rafmacalaba/datause-encoder-data huggingface dataset:
social returns taking into account externalities. Since externalities from education are in general positive, the inclusion of externalities would increase the returns.

# **IV- FINDINGS**

This section presents the estimated returns to education. First, it describes the rates of return in the base case scenario that best answers the title question on profitability of formal lifelong learning. Second, we discuss how various individual and educational factors affect the gains from re-entering the education system.

# **IV-1 HOW AGE AFFECTS RETURNS TO FORMAL LIFELONG LEARNING**

Based on the base-case scenario, we find high rates of return to re-entering the education system. For a 20 year-old, the returns to primary, secondary and tertiary education are 8 percent, 14 percent, and 17 percent. On an international scale, these returns are quite high, and point to the need for continued strong emphasis on investment in education and further reforms to facilitate access to education.

The returns differ across education levels. The return to tertiary education exceeds that of secondary education by three percentage points and primary education by 9 percentage points. This pattern of rising return to schooling is similar to that of several of the other major Latin American countries, Sanchez-Paramo and Schady (2003). The driving force is primarily a rising demand for highly skilled labor with which the education system has not been able to keep pace.

**Figure 4 - Rates of return to education by age (base scenario)**

<!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>20 25 30 35 40 45 50 55<br>-5%<br>-10%<br>-15%<br>Age at re-entry to Education<br>Primary Secondary Tertiary<br>Internal Rate of Return<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000 Note: Annex table 7 presents the full estimation results 13
# **<u>Table A.2. Poverty Line, Arab Republic of Egypt by Region, in LCU, Per Capita Annual Consumption in Survey Year Prices</u>**

|||**Food**<br>|**poverty line**<br>|||**Po**<br>|**verty line**<br>||
|---|---|---|---|---|---|---|---|---|
|**Region**|**2012/2013**|**2015**|**2017/2018**|**2019/2020**|**2012/2013**|**2015**|**2017/2018**|**2019/2020**|
|**Urban governorates**|2748|4318|6065.3|7071|4320|6141|9280.1|11285|
|**Urban lower Egypt**|2484|3835|5667.6|6304|3840|5631|8536.9|9755|
|**Rural lower Egypt**|2568|3854|5901.7|6570|3852|5675|8673|10108|
|**Urban upper Egypt**|2568|3968|5752.1|6553|3972|5823|8728.5|10225|
|**Rural upper Egypt**|2496|3760|5896.5|6484|3756|5694|8865.6|10068|
|**Urban frontier**|2736|3990|5924.3|6696|3996|6247|8568.7|10409|
|**Rural frontier**|2688|3979|6304.7|7074|3984|5788|8979.3|10788|
|**Total**|2568|3921|5889.6|6604|3924|6141|8827|10279|

Source: Compiled from various CAPMAS Poverty assessment updates.

45
# **Applied Services Trade Policy**

A Guide to the Services Trade Policy Database and the Services Trade Restrictions Index Ingo Borchert (1) Batshur Gootiiz (2) Joscelyn Magdeleine (3) Juan A. Marchetti (3) Aaditya Mattoo (2) Ester Rubio (3) Evgeniia Shannon (3)<sup>**1,2**</sup>

**JEL codes:** F13, F14, F23, L80

**Keywords:** services, trade policy, investment, STRI, trade restrictions, quantification

_(1) University of Sussex_ _(2) World Bank_ _(3) World Trade Organization_ For Batshur Gootiiz and Evgeniia Shannon, affiliation at the time of writing.

> 1 Acknowledgments:  The authors would like to express their gratitude to all WTO and World Bank colleagues who assisted in the process of creating this Database; in particular Laura Baiker, Renjie Ge, Cecilia Heuser, Giulia Jonetzko, Justine Lan, Anastasiya Letnikava, Clemence Moreau and Fatima Anjum Quraishi. Cooperation by the OECD Secretariat, who made available data from the OECD Services Trade Restrictiveness Database (2016 vintage), which was used as source of information for 43 economies in the World Bank-WTO Services Trade Policy Database, is gratefully acknowledged. This paper is part of a World Bank research project on trade in services, supported in part by the Umbrella Trust Fund for Trade and Development.

> 2 Disclaimer:  This is a working paper, and hence it represents research in progress. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They are not intended to represent the positions or opinions of the WTO or its Members and are without prejudice to Members' rights and obligations under the WTO. They also do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, the Executive Directors of the World Bank, the governments they represent, or any of the aforementioned individuals. Any errors are attributable to the authors.
_-51 -_ from the inverse of price-earnings ratios, where book earnings are corrected to account for inflation's effects on the capital stock, inventories and debt liabijities. The arbitrage assumption requires that this also equal the before-tax return on new issues. For calculations at the industry level, an industry-specific risk premium can be calculated from capital-asset pricing model studies and adjusted for leverage.

_(ii) Inflation Rate_ (ir). Again, King and Fullerton simply assume a given rate of inflation. Alternatively, the expected inflation rate can be estimated using an ARIMA forecast based on the consumers price index as in Boadway, Bruce and Mintz(1984).

_r*v) Real Capital Gains (4Iq,p/p)._ Expected capital gains on capital goods and resources can, in principle, be estimated by the same source procedure ,.3 for inflation, using the appropriate capital good series or resource price index. For resources subjec **t** o royalties, it is also necessary to know the profit margin _(p-c)p._ These can be calculated by using estimates of the short run cost function. In most applications, real capital gains have simply been ignored since their estimates are regarded as not being reliable.

> _(v) Depreciation Rate_ (5). Depreciation rate calculations are typically based upon length of life data for various types of capital. Where necessary, service lives can be aggregated using as weights the proportions of gross investment. Service lives L can be converted to equivalent exponential depreciation rates by the formula 6=2/L. See Hulten and Wykoff (1981).

> _(vi) Holding Period for Inventories (7)._ These can be calculated from the ratio of average monthly inventories to average monthly shipments.

> _(vii) Corporate Tax Parameters (Cf, u, O)._ In most countries, the corporate tax rate and the investment tax credit rates depend upon the type and size of industry, and the type of investment. For each type of capital good, statutory tax rates can be aggregated appropriately according to the share of income taxable at various rates. A similar procedure can be used for the investment tax credit. For depreciation rates, when the tax system allows declining balance write-offs, a can be calculated as an average of th-e rates applicable to various types of capital using as weights the amounts of gross
Another issue in the IV interpretation of rainfall is that it may be capturing not only agricultural productivity shocks but also resulting price changes. In a completely segmented rice market at the upazila level, a rainfall shock would a¤ect the equilibrium rice price through income e¤ect. However rice market is the most developed and spatially integrated market in Bangladesh (see, for example, Golleti, Ahmed and Farid (1995), Hossain and Verbeke (2010)). In addition, we control for the distances to the main city markets, which would capture spatial price dispersion due to transport costs. The theoretical model assumes that rice price is pinned down by the international market, and available evidence on rice markets in Bangladesh clearly supports this assumption.

# (5.2) Rainfall, Agricultural Productivity and Labor Market Outcomes

We start by presenting the regression results for wages for hired daily laborers employed in farming.<sup>20</sup> Consistent with proposition (1) of the theoretical model, the results in column (1) of Table 3 show a statistically signi...cant and positive impact of rainfall on agricultural wages. This result suggests that the income of unskilled workers employed in agriculture, who are mostly landless poor people, gets a boost from higher agricultural productivity. The estimated coe¢ cient implies that a one percent increase in rainfall increases agricultural wages by 0.46 percent. Our theoretical results imply that even when agricultural productivity increase has a signi...cant positive e¤ect on the wages of unskilled farm labor, hired labor in farming may increase or decrease depending on the relative shifts in the labor demand (due to productivity growth) and supply (due to reallocation of labor from home to market work).

> 20The number of observations for wage regression is slightly less (341 upazilas). This is because the wage data is trimmed by dropping 2.5% of observations in both upper and lower tails of the distribution. While such trimming is done to correct for coding mistakes. However, such trimming does not a¤ect our result: if anything the estimated coe¢ cient of rainfall is larger in the untrimmed data.

25
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# <u>Table A2. Analysis of differential attrition (Panel B)</u>

||N|(1)<br>**Full data**<br>Mean[SD]|N|(2)<br>**No skills measures**<br>Mean[SD]|N|(3)<br>**Full attrition**<br>Mean[SD]|t-test<br>Diff<br>(1)-(2)|t-test<br>Diff<br>(1)-(3)|Total obs.|
|---|---|---|---|---|---|---|---|---|---|
|**Location - 2016**||||||||||
|Original village|4375|0.74<br>[0.44]|562|0.42<br>[0.49]|552|0.71<br>[0.45]|0.32***<br>(0.02)|0.03<br>(0.03)|5489|
|Within district|4375|0.15|562|0.12|552|0.11|0.02|0.04***|5489|
|||[0.35]||[0.33]||[0.31]|(0.02)|(0.02)||
|Within country|4375|0.09|562|0.21|552|0.13|-0.12***|-0.04**|5489|
|||[0.29]||[0.41]||[0.34]|(0.02)|(0.02)||
|Outside country|4375|0.02|562|0.25|552|0.05|-0.22***|-0.03**|5489|
|||[0.16]||[0.43]||[0.22]|(0.02)|(0.01)||
|**Individual - 2016**||||||||||
|Respondent is female|4337|0.49|531|0.33|339|0.54|0.16***|-0.05*|5207|
|||[0.50]||[0.47]||[0.50]|(0.02)|(0.03)||
|Age|4337|22.36<br>[3.72]|531|23.83<br>[3.17]|339|22.73<br>[3.46]|-1.48***<br>(0.15)|-0.38**<br>(0.18)|5207|
|Ever married|4337|0.38<br>[0.48]|531|0.40<br>[0.49]|339|0.29<br>[0.45]|-0.03<br>(0.02)|0.09***<br>(0.03)|5207|
|Enrolled|4173|0.15<br>[0.36]|381|0.03<br>[0.17]|299|0.19<br>[0.39]|0.12***<br>(0.01)|-0.03<br>(0.02)|4853|

* p<.10  ** p<.05  *** p<.01

_Notes_ . Columns (1) to (3) display the means for the group that answered all parts of the survey, the group surveyed that did not complete the cognitive and socioemotional assessments (either because they were surveyed over the phone or information was collected indirectly), and the group who attrited fully. Standard deviations are shown in brackets. Column (4) is the difference between the mean of respondents with complete data and those who do not have skills measures. Column (5) is the difference between the mean of respondents with complete data and the mean of attritors. Panel A shows differences in variables measured in this latest round of data collection, in 2018, so we cannot report data on full attritors. Panel B reports data measured during the tracking exercise of 2016. Panel C reports data measured in 2003 at the individual level. Panel D reports data measured in 2003 at the household level. Differences in means are computed by OLS regressions. All standard errors in parentheses are clustered at the village level.

.

77
**Table 10. Access to finance, and corporate governance and control**

|State banks|Shareholding|City comm.|Non-gov’t|Individual|Foreign|<br>Account|
|---|---|---|---|---|---|---|
||banks|banks|institution||banks|payable|
|**Panel A: Distribution of financi**|**al access over**|**time (in 10,000**|**Yuan)**||||
|2000<br>320.3|.|.|71.11|.|.|.|
|2002<br>251.4|.|.|27.42|.|.|46.26|
|2004<br>492.5|65.99|105.8|32.98|.|.|69.51|
|2006<br>407.4|58.41|128.0|5.865|42.28|0.293|63.26|
|2008<br>471.9|71.41|165.5|15.42|38.03|4.529|74.79|
|2010<br>1390|.|.|.|.|.|83.25|
|2012<br>1636|.|219.4|48.33|.|.|250.5|
|Total<br>759.2|65.41|161.2|28.74|40.08|2.490|106.7|
|**Panel B: Bank loan and trade c**|**redit: Inland r**|**egions v. Coas**|**tal regions (n**|**ormalized by**|**asset)**||
|Inland<br>0.535|0.013|0.150|0.090|0.124||0.091|
|Coastal<br>0.550|0.023|0.184|0.069|0.099||0.139|
|**Panel C: Political connection an**|**d financial acc**|**ess (normaliz**|**ed by asset)**||||
|Non party mem.<br>0.537|0.013|0.148|0.086|0.104||0.111|
|Partymem.<br>0.620|0.026|0.192|0.079|0.121||0.134|
|**Panel D. Corporate control and**|**governance st**|**ructure**|||||
|Year<br>Share held by|Major decisions|<br>Board of|Board of|Board o|f|Using|
|entrepreneur|made by|shareholders|directors|superviso|rs|professional|
||entrepreneur|||||manager|
|**D1. Corporate control and gove**|**rnance structu**|**re over time**|||||
|1993<br>.|0.64|.|0.26|.||0|
|1995<br>0.91|0.57|.|0.28|.||0|
|1997<br>0.78|0.55|.|.|.||0|
|2000<br>0.78|0.44|0.28|0.44|0.23||0.03|
|2002<br>0.76|0.40|0.34|0.48|0.27||0.02|
|2004<br>0.66|0.35|0.59|0.75|0.37||.|
|2006<br>0.68|0.38|0.58|0.63|0.37||0.07|
|2008<br>0.66|0.34|0.58|0.57|0.34||0.06|
|2010<br>0.64|0.52|0.57|0.58|0.32||0.12|
|2012<br>0.75|0.48|0.61|0.58|0.32||0.25|
|Total<br>0.73|0.45|0.51|0.54|0.32||0.08|
|**D2. Corporate control and gove**|**rnance structu**|**re over firm s**|**ize (Asset qua**|**ntiles)**|||
|0-25%<br>0.78|0.60|0.50|0.33|0.21||0.050|
|26%-50%<br>0.75|0.51|0.49|0.45|0.27||0.060|
|51%-75%<br>0.73|0.43|0.51|0.55|0.31||0.070|
|76%-100%<br>0.69|0.30|0.56|0.73|0.47||0.100|

_Data sources: National Surveys of Privately-Owned Enterprises in China_ 46
- Verhoeff, A. P., G. Hoek, J. Schwartz, and J. H. Van Wijnen. (1996). Air Pollution and Daily Mortality in Amsterdam. _Epidemiology_ 7(3, May):225-30.

- Wilson, R., and J. Spengler. (1996) _Particles in Our Air._ Harvard University Press, USA.

- Wordley, J., S. Walters, and J. G. Ayres. (1997). Short-tenn variations in hospital admissions and mortality and particulate air pollution. _Occup. Environ. Med._ 54:108-16.

- Zmirou, D., T. Barumandzadeh, F. Balducci, P. Ritter, G. Laham, and J. P. Chilardi. (1996). Shortterm effects of air pollution on mortality in the city of Lyon, France, 1985-90. _J. Epidemiol. Community Health 50_ (April): Suppl I :S30-S35.

_51_
# **5. DISCUSSION**

The purpose of this review was to comprehensively triangulate, describe, and analyze longitudinal HIV incidence trends in select Sub-Saharan African countries between 1990 and 2012. Proxies of HIV incidence (HIV prevalence in young females) from nationally reported sources and published studies were compared to each other, and UNAIDS modeled HIV incidence curves were compared to HIV incidence empirically obtained from trials and studies. In addition, HIV incidence trends were related to temporal trends in ART scale-up from 2004 to 2012.

One challenge encountered in this review was the scarcity of _measured_ HIV incidence data in many countries. There is a dearth of published studies reporting directly measured HIV incidence, including those from the ALPHA Network, and for many countries, no measured HIV incidence data could be identified. The scarcity of data is most profound before 2001, which presents a challenge to the certainty in which national HIV epidemics can be modeled prior to this year. Uganda, Zimbabwe, and South Africa are exceptions to this general lack of measured incidence data, as a number of studies reporting HIV incidence rates from intervention trials or observational studies have been carried out in these countries. In general, measured HIV incidence rates tend to be higher in sub-national studies in specific demographics than corresponding UNAIDS incidence estimates for these three countries, though incidence rates measured within the general population (not specific demographics or high-risk groups) of Uganda tend to be similar to the UNAIDS estimates and the overall incidence trend in Zimbabwe mirrors that projected by Spectrum.

Another difficulty in longitudinally assessing incidence trends was the scarcity of _repeat_ cross-sectional, nationally representative HIV prevalence surveys. In many cases, only a few ANC and DHS/AIS reports through the years of interest could be readily found, leaving few data points from which to infer long-term incidence trends. While many of the individual reports from sentinel surveillance or DHS/AIS surveys could be obtained from Measure DHS, governmental websites, or other online sources, a central repository for these reports, even within the UNAIDS website, would be a useful resource for those conducting similar analyses. Recently (as of late 2013, before compilation of data in this review), national Spectrum files used to estimate prevalence were made available by UNAIDS through request at <u>http://apps.unaids.org/spectrum/.</u> ANC data was more abundant than DHS/AIS data, which comes as no surprise due to the logistical and financial challenges of carrying out household-based surveys. However, as pointed out by Montana et al., in the cases of Ethiopia and Tanzania (Mahy, GarciaCalleja, and Marsh 2012), and also found here in other countries (Lesotho, South Africa, and Botswana), HIV prevalence in young ANC clients aged 15-24 is higher than prevalence measured in females from DHS/AIS in the same age bracket, a chief reason being different sexual behaviors in these two groups. Also, ANC data are subject to a 46
through interviews with bank officials to describe ongoing changes in the relation between banks and SMEs. To complement the information coming from banks, we also analyze data obtained from existing SME surveys.

The data come from 3 different sources. First, we use data gathered for this project across banks for a group of countries via a specially designed questionnaire, which was completed during on-site interviews with bank business and risk managers in Argentina, Chile, Colombia, and Serbia by different teams of the World Bank during late 2006 and early 2007.<sup>18</sup> This type of information provides a good picture of the market environment and structure in which banks serve SMEs in specific countries. In all of the countries included in the sample, surveyed and interviewed banks account for a large share of each country’s banking sector assets (normally around or over 70 percent). Second, we analyze data collected during 2006 by the International Finance Corporation (IFC) via surveys conducted across 8 developed and developing countries: Australia, Brazil, India, the Netherlands, Poland, Thailand, the UK, and the US.<sup>19</sup> This information complements well the mentioned within-country surveys because it depicts how banks that are leaders in the SME business segment of the market operate, especially in terms of business models and risk management processes. Third, we use surveys of SMEs conducted annually during 2002-2006 in 7 economies: Argentina, Chile, Colombia, Mexico, Peru, Puerto Rico, and Venezuela. These surveys, which were undertaken by FRS (Inmark Group), a consulting firm specializing in financial services research and

> 18 Complete case studies are available for each of these countries describing the institutional and macroeconomic contexts, their banking industries and trends, and the data in detail. See World Bank (2007a and 2007b) and Stephanou and Rodriguez (2008).

> 19 See IFC (2007) for a complete description and analysis of the IFC surveys.

7
integration with other countries in order to support the hypotheses discussed in the paper.<sup>2</sup> In this paper, I draw on the public finance/fiscal federalism literature to shed light on the positive and normative aspects of the relationship between economic integration and institutional standardization/harmonization. In one sense, economic integration among different states within a nation or of different countries in a global economy can be viewed as a set of points along a continuous line representing larger and larger entities—with one glaring difference at the political economy level. Nation states are overseen by an entity that (ideally) promotes the overall growth and poverty reduction goals within a country and that will take the redistributive measures necessary to achieve these goals, while the international economy does not have such a sovereign entity. In terms of the influence exerted, the international setting is dominated by wealthier or larger countries and large multinational firms (at the country level the analogy being large firms or wealthy landowners). Imbalances in the influence exerted by different countries and the absence of a sovereign power make choosing any international rule difficult, and may be expected to bias the outcome of such a rule to a greater extent in favor of the economically powerful.

Alesina and others (1997, 1999, 2001, 2004, 2002), Feldstein (1997), Casella and Feinstein (1990), and Sachs and Sala-i-Martin (1997) among others have differentiated between the economic and the political desire to unify or harmonize countries’ policies and institutions in the context of multiple sovereign nations trading together. Much of this work has focused on the particularities of the progressively tighter links between the

> 2 “modified” in the sense that the paper does not discuss one country over a period of time but several countries at a point in time.

3
on empowerment of female host-community members are missing in the literature. We add to these studies by looking at the presence of IDPs as a mechanism through which the spatial spillover of conflict potentially operates.

# **2.2 Violence and Forced Displacement in Nigeria**

According to the Global Terrorism Index 2020, Nigeria was the country third most affected by terrorism, after Afghanistan and Iraq. Nigeria accounts for almost half of all deaths caused by organized violence in Africa (Pettersson and Öberg, 2020). Together Boko Haram and Fulani extremists account for almost 80 percent of terror attacks and roughly 90 percent of terror victims in Nigeria.

Nigeria is the seventh country in the world ranked by number of people who are internally displaced, with 2.73 million in 2020 (IDMC, 2021). Most of the displacement is due to violent conflict, in particular the Boko Haram insurgence. The Islamist group was founded in 2002 to counter Western education and democracy. Its Hausa name translates to “Western education is forbidden.” The group’s focus on forbidding Western education is unique among jihadist groups. The group has been responsible for much of the surge in terrorist activity in Nigeria and for thousands of attacks and deaths in the Chad Basin region. Since 2011, Boko Haram activity has led to over 20,000 terrorism-related deaths and 37,500 combat-related deaths. To date, Boko Haram is the deadliest terrorist group in Sub-Saharan Africa and the fourth-deadliest group worldwide (IEP, 2020).<sup>5</sup> Attempts of the Nigerian government to neutralize the group started in 2012, after which the conflict escalated quickly and peaked in 2014–15. In 2014, the group kidnapped almost 300 schoolgirls from the town of Chibok, more than 100 of whom were still missing as of 2020 (Strochlic, 2020). In the same year, the group was responsible for the first attack on the capital of Abuja, targeting a bus park where more than 70 people were killed. After 2014, the number of displaced people originating from Borno State increased rapidly. The group pledged allegiance to the Islamic State of Iraq and Syria (ISIS) in 2015 and since then has broken into factions.

> 5After Al-Shabaab, the Taliban, and the Islamic State of Iraq and the Syrian Arab Republic.

12