rafmacalaba/datause-encoder-data huggingface dataset:
common among women than men: about 1 percent of all Nigerian men are widowers while 9 percent of women are widows. Strikingly, while the share of widowers among men aged 75 and older is about 11 percent, it is 77 percent for women of the same age. This pronounced divergence by gender reflects a combination of large age gaps between spouses, higher remarriage rates as well as surviving spouses for polygamous men, and the longer life expectancy of women.

**_Inheritance_** : In Nigeria’s patrilineal society, a woman’s rights to property, such as land or housing, are typically dependent on her relationship with a man—usually a father, husband or brother. A widow’s right to inherit from her late husband is formally recognized by statutory law. But this is a moot point since the majority of marriages are contracted under customary or Islamic law.<sup>8</sup> Under the former, women are not in general accorded inheritance rights (Ostien and Dekker 2010; Hallward-Driemeier and Hasan 2013).<sup>9</sup> In some instances and with variation across ethnic groups, this can depend on whether the widow has children with the deceased husband, whether any are sons, and whether she has remarried, with further distinction based on whether this is within the husband’s lineage and in a polygynous or monogamous relationship (NIALS 2013). Widows with children, and particularly sons, may be allowed to retain possessory (not proprietary) rights on the conjugal home or hold the estate in trust for male children who are minors. Others are deprived of the custody of their children under the customary view that offspring belong to the deceased’s lineage. Under customary law, childless widows can be asked by their in-laws to leave the matrimonial home. Numerous anecdotes suggest that this happens also to widows with children. Examples of property seizures and abandonment by in-laws are legion in the South of Nigeria. Attesting to the extent and abusive aspects of such events are the many NGOs active in the country’s Southern States who focus on the rights of Christian widows, heightening awareness among the public, providing counseling, legal advice, and other forms of support.<sup>10</sup>

Inheritance practices under Islamic law are more favorable to women. The Koran instructs that a deceased man’s property be inherited by his widow(s), his (male and female)

> 8 It is still rare for Nigerians to contract solely a statutory marriage, and conflict between customary and statutory law often results (Rahamatian 1996).

> 9 The Women's Economic and Legal Empower Database for Africa (Women LEED Africa) can be retrieved at: http://siteresources.worldbank.org/EXTGENDER/Resources/Women_LEED_Africa_Database.xlsx.

> 10 For example, Widows Development Organization (WIDO) active in Enugu State; and the Nigerian branch of the International Federation of Women Lawyers (FIDA).

7
school. Sixty-one percent of the evaluation sample is female. The majority have worked previously, with 63 percent having worked before, and an average work experience of more than three years. Nonetheless, 37 percent have never worked before, and a further 20 percent have worked for less than one year. Baseline responses to current employment are consistent with these individuals being unemployed: only 12 percent have worked even one hour in the previous month, and only 2 percent have worked at least 20 hours per week in the past month.

A comparison of the applicants to a sample of all urban unemployed using the 2009 Labor Force survey reveals that applicants are younger, more likely to be female, have less work experience, and are better educated than the average unemployed individual. Only 30 percent of the unemployed are female (reflecting low labor force participation rates among women), compared to 61 percent of applicants; only 31 percent of the unemployed are youth, compared to 45 percent of applicants; and only 42 percent have completed high school, compared to 73 percent of applicants. These differences reflect both differences in which types of unemployed individuals are more likely to apply for training courses, as well as the supply of courses, since many courses are designed for people with at least medium levels of education. Our results are therefore informative as to the effect of training for the types of unemployed who apply for vocational training courses being offered, but need not necessarily reflect the returns to vocational training for the broader population of the unemployed.

# **4.4 Estimation Methodology**

We can measure the intent-to-treat (ITT) effect of vocational training on a particular outcome of interest by estimating the following equation, analogous to equation (1): Outcomei = α+ βAssignedtoTraining+ ∑457s=1 θsδi,s + εi (2) We do not control for baseline levels of outcomes since they are close to zero and provide little variation. β is then the average effect of being selected for a vocational training course on this outcome.

We can also estimate the impact of actually completing training by replacing _AssignedtoTraining_ with _CompletedTraining_ in (2), and instrumenting this with treatment assignment. Under the assumption that assignment to training has no impact on outcomes for those who do not complete the course, and that there are no individuals who would take courses only if assigned to the control group, this yields the local average treatment effect (LATE). This 12
# **Procurement and Management of Critical Inputs**<sup>**10**</sup>

The rules regarding what critical education and health inputs are procured, how and by whom they are procured, and what processes regulate availability, quality, and cost constitute another important dimension of governance systems. While health and education services require many inputs, textbooks and especially pharmaceuticals are often considered critical for quality of service. Here we discuss indicators related to pharmaceuticals, an area in which there has been particular interest both at the international and national level in many developing countries.

The World Health Organization has developed a methodology for assessing the transparency of pharmaceutical policies that can serve as a useful example of a multidimensional, expert-opinion-based system of measurement of governance policies. It covers 8 dimensions: medicines registration, licensing of pharmaceutical establishments, inspections of those establishments, control of medicine promotion, control of clinical trials, selection of medicines, procurement and distribution (World Health Organization 2009).

In each of these dimensions, the WHO defined a set of indicators to assess the transparency of the policies that govern that specific area. For example, for the area of medicines registration, the assessment considers such questions as:

- Is there an up-to-date list of all registered pharmaceutical products available in the country?

- Does it provide a minimum level of information?

> 10 Health indicators draw from WHO, 2008.

22
12 about environmental benefits of consuming certain goods. Some overlaps exist, such as between Types 1 and 3. This division seemed a promising basis for a matrix, and it was disappointing that the Secretariat did not make more use of it.

The study herein, with a broader scope than just renewable energy, develops a new taxonomy in Table 1 below that distinguishes subsidies for the purposes of economy-wide investment, government operations, transfers to domestic private actors, and foreign transfers. See Table 1 below:

|**Ta**<br>|**ble 1: How Governments Carry Out**|**Green Subsidies**<br>**Examples**|
|---|---|---|
|**Investment**|||
||Generally available infrastructure|Smart electricity grid, Water<br>desalinization, Communications<br>infrastructure for telecommuting,<br>Sewage treatment plan, Toxic waste<br>dump|
||Fundamental research|US National Labs|
||Technology-specific research|USDOE H Prize<br>US ARPA-E|
|**Government Operations**|||
||Infrastructure for government|Hanford Vitrification Plant|
||Procurement|Electric car fleet|
|**Contingent Payments to**<br>**Domestic Economic**<br>**Actors**|||
||Contingent on production of good|EU capital, project, and contingent<br>grans to SMEs for renewable<br>energy equipment<br>Malaysia 15-year tax holiday for<br>solar manufacturers|
In rural societies, a large fraction of households’ endowment of physical capital is in the form of land, a key asset that serves not only as a source of livelihood but also a source of old-age support and status (Agarwal 1994). Restrictions on women’s land rights and tenure security are likely to affect not only their bargaining power but have also been shown to lead to significant productivity losses (Udry 1996, Goldstein and Udry 2008). In many customary systems, inheritance, which often constitutes the main avenue for accessing land, remains heavily biased against females (World Bank 2001), that is often considered a key reason for persistence of gender bias over time. This is in line with evidence that differences in inheritance legislation affect economic outcomes and entrepreneurial activity across countries (Panuzzi et al. 2009).

The discussion of inheritance patterns in the literature has mostly abstracted from gender aspects. The wealth model (Becker and Tomes 1979) implies that altruistic parents provide children who have different abilities with different but efficient amounts of human capital, equating marginal returns to investment in schooling with the return to financial assets whereas the strategic bequest model (Bernheim, Schleifer, and Summers 1985) hypothesizes that parents assign bequests (inter vivos or post mortem) to children in return for care and old-age support. In a developed country setting, an equal allocation rule has been shown to generally prevail (Behrman and Rosenzweig 2004). However, in developing countries, interactions between pre-existing norms and structural changes make issues more complex. For example, exogenous changes in technology can shift land access within the household in favor of women (Quisumbing et al. 2004). In addition, while the nature and direction of payments at marriage is affected by several socio-economic factors (Anderson 2003), legal changes in family law can have far-reaching impacts as illustrated in Bangladesh where the value of dowry and prenuptial agreements increased when constitutional changes erected legal barriers to polygamy and decreased after additional divorce costs were imposed on men (Ambrus, Field, and Torero 2009).

An area of legislative change that has generated considerable empirical research relates to divorce legislation in the US (Peters 1986; Allen 1992; Friedberg 1998; Wolfers 2006). By removing the requirement for spousal consent for divorce, the law changed the terms 7
Table 4: Impact on Knowledge of Insurance and Remittance Costs

||(1)<br>Knowledge of<br>Migrant<br>Insurance|(2)<br>Prefers to Send<br>One Large<br>Remittance|(3)<br>Fixed Fee on<br>Sender: Correct|(4)<br>Fixed Fee on<br>Recipient:<br>Correct|(5)<br>Exchange Rate<br>Commission:<br>Correct|(6)<br>Interest Rate:<br>Correct|(7)<br>Knowledge of<br>Remittance<br>Costs:<br>Aggregated|
|---|---|---|---|---|---|---|---|
|Migrant-Only Training (A)|0.057*|0.031|0.058|0.057|0.048**|-0.001|0.039|
||(0.032)|(0.034)|(0.045)|(0.045)|(0.022)|(0.039)|(0.026)|
|Family-Only Training (B)|0.135***|0.030|0.087*|0.035|0.025|0.050|0.046*|
||(0.036)|(0.036)|(0.045)|(0.046)|(0.021)|(0.040)|(0.026)|
|Migrant and Family Training (C)|0.256***|0.029|0.125***|0.069|0.047*|0.123***|0.079***|
||(0.046)|(0.035)|(0.047)|(0.047)|(0.024)|(0.043)|(0.028)|
|R-squared|0.067|0.018|0.040|0.059|0.012|0.090|0.077|
|Sample Size|1059|1133|1133|1133|1133|1133|1133|
|Number of Clusters|384|394|394|394|394|394|394|
|Mean of Dependent Variable in Control Group|0.097|0.808|0.564|0.509|0.049|0.300|0.446|
|Test: A-B = 0 (p-value)|0.046|0.973|0.514|0.623|0.320|0.200|0.801|
|Test: A-C = 0 (p-value)|0.000|0.968|0.144|0.790|0.949|0.004|0.140|
|Test: B-C = 0 (p-value)|0.019|0.996|0.401|0.466|0.387|0.091|0.219|
|Test: A = B = C = 0(p-value)|0.000|0.799|0.054|0.459|0.102|0.013|0.041|

This table presents pooled regression analysis across three follow-up survey rounds for measures of knowledge of the insurance product for migrants and remittance costs. Column 7 is an aggregate measure of Columns (2)-(6). Separate regression analysis by follow-up survey round is presented in Appendix Table A6. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
26

**Figure 8:** _Evolution in the factor content of Turkish Pan-European-destined exports over 19902003(in percent)_

<!-- Start of picture text --> 55<br>45<br>35<br>25<br>15<br>5<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Natural Resources Unskilled Labor<br>Capital Intensive Skilled Labor<br><!-- End of picture text --> Source: As in Table 13.

Developments in factor intensity of exports point to the growing importance of Turkey’s links to EU-8 as well as other Pan-European countries (Bulgaria, Romania and EFTA) aside from the EU-15. Pan-European markets other than those of the EU-15 have become increasingly receptive consumers of skilled labor and capital intensive products made in Turkey and engines of Turkey’s export growth. While Turkish exports to these markets have registered a stronger growth than in exports to EU-15 across all categories of products in terms of their factor intensities, growth differentials, however, were particularly large in capital and skilled labor intensive products. The value of exports of these products increased almost four-fold between 2000 and 2004 from US$ 533 million to US$ 2,540 million. The growth of these exports was twice as fast as EU-15-destined exports of these products.

**Table 14** : _Developments in exports to non-EU-15 Pan-European economies in 1990, 2000 and 2003 (in percent)_

||Compo<br>|sition<br>||Least Squ<br>|are Growth<br>||Non-E<br>Pan-Eu<br>|U-15 in T<br>ro Expor<br>|urkey's<br>ts<br>|
|---|---|---|---|---|---|---|---|---|---|
|Factor Intensity|1990|1996|2004|1990-95|1996-00|2000-04|1990|1996|2004|
|Natural Resource|49.1|35.1|13.4|6.9|-0.4|17.6|6.3|9.5|13.4|
|Unskilled Labor|41.1|31.0|35.8|11.0|15.5|30.8|3.1|4.2|11.1|
|Capital|4.3|16.2|14.0|43.9|2.4|37.0|3.0|13.2|19.0|
|SkilledLabor|5.4|17.7|36.8|36.3|21.3|41.5|2.3|9.4|14.0|
|All abovegoods|100|100|100|15.0|10.1|32.7|4.0|7.0|13.2|

Source: Computations based on Pan-Euro and EU import data from UN COMTRADE Statistics.

In contrast to export performance in 1990-95, skilled labor intensive products have subsequently become the major levers of export expansion to non-EU-15 Pan-European markets. While capital intensive products followed by skilled labor intensive exports drove the growth in 1990-96, the latter drove the expansion in 1996-2003. The share of EU-15 in Turkish PanEuropean exports of skilled labor products fell from 98 percent in 1990 to 916 percent in 1996 and 86 percent in 2004.

Simultaneously, the factor content gap between EU-15-destined exports and those to other pan-European markets has been falling. While through the 1990s the EU-oriented export basket had a lower presence of natural resources intensive products and a larger presence of
# <u>Figure 3. Evolution of the Average World Incidence of Various Disasters</u>

The different panels of the figure display the evolution of the average world incidence of disasters, corresponding to the total number of disasters of a given type in a year divided by the total number of countries in the sample, during the 1960-2006 period, as well as a fitted trend line showing the evolution of each series <!-- Start of picture text --> A. Geological Disasters B. Climatic Disasters<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>C. Other Disasters D. Windstorms<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>E. Floods F. Droughts<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>.08 .4<br>.06 .3<br>.04 .2<br>AverageWorld Incidence Average World Incidence<br>.02 .1<br>0 0<br>.05<br>.1<br>.04<br>.08<br>.03<br>.06<br>.02 .04<br>Average World Incidence Average World Incidence<br>.01 .02<br>0 0<br>.2 .2<br>.15 .15<br>.1 .1<br>AverageWorld Incidence AverageWorld Incidence<br>.05 .05<br>0 0<br><!-- End of picture text --> 20
# Table 4.1: Main results of MDG simulations

||2004|Base|MDG scen|arios||
|---|---|---|---|---|---|
|||ftr<br>scenario|fb|tax|db|
|Macroeconomic aggregate|Bn. YR(1)|_Percent average annu_|_algrowth rat_|_e, 2004-15_||
|Consumption -private|1,691|4.9<br>7.0|7.0|0.0|-2.0|
|Consumption -government|326|6.5<br>11.0|11.0|12.3|12.5|
|Fixed investment -private|274|4.8<br>7.8|7.8|-0.2|-12.7|
|Fixed investment -government|263|6.4<br>12.8|12.8|15.3|16.5|
|Exports|931|1.6<br>-2.7|-2.7|0.6|-0.1|
|Imports|909|3.8<br>8.3|8.3|3.5|2.6|
|GDP at factor cost|2,635|5.1<br>5.8|5.8|4.5|3.6|
|Total factor employment(index)|100|2.9<br>4.1|4.1|4.0|3.7|
|Total factorproductivity (index)|100|2.1<br>1.7|1.7|0.4|-0.1|
|Real exchange rate(index)||2.7<br>-1.3|-1.3|2.2|2.0|
|Government expenditure|Bn. YR|_Percent average annu_|_algrowth rat_|_e, 2004-15_||
|Consumption||||||
|Basic education(grd1-grd6)|55.8|5.8<br>15.2|15.2|15.8|16.1|
|Health|19.6|5.8<br>22.0|22.0|27.7|28.6|
|Water and sanitation|0.1|5.1<br>68.9|68.9|72.6|73.8|
|Capital stocks(2)||||||
|Basic education(grd1-grd6)|8.9|6.2<br>14.8|14.8|15.4|15.5|
|Health|21.1|6.1<br>20.6|20.6|26.2|26.8|
|Water and sanitation|13.1|6.2<br>15.1|15.1|15.5|15.6|
|Government expenditure|GDP%|_Percent average GDP_|_share, 2004-1_|_5_||
|Consumption|3.0|2.8<br>6.8|6.8|8.4|8.7|
|Basic education(grd1-grd6)|2.2|2.0<br>4.2|4.2|4.4|4.5|
|Health|0.8|0.8<br>2.2|2.2|3.4|3.6|
|Water and sanitation|0.0|0.0<br>0.5|0.5|0.6|0.7|
|Fixed investment|1.7|1.8<br>7.5|7.5|12.5|13.7|
|Basic education(grd1-grd6)|0.3|0.4<br>1.1|1.1|1.3|1.3|
|Health|0.8|0.9<br>4.8|4.8|9.3|10.4|
|Water and sanitation|0.5|0.6<br>1.6|1.6|1.9|2.1|
|Total|4.6|4.6<br>14.3|14.3|20.8|22.5|
|MDG indicator(3)|Value|_Value in 2015_||||
|MDG 1: Povertyrate|34.8|34.1<br>27.6|27.6|49.3|65.5|
|MDG 2: Net enrolment rate in basic education|16.8|55.2<br>92.6|92.6|92.5|92.5|
|MDG 4: Under-five mortalityrate|93.4|64.6<br>40.6|40.6|40.6|40.6|
|MDG 7w: Access to safe water|43.9|48.2<br>67.4|67.4|67.7|68.0|
|MDG 7s: Access to improved sanitation|15.9|24.1<br>56.5|56.5|55.3|55.3|
|Government financing|GDP%|_GDP sharefor average_|_2004-15 or 2_|_015(%)_||
|Foreign debt(2015)|29.9|33.6<br>19.6|125.9|32.0|34.0|
|Domestic debt(2015)|10.0|17.5<br>14.7|14.7|17.8|51.3|
|Foreigngrants(2004-15)|1.6|2.1<br>12.8|1.7|2.5|2.5|
|Direct taxes (2004-15)|5.7|5.8<br>5.9|5.9|22.5|22.4|

(1) YR = Yemeni rials.

(2) Value for 2004 refers to investment.

(3) Units: percent for MDGs 1, 2, 7w, and 7s; per 1,000 for MDG 4.

References: fb=foreign borrowing; db=domestic borrowing; tax=direct taxes; ftr=foreign grants 24
# **V. Using Credit Ratings to Estimate the Expected Cost of a Guarantee of a Power-Purchase Agreement**

If we substitute for debt-service payments the payments that the utility must make to an IPP under a PPA, we can apply the same approach to a PPA guarantee.

We assume for the purpose of the illustration that the possible guarantee covers the entire term of the PPA and applies only to routine (energy or capacity) payments. In particular, we do not consider any guarantee of a termination payment. If the guarantee of routine payments is for a term significantly shorter than the entire PPA, a guarantee of a termination payment may have a large additional cost. But this is not the case if the guarantee covers routine payments for the entire term of the PPA: the effect of termination may be mainly to require an immediate payment roughly equal to the present value of the routine payments that would have been made if the contract had not been terminated. In that case, summing the cost of the guarantee of the routine payments and the guarantee of the termination payment would be double counting.

_PPA with only energy payments_ Consider first an energy-payment-only PPA. It might be a PPA for a wind farm or a solar plant or run-of-the-river hydro plant.

We suppose first that, by reading the PPA and seeking the advice of lawyers and other experts familiar with the project, we get the following information:

- The PPA has a term of 15 years.

- The PPA provides that the utility will purchase all the energy produced or deemed produced by the IPP at a year-zero price of 100 pesos per megawatt-hour increased by the actual rate of local consumer price inflation.

For simplicity, we assume all payments are made at the end of year, that the first payment will occur in exactly one year’s time (and will be adjusted for actual inflation), and the guarantee is issued today.

We assume next that advice provided by macroeconomists and electricity engineers familiar with the project suggests the following two assumptions:

- Peso inflation is forecast to be 5 percent a year over the term of the PPA.

- The annual electricity production of the plant is forecast to be 300,000 MWh. (This forecast might be derived from data on the maximum

11