rafmacalaba/datause-encoder-data huggingface dataset:
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Marshall, Monty, 2001. _The Indian Problem Set_ . Unpublished data and documents.

Marshall, Monty G. & Keith Jaggers, 2000. ‘Polity IV Project: Political Regime Characteristics and Transitions, 1800–1999'. University of Maryland, MD: CIDCM. Codebook and data available at <u>http://www.cidcm.umd.edupolity/index.html.</u> McKenzie, David, 2008. ‘A Profile of the World’s Young Developing Country International Migrants’, _Population and Development Review_ 34(1), 115-135.

Mesquida, Christian G. & Neil I. Wiener, 1996. ‘Human Collective Aggression: A Behavioral Ecology Perspective’, _Ethology and Sociobiology_ 17:247-262.

Mroz, Thomas A. & Timothy H. Savage, 2001. _The Long-term Effects of Youth Unemployment_ . Washington DC: Employment Policy Institute. Available at http://epionline.org/studies/mroz_102001.pdf Oyefusi, Aderoju, 2008. ‘Oil and the Probability of Rebel Participation Among Youths in the Niger Delta of Nigeria’, _Journal of Peace Research_ 45(4): 539−555.

Salehi-Isfahani, Djavad & Navtej Dhillon, 2008. _Stalled Youth Transitions in the Middle East: A Framework for Policy Reform_ . Washington DC & Dubai: Wolfensohn Center for Development & Dubai School of Development.

Sommers, Marc, 2006. ‘Fearing Africa’s Young Men: The Case of Rwanda’, _World Bank Social Development Papers: Conflict Prevention and Reconstruction Unit Working Paper_ No. 32. Washington, DC: World Bank.

Stavrou, Stavros; Robert Stewart & Amanda Stavrou, 2000. ‘The Reintegration of Child Soldiers and Abducted Children: A Case Study of Palaro and Pabbo, Gulu District, Northern Uganda’ in Elizabeth Bennett, Virginia Gamba & Deirdre van der Merwe, eds, _ACT Against Child Soldiers in Africa: A Reader_ . Pretoria: Institute of Security Studies.

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21
<!-- Start of picture text --> First two children girls<br>(a) 1972-1984 (N=735) (b) 1985–1994 (N=1,681) (c) 1995–2006 (N=1,015)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>First two children one boy and one girl<br>(d) 1972-1984 (N=1,416) (e) 1985–1994 (N=3,506) (f) 1995–2006 (N=2,367)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>85 85 85<br>80 80 80<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>85 85 85<br>80 80 80<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 9: Predicted probability of having a boy and probability of no birth yet from nine months after second birth for urban women with 8 or more years of education by quarter. Predictions based on age 24 at second birth. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

be explained by the increase in the use of sex-selective abortions for second births combined with lower desired fertility. Fewer women have two girls and given the apparent ready access to prenatal 34
# **<mark>Exhibit 3</mark>**

<!-- Start of picture text --> :<br><!-- End of picture text --> hypothesis) given a certain number of disease deaths (E, the evidence) by confusing it with the probability of such deaths occurring given a disease outbreak (in probability notation by confusing P(H│E) with P(E│H) ).

A number of surveys taken during the SARS outbreak provide direct evidence on people’s subjective probability assessments. Leung et al (2004) report that nearly a quarter of respondents in Hong Kong thought that they personally were either very or somewhat likely to become infected with SARS (Exhibit 3), even though the _ex post_ infection or attack rate in Hong Kong (probable cases reported to WHO relative to population) was only 26 per 100,000 population ( 0.0026 percent). In the United States some 16 percent of respondents thought that the event that they or their immediate family would get infected by SARS in the next twelve months was very or somewhat likely, despite an _ex post_ attack rate not sensibly different from zero. (Blendon et al 2003).

Strictly speaking one cannot infer that these subjective probabilities were necessarily biased or too high by simply comparing them to _ex post_ attack rates, because of the possibility that low _ex post_ rates were themselves the endogenous result of preventive action taken by people, based on their subjective risk perceptions. The correct comparison might instead be to the theoretical counterfactual of what the ex post infection rate for SARS would have been had people not taken preventive actions, which is not an easy thing to calculate. The large gap between subjective risk perceptions and actual outcomes is suggestive nonetheless.

Survey evidence from Taiwan (China) throws light on some other aspects of risk perception, in particular the perceived risk of fatality in the event of infection and individuals’ willingness to pay (WTP) to avoid infection. (Liu et al 2003). Exhibit 4 shows that 76 percent of respondents rated the likelihood of fatality after contracting SARS as 4 or 5 on a 5 point scale, which seems high compared to an actual case fatality rate in Taiwan (China) of 11 percent. This may provide clearer cut evidence of subjective overestimate of risk, since the actual case fatality rate after infection is unlikely to have been much affected by people’s actions or risk perceptions. (The exhibit also shows that most respondents thought they had a high level of knowledge about the disease, as well a moderate level of control over the disease.) Liu et al (2003) also asked people about their willingness to pay for a theoretical vaccine that eliminated the chance of infection given realistic odds of infection and mortality (3 or 5 per 100,000 per month for infection risk and 5 or 10 percent risk of mortality after infection). From these responses the study inferred a Value per Statistical 14
by the new global trade environment. What to do to improve infrastructure, and what role can aid play in that context, are key elements of the debate.

Table 1 Caribbean Countries: Real GDP Growth, 1997-2006

||(Inperce<br>|nt)<br>||
|---|---|---|---|
||**Average**<br>**1997-2000**|**Average**<br>**2001-2006**|**Average**<br>**1997-2006**|
|Antigua and Barbuda|**3.7 **|**5.5**|**4.8**|
|Bahamas|**4.4**|**2.1**|**3.0**|
|Barbados|**3.4**|**1.8**|**2.4**|
|Belize|**7.2**|**5.5**|**6.2**|
|Dominica|**1.7**|**1.3**|**1.5**|
|Dominican Republic|**7.6**|**4.9**|**6.0**|
|Grenada|**6.6**|**2.8**|**4.3**|
|Guyana|**1.7**|**1.2**|**1.4**|
|Haiti|**2.1**|**-0.1**|**0.8**|
|Jamaica|**-0.1**|**1.6**|**0.9**|
|St. Kitts and Nevis|**3.9**|**3.5**|**3.7**|
|St. Lucia|**2.3**|**3.7**|**3.2**|
|St. Vincent and the<br>Grenadines|**3.7**|**3.3**|**3.5**|
|Suriname|**1.7**|**5.5**|**4.0**|
|Trinidad and Tobago|**7.7 **|**9.2 **|**8.6**|
|**Average**|**3.9**|**3.5**|**3.6**|

Source: World Bank (2008).

The remainder of the paper is organized as follows. Section II analyzes various arguments that have been offered to justify aid-for-trade (AFT) programs for developing economies. Section III discusses the relevance and implications of these various arguments for trade policy in Caribbean countries—particularly for some of the poorer countries in the region, where lack of public infrastructure remains a serious impediment to reaping the benefits from trade reform and spur growth. Section IV presents numerical experiments using a quantitative model for the Dominican Republic—a country where, despite its middle5
# **Figure 2. Regional variation in health outcomes and service provision**

_Despite some remarkable successes, provision of basic public services to reduce mortality and morbidity and enable basic economic activity, remains a challenge in many EMDEs, especially in Sub-Saharan Africa, but also in parts of other EMDE regions._

# **A. Under-5 mortality rate, 2015**

<!-- Start of picture text --> Number per 1,000 live births<br>150<br>EAP ECA<br>LAC MENA<br>120 SAS SSA<br>BRICS<br>90<br>India<br>60<br>South Africa<br>30 China Russian<br>Brazil Federation<br>0<br>0 5 10 15 20<br>GDP per capita, US $ thousands<br>C. Improved water source, 2012<br>100Percent of population with access India China Brazil<br>Russian<br>South Africa Federation<br>80<br>60 EAP ECA<br>LAC MENA<br>SAS SSA<br>40<br>0 5 10 15 20<br>GDP per capita, US $ thousands<br>E. Percent of population without access to<br>electricity, 2012<br>Percent of population<br>90<br>75<br>60<br>45<br>30<br>15<br>0<br>EMDE EAP ECA LAC MENA SAS SSA<br><!-- End of picture text -->

# **C. Improved water source, 2012**

# **E. Percent of population without access to electricity, 2012**

# **B. Access to improved water and sanitation, 2011-15**

<!-- Start of picture text --> Percent<br>70<br>Without sanitation facilities<br>60 Without water source<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

**D.  Improved sanitation facilities, 2015**

<!-- Start of picture text --> Percent of population with access<br>100<br>90<br>80 Brazil<br>70 China FederationRussian<br>60 South Africa<br>50<br>40 India<br>EAP ECA<br>30<br>LAC MENA<br>20 SAS SSA<br>10 BRICS<br>0<br>0 10 20 30<br>GDP per capita, US $ thousands<br><!-- End of picture text -->

# **F. Access to electricity, controlling for per capita GDP, 2012**

<!-- Start of picture text --> Percent of population<br>25<br>15<br>5<br>-5<br>-15<br>-25<br>-35<br>EAP ECA LAC MENA SAS SSA<br><!-- End of picture text --> Source: World Bank.

Notes: EAP is East Asia and Pacific, ECA is Europe and Central Asia, LAC is Latin America and the Caribbean, MNA is Middle East and North Africa, SAS is South Asia, SSA is Sub-Saharan Africa. BRICS are Brazil, Russia, India, China and South Africa. Regional aggregates are simple averages.

A. Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five. B. C. D. Improved sanitation facilities include flush/pour flush, ventilated improved pit latrine, pit latrine with slab, and composting toilet. An improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection). E. F. Vertical bars indicate 25<sup>th</sup> -75<sup>th</sup> percentile range.

F. To control for per capita income, Figure shows deviation of access to electricity from results of a linear regression of access to electricity on per capita income.

5
<!-- Start of picture text --> re<br>ri ‘ ay<br>an ¢ah A ys Ven|]inMissing 1993/ InformationForeigners in 1993<br>54 ah [__]0.000<br>PCT aa | [HHHE 0.00010.3001 -- 0.3000 0.7000<br>, 2 » = J0.7001- 0.9700<br>? Sp HM 0.9701 - 1.0000<br>wi\, og aeMe‘ eg<br>nS" aay.ekMig: |SAE<br>P s os .<br>RZ } a<br>a<br>Rica lat ,<br>‘4 .ey,a 7 a<br>“ |<br>opAe = |<br><!-- End of picture text -->

<!-- Start of picture text --> sagt. aA [_]0.0000PEP Holders/ Pop. in 2018<br>D>: ae [ly 0.0001- 0.0003<br>aFo oe:aFe HEHM 0.00040.0013-- 0.00120.0037<br>- 1.6024<br>yp ay HMM 0.0038<br>yc “ak<br>Mihaialeae a SEee‘ ales<br>eGee oeSh GE A<br>P a ¥ <=) \<br>Cavabrs eect ‘ &<br>Gere<br>SNe Le LY t<br>xaeWs WA<br><!-- End of picture text -->
# **Table 3 Intensity of foreign capital and technology development stocks by industry, 1999-2004 (relative to total capital stock)**

||**Foreign**|**capital st**|**ock**||||**Technol**|**ogy dev**|**elopment**|**stock**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**1999-**<br>**2004**|**1999**|**2000**|**2001**|**2002**|**2004**|**1999-**<br>**2004**|**1999**|**2000**|**2001**|**2002**|**2004**|
|**Pulp and**|13.9%|7.8%|16.2%|16.8%|19.2%|9.5%|36.8%|31%|34.8%|34.4%|36.7%|43%|
|**Paper**|||||||||||||
|**Cement**|20%|22.6%|20%|20.6%|18.8%|20%|4.7%|2.7%|3.4%|3.7%|5.3%|6.9%|
|**Iron and**<br>**Steel**|1.5%|1.4%|1.1%|1.3%|1.3%|2.5%|38%|40%|32.4%|33.6%|35.9%|35%|
|**Aluminum**|4%|7.2%|4.3%|4%|3.8%|1%|15%|6.6%|7.4%|12%|14%|27.5%|

**Table 4: Number of firms with missing years of observations**

|**Missing years of**<br>**observations**|**Pulp and Paper**<br>**industry**|**Cement**<br>**industry**|**Iron and Steel**<br>**industry**|**Aluminum industry**|
|---|---|---|---|---|
|**0**|28|58|35|11|
|**1**|24(8)<sup>*</sup>|59(23)<sup>*</sup>|24(9)<sup>*</sup>|14(3)<sup>*</sup>|
|**2**|32|114|47|16|
|**3**|45|80|34|7|
|**4**|63|146|52|13|
|**5**|71|171|95|37|
|**6**|153|415|232|64|
|**7**|517 (405)<sup>**</sup>|921 (610)<sup>**</sup>|1597 (1459)<sup>**</sup>|306 (249)<sup>**</sup>|

- Number of firms only missing year 2004 inside parenthesis

- **  Number of firms only reporting in year 2004 inside parenthesis

**Table 5: Mean of gross value industrial output (2004)**

||**Unbalanced data set**|**Balanced data set**|
|---|---|---|
|**Pulp and Paper industry**|237,508|565,843|
|**Cement industry**|132,386|162,739|
|**Iron and Steel industry**|619,253|3,538,707|
|**Aluminum industry**|504,413|1,386,412|

29
reduces the magnitudes of both the estimated slope and intercept which confirms that ability bias is positive, as widely argued in the literature. The effects of ability controls on the quadratic CEF estimates are as follows: (i) the intercept and linear coefficients are lower, and (ii) the estimated quadratic coefficient is not affected in a substantial way. More important for our analysis of rural-urban divide, the pattern of rural-urban differences in the estimated coefficients we found earlier in Table 2 without any ability controls remain intact: (i) there is no significant differences in the intercepts, (ii) the linear coefficient is larger in the urban areas, and (iii) the quadratic coefficient is smaller in the urban areas. The estimates of the relative and absolute mobility estimates in the lower panel also paint a picture largely consistent with what we found in Table 2 in the previous subsection.

# **(5.2) Estimates of the Investment Equation**

In the Becker-Tomes model, parent’s financial investment in education plays a prominent role in generating intergenerational persistence in economic status under the plausible assumption of imperfect credit markets. It is thus curious that none of the published studies on intergenerational educational mobility we are aware of provide estimates of the parameters of the investment equation (8) in section 2 above. To understand possible differences in the financial investment by parents in rural vs. urban areas, we estimate the following regression specification: where _D_<sup>_r_</sup> is the rural dummy taking on the value 1 for a household located in villages, and zero otherwise. The estimated parameters of equation (21) are related to the parameters in the investment equation (8) as follows: _θ_ 0<sup>_u_=</sup><sup>_ρ_0,</sup><sup>_θ_</sup> 1<sup>_u_=</sup><sup>_ρ_1,and</sup><sup>_θ_</sup> 0<sup>_r_=</sup><sup>_ρ_0+</sup><sup>_ρ_2,</sup> _θ_ 1<sup>_r_=</sup><sup>_ρ_1+</sup><sup>_ρ_3.</sup> We use the pooled sample from four earlier rounds of the IFLS panel data to estimate equation (21) above.<sup>22</sup> The estimates of the regression equation (21) are reported in Table 4; we report estimates from three specifications : the first column contains the estimates of equation (21) without any controls, the second column controls for the number of children in

> 22IFLS 2007 (wave 4), IFLS 2000 (wave 3), IFLS 1997 (wave 2) and IFLS 1993 (wave 1).

20
9 Complementarity between reforms and the role of policy learning also need to be considered. Reforms in one area, such as trade and product markets, may amplify the benefits of reforms in other areas, such as the labor market. Moreover, labor market reforms may allow for the compensation of losers from trade and product market reforms.<sup>8</sup> Synergies in economic impact (for example, reforming land markets and contract enforcement also improves access to financing) and in political economy (combining trade reforms with social protection can diffuse opposition) also come into play. And reform priorities often emerge through a process of policy learning and experimentation. China is a striking example of a country that designed and sequenced reforms by learning from its own policy experiments and, to some extent, from the experiences of other economies as well (see page 18 below).

# **SEIZING OPPORTUNITIES TO GET REFORM ONTO THE POLICY AGENDA**

Once the priorities for reform are determined, what makes reform possible? One popular view is that reform is possible only in times of crisis or under a new government, when reformers benefit from strong political and popular support that makes it easier to counter opposition from interest groups. Indeed, our cases suggest that policymakers more often than not use these events to sharpen the focus on a problem and bring it onto the policy agenda. But crisis and political change are not the only factors that make reform possible.

Reformers often take advantage of periods of growth to make hard choices, because public support is high and resources are available to compensate losers. Two other factors also appear to play an important role (figure 2):

- Increasing competition, created by global integration and technological changes more broadly, and by spillovers from other policy initiatives such as trade liberalization or international agreements; and

- New information and diagnostics that expose a policy problem and create awareness of the need for reform, as well as new policy ideas generated from past experiences and an ongoing process of learning and diffusion.

Our analysis also suggests that identifying a single cause of reform can be difficult: only a few of our cases highlight just one factor as being important (see annex B). More often it is a combination of factors—and players—that helped create a window of opportunity to get reform onto the agenda. Indeed, in all our cases, _political leaders_ played a critical role in recognizing a problem and seizing the brief windows of opportunity to get reforms onto the agenda. Moving quickly was seen to be important, given that windows for reform remain open only for brief periods before policymakers feel they have addressed the problem, before events or personnel change, or before interest groups coalesce against

> 8 An analysis and more detailed discussion of reform complementarities, drawing from reform experiences in OECD countries over a five-year period, can be found in the IMF’s _World Economic Outlook, April 2004_ (IMF 2004).
- 34 -

knowledge about borrowers' working environment--fcrces them to put heavy financial demands or goverrnent to compensate for the bad debt.<sup>1 2</sup> Second, investment in institutional development activities has allowed Grameen to be highly successful in targeting low income, disadvantaged groups--particularly women, who are usually missed by traditional credit schemes. For Grameen, this group is defined as persons belonging to households that own less than 0.5 acre of cultivable land. A 1985 field survey of 975 randomly selected borrowers showed that only 4.2 percent of the borrowers belonged to nontargetgroup households (Hossain 1988:44).

It is also important to point up that institutional investments can have declining cost structures which may justify Grameen's access to subsidized credit. Start up costs have accounted for over half of Grameen's administrative costs. Banks that have been in operation from six months to a year have had expenses that account for 16.2 percent ot outstanding loans. After banks have been in operation for more than three years, this figure drops to 5.7 percent (see Table 8).

There are an additional set of non-financial benefits, or **positive** externalities, that are associated with institutional intervent!ons that are difficult to quantify. For Grameen, credit has served as a catalyst for 12 Mosley and Dahal argue that if ti e credit institutions he analyses were to charge an interest rate to borrowers that covered the cost of their bad debt: Grameen would have to charge 19% as opposed to the 16% it currently charges, SFDP would have to charge 28%, and farm credit institutions would have to charge 88%. These figures are based on the following formula: (i+a+R) - interest rate that will cover costs 1-p p the probability of financial loss a - administrative costs i - the interest rate charged to banks which is estimated to be 10%