rafmacalaba/datause-encoder-data huggingface dataset:
43 undertaken on the household side, using similar information from the Global Findex database (Demirguc-Kunt and Klapper, 2013).

# **7. Egypt – Applying macro and micro data to identify a financial system’s position**<sup>**17**</sup>

Macro and micro data can give different and sometimes contradictory insights, as we will highlight in the following for the case of Egypt.

# **7.1. Background**

Egypt has undergone significant financial sector reform in the mid-2000s that aimed at making the financial system not only more stable but also more efficient. The reforms included privatization of one of the four commercial state-owned banks and financial, operational and institutional restructuring of the remaining three government-owned banks. The banking sector experienced a consolidation process driven by higher minimum capital requirement and by the exit of several weak banks, with the number of banks dropping from 57 banks in 2004 to 29 in 2010. Bank supervision has undergone significant changes, moving from a compliance-based toward a risk-based system. As a result of the reform program, Egypt’s financial system transformed itself over the past seven years, becoming more stable, mostly due to addressing loan losses in state-owned banks, increase in provisioning and capital and the aforementioned increase in minimum capital. There has also been progress in the financial infrastructure, most notably through the establishment of the credit bureau I- Score as well as improvements in the payment system. In 2007, a second-tier market – Nilex - was established by the government to offer funding to SMEs by offering relaxed listing rules.

> 17 This section relates to early work by the two authors for an internal World Bank report on Egypt’s postrevolution challenges in the financial sector.

43
Table 4: Results by Sector

|DepVar: TFP Index|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Communications rest1|-4.606<br>(6.090)|||||
|Construction rest1||-7.871<br>(11.85)||||
|Distribution rest1|||-0.250<br>(0.486)|||
|Electricity rest1||||-12.36**<br>(4.893)||
|Transport rest1|||||-2.258**<br>(0.912)|
|Policy Restrictiveness in Manufacturing FDI|-0.187|-0.206|-0.167|-0.300|-0.328|
||(0.723)|(0.722)|(0.740)|(0.710)|(0.649)|
|Tariffs on Inputs (Effective)|-0.00707|-0.00705|-0.00683|-0.00695|-0.00885*|
||(0.00504)|(0.00506)|(0.00506)|(0.00506)|(0.00520)|
|% of Output by Foreign Firms in the Sector|-0.000264|-0.000300|-0.000305|-0.000240|-0.000279|
||(0.000388)|(0.000381)|(0.000402)|(0.000393)|(0.000385)|
|Output Tariffs (Effective)|-0.000905|-0.00100|-0.00105|-0.00138|-0.000960|
||(0.00354)|(0.00356)|(0.00350)|(0.00359)|(0.00356)|
|Constant|0.316***<br>(0.0310)|0.322***<br>(0.0340)|0.322***<br>(0.0325)|0.361***<br>(0.0352)|0.386***<br>(0.0400)|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Firm Fixed Effects<br>Standard Errors|Yes<br>Clustered|Yes<br>Clustered|Yes<br>Clustered|Yes<br>Clustered|Yes<br>Clustered|
||ISIC2/Year|ISIC2/Year|ISIC2/Year|ISIC2/Year|ISIC2/Year|
|Observations|40,208|40,208|40,208|40,208|40,208|
|R-squared|0.029|0.029|0.029|0.030|0.030|
|Number of id|25,425|25,425|25,425|25,425|25,425|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 27
SYS-REVIEW/HI-LMIC Reimbursement mechanisms may play a role; many families have little cash, are credit constrained (Pitt and Khandkar 1998) and cannot take on the financial burden at the point of care when reimbursement is often delayed (Shi et al _._ 2010).

From a governmental perspective, insurance allows a separation between the provision and the funding of care. Despite a public sector that offers care largely free of cost to recipients, the insurance system can take advantage of the pluralism in the supply of medical care that prevails in most low- and middle-income countries. The use of the private sector through government financing of health insurance would reduce the administrative burden within the allocation and subvention processes, the incidence of side payments and, perhaps, corruption.

When insurance is offered free, it often involves prescribed care and care givers. Some of these may be offered at the national level and some at the community level. Some community-level insurance with subsidized entry fees may have limited risk pooling because a specific community may be small. Most insurance that we examined only offered a set of well-defined interventions; thus, limited risk pooling at the community level may yield the same coverage as nationally sponsored insurance in terms of illness. Nationally sponsored insurance may allow for wider access to providers.

We examine studies of schemes that meet all of the following criteria:

- (1) Schemes that seek to offer financial protection for people facing health shocks to cover health care costs; these schemes involve some tax financing

- (2) Schemes that include a component in which poorer households can or must enroll through some formal mechanism at a rate much lower than the actuarial cost of the package or even free of charge; in return, these households receive a defined package of health care benefits

- (3) Schemes that are offered in one of two ways:

5
# **Overall effect**

The overall effect results from adding the expenditure and poverty line effects above together. If they both go in the same direction (basically reducing poverty), they reinforce each other and magnify the final impact on the poverty indexes. If they go in opposite directions, the direction of the net effect will depend on the difference between the two, and the magnitude of the overall effect will be smaller in absolute terms than either of the two effects individually.

In our case, the overall effect of using all components of FAFH on extreme poverty is always positive and significant. The poverty rate increases by 1.1 percentage points, a change that represents an 18 percent increase over the baseline benchmark results. The increase on the poverty gap and severity of poverty is even more pronounced, changing by about 25 percent. Extreme poverty increases because the impact of a higher poverty line outweighs the impact of higher expenditures, and it is driven by adult consumption away from home.

By contrast, the overall effect of all components of FAFH on moderate poverty is always negative and significant. The moderate poverty rate falls by almost 6 percentage points, a change that represents a 16 percent decrease over the benchmark. Similarly, the poverty gap and severity of poverty go down, by 20 and 22 percent respectively. Contrary to the case of extreme poverty, it is now the expenditure effect that outweighs the poverty line effect.

A second difference relative to extreme poverty is that now it is not the case that adult consumption explains virtually the totality of the effect. When analyzing the expenditure and poverty line effects individually, the absolute magnitude of the effects is always stronger when including adult consumption relative to either of the other two components. However, taken together that is no longer the case. The reason behind it is that the expenditure and poverty line effects move in opposite directions when we account for adult consumption. However, both effects move in the same direction when accounting for child consumption or take-out. Individually, each of the three components has a statistically significant impact on poverty and the three effects are of comparable magnitudes.

In sum, when looking at extreme (moderate) poverty, the inclusion of FAFH increases (decreases) the number of individuals who are poor, increases (decreases) the poverty gap among the poor and the poverty line, and increases (decreases) the severity of poverty among the poor.

21
> <sup>14</sup>

# **Measuring expressions of partisan preference**

The dependent variable, whether respondents express a partisan preference, is based on question 85. All respondents who answered question 85 with “do not feel close to any party”, refused to answer or did not know are coded as having no partisan preference.

Among these respondents, the vast majority answered “not close to any party”.<sup>5</sup> Rates of partisan indifference are high, by itself strong evidence that political parties across the 16 countries struggle to mobilize support with credible pre-electoral commitments to voters. Partisan indifference is near or above 50 percent in four of them and above 30 percent in eleven. While Eifert, et al. (forthcoming) find that elections matter for the salience of ethnicity, rates of partisan indifference appear to be high regardless of election proximity. In the six countries with elections in 2004, just before the surveys were conducted (Botswana, Ghana, Malawi, Mozambique, Namibia and South Africa), 31 percent of respondents did not express a partisan preference (Mozambique was lowest, with 18.1 percent). The competitiveness of these elections seems also not to have mattered. Botswana, Ghana and Malawi had the most competitive elections (the government party received 52 percent, 56 percent and 47 percent of the vote respectively), and exhibited rates of partisan indifference of 22, 34 and 40 percent, respectively. Rates of partisan preference are low even in countries where ethnic voting is notable. Although Kenya exhibits high rates of ethnic voting, between 35 and 40 percent of Kikuyu, Kalenjin and Luo express support for no party.

These numbers are high when compared to surveys undertaken by the American National Election Studies from 1952-2004, asking “Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what?” It is plausible that 5 For example, this is true for all 403 of the Ghanaian respondents coded as having no preference.
Moldova, though in Georgia more rural than urban residents reported getting medical care when needed.<sup>46</sup> A factor potentially reducing the effective access and use of medical services is the increased payments required. Moreover, in many countries health staff in secondary cities as well as rural areas have left the health facilities because of general deterioration in their incomes and working conditions. It is likely that the numerous economic and infrastructural problems in secondary cities have reduced much of the advantage in health access, quality and outcomes that they may have enjoyed. Figure 4.34 below indicates little difference across the settlement types regarding the percentage of households reporting that at least one member interrupted his/her activity due to health problems.

**Figure 4.34. Health outcomes** (% of households)

<!-- Start of picture text --> Capital Other urban Rural<br>25<br>20<br>15<br>10<br>5<br>0<br>Activities interrupted by health problems<br><!-- End of picture text --> Source: see Table 2.1 Note: average among 12 ECA countries.

**Education.** Access to education remains higher in urban areas than rural areas in the region.  In Albania (World Bank, 2003), the gap in enrolment rates between rural and urban areas was most evident in postprimary education, with less than 3 out of 10 secondary school-age children enrolled in rural areas compared to 7 in Tirana and 6 in other urban areas.  However, poverty assessments report that absenteeism and withdrawal of children from school for economic reasons is a growing issue in urban areas where poverty is a serious problem, such as in Armenia (particularly in the secondary cities).<sup>47</sup> In Tbilisi (Georgia), youths reported avoiding university classes because of fear of appearing poorly groomed or badly dressed.<sup>48</sup> In Albania, residents of peri-urban areas report serious inadequacies of access to health services and schools, as well as water and sanitation.  Concerns with physical insecurity deter female students.  In some of the newly formed peri-urban settlements illiteracy is becoming an emerging issue.  Overcrowding of schools is also becoming a problem in urban areas receiving large numbers of migrants from rural areas.<sup>49</sup> In short, conditions of educational access and quality are diverse and changeable within the countries of the region.

**Nutrition.** In several of the poverty assessments and qualitative poverty studies, poor households, especially poor urban households, report difficulties affording both food purchases and utility bills, and

> 46 “Tracking Non-Income Dimensions of Poverty in ECA”, PREM, HD Sector Unit, ECA Region, World Bank, April 7, 2003 draft,  table 2.4.

> 47 World Bank, “When Things Fall Apart: Qualitative Studies of Poverty in the Former Soviet Union”, p. 194. 48 Ibid, p. 217.

> 49 Ibid., p. 59-63.

55
from education, schooling is interacted with a dummy for schooling in the bottom twothirds across the sample. A dummy is included for commodity exporters during the period 2003-07. This dummy is intended to capture the impact of the exceptionally large commodity price boom that temporarily lifted commodity exporters’ growth during this period. Potential TFP is thus: + _α_ 5 Dcebi,t + _α_ 6 Δ invi,t + _ε_ i,t , where Δ tfpi,t is the logarithmic first difference of trend TFP, GDP per capitai,t is GDP per capita in percent of advanced-economy per capita GDP, wapi,t is the working-age share of the population, educationi,t is the percent share of the population who completed secondary school, Δ invi,t is the five-year moving average of real investment growth, Dedu is a dummy variable taking the value of 1 if the secondary completion rate is in the bottom two-thirds of the distribution, and Dcebi,t is a dummy variable for the period 2003-07 taking the value 1 if the country is a commodity exporter.<sup>18</sup> The data were compiled using a wide range of sources: UN Population Statistics (for population growth, the working-age share of the population); Barro and Lee (2013) (for secondary school completion); the World Development Indicators (for secondary school completion and GDP per capita relative to the advanced economies); and Haver Analytics (for investment).

The regression results are broadly in line with the previous literature (table 5). TFP growth slows as per capita incomes converge toward advanced-economy levels (Barro and Sala-i-Martin 1997). A better-educated population and accelerated investment growth are associated with higher TFP growth. However, the impact of education diminishes as education levels rise toward advanced-economy levels (Benhabib and Spiegel 1994, 2005; Coe, Helpman, and Hoffmaister 1997; Kato 2016). As a result, the coefficient on secondary school completion rates is only significant for countries with completion rates below the top third.

The results are broadly robust to a number of alternative specifications (tables 5 and 6). Two different methodologies are used to estimate trend TFP growth (a linear-quadratic trend and 3, 5, and 7-year moving averages) instead of the HP-filtered trend. The 3- and 7-year rolling averages of investment growth are used. In most specifications, the coefficient estimates remain significant and retain their signs; however, the working-age

> 18 This approach is similar to Abiad, Leigh, and Mody (2007) and Bijsterbosch and Kolasa (2010). Abiad, Leigh and Mody (2007) estimate five-year non-overlapping averages of TFP growth as a function of per capita GDP, schooling, population growth, trade openness and a nonlinear function of current account deficits and FDI for a sample of 22 European countries for 1975-2004. Bijsterbosch and Kolasa (2010) estimate five-year non-overlapping averages of labor productivity growth as a function of relative productivity levels (which here is proxied with relative per capita GDP), the share of high-skilled workers in employment, and investment in percent of value added for sectoral data for eight European countries for 1996-2005.

33
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

## **Achievements**

Benin has made important progress in reducing households’ reliance on surface water. At the national level use of surface water declined from 21 percent in 1996 to 13 percent in 2006, a level comparable to the average middle-income country in Sub-Saharan Africa (table 9).

Benin has managed to move its population slowly up the water and sanitation ladder by extending access to high-cost technologies. Access to piped water increased from 27 percent in 1996 to 33 percent in 2006, three times the average access to piped water in comparable low-income countries. Access to standposts almost doubled between 1996 and 2006 from 6 to 11 percent in 2008. On the sanitation side, access to septic tanks was almost nonexistent in 1996 but increased to 4 percent in 2006. Use of improved latrines increased from 18 percent in 1996 to 27 percent in 2006.

**Table 9. Benchmarking water supply and sanitation indicators**

||Unit|Low-income<br>countries|**Benin**||Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|||Mid-2000s|1996|2002<br>2006|Mid-2000s|
|Access to piped water|% pop|9|27|30<br>33|61|
|Access to stand posts|% pop|17|6|6<br>11|22|
|Access to wells/boreholes|% pop|39|46|47<br>43|5|
|Access to surface water|% pop|34|21|17<br>13|11|
|Access to septic tanks|% pop|5|0|3<br>4|48|
|Access to improved latrines|% pop|18|18|21<br>27|34|
|Access to traditional latrines|% pop|39|8|5<br>8|7|
|Open defecation|% pop|38|74|72<br>62|11|
||||2000|2005<br>2009||
|Domestic water consumption|liter/capita/day|72|24|22<br>24|166|
|Revenue collection|% sales|93|102|91<br>91|100|
|Distribution losses|% production|34|20|26<br>28|27|
|Cost recovery|% total costs|56|49|57<br>75|81|
|Operating cost recovery|% operating costs|65|69|80<br>105|145|
|Labor costs|connections per employee|159|158|209<br>207|369|
|Total hidden costs as % of revenue|%|163|139|58<br>43|140|
|||2005|2008|Non-scarce<br>water<br>resources|Other<br>developing<br>regions|
|Average effective tariff|U.S. cents per m3|60|7|1<br>60–121|3–60|

_Source_ : Demographic and Health Survey and AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Access figures from Demographic and Health Surveys (1996 and 2006) and census (2002). _Note_ : A country is considered non-water-scarce if its renewable water resource per capita is greater than 3,000 cubic meter per year. — = data not available.

These improvements represent progress toward the Millennium Development Goals for 2015 in both water supply and sanitation. Access to improved water increased from around 57 to 67 percent of the population between 1996 and 2006. Access to improved sanitation rose from 18 percent in 1996 to 30 23
b) Multilateral Creditors c) Commnercial Banks d) Other Private Creditors Prior to 1991 contractual obligations were broken down only into Official and Private Creditors.

Finance Credit Risk (FINCR) Intemal Records IBRD History of Nonaccrual.

Global Development Finance 1999: Analysis and Summary Tables Paris Club and Commercial Bank Rescheduling Agreements from Tables A3.2 and A2.3 respectively.

This source was supplemented by information from _The Institute of International Finance Surveys of Debt Restructuring by Commercial Banks/Official Creditors_ and the IMF reports on _Official Financingfor Developing Countries_ and _Private Market Financingfor developing countries._

# - PRS Intemational Country Risk Guide (ICRG) 1984-99

The stated aim of the PRS political risk rating is to provide a means of assessing the political stability of the countries covered by ICRG on a comparable basis. This is done by assigning risk points to a pre-set group of political risk components. Points are assigned by the ICRG editors on the basis of a series of pre-set questions for each component, to ensure consistency between countries and over time. The minimum number of points that can be assigned to each component is zero, while the maximum number of points depends on the fixed weight that component is given in the overall political risk assessment. In every case the lower the risk point total, the higher the risk, and the higher the risk point total the lower the risk. We use the total political risk index, representing the sum of all components, together with the individual components themselves. Data are reported on a monthly basis, and hence we use the monthly average over the year for a given series.

The components and weights are as follows:

|Component|Weipht|
|---|---|
|AGovernmentStability|12|
|BSocioeconomicConditions|12|
|CInvestment Profile|12|
|D InternalConflict|12|
|EExternalConflict|12|
|FCorruption|6|
|GMilitaryinPolitics|6|
|HReligioninPolitics|6|
|ILawand Order|6|
|JEthnic Tensions|6|
|KDemocratic Accountability|6|
|LBureaucracyQuality|4|

A brief description of each component is provided below:<sup>24</sup> _Government stability -_ This is a measure both of the government's ability to carry out its declared program(s), and its ability to stay in office.

_Socioeconomic conditions -_ This is an attempt to measure general public satisfaction, or dissatisfaction, with the govermment's economic policies. Socioeconomic conditions cover a broad spectrum of factors ranging from infant mortality and medical provision to housing and interest rates. PRS attempts to identify those factors that are important for the society in question, i.e. those with the greatest political impact, and assess the country on that basis.

24 Descriptions are taken from those given by PRS in their June 1999 ICRG.

32
terms of worse predictive power relying on this simple and transparent classification rule relative to more sophisticated alternatives such as the random forest classifier.

|**Annex Table 1: Pre**|**dictive Perf**<br><br>**Mode**|**ormanc**<br>**l 1 -- HS T**|**e of Clas**<br><br>**op 10 Va**|**sification R**<br><br>**riables**|**ules in the**<br><br>**Mod**|**Hegre-Sa**<br>**el 2 -- HS T**|**mbanis D**<br><br>**op 20 Vari**|**ataset**<br><br>**ables**|
|---|---|---|---|---|---|---|---|---|
||PC|RF|LC|TC|PC|RF|LC|TC|
|**Full Sample**|||||||||
|Type 1 Error|0.073|0.122|0.098|0.134|0.098|0.244|0.122|0.256|
|Type 2 Error|0.497|0.504|0.439|0.370|0.454|0.244|0.383|0.295|
|Objective Function|0.285|0.313|0.268|0.252|0.276|0.244|0.252|0.276|
|**Split Sample at 1980**<br>**In-Sample**|||||||||
|Type 1 Error|0.204|0.163|0.082|0.184|0.102|0.347|0.082|0.286|
|Type 2 Error|0.365|0.494|0.445|0.310|0.449|0.125|0.414|0.208|
|Objective Function|0.284|0.329|0.263|0.247|0.276|0.236|0.248|0.247|
|**Out-of-Sample**|||||||||
|Type 1 Error|0.333|0.061|0.121|0.303|0.102|0.333|0.082|0.286|
|Type 2 Error|0.410|0.628|0.549|0.368|0.449|0.261|0.414|0.208|
|Objective Function|0.371|0.344|0.335|0.336|0.276|0.297|0.248|0.247|
|**Split Sample at 1990**|||||||||
|**In-Sample**|||||||||
|Type 1 Error|0.197|0.409|0.076|0.167|0.091|0.152|0.091|0.227|
|Type 2 Error|0.380|0.242|0.458|0.320|0.434|0.305|0.396|0.354|
|Objective Function|0.288|0.325|0.267|0.243|0.262|0.228|0.243|0.291|
|**Out-of-Sample**|||||||||
|Type 1 Error|0.188|0.375|0.125|0.313|0.375|0.125|0.250|0.188|
|Type 2 Error|0.410|0.306|0.528|0.419|0.376|0.520|0.371|0.358|
|Objective Function|0.299|0.325|0.327|0.366|0.375|0.322|0.311|0.273|
|Notes: This table reports the<br>function,for in-sample and o|Type 1 and T<br>ut-of-sample|ype 2 erro<br> predictive|r rates tog<br> performa|ether with th<br>nce,splitting|e minimized v<br>the sample at|alue of the<br>the indicat|prediction<br>edyear.|loss|

35