rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2014 2017<br>skilledBirth |is skilledBirth |<br>deliverylnstitutional | wealth] Hi<br>wealth 7 fl deliveryPublic 7 Bf<br>deliveryPrivate 7 I division 7 Il<br>equipment_basic 7 | improvedSanitation 7 I<br>staff_del7 | amenities_basic 7 I<br>amenities_basic 7 | labtests 7 I<br>mediaAcess4 | staff_del7 |<br>education 4 | ready_del |<br>division + | awareComClinic 4 |<br>(e) 20 40 60 (e) 20 40 60<br>Relative importance Relative importance<br><!-- End of picture text -->
12 The first approach looks at earnings of persons and/or their re-employment probabilities who have been on a ALMP scheme and compares them with the earnings/re-employment probabilities of a "control group". Training measures e.g. intended to raise the productivity of participants should be mirrored, ceteris paribus, in higher wages relative to persons with similar characteristics who have not been given training. This approach uses micro data and tries to ensure that unobservable individual-specific determinants of earnings are controlled for (cf. e.g. Ashenfelter and Card (1985). More generally, participation in any ALMP measure should enhance human capital relative to non-participants and should express itself, ceteris paribus, in higher re-employment probabilities than those who have not participated.

The second approach, known as "transition methodology"?1 , uses in one application flow analysis of administrative macro data to establish the overall effect of a measure on outflows from unemployment. The idea behind this approach, formulated by Haskel and Jackman (1988) among others, is that a measure which is administered on a large scale can only be considered effective if there is a statistically significant positive correlation between such a measure and outflows from unemployment. One of the strong points of such an approach is the ability in principal to take account of dead weight loss and substitution effects. For example, if we model the determination of overall outflows from unemployment, a positive impact of a measure can be considered its net effect after all distortions have been accounted for.

A very simple and general model of an outflow function might be presented to heuristically demonstrate how ALMPs can be evaluated.

Outflows from unemployment (0) are determined by a vector of labour market - variables **(x1)** the stocks of vacancies and unemployment plus a time trend - and possibly by some or all elements of a vector of ALMPs **(x2),** where f is a general function. An econometric test of the effectiveness of ALMPs consists then in establishing whether elements in vector **x2** have some predictive power. Such a test is especially sensible if the application of an ALMP scheme does not entail

> 111n general the term "transition methodology" refers to analyses of **transitions between different** labour market states using micro or macro data. Such analyses were undertaken in developed market economies since the early seventies.
management techniques. The training sessions were divided into two three-day modules, with about a threemonth gap in between the two sessions. In one variant of the training session, only the village head attended. But, due to concerns that simply informing village heads of the laws regulating their activities would not be sufficient to change their behavior, there was also a second variant of the training sessions in which another community leader was trained alongside the village head on the rules constraining them. _By comparing the effectiveness of these two variants, we can parse the effects of civil society leader involvement from the effects of training the village head._ This effect is estimated contingent on the village head also being trained, as it was not deemed feasible to have a third variant of the intervention in which only the community leader was trained.

Crucially, both the timing and the variant of the training to which villages were exposed were randomly assigned. Specifically, we randomly selected the villages that would be trained in the first year of the analysis, and those that would not be trained until year 2. Then, within the villages selected for training in year 1, we randomly determined in which villages only the village head would be trained and in which villages both the village head and a community leader would be trained. At the end of the first year of training, the year 2 villages served as the comparison group to determine the effectiveness of each training variant.

In total, 270 villages were included in the randomized roll-out of the intervention. The roll-out of the two interventions across villages is described in table 3. Randomization was done by blocks of villages in the same ward and on the same land classification to improve the likelihood of achieving balance across the experimental group. Balance statistics, which are compiled in Table 1 of the appendix, indicate good balance across treatment arms.

Compliance with treatment assignment was very high. Fully 96 percent of the village heads assigned to the “village head only” training attended the sessions, 98 percent of the village heads assigned to the “village head plus civil society leader” training attended, and only 1 percent of the village heads assigned to the “no training” variant attended training sessions. Similarly, 97 percent of civil society leaders assigned to the “village head plus civil society leader” training attended sessions, and civil society leaders from just 4 percent of the villages assigned to the “village head only” training and 1 percent of the villages assigned to “no training” attended training sessions.

15
||Severe (<70g/L)|2 (0.6)|12 (0.6)|0 (0.0)|14 (0.7)|
|---|---|---|---|---|---|
||Moderate (70-89 g/L)|10 (3.1)|33 (1.6)|7 (2.3)|48 (2.2)|
||Mild (90-109 g/L)|66 (20.4)|464 (22.6)|55 (17.9)|463 (21.6)|
||None (≥110 g/L)|246 (75.9)|1540 (75.2)|246 (79.9)|1618 (75.5)|
|**Hemoglobin (g/L)**|Mean (sd)|117.7 (13.6)|117.3 (12.9)|118.9 (13.3)|117.3 (13.7)|
|**_P.falciparum_ prevalence**<sup>b</sup>||-|-|26 (8.6)|285 (13.6)|
|**Class 1**<sup>c</sup>||||||
|Score: 0-20|Sustained attention<sup>d</sup>|11.9 (6.7) [0, 20]|11.9 (6.7) [0, 20]|11.8 (6.6) [0, 20]|12.2 (6.6) [0, 20]|
|Score: 0-20|Spelling|8.0 (4.2) [0, 19]|8.7 (4.5) [0, 19]|7.4 (4.5) [0, 19]|7.7 (4.4) [0, 20]|
|Score: 0-30|Arithmetic|2.4 (2.3) [0, 12]|2.6 (2.4) [0, 17]|2.3 (2.6) [0, 13]|2.6 (2.5) [0, 15]|
|**Class 5**<sup>c</sup>||||||
|Score: 0-20|Sustained attention<sup>d</sup>|9.9 (6.1) [0, 20]|9.9 (6.0) [0, 20]|9.6 (5.7) [0, 20]|10.6 (5.7) [0, 20]|
|Score: 0-78|Spelling|24.0 (11.6) [0, 51]|28.6 (11.7) [0, 63]|24.2 (11.1) [0, 56]|26.1 (11.2) [0, 59]|
|Score: 0-38|Arithmetic|28.6 (6.1) [5, 38]|29.5 (5.5) [0, 38]|27.2 (7.0) [1, 38]|28.8 (5.5) [0, 38]|

a % of non-missing children in each study group presented for categorized data. For continuous data mean(sd) [min,max] is presented; b Not measured at baseline in the control group; c Presented as mean(sd) [min,max] d In class 1 sustained attention was measured by the “pencil tap test” and in class 5 sustained attention was measured by the “two digit code transmission test”.

**Table S2. Baseline measures for 5233 study children with missing 24 months follow-up health data vs. those not missing 24 months follow-up health data across both the control and IST intervention groups.**

|Characteristic; n (%)<sup>a</sup>|**CONTROL GROUP**||**INTERVENTION GR**|**OUP**||
|---|---|---|---|---|---|
||Missing outcome<br>data|Outcome data<br>available|Missing outcome<br>data|Outc<br>availa|ome data<br>ble|
|Child characteristics|N=496|N=2027|N=536|N=21|74|
|**Age** <br>Mean (sd)|10.5 (3.1)|10.0 (2.8)|10.9 (3.1)|10.2|(2.7)|
|5-9|196 (39.5)|845 (41.7)|184 (34.3)|885|(40.7)|

33
Defining skilled workers as those with some college (complete or incomplete) and the unskilled as those without any college education, the corresponding skilled-unskilled wage premium is given by: where _βit_ is the coefficient associated to educational level _i_ in the Mincer equation at time _t_ , and γ _ik_ = _Ei_ / _Ek_ is the share of employment of the individuals with the educational level _i_ in the employment of input _k_ in a base period.<sup>21</sup> Analogously, the high school-dropout wage premium or differential can be obtained as: To sum up, the remuneration for each type of work (skilled and unskilled) is obtained as a weighted average of the returns to education of each educational level, where the weights are given by the proportion of individuals with the corresponding educational level, and the wage premiums are obtained as the ratio of these remunerations (in logs). These two variables are based on six educational levels included in the Mincer regression: complete college ( _c.coll_ ), incomplete college ( _i.coll_ ), complete secondary ( _c.sec_ ), incomplete secondary ( _i.sec_ ), complete primary ( _c.pri_ ) and incomplete primary, the excluded category.

# **_B3.Construction of relative supplies_**

The relative supplies are also computed from individual microdata from the sixteen countries in our sample. We use the working age population as a basis for the relative labor supplies, although the trends described in this paper are qualitatively the same if instead of using the population we construct our measures of relative supply, as in Manacorda et al. (2010), based on the labor force, those in employment or hours of work.

The relative supplies represent a weighted sum of individuals in each of the three skill categories defined above. Following Katz and Murphy (1992) and Goldin and Katz (2007), we first compute _efficiency units_ for each country dividing the population into 24 groups (cells) according to their gender, educational level and potential experience.<sup>22</sup> For each cell, we compute the average wage for all years with respect to the wage of the largest group in the last year of the sample. These relative wages constitute the factors used to weight the supply for each cell in efficiency units, and the supply for high school dropouts, high school graduates and those with some tertiary education is simply the sum of the supplies of the individual cells (in efficiency units). For skilled labor, for instance, it is the sum of the supplies of the cells with individuals with some college education. Finally, the relative supplies are computed as the ratios of each input’s supply.<sup>23</sup>

> 21  As in Katz and Murphy (1992), the base period is defined here as the average of all years for each country.

> 22 Four educational levels (complete college, incomplete college, complete secondary and less than complete secondary) and three groups of potential experience (0 to 15 years, 16 to 30 years, and more than 30 years) are considered. 23 In the figures below, we express this relative supplies in log terms.

38
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles decrease over time reaching the reproduction level of about 210% by 2075. The assumed changes in mortality rates are discussed in more detail in the next section as they play a key role not only in the population projections per se but in the projections of life expectancy for the population covered by the pension system. Based on these assumptions, the population is projected to grow from the current estimated 24 million to about 67 million by the end of the simulation period.

# **Mortality table**

As mentioned above, mortality tables are very important in pension system financial projections as they determine – through life expectancy changes – how long retirees will receive their pensions. The assumed reduction in age and gender specific mortality rates result in an increase in life expectancy as reported in Table 17. The projected growth of life expectancy at birth is relatively rapid: by about 5 years over the next 15 years and by 11 years over the next 30 years, with life expectancies reaching the level currently observed in developed countries by the end of the simulation period. One of the underlying mortality assumptions used in these projections is that the current trend of declining HIV prevalence rates will continue and HIV/AIDS will not have any significant impact on future mortality rates. Our discussions with Ugandan demographers confirmed this assumption to be reasonable.

**Table 17: Projected life expectancy* for the general population**

|**Year**|**2003**|**2004**|**2005**|**2020**|**2050**|**2075**|
|---|---|---|---|---|---|---|
|_Male_|||||||
|Life expectancy: At birth|42.5|42.8|43.1|48.2|59.5|70.4|
|At Age 20|32.3|32.6|32.9|37.0|45.7|53.0|
|At Age 60|13.3|13.3|13.4|13.7|15.0|17.5|
|At Age 65|10.7|10.7|10.7|11.0|12.0|14.0|
|_Female_|||||||
|Life expectancy: At birth|43.1|43.4|43.8|48.9|60.6|74.8|
|At Age 20|32.7|33.0|33.4|37.4|46.0|56.7|
|At Age 60|14.0|14.0|14.0|14.1|15.6|20.0|
|At Age 65|11.2|11.1|11.1|11.2|12.5|16.1|

**Notes** : *Average remaining years of life expected at specific ages **Source** : WB calculations based on PROST

In a closed group, like the PSPF, it is very likely that life expectancy of the population covered by the pension system significantly differs from that of the general population. This is often the case in developing countries where living conditions, including access to health care, for civil servants are notably better that for the majority of the population resulting in their higher life expectancy. As life expectancy for the retired population is one of the essential determinants of pension system expenditure, financial projections are very sensitive to the mortality tables used in the simulations.

Our consultations with Ugandan demographers strongly supported the idea that civil servants in Uganda might on average live longer than the non-covered population.

- 82 -
these two numbers are not comparable for two additional reasons. First, the NFHS calculated the prevalence of domestic violence in 2005-06, about 10 years prior to this study. Second, the manner in which the NFHS asks questions about physical violence—as a form of domestic violence—is very different from the manner in which the list randomization method poses these questions. The NFHS specifically defines what physical aggression is, whereas this study left the definition of physical aggression open to interpretation by the respondent. It could be that the respondent does not consider certain acts, such as pushing, as acts of violence by the husband, which would contribute even more to the underreporting of domestic violence. Future studies need to combine the inquiry methodology used by this study (list randomization) and the wording of questions as used by the NFHS. This will lead to an even more accurate estimation of domestic violence, at the same time contributing to alternate and shorter methods of data collection—particularly on sensitive issues like gender based violence.

19
industrialised country imports only increased by 3.2 percent per annum, reflecting the increasing importance of intra-developing country trade in fish and fishery products.

# **Figure 2. World trade in fish and fishery** **<u>products, 1993-2000:</u>**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000<br>Developing country exports Developing country imports<br>Industrialised country exports Industrialised country imports<br>US$ billion<br><!-- End of picture text --> _Source: FAO (2002)._ Africa accounts for less than 5 percent of global fish and fishery product exports. However, the value of exports increased significantly following the mid-1970s and particularly through the 1990s. Thus, exports in 2000 exceeded US$2.5 billion, compared with less than US$0.5 billion in 1976.

Net receipts of developing countries from fish and fishery product exports increased significantly over the last two decades from US$3.7 billion in 1980 to US$18.0 billion in 2000 (FAO, 2003). This represents an increase of 250 percent in real terms. For many developing countries, exports of fish and fishery products are an important source of foreign currency earnings. Indeed, the aggregate value of net exports over the period 1980-2000 was greater than for many traditional commodity exports, including coffee, bananas, tea and rubber.

At the micro level, fish and fishery products are an important source of livelihood in rural areas of developing countries. It is estimated that 34.5 million people were directly engaged in fishing or fish farming as a full or part-time occupation in 2000 (FAO, 2003). This compares with 28 million in 1990, an increase of 25 percent. Of these, 85 percent are in Asia and seven percent in Africa.

The number of people directly engaged in fish production across Africa is estimated at around 2.6 million. The number increased significantly (35%) during the 1990s, although remains a small fraction (1.4%) of the population that is economically active in agriculture. In contrast to other developing regions, in particular Asia, a very small proportion (2.3%) is engaged in fish farming, emphasising the dominance of capture, and in particular marine capture, fisheries.

4
significant over-triggering tendency, potentially leading to alarm fatigue. Nevertheless, its loss values, specificity. Our further analysis shows that the indicator performs much better when taking a high false negative rate (FNR) of 0.94 for alerts and 0.96 for alarms. This extreme conservatism, reflected more balanced error mixture), reaching similar loss values as the conflict and displacement indicators. Conflict and displacement indicators, employing RSI of the EMA of local and neighboring conflict fatalities, highlight a different challenge. With notably high FNRs at the alert level (0.49 for conflict and a propensity to under-predict emergencies. Their loss values reflect this, with a significant reduction in predictive error from alerts to alarms, for conflict (from 0.42 to 0.34) and displacements (from 0.42 to <!-- Start of picture text --> Food price index (Average of top 5 food items) Displacements index (Local displacements)<br>8 0.07<br>0.06<br>® 0.05<br>, 3 004<br>& & 0.03<br>2 0.02<br>° LAA . /\ PR<br>os 0.9 1.0 11 1.2 13 00 oO 20 40 60 80 100<br>Percentage of EMA RSI of EMA<br>Conflict index (Regional fatalities) Fuel price index (Average of diesel and petrol)<br>0.025<br>2.5<br>0.020<br>2.0<br>2 0.015 4 15<br>0.005 05<br>8 0.0100.000 I Pv) 4 100.0 eee<br>oO 20 40 60 80 100 0.5 1.0 LS 2.0 2.5 3.0<br>RSI of EMA Percentage of MA<br>Exchange rate index (YER to USD) Drought index (Standardized Precipation Index)<br>0.030 04 — Heightened threshold<br>— Critical threshold<br>0.025<br>0.3<br>2 0.020 2<br>0.010<br>0.1<br>0.005<br>Fi 0.015 i | | 5 0.2<br>0.000 [~~ 0.0<br>20 40 60 80 100 2 oO 2 -4 -6<br>RSI ‘SPI<br><!-- End of picture text -->
stratification variable _z_ . _Xi_ ,1 is a set of time-invariant characteristics of the union and of each spouse determined at the time of marriage and not affected by postmarriage outcomes, to avoid reverse causality issues.<sup>11</sup> In our main empirical specification, we control for both spouses’ age at marriage, ethnicity, education, having been fostered before the age of 15, the education and activity of both spouses’ parents, and the location of the union (Dakar and other urban areas, as opposed to rural areas).<sup>12</sup> Prediction of the Risk of Polygamy Based on equation (1), we predict for each union, at each survey wave, the failure function, which represents the probability to become polygamous before time _t_ . In this specific application, we first argue that the failure function, which is a cumulative distribution function, better captures the relevant perception of the risk of polygamy than the hazard rate, which may be interpreted in a continuous setting as the instantaneous transition intensity to polygamy at time _t_ . Second, saving decisions are likely to be made with a medium to long time horizon, rather than be sensitive to instantaneous risks. Third, contrary to the hazard rate, the failure function is a probability, making the interpretation of the magnitude of the effects more intuitive.

The predicted failure function ���,�� is obtained for each union _i_ from the predicted survival ���,��: The predicted failure thus depends on the estimated stratified nonparametric baseline survival, and the predicted hazard ratio, depending on the characteristics _Xi_ ,1 of union _i_ . ���,��, is then included as a regressor in our second step resource allocation equations.

More specifically, the second step of our estimation strategy uses the two waves of the survey collected on average 4.3 years apart and focuses on the subsample of unions that have remained monogamous. We are thus interested in the difference between the predicted failure function at the two survey dates (������ ���), which represents the probability of becoming polygamous between the two survey waves, denoted, respectively, _w_ 1 and _w_ 2. As noted above, the set of individual- and union-level characteristics _Xi_ ,1 only contains variables that are predetermined at the 11 Since inheritance, and in particular housing inheritance, may help husbands to afford the cost of taking a second wife (Lambert, Ravallion, and van de Walle 2014), we estimated an additional specification with a dummy equal to one for the death of the husband’s father between the two waves as a time-varying variable likely to affect transitions to polygamy and found similar second step results (available upon request).

12 Both our first- and second step results are robust to alternative first step specifications. See the Estimation of the Risk of Polygamy section and table S.3 in the supplemental appendix. Second-step results for alternative specifications are available upon request.

8
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure A-7: Comparison of Growth in Cote d’Ivoire to West Africa**

<!-- Start of picture text --> Cote d'Ivoire versus West Africa: Real Per Capita Growth<br>12.0<br>10.0<br>West Africa<br>Cote d'Ivoire<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>-2.0<br>-4.0<br>Percent Per Capita Growth in GDP (1995 US$)<br><!-- End of picture text --> <!-- Start of picture text --> Years 1993-2002<br><!-- End of picture text -->

**Figure A-8:  Comparison of Growth in Cote d’Ivoire to West Zone CFA Countries**

<!-- Start of picture text --> Cote d'Ivoire Verus Eight Western Zone CFA Countries:<br> Real GDP Growth Per Capita<br>8.00<br>Cote d'Ivoire<br>Western Zone CFA<br>6.00<br>4.00<br>2.00<br>0.00<br>(2.00)<br>(4.00)<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Years: 1993-2002<br>Percent Growth in Per Capita GDP (1995 US$)<br><!-- End of picture text -->

**Western CFA Franc Zone:** One narrower  peer group for comparison is the eight countries that share the CFA Franc in the CFA Western zone. They share to some extent culture, regional ties, and colonial heritage<sup>35</sup> , as well as sharing a currency. The Banque Central des Etats d l’Afrique de l’Ouest (BCEAO) acts as a common central bank for the

> 35 Except for Guinea Bissau, a former Portuguese colony.

38