be rising over time (population averages are improving), and closing in on the vertical line on the x-axis (inequalities are falling). For the malnutrition and child mortality indicators, we see the markers falling over time, but no clear pattern in terms of inequality. We come back to this in a moment. Fourth, inequalities favoring the better off are still very much in evidence even in the latest round of surveys.

> 8 Concentration indices were computed from the individual-level data not the quintile data using the convenient covariance method (see e.g. Jenkins 1988), except in the case of the mortality variables whose concentration indices were computed from quintile data (see e.g. Kakwani, Wagstaff and van Doorslaer 1997).

13
and after incidents but all within 1 kilometer from them– further support our findings. We also find that perceptions of police competence worsen, which could erode trust in formal institutions and ultimately hinder the government’s legitimacy and capability for implementing policies.

The results are in line with the disruption that could be expected from a terrorist attack against civilians. However, these findings cannot necessarily be extrapolated to other contexts or periods due to differences in the size, structure and operation of other criminal organizations. The stage and duration of the conflict could also lead to different results. Moreover, we only capture immediate impacts due to data limitations. Further research is needed to assess if the effect on consumption and poverty is transitory or permanent and if it varies depending on whether attacks are more common in certain regions, as well as to understand displacement and other longer term effects on welfare.

Nearly two-thirds of the world’s poor will be concentrated in conflict-affected countries by 2030.<sup>33</sup> Therefore, it is important to shed light and improve our understanding on the links between conflict and poverty. We contribute to the literature and policy debate by quantifying the impact on consumption and poverty, describing which households are affected by such incidents and the mechanisms through which this is likely to occur. A terrorist attack against civilians can lead to increases in poverty and vulnerability, among other adverse outcomes. In this context, policies could provide support to affected households through a combination of cash and in-kind food assistance to ameliorate the sharp decrease in consumption, mainly of food items. Beneficiaries can be identified using geographical targeting, covering those within 4 kilometers from the incident. Effective labor market interventions that support continuous employment could help by providing certainty and stability to households’ incomes. Nevertheless, accelerating poverty reduction will be challenging until security conditions improve. Al-Shabaab has filled a vacuum of political power and gained control over several towns and villages across Somalia. National and international efforts should prioritize achieving peace, which is a fundamental first step for increasing welfare conditions that will also bring other wider long-term benefits in Somalia.

> 33 World Bank (2020).

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**Table 2: Demographic characteristics of migrants: result from the census survey**

||**Internatio**<br>|**nal migrants**<br>|**Domest**<br>|**ic migrants**<br>|
|---|---|---|---|---|
||**Current**|**Past**|**Current**|**past**|
|Average age|31.35|40.33|25.42|33.81|
|Percentage male|90.64|95.20|77.54|75.96|
|Percentage who are head of<br>household|13.30|61.92|14.50|46.47|
|Percent who are spouse of head<br>of household|12.87|2.81|5.20|11.32|
|Percentage who are either<br>son/daughter of household head|51.08|24.50|56.24|34.29|
|Percent who are sibling of<br>household head|10.90|7.12|4.19|2.14|
|Percentage who are married|59.53|86.26|45.33|66.56|
|Average education level (in<br>years)|6.87|5.99|6.65|5.18|
|Average migration duration in<br>years||6.08||4.6|
|Key destinations||% of migrants||% of migrants|
|Saudi Arabia||42.13|||
|UAE||16.24|||
|UK||10.81|||
|Kuwait||10.02|||
|Dhaka||||60.5|
|Other urban centers||||17.53|
|Chittagong||||12.34|

25
|**Table 7. Marginal effects atpolicy-rel**|**evant cutoffs**||
|---|---|---|
||OLS|UQR|
||_Mean_|_Policy-relevant_<br>_cutoff_|
|LogReal Value of MonthlyPer Capita Food Consumption|-0.425***|-0.298***|
|Cutoff: Value = 687.13|[0.0364]|[0.0437]|
|LogDailyPer Capita Calorie Intake|-0.184***|-0.115***|
|Cutoff: Intake = 2,100|[0.0244]|[0.0279]|
|LogFood Consumption Score|||
|Mean|-0.189***||
||[0.0270]||
|Poor Diet Cutoff: Score= 28||-0.163***|
|||[0.0565]|
|Borderline Diet Cutoff: Score= 42||-0.256***|
|||[0.0518]|
|See Table 6 notes. Below the dependent variable, we list the p<br>marginal effect is evaluated using UQR.|olicy-relevant c|utoff at which the|

30
sirable. This is because anticipation of high taxes induces agents to exert low effort; as the result of this, the bulk of income heterogeneity in the distribution is due to luck, which in turn makes it optimal ex post to redistribute.

The 2006 World Development Report on _Equity and Development_ provides a rich account of how political, social, and economic inequalities combine to stifle social mobility and create inequality traps (World Bank, 2005). Furthermore, empirical evidence from around the world suggests that an important part of overall inequalities in outcomes, commonly measures in the space of incomes or household expenditures, is, in fact, due to inequality of opportunity, or inequality due to differences in circumstances for which an individual would not normally be held responsible, and thus inequality that is objectionable on normative grounds (Bourguignon et al., 2003; Roemer et al., 2003; Ooghe et al., 2007; Checchi and Peragine, 2010; Cogneau and , Mesple-Somps; Ferreira and Gignoux, 2008; Ferreira et al., 2008, 2010; Lefranc et al., 2008, Lefranc et al., 2009).

This paper aims to contribute to this literature, but its focus is somewhat different – it examines the link between _perceived_ inequality of opportunity and _perceived_ future mobility. I am not aware of other studies that examine directly the links between subjective perceptions of inequality and subjective expectations of mobility. Clearly, actual and perceived inequalities are correlated, although not perfectly. Figure 1 plots perceived inequality of opportunity against available estimates of relative inequality of opportunity from the Equalchances.org database. Likewise, actual and perceived mobility are also correlated, even if imperfectly, as theoretical models have demonstrated. This is also consistent with empirical evidence. Earlier studies have found actual occupational mobility to be an important predictor of perceived occupational mobility (Kelley and Kelley, 2009), and parental social class to be correlated with occupational aspirations of adolescents and with parental aspirations for their children (Schoon and Parsons, 2002; Guyon an Huillery, 2016). Those who experienced mobility personally tend to have more positive views about the chances of children born in low income families to have high incomes when they grow up (Alesina, Stancheva and Teso, 2016). Entrenched poverty has been shown to lead to depression (Case and 6
P a g e <u>|</u> **33**

productivity. This was the case in large cities of Asia like Hong Kong SAR, China, Bangkok and Seoul. All of these cities were able to accommodate a large influx of poor migrants for decades while the percentage of slums was decreasing. It is only when the supply of housing is quite flexible, as in the case of Chile, that targeted demand side subsidies can reduce the residual of very poor households who cannot afford a formal home (Bertaud: 2010).

- **D. Market Analysis and Its Consequences for Developing an Affordable Housing Policy in Ahmedabad and Mumbai**

Figure 6 in section B. sets out a framework for a strategy to make housing more affordable and improve living conditions on a large scale in urban India. In this section, we sketch out how this strategy could be applied using two city examples: Ahmedabad and Mumbai. Aside from illustrating how specific data on real estate policies and market outcomes can be used to formulate a strategy, these examples demonstrate how important it is to design policy based on each city’s unique real estate market conditions. National standards and templates offer blunt edged and potentially expensive or redundant tools for resolving the slum problem.

# **Understanding the level and distribution of household income**

The first task in defining a housing policy in a metropolitan area is to understand the parameters of the housing affordability problem in the city. Essentially, this involves measuring purchasing power in comparison to prevailing prices. This analysis must start by obtaining the distribution of income among slum dwellers and the population living in formal dwellings. The task is not easy because many slum dwellers work in the informal sector so their income is difficult to measure with accuracy and can be quite variable. However, in India there are many surveys on income and consumption of slum dwellers that have been conducted by universities and NGOs, so it should be possible for every major city to have a fair idea of income distribution within the slum community and how these incomes overlap with the rest of the population.

The distribution of income among urban households is indispensable to assessing housing policy. The distribution of income evolves constantly and in a different way in each city. Earlier, the establishment of the traditional income categories such as EWS, LIG, MIG etc. were an attempt to understand housing needs based on income. Unfortunately these categories have been used as “norms” to establish prospective benefits or even housing “rights” rather than to quantify demand and 33
Egypt region experienced the largest gains and jumped one place in the inter-regional ranking, and Lower Egypt reached levels of opportunity in education and nutrition more comparable to the Metropolitan region.

- The five most unequalizing circumstances are parents’ education, income per capita, urbanrural location, number of siblings, and regional location. These are at least twice as important as gender, presence of elderly family members, and presence of both parents in the household.

- The most unequalizing set of circumstances differs across sectors; therefore, policy makers should adjust targeting mechanisms accordingly.

- For nearly all opportunities (14 of 16), demographic circumstances considered jointly lead to greater inequality than location circumstances.

Four policy recommendations follow from these findings:

- The fact that the most unequal set of circumstances varies across sectors must be disseminated and discussed with government officials as a key reference to revise targeting designs _within each sector,_ in order to remove the barriers to access for children in the most unfavorable circumstances and improve equality of opportunity. Special attention should be paid to children with more than two or three unfavorable circumstances because the cumulative effects multiply the barriers to access.

- An agenda for reforming expensive and inequitable subsidies for food and fuel is clearly justified in order to promote equality of opportunity among Egyptian children. The relatively poor performance of the HOI nutrition indicators (non-stunting, 2-17, and nonwasting, 0-4), and the poor targeting of food subsidies are good reasons to reform those programs.

- Moreover, the substantial resources that could be saved in the fuel and food subsidy programs might be devoted to conditional cash transfer nutrition programs, which would compensate for the most unfavorable circumstances (income per capita and number of siblings) that have been found to prevent access to nutrition opportunities.

- Finally, to increase awareness of inequality of opportunities among stakeholders and policy makers, the computation of opportunity indicators should be updated and disaggregated. HOIs available by sub-region and municipality -using census data– would provide relevant information to sub-national governments to implement more equitable policies for human development.

30