rafmacalaba/datause-encoder-data huggingface dataset:
The h-index captures both the volume of publications and their citations. On this, the Bank ranks 11<sup>th</sup> on the SSCI data (Table 7) and seventh on the SCOPUS data (Table 8). Broadening the publication base to the Google Scholar data takes the Bank to fifth position on the h-index, behind Berkeley, Chicago, Harvard and Stanford.

Figure 7 shows the citation curve for the Bank alongside those of Harvard, Oxford, Princeton and Yale;<sup>18</sup> panel (b) enlarges the area around the origin to show more clearly the relative positions of the curves. In terms of the h-index, the raking is clear: Harvard has the highest index, followed by the World Bank, then Princeton, Yale and Oxford. However, with sufficiently high value of _k_ , the generalized h-indices start to show re-rankings as there are a number of intersecting citation curves, implying that robust comparisons are impossible if one allows for any increasing influence function. The claim that Harvard has greater aggregate influence than Oxford is robust, as is the claim that the World Bank dominates Oxford in influence. But the rankings of Harvard, Yale and the World Bank are ambiguous; measures of influence that give sufficiently high weight to high citations would put Yale on top, with the World Bank close behind. The ranking of Princeton and Yale also depends crucially on the weights; the higher the weight given to highly cited publications the more Yale is favored.

In Ravallion and Wagstaff (2010) we analyze these institutional rankings further by imposing more structure on the function relating citations to scholarly influence. In particular, we find that if we restrict the analysis to functions that exhibit declining marginal influence from citations then a clearer ranking emerges, with Harvard first, followed by Berkeley, World Bank, Princeton, Yale and Oxford. This is exactly the same ranking as the h-index. So, for these comparisons at least, the h-index ranks consistently with the theoretically ideal measured proposed by Ravallion and Wagstaff (2010).

Figure 8 gives the normalized citation curves for assessing the robustness of the comparison of average influence per publication. Very few unambiguous rankings are possible for any increasing influence function. The Harvard-Yale ranking switches when high citations are favored, which also favors the Bank over Princeton.

In Table 2 we saw that the Bank ranks first in terms of the number of articles it publishes in development. In Table 7 we see that the Bank’s articles in the top 16 development economics

> 18   Ravallion and Wagstaff (2010) provide citation curves and dominance test for a larger set of universities.

21
21 Table 1. Summary Data on Non-Remittance and Remittance-Receiving Households, Guatemala, 2000

|**Variable**|**Receive no**<br>**remittances**|**Receive internal**<br>**remittances (from**<br>**Guatemala)**|**Receive**<br>**international**<br>**remittances (from**<br>**USA)**|**t-test (No**<br>**remittances vs.**<br>**internal**<br>**remittances)**|**t-test (No**<br>**remittances vs.**<br>**international**<br>**remittances**|
|---|---|---|---|---|---|
|Mean household size|5.32<br>(2.46)|4.74<br>(2.69)|5.18<br>(2.68)|6.50**|0.52|
|Mean number of males in<br>household over age 15|1.35<br>(0.84)|1.19<br>(0.98)|1.21<br>(1.00)|6.67**|3.52**|
|Mean number of children<br>in household under age 5|0.88<br>(0.96)|0.62<br>(0.89)|0.69<br>(0.96)|8.14**|5.49**|
|Mean number of<br>household members over<br>age 15 with secondary<br>education|0.94<br>(1.07)|1.01<br>(1.19)|1.09<br>(1.17)|-2.07*|-4.17**|
|Household has electricity<br>(1=yes, otherwise zero)|0.71<br>(0.45)|0.78<br>(0.41)|0.83<br>(0.38)|-5.44**|-8.54**|
|Area (1 = urban, 2 = rural)|1.58<br>(0.49)|1.49<br>(0.50)|1.52<br>(0.49)|6.51**|5.02**|
|Mean annual per capita<br>income (excluding<br>remittances) in<br>Guatemalan quetzals|6,798.98<br>(14,021.5)|6,523.24<br>(8,351.7)|7,042.01<br>(13,307.4)|0.45|-0.12|
|N|5,665|1,063|593|||

Notes: N = 7,276 households; 88 households receive both internal remittances (from Guatemala) and international remittances (from USA). All values are weighted; standard deviations are in parentheses.

In 2000, 1 Guatemalan quetzal = US$0.128.

Source: Guatemala 2000 ENCOVI Survey, Instituto Nacional de Estadistica.

*Significant at the 0.05 level. **Significant at the 0.01 level.
- 24 -

Table 1. Indicators of Crises: A Review of the Literature

|Study|Sample and<br>Frequency|Country<br>Coverage|Indicators|Comments|
|---|---|---|---|---|
|**Eichengreen,**<br>**Rose and**<br>Wyplosz<br>(1995)|1959-1993,<br>quarterly|20 industrial countries;<br>78 crises, 33 successful<br>attacks and<br>45 successful defenses.|The authors mention that many of<br>(1)-(16) are defined with respect to<br>the same variable in Germany, but do<br>not specify which of them.<br>(1) change in international reserves<br>(2) real effective exchange rate<br>(3) credit growth<br>(4) Ml growth<br>(5) bond yield<br>(6) interest rates<br>(7) stock prices<br>(8) inflation<br>(9) wage growth<br>(10) GDP growth<br>(11) unemployment rate<br>(12) employment growth<br>(13) fiscal deficit/GDP<br>(14) current accountlGDP<br>(15) change in exports<br>(16) change in imports<br>(17) government victory<br>(18) government loss<br>(19) elections<br>(20) change in government<br>(21) capital controls<br>(22) left-wing government<br>(23) new finance minister<br>(24) past exchange market crisis<br>(25) past exchange market event|The behavior of (1)-(16) is<br>examined during the four years<br>around crises and events<br>compared to the evolution of<br>these variables around periods of<br>tranquility. " Events" include<br>significant changes in exchange<br>arrangements (such as<br>devaluations, decisions to float,<br>and widening of exchange rate<br>bands); " crises" overlap with<br>events but includes unsuccessful<br>speculative attacks and excludes<br>changes in exchange<br>arrangements not associated with<br>market pressures. The association<br>between (17)-(23) and foreign<br>exchange market events is also<br>examined. A subset of (1)-(25) is<br>then used to estimate the<br>**probability of:**a successful<br>defense, devaluation, revaluation,<br>floating, fixing, and other events.|
|**Flood and**<br>Marion (1995)|1957-1991,<br>monthly|17 Latin American<br>countries; 80 peg<br>periods of a duration of<br>at least 3 months.|(1) drift of the real exchange rate<br>(2) variance of the real exchange rate|A model is developed and tested<br>that examines the size and the<br>timing of devaluations-that is<br>the duration of the peg; the focus<br>is on trade-off between the cost<br>of realigning and the costs of a<br>misalignment.|

Note: Additional details on the individual countries included in the larger cross-country studies are available in the original studies.
**Table 2: Selective mortality (probability of death) by socio-economic status, 1975-1979**

||Family of Rural<br>Origin|Family of Urban<br>Origin|Sister with<br>secondary<br>education prior to<br>1975|Sister with less<br>than secondary<br>education prior to<br>1975|
|---|---|---|---|---|
|Both genders|0.0889|0.1472|0.1644|0.1068|
||[0.0015]|[0.0044]|[0.0120]|[0.0028]|
||N = 33677|N = 6416|N = 954|N = 12085|
|Males|0.1169|0.1848|0.2551|0.1472|
||[0.0024]|[0.0068]|[0.0204]|[0.0045]|
||N = 16725|N = 3196|N = 457|N = 5957|
|Females|0.0612|0.1104|0.07951|0.0677|
||[0.0018]|[0.0055]|[0.0121]|[0.0032]|
||N = 16906|N = 3204|N = 494|N = 6109|
|Family of Rural|||0.1221|0.1018|
|Origin|n.a.|n.a.|[0.0145]<br>N = 507|[0.0029]<br>N = 10282|
|Family of Urban|||0.2232|0.1450|
|Origin|n.a.|n.a.|[0.0200]|[0.0086]|
||||N = 434|N = 1671|

[standard error in brackets] **_Source_ :** Cambodia DHS (2000).

37
22 ensure that subjects understood the stakes of the game; it is possible that this lack of confidence reflects individual characteristics that influence both pro-social behavior and entry into government.

**Table 5:  Are real public sector workers more pro-social?**

|Dependent Variable: Amount Se|nt to Charityin|Dictator Gam|e<br>Accountants|
|---|---|---|---|
|Subjects|All<br>I|All<br>II|Only<br>III|
|Private sector (0) or public sector (1)?|60.969**|47.463*|82.961*|
|(0 = Universityof Indonesia;1 = STAN)|(28.18)|(27.92)|(48.91)|
|Age (in years)|-6.326|-7.022|-19.010|
||(10.90)|(10.77)|(19.67)|
|Gender (D)|54.19*|59.02**|35.390|
|(1 = Female)|(28.24)|(27.91)|(47.78)|
|Trust in Others|10.16*|8.860|-1.277|
|(9 = Believes others are trustworthy)|(5.50)|(5.44)|(9.61)|
|Belief that Charity was Paid|23.560|10.440|-4.703|
|(5 = Complete Confidence)|(16.36)|(16.29)|(28.82)|
|Charity Rating||57.882***|50.240***|
|(7 = Most effective)||(8.82)|(15.23)|
|Constant|531.112**|300.788|686.550|
||(229.40)|(229.40)|(418.90)|
|R-squared|0.008|0.032|0.024|
|P-Value|0.018|0.000|0.024|
|Observations|1723|1723|596|

Note: * p<0.1, ** p<0.05, *** p<0.01. OLS estimates, standard errors in parentheses. All subjects from the University of Indonesia (non-public) and STAN (public).

The results, presented in Table 5, show a significant difference between public officials (STAN students) and the students at UI. Subjects who have selected into public service (STAN) make significantly larger donations to the Indonesian Red Cross. They donate about 61 more tokens, or about eleven percent, than non-public officials. This is a large difference in the context of dictator games, in which ten percent differences in contribution rates are considered quite large. The second column adds subject _i_ 's rating of the effectiveness of the charity. This variable controls for subjects’ private information regarding the charity. However, their ratings of charity effectiveness are likely to be more heavily influenced by subjects’ overall pro-sociality, precisely what we capture with the amount sent in the dictator game. In fact, the two are highly correlated. Nevertheless, even controlling for the charity rating, students at STAN behave more pro-socially than students at UI. Subjects who rate the charity as more effective also, unsurprisingly, give significantly more. In addition, consistent with previous dictator games, women are more generous than men. These results are robust to additional controls such as religiosity, personal finances, and risk-taking.
ascertain the causality. Further, a younger bride may be less able to assert power and authority in her marriage especially given that women marry men who are on average several years older.<sup>6</sup>

# **Empirical Strategy and Data**

Our empirical strategy is to examine the propensity for young adults to be married as a result of a mortality event in their household, conditional on a set of background (pre-martial) conditions. Our data, discussed below, contain a sample of individuals who are children in 1991-1994 and who are re-interviewed as young adults in 2004. We use these data to estimate the effect of adult mortality on probability that an individual is married by 2004 using the following basic specification:

> where subscripts index individuals ( _i_ ), households ( _j_ ), time of first KHDS interview ( _t_ ), and number of years between baseline interview and last interview ( _s_ ) (which, in our data, is 10-13 years depending on the round in which the child was interviewed last at baseline). _Y_ is an indicator for having ever been married; for most of the sample, this variable captures the marital status in 2004 as few are divorced or widowed. Since marriage is universal in Tanzania, we interpret the probability of being married in 2004 as reflecting the probability of earlier marriage. We use an indicator for ever married rather than age at first marriage, which would allow for a more refined estimation, because we do not have this information. _X_ and _I_ contain sets of controls for household and individual characteristics, respectively. Orphan status ( _orphan_ ) captures the event of being orphaned prior to age 15. It is separated into being a maternal and paternal orphan.<sup>7</sup>

> 6 These are points also made by Mensch _et al_ . (2005) in their global study of trends in age at first marriage. 7 There are few double orphans in the sample and generally there is no additional impact of being a double orphan on the probability of being married in 2004, beyond the additive effect of the maternal and paternal coefficients. Therefore, an indicator for becoming a double orphan by age 15 years was omitted from the final estimations presented here. Likewise, for children who both become orphaned and experience a

4
**Figure 5: Kernel densities of non‐cognitive measures**

<!-- Start of picture text --> A. Conscientiousness B. Emotional stability<br>Z-Score of Goldberg Big 5 factor - z_gldconsc Z-Score of Goldberg Big 5 factor - z_gldemotstab<br>. -4 -2 Scores 0 2 .-4 -2 Scores 0 2<br>C. Kindness D. Cooperation<br>Z-Score of Goldberg Big 5 factor - z_gldkindness Z-Score of Goldberg Big 5 factor - z_gldcooperat<br>.-6 -4 Scores-2 0 2 .-6 -4 -2 Scores 0 2 4<br>E. Extraversion F. Openness to experience<br>Z-Score of Goldberg Big 5 factor - z_gldextrav Z-Score of Goldberg Big 5 factor - z_gldopeness<br>-3 -2 -1 0 1 2 -4 -2 0 2<br>Scores Scores<br>. .<br>G. Perseverance of effort (grit) H. Consistency of interest (grit)<br>Z-Score of Duckworth Grit factor - z_grt2persist Z-Score of Duckworth Grit factor - z_grt2consist<br>-4 -2 0 2 -2 -1 0 1 2 3<br>Scores Scores<br>. .<br>.5 .5<br>.4 .4<br>Density .3.2 Density .3.2<br>.1 .1<br>0 0<br>.5 .6<br>.4<br>.4<br>Density .3.2 Density<br>.2<br>.1<br>0 0<br>.4<br>.4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text -->

**26 |** P a g e
# **<u>Acronyms and Abbreviations</u>**

|<br>ACD|<br>Armed Conflict Dataset|
|---|---|
|AFD|French Development Agency|
|ARE|United Arab Emirates|
|BHR|Bahrain|
|BP|British Petroleum|
|DJI|Djibouti|
|DZA|Algeria|
|EAP|<br>East Asia and the Pacific|
|ECA|Europe and Central Asia|
|EE/FSU|Eastern Europe/Former Soviet Union|
|EGY|Egypt|
|GDP|Gross Domestic Product|
|GNI|Gross National Income|
|IDB|Inter-American Development<br>Bank|
|IMF|International Monetary Fund|
|IRN|Iran|
|IRQ|Iraq|
|JOR|<br>Jordan|
|KWT|Kuwait|
|LAC|Latin America and the Caribbean|
|LBN|Lebanon|
|LBY|Libya|
|MAR|Morocco|
|MENA|Middle East and North Africa|
|MTBF|Medium-Term Budget Framework|
|MTEF|Medium-Term Expenditure Framework|
|MTFF|Medium-Term Fiscal Framework|
|MTPF|Medium-Term Policy Framework|
|NRM|Natural Resource Management|
|OECD|Organisation for Economic Co-operation and Development|
|OGM|Oil, Gas, and Minerals|
|OMN|Oman|
|PE|Political Economy|
|PEA|Political Economy Analysis|
|PFM|Public Financial Management|
|PPA|Public Policy Attributes|
|PREM|Poverty Reduction and Economic Management Network|
|PRIO|Peace Research Institute Oslo|
|PRMPS|PREM Public Sector Governance Anchor|
|PSD|Private Sector Development|
|PSM|Public Sector Management|
|QAT|Qatar|
|<br>SAU|<br>Saudi Arabia|
|SME|Small or Medium-Sized Enterprise|
|SSA|<br>Sub-soil Assets|
|SYR|Syria|
|TUN|<br>Tunisia|
|UAE|United Arab Emirates|
|WDI|World Development Indicators|
|YEM|<br>Yemen|

iv
indicators and determinants), to which the MAMS baseline simulation is calibrated. (See Appendix 1 for more details.) The database for the current application was designed in light of data availability and the analytical objective of shedding light on the impact of alternative changes in government spending and taxation on MDGs and other major social and economic indicators. It includes 30 activities and commodities with one-to-one mapping (i.e., one commodity output matches every activity and vice versa), including 5 in agriculture, 6 in industry, and 19 services, 9 of which are provided by the government. Among the 19 services, 9 (5 government and 4 private) are related to human development. The government sectors also include infrastructure services and capital stocks, split into agriculture, energy, and other. The factors of production are split into two types of land (for large and small farms) and different types of labor and capital.

# <u>4. SIMULATIONS</u>

# BASE

The _base_ scenario is designed to represent a plausible, business-as-usual projection for Pakistan’s economy for the period up to 2022, responding to the question: what is the likely evolution of Pakistan’s economy up to 2022 given it current state and in the absence of major policy changes? It also serves as a benchmark for comparisons with alternative simulations. The simulations start in 2008, the base-year for the SAM and the bulk of the database. In the reports, 2012 is treated as the starting point given the objective of providing comparisons to the current situation and given the forward-looking nature of this analysis.<sup>11</sup> Our presentation of the _base_ scenario starts with the key assumptions, followed by an analysis of its simulation results.

# <u>Key Assumptions</u>

For the _base_ scenario (but, unless otherwise noted, not for the other scenarios), growth in GDP at factor cost is exogenous. Drawing on the current World Bank assessment, for 2013-2016 and 2017-2022, the annual growth rates are set at 4.1 and 4.5 percent, respectively, the latter coinciding with Pakistan’s long-term trend growth. This growth exceeds the annual average of 2.8 percent that was observed for 2009-2012. Among other assumptions, it is important to take note of the following:

- Government receipts. Taxes (direct, domestic indirect, and import) and foreign grant aid, all expressed as shares of GDP, evolve on the basis of IMF projections up to 2016

> 11 The assumptions and the results for the period 2008-2012 are identical across all simulations. They are focused on the replication of changes for this period (as opposed to year-to-year changes). The solution for 2012 is calibrated to a macro SAM for this year (see Table A2.2), ensuring that the solution for 2012 replicates available macro data and projections. The macro SAM is primarily based on data in IMF (2012).

21
# **3 Data**

We compiled data on salaried workers from three waves of nationally representative household surveys in Afghanistan: the National Risk and Vulnerability Assessment (NRVA) in 2007, the Afghan Living Condition Survey (ALCS) in 2014, and the Income, Expenditure, and Labor Force Survey (IEL) in 2020. The research team obtained these datasets from the National Statistical and Information Authority (NSIA) under a restricted use agreement. We believe that combining data from the three survey waves offers a more comprehensive understanding of the returns to education by accounting for macro-level shocks, which can drive results in a single period. The analytical sample includes working-age individuals (14–65 years old) who reported their monthly income. NRVA 2007 reports labor force participation and wages for household members 16 years of age and older, while ALCS 2014 and IEL 2020 report labor information for household members aged 14 years of age and older. To calculate annualized income, we first converted the monthly income reported in Afghan currency to United States dollars using the official exchange rates from the annual statistical books published by the NSIA. We then multiplied the converted amount by 12 to estimate yearly income. To simplify the interpretation of returns to schooling on annualized income as a percent change in a person’s income, we took the natural logarithm of the annualized income.

The surveys report individuals’ education levels, which we convert into years using the official education system structure. School education in Afghanistan follows a 6-3-3 system: 6 years of primary education, 3 years of secondary education, and 3 years of high school. In tertiary education, individuals can pursue either a 2-year associate degree or a 4-year undergraduate degree, followed by a graduate degree.

We report the descriptive statistics for the analytical sample in Table 1. Column 1 provides pooled summary statistics, while columns 2 to 4 show survey-specific statistics. The average annualized income is $1,892, and 10 percent of the sample is women. Two-thirds of salaried workers are employed in the public sector, and the average age is 34. About 20 percent were of the age that would have been affected by an increase in compulsory general education from grade 6 to grade 9 at the time of the 2004 constitution—the constitution was formally ratified on January 26, 2004, and the academic year in majority of the country starts on March 21, 2004; hence, we expect them to be the first cohort impacted by the policy change. Education was compulsory up to grade 6 until the 2004 constitution, which increased the mandatory years of schooling to grade 9. Therefore, the first cohort likely to be affected by this policy is individuals born in 1991, as they were on the verge of transitioning to secondary education and starting grade 7.

6
gaining more education (Cox-Edwards and Ureta 2003 for El Salvador, Yang 2008 for Philippines, UNDP 2009).<sup>7</sup> Evidence from rural Pakistan suggests that temporary migration is associated with higher school enrollment, especially for girls (Mansuri 2006). Furthermore, migration has been observed to increase health knowledge in addition to the direct effect on wealth, which has led to lower rates of infant mortality and higher birth weights in Mexico (Hildebrandt & McKenzie 2005). Visiting and returning migrants may also bring back health-improving practices such as drinking safe water and better sanitation (UNDP 2009, p. 79).

On the other hand, migration as such might also present a threat to migrant health as certain jobs expose migrants to occupational hazards, such as tuberculosis, pneumoconiosis and workplace injury by mine workers (Kahn _et al._ 2003). Increased mobility of workers has also contributed to a rapid spread of communicable diseases such as HIV (Decosas _et al._ 1995; Lurie 2000; Lurie _et al._ 2000; Brummer 2002). For instance, Kane _et al_ . (1993) find that 27 percent of the male Senegalese migrants were HIV positive compared to 1 percent for non-migrants males from the same area. Sexually transmitted infections are also more likely to spread among migrants themselves as well as their permanent partners residing in the sending communities (Kahn _et al._ 2003).

**High-skilled emigration or the so-called “brain drain” can imply a loss of public resources invested in their education, can reduce the sending country’s productive capacity, and can worsen the business environment, especially in small economies.** The emigration of the highly skilled can be particularly important in the education and health sectors in small countries that face severe shortages of health workers (Docquier _et al._ 2010b).<sup>8</sup> Moreover, the departure of doctors may result in underemployment of nurses and other auxiliary staff (Commander _et al._ 2004). However, as argued by Dustmann _et al_ . (2010), return migration can lead to mitigation of the brain drain, if not a net ―brain gain‖. When the migrants return, they have usually acquired skills that are needed in the sending community as manifested by a sizable wagepremium paid to the returned migrants (Wahba 2007). Furthermore, Mountford (1997)

> 7 Contradicting evidence has been found in Mexico (McKenzie & Rapoport 2009).

> 8 For example, the vacancy rate in Ghana’s public sector health positions was 47 percent for doctors and 57 percent for nurses in 2002 (Dovlo 2003).

7