rafmacalaba/data-use-mentions-extended huggingface dataset:
16 in 1992) and cohorts who should have completed their mandatory school education before the conflict started. The education of the “young” cohort may have been affected by various disruptions associated with the conflict, such as lack of school facilities and teachers, decrease in household income, uncertainty and insecurity associated with the conflict.

Figures 1 and 2 show the average years of schooling completed by women and men by year of birth using the 2003 and 2007 data respectively. The solid lines represent the average educational attainment by individuals who lived in the regions not significantly affected by the war (mostly in Sugd and GBAO) while the dashed line represents the average educational attainment by cohorts of individuals who lived in the more conflict affected regions (mostly in Dushanbe, Khatlon and RRS). Figure 1 indicates that the younger cohort of women in the conflict affected regions (age 2-16 in 1992) obtained about 0.54 fewer years of schooling than women who were of the same age but lived in the lesser affected regions.

Figure 2 presents longer-term evidence of the effect of the conflict on education. The gap in education between the more and less conflict affected areas is greater for the younger cohorts (aged 2-16 in 1992) (0.52 years less) than the gap for women who were aged 18-34 in 1992 (0.21 years less) and who should have completed their schooling before the conflict started. Interesting observation: The gap is the largest for those aged 9-16 in 1992, averaging 0.62 years of schooling, and those aged 3-5 in 1992, averaging 0.71 years of schooling. The gap is the smallest for those aged 6-8 in 1992, at 0.28 years.

# **Regression results**

In the regression analysis that follows, I first use the 2007 TLSS data to estimate the determinants of completion of “basic level of schooling or higher” (that constitutes eight or nine grades depending on when an individual entered schooling) and “secondary school or higher” levels of education
> **Figure 11 Total fiscal risks from early termination of public-private partnership portfolio in sample countries as percentage of annual government revenue**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>Low Medium less low High less medium<br>AlbaniaArgentina Bhutan BrazilBulgariaBurundiEthiopiaGeorgia GhanaIndonesia Kenya Kosovo PeruRomaniaSolomon IslandsSouth AfricaUkraine<br>Percent of annual government revenue<br><!-- End of picture text --> _Source:_ Authors’ calculations.

_Note:_ Total fiscal risks are the maximum expected loss at the 99 percent confidence over the entire contract period, expressed as percentage of GDP of a single year.

# _5.5.2 Adverse Macro-Financial Shock_

Multiple types of macroeconomic shocks have significant effects on the probability of early termination (see table 2). The fiscal risks presented so far assume no macro-financial shock, such as depreciation of local currency or a stress in the banking sector.

This section simulates the results of a macro-financial shock. The Systematic Banking Crises dataset of Laeven and Valencia (2020) identifies 104 banking crisis episodes among the countries included in the PPI dataset, 13 of which also involved both sovereign debt and currency crises. During these 13 episodes, the maximum yearly deviation in depreciation rate from its long-run average ranged from 15.1 to 116.0 percentage points, with an average of 48.3 percentage points.<sup>16</sup> The simulation assumes a 48.3 percentage-point depreciation shock and the occurrence of both a banking and a debt crisis in year 0.<sup>17</sup> The PPP portfolio of each country is assumed to

> 16 The statistics exclude two episodes in Ecuador, because the adjusted currency conversion factor remained stable even though the official rate of depreciation was 56.8 percent in the banking crisis of the early 1980s and 77.2 percent in the crisis of the late 1990s.

> 17 The scenario abstracts from
0.0001)|0.129<br>(0.0001)|0.080<br>(0.0001)|
|Education<sup>2</sup><br>(x 100)|0.225<br>(0.0001)|0.181<br>(0.0001)|0.283<br>(0.0001)|0.438<br>(0.0001)|
|Experience|0.075<br>(0.0001)|0.085<br>(0.0001)|0.087<br>(0.0001)|0.062<br>(0.0001)|
|Experience<sup>2</sup><br>(x 100)|-0.105<br>(0.0001)|-0.119<br>(0.0001)|-0.121<br>(0.0001)|-0.080<br>(0.0001)|
|Gender|-0.638<br>(0.0001)|-0.590<br>(0.0001)|-0.635<br>(0.0001)|-0.493<br>(0.0001)|
|_______________|0.525|0.538|0.547|0.474|

Source: Authors' calculations based on the "Pesquisa Nacional por Amostra de Domicilios" (PNAD). P-values in parentheses.

# **[See Figure 4 in Appendix 4]**
enroll the vast majority of students in Jordan.<sup>5</sup> One can clearly see that the growth in the supply of public basic schools started to take off for the cohort born in 1950 and continued at a rapid pace through the mid-1970s cohort. Initially more boys’ schools were being built, corresponding to the early school acquisition of Jordanian males (panel [A] of figure 2). The supply of girls’ and mixed schools was also rising for the cohorts born in 1950 onward, but by the 1965 cohort, there appears to have been a concerted effort to dramatically increase the supply of mixed schools. This corresponds roughly to the cohort of Jordanian women that have experienced the largest increase in schooling relative to previous cohorts. Indeed, an examination of the 2010 school census data reveals that the majority of students in mixed schools are girls at both the basic and secondary levels (70 percent and 86.5 percent respectively), which suggests that the rapid increase in schooling for females was primarily accounted for by the growth in mixed schools. On the other hand, the growth in the supply of boys’, girls’, and mixed secondary schools was steady across the observed birth cohorts (panel [B] of figure 2). The number of boys’ secondary schools being equal to the number of girls’ and mixed secondary schools combined, bearing in mind the fact that mixed secondary schools were _de facto_ girls’ schools.

Since the empirical analysis is contingent on the exogeneity of the growth in the supply of schools at the local level, controlling for governorate-level time-varying heterogeneity, an analysis of its association with the initial observable characteristics of the local populations is necessary. Because there are no reliable historical data on the characteristics of subdistricts in Jordan, the characteristics of those 60 and older from the 2004 population census (who have

> 5. Data from the JLMPS 2010 indicate that 94.1 percent of Jordanians between the ages of 25 and 35 who went to basic schools within Jordan
of the world, in particular, developing countries. When MRAs are open to ...rms regardless of origins, both intra-regional trade and trade with the rest of the world, especially with developing countries, rise substantially.

We noted in the introduction that multilateral rules on goods trade have taken a permissive approach to regional agreement on standards. It is neither feasible nor desirable to restrict the freedom of countries to harmonize or mutually recognize their standards. However, more could be done to protect the rights of countries that are excluded from such agreements. This is particularly important because few of the agreements on standards include developing countries, and the big di¤erences in social preferences over issues such as safety and the environment suggest that few developing countries are likely to be party to such agreements with industrial countries in the foreseeable future. Multilateral rules should make it more di¢ cult to conclude agreements of the type that impose an unnecessarily high price of exclusion, especially on developing countries. Thus, the imposition of restrictive rules of origin, which deny the bene...ts of mutual recognition agreements to third countries for no legitimate reason, should be outlawed. It is harder to prevent the upward harmonization of standards. But it may be possible to build on the presumption that international trade law already creates in favor of the use of international standards by individual countries (in Articles 2.4 and 2.5 of the WTO’s Agreement on Technical Barriers to Trade) and oblige countries that harmonize their standards to demonstrate why international standards or the less strict of the original standards are not adequate to meet their regulatory goals.

This paper should be seen as the beginning of a research program, and there remains much scope for deepening the analysis. In particular, two types of industry-level data would help: ...rst, on how the level of harmonized standards compares with the standards that countries originally imposed; second, the implications of complying with standards for the cost of ...rms, across industries and
, however, within the services sector male workers dominate all the sectors except education, health and manufacturing. Women predominate in the expected sectors of health and education, as most teachers, nursing and medical staff are female, and are well represented in industry groups that rely on professional services such as insurance and banking. Even service sectors such as accommodation (hospitality), wholesale and retail, and agriculture where women could reasonably be expected to be employed in larger number, employ more male workers.<sup>23</sup> Just like other countries in the region, the gender wage gap is high in Sri Lanka as well. The Global Gender Gap report (2017) highlights that Sri Lanka has widened its gender gap in wage equality for similar work despite a modest increase in parity in estimated earned income.

**Table 8: Gender Wage Gap by Economic Activity in Sri Lanka in 2010**

|**S.No.**|**Economic activity**|**Men**|**Women**|**Wage**<br>**Gap (%)**|
|---|---|---|---|---|
|||**Nomina**<br>**earning**|**l annual**<br>**s (in US$)**||
|1|Hotels and restaurants|1596|816|51.1|
|2|Electricity, gas and water supply|2292|1440|62.8|
|3|Manufacturing|1536|984|64.1|
|4|Activities of private households as employers|1128|732|64.9|
|5|Extraterritorial organizations and bodies|2592|1728|66.7|
|6|Not elsewhere classified|888|600|67.6|
|7|Agriculture, hunting and forestry|924
|Thailand|n|0|197|28|1|2|18|3|1|250|
|---|---|---|---|---|---|---|---|---|---|---|
||%|0|78.8|11.2|0.4|0.8|7.2|1.2|0.4|100|
|Turkey|n|0|24|195|2|9|18|1|1|250|
||%|0|9.6|78|0.8|3.6|7.2|0.4|0.4|100|
|Vietnam|n|0|218|31|0|0|3|1|0|253|
||%|0|86.17|12.25|0|0|1.19|0.4|0|100|
|**Total**|**n**|**9**|**1,092**|**827**|**46**|**40**|**323**|**36**|**51**|**2,424**|
||**%**|**0.37**|**45.05**|**34.12**|**1.9**|**1.65**|**13.33**|**1.49**|**2.1**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

**Table 3. Respondents by income classification of foreign
</sup> A good example of an algorithm to identify FUAs is that used in the construction of the OECD’s Metropolitan Areas database.<sup>19</sup> Hence, this algorithm defines FUAs by first identifying densely inhabited urban cores using gridded population data, and then adding non-contiguous interconnected urban cores, as well as other surrounding municipalities, based on commuting patterns (OECD, 2012). More recently, Duranton (2015) has proposed an algorithm to identify FUAs which dispenses with the need to pre-define an urban core, and instead relies solely on a minimum commuting threshold. Duranton tests this algorithm on data for Colombia, obtaining good results.

Unfortunately, the commuting data necessary to implement either the OECD’s or Duranton’s algorithm for identifying FUAs are not available at a global scale. In this context, the AI can be seen as a methodology which adopts a similar labor market perspective to defining urban areas, but which instead relies on estimated travel times in combination with data on population density to allow for a consistent method of measuring urbanization levels that can be feasibly implemented for a broad cross-section of countries. More specifically, the AI identifies as “urban, agglomerated, or dense” those areas that meet a certain population density threshold and which are within a given travel time radius of a “sizeable” settlement, where “sizeable” settlements are identified on the basis

> 17 The proposal of new methodologies has also been aided by the increasing availability of high-resolution global gridded population data sets and data sets on built-up area derived from satellite imagery.

> 18 The use of commuting data to identify metropolitan areas in the United States dates back to the 1960s (Berry, Goheen and Goldstein, 1969). This approach is also similar to the delineation of Labour Market Areas in Europe, or Travel to Work Areas in the UK, with the main difference being that these other exercises aim to exhaustively divide up an entire country or region into distinct areas based on commuting patterns, rather than identifying
in Sweden. To document this, the authors use the FLYDATA, which offers detailed information on all refugees who received a residence permit between 1987 and 1991. Data on labor market outcomes come from the Sweden Longitudinal Individual Study. The authors are thus able to follow individuals over time until 1998. In comparing refugees entering Sweden during an economic boom and during severe and unexpected recession, the authors show that high unemployment rates at arrival have negative effects for at least ten years.

Aksoy et al. (2020) focus on refugees who arrived in Germany between 2013 and 2016 and were subsequently interviewed in the framework of the IAB-BAMF-SOEP survey. The authors examine the role of labor market conditions and attitudes towards migrants at the time of refugees’ arrival. Their identification relies on the quasi-random allocation of refugees across counties. They find that high unemployment rates at arrival predicts future lower employment rates, lower earnings, and lower human capital investment. Conversely, positive attitudes towards migrants promote economic and social integration. Furthermore, integration outcomes are equally affected by both factors.

Another stream of the literature has examined the role of spatial concentration of co-ethnics, and how this affects refugees’ pattern of integration in society. Specifically, existing studies have examined residential integration by focusing on the size of the ethnic enclaves. In the sociological literature, early descriptive studies examined segregation through the enclave setting and its role in shaping the integration of migrants into the labor market of the host country (e.g., Wilson and Portes (1980); Sanders and Nee (1987); Massey and Denton (1988)).

More recently, in the economics literature, Edin et al. (2003) look at the impacts of living in ethnic enclaves on the labor market outcome for refugees in Sweden. They rely on the placement

> 16. For example, a few studies have examined the role of employment bans. Fasani et al. (2020a) investigate the medium to long-term effect of temporary employment bans on labor market outcomes for refugees in 19
agenda after 1948, resulting in a swift and successful abolition of rent collecting intermediaries ( _zamindars_ ). Two other measures which the 1955 Land Reform Act introduced were (i) the ability to expropriate any land held by a household above a given ceiling to vest it with the state for subsequent redistribution to the poor; and (ii) tenancy laws that provided sitting tenants with permanent and registered use rights. Both remained controversial, difficult to implement,<sup>1</sup> and had undesired adverse side effects as landlords not only evicted tenants to prevent them from becoming eligible for such reforms (Appu 1997) but also failed to enter into new lease agreements.<sup>2</sup> Overall reform impacts are thus ambiguous, depending on state level implementation (Besley and Burgess 2000; Deininger _,_ Jin, and Nagarajan 2009; Ghatak and Roy 2007). Enforcement was a key reason: although the law provided tenants with permanent and inheritable use rights, landlords’ ability to take back land for ‘personal cultivation’ created a loophole that was routinely used as a pretext

> 1 As the Indian constitution makes land a state subject, implementation of laws in this area is the responsibility of individual states.

> 2 Estimates from India’s national sample survey, the share of households participating in land rental markets decreased from 26% to 11% nationally and from 35% to 14% in West Bengal (World Bank 2007).

3
peers. In the middle are several economies making strides for better data systems but are still far away from the frontier. Do note that the cross-sectional comparison of data systems in MENA is largely based on the year 2018, which is the year when data availability overlaps for the SPI, SCI, and ODIN.

# **Stylized Fact 1: Developing MENA economies have poor data ecosystems, largely due to the prevalence of conflict**

Data systems across the developing MENA economies are poor in comparison to other developing regions. The MENA region scores the lowest for both the SCI and SPI (figure 1). The ODIN overall score places MENA at a virtual tie with East Asia and the Pacific, both being above Sub-Saharan Africa. This is mainly due to a higher ODIN coverage score for the MENA region, indicating a higher prevalence of indicators in NSO websites available at a disaggregated level. Note that ODIN does not consider access to micro data or the adherence of data to international standards.<sup>3</sup>

**Figure 1: Data Systems across Developing Economies by Region**

<!-- Start of picture text --> SCI (2018) SPI (2018)<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br><!-- End of picture text -->

**ODIN, 2018**

> 3 One element not discussed in the study is the openness and coverage of gender statistics as provided by the ODIN indicator. The MENA region has the second lowest score of all regions, but it is quite similar to the other regions except for North America and Europe and Central Asia.

7
presents more details on these variables.

Despite the relevance of participant characterization in terms of violence before the intervention, the dangerous public school context in El Salvador prevented us from directly asking students about violence during the registration phase. Specifically, it was impossible for the NGO or schools to ensure that this personal information would be kept confidential during the study, an impediment that could endanger both children and instructors.

School behavior reports for each student were the only available administrative data that could have been used as a proxy for violence. Although these reports describe delinquent and violent actions committed within school facilities, they are not a comprehensive measure of violence because they do not include actions committed by children in other relevant domains such as at home and in their communities.

To address this constraint and to estimate a comprehensive proxy for violence, we rely on Dinarte (2018)’s prediction of violence for each enrolled child. Using available baseline data, Dinarte followed Chandler et al. (2011) and estimated a predictive model of violence and crime from existing data (FUSADES, 2015) using a two-sample least-squares strategy. First, the author estimated the likelihood of having committed a violent act _Vf_ as a function of a wide range of covariates _Xf_ , such as student characteristics (e.g., age, gender, time spent alone at home, and education level); children’s household variables (e.g., residence area, mother’s education, household composition); and school-level controls (e.g., school location and commuting time to school). Then, exploiting the availability of these variables in the registration forms of enrolled students, she predicted the measure of propensity for violence (IVV) for each child, using the vector of estimated coefficients.

In Dinarte (2018) a full section explains and discusses this estimation, including some robustness checks. Since the IVV may not be a perfect measure of violence, she provides some evidence that it is clearly the best proxy of propensity for violence
countries, producing large fluctuations in capital flows.<sup>8</sup> In this paper, we show micro-evidence that suggests that shocks to the supply side of funds seem important in the transmission and amplification of shocks. We are able to measure different effects inside financial intermediaries, which other papers that focus on aggregate capital flows (even by type) cannot do. In particular, we disentangle the actions of investors injecting and withdrawing capital from openend funds, possibly as a way to discipline managers, and the behavior of managers actively allocating country portfolios and reacting to shocks from investors and returns. Our results support the claims that shocks to financial institutions and their inner-workings are important to understand crises.

Fourth, there is an increasing interest in studying how portfolios are managed when investing around the world and how shocks impact them.<sup>9</sup> Important among the shocks are valuation effects. One advantage of using mutual fund data is that we can work with actual portfolios. This is helpful because, while there is much discussion on portfolio reallocations, there is limited information on how portfolios are allocated and managed. There are no data on the portfolios of households and little data on those of other institutions like banks and hedge funds. Moreover, unlike country portfolios, the data we use are not inferred from capital flow data. In our case, we link movements in asset allocations to capital flows by an important group of foreign portfolio investors (international mutual funds). Moreover, we analyze in detail what role valuation effects play in changes in portfolio compositions.

The rest of the paper is organized as follows. Section 2 briefly describes the data and provides some basic statistics on mutual fund holdings. Section 3 discusses the shocks to managers and studies the variation in fund allocations (the manager’s decisions). Section 4 analyzes how managers and investors react to crises. Section 5 studies how the variations in the investor and manager responses affect capital flows to different countries. Section 6 concludes.

#
**FIGURES**

Figure 13- Poverty Rates, National 2006-2013 _Notes_ : OLS imputed income poverty, spatially deflated. _Source_ : LFS, SILC 2006-2013; authors’ calculations.

Figure 14 Poverty Measures ($5 PPP, %) and Houshold Liabilities (annual, per capita $PPP)<sup>17</sup> <!-- Start of picture text --> 36 2800<br>34 2600<br>32 2400<br>2200<br>30<br>2000<br>28<br>1800<br>26<br>1600<br>24<br>1400<br>22 1200<br>20 1000<br>18 800<br>2006 2007 2008 2009 2010 2011 2012 2013<br>Income Poverty Consumption Poverty Household Liabilities (right axis)<br>Poverty Rate (%)<br>Household Liabilities<br><!-- End of picture text --> _Source_ : LFS, SILC 2006-2013 authors’ calculations. ECAPOV data. Household liability figures from Turkish Central Bank, Financial Stability Reports (2006-2013)

> 17 Household liabilities consist of credits taken from banks and consumer financing companies, credit card balances, liabilities relating to non-performing personal loans that have been transferred to wealth management companies and unpaid mortgages to the Housing Development Administration of Turkey.  Population of 2006 back-casted from the 2007 population taken from the Address Based Population Registration System of Turkish Statistical Institute.

30
an estimate of the fourth moment of the measurement error, along with an estimate of the cross-sectional variance of true income.

In practice this methodology offers several advantages over using genuine panel data. In many situations genuine panel data are not available, or are available for only short periods, whereas much larger samples over longer periods

> 8 More generally, we can allow for a fixed component in the measurement error term for each cohort, which gets captured in the cohort-specific effect αc.

13