rafmacalaba/datause-encoder-data huggingface dataset:
# **2.3 Evaluation of performance**

In order to tune the algorithms listed above, compare the different approaches for nowcasting poverty presented in Table 1, and report the final out-of-sample errors, we rely on nested 5-fold cross validation (Stone, 1974; Iizuka _et al._ , 2003; Varma and Simon, 2006). Intuitively, nested cross-validation works by iteratively the into three different a on splitting sample subsamples; training subsample which variants of a particular machine learning method are estimated, a testing subsample on which these variants are compared against each other and the best performer is selected, and a validation subsample which for each machine learning method is used to calculate the out-of-sample errors. The data points going into these three subsamples are reassigned multiple times to maximize the power of the data. Nested cross-validation is an extension of regular k-fold crossvalidation, which reduces a bias with the latter towards selecting a model with many tuning options and a downward bias in the final out-of-sample errors obtained. Appendix A contains a more thorough and technical discussion of nested cross-validation.

Once we are done with nested 5-fold cross validation, we will have one out-ofsample estimate of the poverty rate for each household survey (except for the earliest one for each country, as the methods that rely on changes since the last data point will not work in those cases), for each machine learning method, and for 1. each approach to estimating poverty from Table To evaluate the performance of the various methods, our primary loss function will be the mean absolute deviation between the predicted and true poverty rate. To assure that we do not select models that only work well for a few countries that happen to have many poverty estimates, we weigh each country by the inverse of number of surveys, such that the total weight for each country equals one.

We use the mean absolute deviation rather than the mean squared error since we are interested in minimizing the deviations between the true and predicted poverty rates while giving equal weight to all deviations. Using the mean squared error tends to give more weight to the prediction of outliers. We think this can be problematic since data incomparabilities can create some strong outliers at times, and we do not want to judge the methods by how well they predict these outliers. We focus on percentage point deviations rather than percentage deviations, as the latter sometimes can be very large for countries with low poverty rates. If a country has a poverty rate of 1% but our model predicts a poverty rate of 2% the error in percentage terms would be 100% which would give this observation a large impact. Our focus on percentage point deviations thus implicitly gives a 12
of the extra variation at the individual level and the better accuracy in measuring girls’ exposure to the program. The outcomes of interest at this level include relative changes between baseline (2003) and follow-up (2007-08) in partial measures of school attainment (middle school completion, and high school enrollment and completion), labor market participation, marriage, and fertility. The comparison is made between cohorts of girls with various levels of exposure to the program in stipend districts (treatment cohorts) and similar cohorts in non-stipend districts (control cohorts). Program impact estimates at the school and household levels should be interpreted as impacts on all eligible girls, whether or not they participated in the program (intent-to-treat impacts).<sup>18</sup> In what follows, both approaches are explained in more detail.

# **_School-level Analysis_**

The treatment and comparison cohorts of girls for this analysis are constructed synthetically based on their enrollment at different grades. The treatment cohorts were enrolled in public middle school in stipend districts; thus, they should have been exposed to the program for at least one year between baseline (2003-04) and subsequent academic years up to 2009-10. Based on these criteria, seven distinct cohorts can be identified (Table 4). The counterfactuals consist of the corresponding cohorts enrolled in public schools in non-stipend districts. For example, a cohort that consists of girls in grade 6 in stipend districts in 2004-05 is expected to progress to grade 7 through 10 in the succeeding years between 2005-06 and 2008-09. If their progress through school is consistent, they should have been exposed to the program for all three years of middle school (grades 6 to 8) between 2004-05 and 2006-07, and to the expansion of the stipends to girls in high school (grades 9 and 10) which started in 2006-07. Girls in the corresponding cohort but in non-stipend districts should not have been exposed.

The outcome of interest for this analysis is the percentage change in grade level enrollment (grades 6-10 each), which is estimated using the following specification:

> 18 In the context of the GSP, the intent-to-treat analysis measures changes in female enrollment of public schools in stipend districts relative to public schools in non-stipend districts even though not all girls in the stipend districts participated.

13
Data Description: Data Sources:

# Growth of GDP

# IHF-IFS

|CPI Inflation|Period averages|
|---|---|
|Curr.Acco.Deficit<br>to GDP ratio|Current Account Deficit as percentage<br>of GDP|
|Exports (GNFS)<br>to GDP ratio|: Exports of goodsand services as percentage of GDP|
|Growth of Exports<br>(real)|: Rate of growth of real exports|
|For.Debt Service<br>to Exports ratio|U.DebtT.<br>: Foreign Debt Service (interest<br>+ amortization)<br>as percentage ofexports|
|Foreign Debt<br>to Exports ratio|n<br>Public and publicly guaranteed|
|Reserves<br>to Imports ratio|IMF-IFS<br>Total reserves minus gold as percentage of annual imports|
|Real Exchange Rate<br>Index|Real, dollar weighted exchange rate index (annual averages)|
|Central<br>Government<br>Expenditure to GDP ratio|IMF-GFS|
|Central Government<br>Revenue to GDP ratio||
|Public Sec. Borrowing<br>Requirement to GDP ratio|The World Bank Country Study, 1990 and C.E.H., Oct.1991<br>: Public Sector Borrowing Requirement<br>to GDP ratio|
|Growth of Money (M2)|IHF-IFS|
|Central Government Wage<br>Bill to GDP ratio|IHF-GFS|
|Real Wage Index in Hanuf.<br>(cpi (IL) deflated, Index)|UNIDO<br>: Real wage bill per worker in manufacturing<br>sector .TL CPI deflated, index 1985=100)|
|Real Wage in Manuf.<br>(wpi (TL) deflated, index)|Real wag, bill per worker in manufacturing<br>sector (TL UPI deflated, index1985=100)|
|Dollar Wages in Hanuf.|Uage bill per worker in manufacturing<br>sector (in U.S. dollars).|
|Uneaployment rate|S.P.O. andS.l.S.|
education expenditure on the child, frequency of tutoring attendance, and expenditure and hours spent on tutoring—as well as with different specifications and definitions of family size.

Our results provide evidence that parents in Vietnam are indeed making a child qualityquantity tradeoff. The results suggest further that by lowering the relative cost of child quality and encouraging families to invest in quality, the availability of family planning services has increased investment in education in Vietnam. Finally, the analysis suggests that, compared with traditional indicators like enrolment, data on tutoring may be a more illuminating indicator of parents’ willingness to invest in the quality of education of their children. Indeed, the hypothesized quantity-quality tradeoff appears much more strongly in the tutoring-based measures than in the simple enrollment decision, which may be a coarser indicator of the household’s desire to invest in human capital. These results suggest the need for more research into these quality-oriented measures of schooling investment, with a view to providing better empirical evidence on the quantity-quality tradeoff in other settings.

35
- 14-

The general model for all-cause mortality, chosen by the author as most representative, is indicated the following:

# Daily mortality in Santa Clara = 0.0084 COH

# The standard error of the esEimated coefficient mas 0.0029.

To obtain the effect of a 10 _,gqmh_ change in PM1O on the percent change in mortality, several adjustments must be made. First, as discussed by dhe author, only readings from the central city monitor were used in the study. This monitor averaged about one-third higher concentrations than the metropolitan-wide average, a metric typically used in the other studies. The author also found that the COH to TSP ratio was at least one. Therefore, we assume tha PMIO is approximately .50*COH. Finally, the study population averaged about 20 deaths per day. Therefore, we adjust the coefficient by (413)(115)(1120) to obtain ?% change in morality = 0.112 1 change in PM10 This implies that a 10 ggm<sup>3</sup> change in PMIO results in a 1 12% change in mortliy. Applying dte standard error, we obtain a range of effect of 0.73 to 1.51 percent associated with a 10 lzgIm' change in PMIO.

_Ozkayzak and Thuwsron._ Additional evidence is provided by a study using cross-sectional data Ozkaynak and Thurstonr examined roughly 100 metropolitan areas in the United States using the 1980 vital statistics. This study controlled for socioeconomic characteristics and conducted additional sensitivity analysis to detemine the impact of certain cities and alternative model specifications. The authors found sutistically significant relationships between mortaliLy ra;es and alternative measures of ambient particulate matter including sulfates and fine particulates. Specifically, the study reports tah existing sulfate concentrations (mean of 11.1 _gImg)_ correspond to a 4 to 9 percent increase in mortality. Assuming a ratio of sulfates to PM10 of 0.25 as above. this suggests that a 10 gg/m3 change in PM1O corresponds to a 0.92 to 2.06 percent change in all-cause mortality, with a mean of 1.49 percent.
28 RENT CONTROL IN KUJMASI, GHANA

- (f) amount of rent for like premises,

- (g) current rate of interest charged by the Ghana Commercial Bank for overdrafts,

- (h) obligations of landlord, tenant or other interested parties under the lease,

- (i) justice and merits of each case.

These appear to be a reasonable basis for assessing standard rents for any premises and for differentiating between premises which represent different quantities of housing benefits. However, they contained no provisions for assessing the ability of tenants to afford rental payments, and only consider part of landlords' opportunity cost.

In addition to case by case assessment, the Minister has the power to assess rents for particular types of property and publish them in an Executive Instrument. This has been done from time to time for rooms occupied singly or in pairs with access to shared services. As rooms with shared services constitute the majority of all property in cities like Kumasi, rents assessed this way have been a dominant factor in rent control for 20 years. Rooms with only some services shared, or with none at all, are treated as if they had shared services. The levels set in 1973 are shown in Table 2.2.

Table 2.2: Prescribed rents for certain premises, 1973

|Premises (room|type and size)|Monthly rent payable|
|---|---|---|
|Sanderete|12ft x 12ft|C7.50|
|or landcrete|12ft x**lOft**<br>loft x lOft|C6.50<br>C5.50|
|Wooden, swish|12ft x**lOft**|C4.50|
|or iron sheet|lOft x loft|C3.50|

Source: Rent (Amendment) Decree, 1973 (NRCD 158), Schedule 1.

The rents handed down in this way have owed little to any of the matters listed above; they appear to have been arrived at more from considerations of what people can afford to pay than from any assessment of the property itself. It is noticeable that there is no mention in the schedules of whether the tenants actually have access to water supply, toilet or electricity in the house, how many others they share them with, or whether the roof is waterproof, a ceiling is present, or mosquito proofing is provided.

It is evident that, where rents are assessed individually or collectively, they tend to remain frozen until new legislation or assessments are made (United Nations, 1979). This has been true in practice, and indeed in intention, in Ghana at least until 1980 (see below for data on informal rent rises since that date). Part four of the Rent Act, 1963, states that no reassessment shall be entertained unless circumstances affecting the question of rent have materially altered since the last assessment, or that the previous assessment was made on erroneous evidence.
Some policy implications arise from this paper. In particular, additional reforms to raise EGX liquidity are worth considering. As some EGX-listed firms are still basically inactive, it makes sense to conduct ensure that EGX’s 2002 de-listing initiative is being effectively implemented and to consider additional measures to accelerate delisting of dormant or non-compliant securities. Given the importance of free float, EGX could consider raising the minimum public float for EGX-listed stocks to 15 percent or more. Lastly, short-selling preparations should be implemented.

# **2 Financial Performance of Egyptian Firms**

There is a clear stratification among Egypt’s firms in terms of financial performance and access to finance. A top tier of 31 firms shows strong performance during 2003-2007 relative to peers in other emerging markets. These firms have been able to sustain rapid sales growth, apparently through heavy reliance on short-term trade credits, some short-term debt, and operating cash flows (retained earnings). There has been some real decline in net fixed assets – presumably reflecting productivity gains, actual disinvestment, or some combination thereof. Additional reforms of Egypt’s equity market and institutional investor base may encourage a sustainable rise in stock market valuations, with resulting benefits for capital formation and economic growth. A larger second tier of 234 much-smaller, publicly-listed companies shows somewhat slower growth and lower profitability. Their access to long-term financing seems notably more constrained, which diminishes their future prospects for capital investment and growth.

Using the Worldscope Fundamentals database from Thomson Reuters, we first compare 31 Top Tier Egyptian companies with 948 similar companies from the following peer group of 11 countries: Colombia, Chile, Hungary, Indonesia, Israel, Jordan, Mexico, Morocco, Pakistan, Philippines, and Turkey (Exhibit 2.1). Because Egypt is a lower-middle income (LMI) country, we also compare the Top Tier with firms in the other LMI countries: Colombia, Indonesia, Jordan, Morocco, and Philippines. Given the focus on firms in the real economy, Top Tier companies only partially overlap with the CASE 30 Index, Egypt’s main stock market index (see Exhibit A.1). Using data from Egyptian data provider Coface, in the second part we compare 234 smaller Second Tier companies with the 31 Top Tier firms.

**<u>Exhibit 2.1 – Firm sample composition</u>**

|**Sector**|**Peer group**||**Egypt**|**Total**|
|---|---|---|---|---|
|||_Top tier_|_Second tier_||
|Construction and materials|178|8|48|234|
|Consumer staples|291|11|74|376|
|<br>Industrial cyclical|409|9|81|499|
|Travel & leisure|70|3|31|104|
|Total|948|31|234|1,213|

_Note: Peer group consists of companies in Colombia, Chile, Hungary,_ _Indonesia, Israel, Jordan, Mexico, Morocco, Pakistan, Philippines, and Turkey._ 4
# **3 Methods**

## **3.1 Econometric model**

We are interested in the predictive power of ENLACE test scores over future schooling and labor market outcomes. In particular, we are interested in the link between test scores and three outcome variables: future educational attainment, student achievement, and labor market outcomes including employment and wages. With this purpose in mind, we write the following model: in which _yi_ denotes an education or labor market outcome of individual _i_ , _enlacei_ is the individual’s ENLACE test score in Grade 6 as a predictor of future education outcomes or test scores at Grade 12 as a predictor for labor market outcomes or university enrollment. and _εi_ is a disturbance term. Our main parameter of interest is _β_ 1 _._ We use the ENLACE panel to study the relationship between Grade 6 test scores and future education outcomes. First we estimate the relationship between Grade 6 test scores and the probability of on-time graduation from lower- and upper-secondary school, proxied by observing student “i” sitting for ENLACE in Grades 9 and 12, respectively. We also use the ENLACE panel to examine the relationship between Grade 6 test scores and test scores three and six years later, conditional on sitting for the tests.

The ENILEMS-ENLACE panel is used to examine the relationship between Grade 12 test scores and university enrollment and, conditional on not being enrolled in university, labor market outcomes. The labor market outcomes included in our analysis are the probability of being employed–conditional on not being enrolled in college–, and, among those employed, the logarithm of the hourly wage, and the probability of having a formal job.

Learning achievement is measured using an aggregated ENLACE test score, the simple average of the math and Spanish scores, in our main specification. However, we also run math and Spanish test scores in separate regressions (the results are presented in the 12
<!-- Start of picture text --> Utility differential between subsidized insurance and zoning<br>* a TM 30<br>20<br>‘@ 40 0.1% | 0.8% | 1.8% | 2.9% | 4.2% | 5.7% | 7.7% 13.8% 37.3%<br>io 10<br>—><br>3 * — °<br>fe}<br>|<br>2<br>a -10<br>Ss 20 | 0.0% 0.5% 1.1% 1.9% 2.9% | 4.2% 5.9% 8.3% | 11.6% 35.3%<br>ro} -20<br>2<br>—<br>2 ° _— *<br>c<br>—<br>2 -40<br>MO 5] 0.1% | -1.4% | -2.6% | -3.5% | -4.2% | -4.5% | -4.4% | -3.5% | -1.4%<br>-50<br>1 10 20 30 40 50 60 70 80 90 99<br>Fraction of flood prone areas, (%)<br><!-- End of picture text -->
# **<u>Table 5: Probability of remarriage: All areas</u>**

||Probability of<br>remarriage for<br>widows|Probability of<br>remarriage for<br>divorcees|
|---|---|---|
|_Ref: dissolution after age 40._|||
|Dissolution before age 25|0.470<sup>***</sup>|0.371<sup>***</sup>|
||(0.055)|(0.070)|
|Dissolution between ages 25-39|0.291<sup>***</sup>|0.261<sup>***</sup>|
||(0.030)<br>|(0.072)<br>|
|Last dissolution; rural resident|0.082<sup>**</sup>|0.109<sup>**</sup>|
||(0.035)<br>|(0.054)|
|No children born from last union|-0.130<sup>*</sup>|0.065|
||(0.078)|(0.069)|
|Had a son at time of dissolution|-0.023|-0.104<sup>**</sup>|
||(0.036)|(0.052)|
|Number of marital dissolutions|-0.016|-0.019|
||(0.034)|(0.060)<br>|
|Ever been to a French school|0.011|-0.121<sup>**</sup>|
||(0.047)<br>|(0.051)|
|Polygamous father|0.098<sup>***</sup>|0.071|
||(0.037)<br>|(0.049)|
|Fostered before age 15|0.123<sup>**</sup><br>(0.048)|0.098<br>(0.068)|
|_Ref: Wolof/Lebou_|||
|Serere|0.048|0.011|
||(0.054)|(0.070)|
|Poular|0.019|-0.054|
||(0.043)|(0.059)|
|Other ethnicity|-0.043|-0.113|
||(0.045)|(0.076)|
|Mean of dep. var|0.27|0.59|
|N|488|353|
|Pseudo R2|0.258|0.146|

Note: Logit models, marginal effects shown. Samples of ever-widowed women (column 1) and ever-divorced women (column 2).<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Source: Authors’ estimations using PSF1.

36
SOUTHWEST ASIA

# 1. **zsti5ates** and Projections

|**_Yw_**|pr puAetin|rtlto|<br>P7riod|**Growth**<br>rate|<br>Cm|Cm|TF4|01<br>Note|F_L.|_in_|alOcG|
|---|---|---|---|---|---|---|---|---|---|---|---|
|19M|1147i|60.6|1985-89|2.83|36.7|8.8|5. 1|62.1|64.9|72|_lC_|
|1990|<br>132319|78.7|1990-94|2.75|35.'|8.0|4.75|63.6|66.5|6a|**s8**|
|1995<br>|<br>151556<br>|78.6|1995-99|2.57|32.6|7.1|4.35|65.2|68.2|55|74|
|2000<br>**2005**|<br>¶72678<br> <br>194627|77.0<br>72.6|2000-04<br>2005-09|2.39<br>2.21|30.2<br>28.2|6.4<br>6.1|3.99<br>3.65|66.8<br>67.9|70.0<br>71.3|46<br>40|62<br>54|
|2010|21739|67.9|2010-14|2.10|26.8|5.8|3.40|69.0|72.7|34|46|
|2015|<br>241416|64.4|2014-19|1.97|25.3|5.6|3.17|70.2|74.1|28|38|
|2020|266411|62.0|2020-24|1.80|23.4|5.4|2.93|71.4|75.6|22|30|
|2025|291571|60.2|2025-49|1.29|18.3|5.6|2.36|74.3|79.1|13|17|
|2050|402478|51.4|2050-74|0.71|14.2|7.2|2.08|77.7|83.1|6|8|
|2075|48073|58.6|2075-99|0.30|12.6|9.6|2.07|79.8|85.6|4|5|
|2100|517262|68.7|2100-24|0.10|12.0|11.G|2.06|81.4|87.7|2|4|
|2125<br>2150|529705<br>535220|71.9<br>73.3|212S-49|0.04|11.8|11.4|2.06|62.0|88.6|2|3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>* e-69<br>60-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5.9<br>0-4<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 3. Distribution of population (percent): 1990 and 2025<br>snaw C' 3) swsr Co M<br><!-- End of picture text --> <!-- Start of picture text --> on<br>Cy51<br><!-- End of picture text --> <!-- Start of picture text --> an C SU)15<br><!-- End of picture text --> 15
As a share of GDP, tariff revenue losses amount to 1.0 percent of GDP in Nigeria,1.7 percent in Ghana, 2 percent in Senegal and 3.6 percent in Cape Verde.

The EPA’s impact on total fiscal revenue is lower for Ghana and Cape Verde in our study, compared with Busse, Borrmann and Grossman’s<sup>20</sup> 2004 study which is based on 2001 data, because of differences in collected (applied) tariff rate, trade flow data (e.g. share of imports from EU) and total government revenue.<sup>21</sup> In percent of GDP, the figures are similar to those of Busse et al. (1.0–2.0 percent of GDP) for Nigeria, Ghana and Senegal, in contrast to Cape Verde where the impact is lower by 0.5 percentage points of GDP in our study.

In comparison with our results, the trade and the budget effects of the EPA for Senegal<sup>22</sup> , as projected by CAPE, are higher in percent of EU imports and in percent of GDP, as can be seen in Table 2. There are several reasons for these differences. The CAPE study applied different elasticities, performed the analysis at a more aggregated level and used a slightly different partial equilibrium model. In addition, CAPE used 2001 (unrevised) customs revenue figures (Busse et al, 2004) (Table 3).

|**Table 3: Comparing the results of the IMF/ World Bank, Busse et al.and CAPEstudies for Senegal **|
|---|

||Our Study<br>|Busse et al.<br>(ECOWAS data,<br>|CAPE Study<br>|
|---|---|---|---|
||(WB/IMFdata,2004)|2001)|(2001data)|
|**Increase in total import**||||
|**from EU (%)**|10.6%|11.5%|29.7%|
|**Tariff Revenue Loss**||||
|million US $ % of Total|154.74|87.90|129.2|
|Government Revenue|10.4%|10.7%|15.7%|
|% ofGDP|2.0%|1.9%|2.8%|

The empirical analyses carried out in this paper show that the impact of implementing the EPA on fiscal revenue will be significant. However, fiscal revenue implications could be more limited for several reasons (discussed in the next section) and, thus, may be manageable.

20 We applied the same model used in Busse, Borrmann and Grossman‘s 2004 study and the same range of elasticities. However, we performed the analysis at an aggregate level, and used more recent trade flows and tariff revenue data.

21 In Ghana for example, Government revenue increased from 18.5 percent of GDP in 2001 to 23.8 percent. For Nigeria, Government revenue averaged 46 percent of GDP in 2001 and 43.1 percent in 2004.

> 22 The study, which  was commissioned by the UEMOA Commission, was limited to the group of eight UEMOA countries (Benin, Burkina faso, Guinea Bissau, Côte d’Ivoire, Mali, Niger, Senegal, and Togo).

13