rafmacalaba/datause-encoder-data huggingface dataset:
basing their government at different times in the south of the country and in the Kingdom of Saudi Arabia (e.g., OCHA 2019).

Importantly, there was a significant increase in violence between 2015 and 2018, our period of analysis (e.g., OCHA 2019). In the initial years of the conflict (2015 and 2016), the numbers of violent incidents and fatalities were approximately 2000 per quarter. However, by the end of 2018, fatalities had more than quadrupled to nearly 9000 per quarter (e.g., Tandon and Vishwanath 2020). Furthermore, the geographic concentration of the violence changed over time as the emphasis of fighting on the front lines changed (e.g., OCHA 2019; Almoayad 2020; etc.).

The manner in which violence increased and safety declined over this period varied across regions and over time. For example, in Taizz, the third largest city in the Republic of Yemen, the front lines of the war bisected the city and people were forced to live amid active fighting and snipers (e.g., Doucet 2020); in some instances, there were active ground battles surrounding military instillations on the outskirts of cities (e.g., Al Jazeera 2020); and in other cities, there were intermittent air strikes and terrorist attacks (e.g., Amnesty International 2019). Furthermore, some regions were captured by different parties to the conflict (e.g., Al Jazeera 2019), which interrupted or completely changed the governance of regions and worsened the displacement crisis.

Although a forced displacement crisis began immediately after the conflict escalated in March of 2015, there is little agreement on the size of the crisis. Official estimates suggest that initially approximately 10 percent of the population became displaced, with a constant flow of new displaced households and returnees that were roughly equal over time (e.g., TFPM 2016; OCHA 2019). However, other sources suggest that displaced households may represent upwards of 25 percent of the population (e.g., World Bank 2017b; WFP 2019; etc.). The difference between the estimates might be due to differences in survey methodologies, where the official estimates are based on key informant interviews at the sub-district level, and other methodologies are based on household surveys. In any case, all methodologies suggest that the initial surge in forced displacement was large, with continued flows of newly displaced households and households returning 6
This paper reviews the experience of the Middle East and North Africa (MENA) region in increasing education attainment over the 40 year period between 1970 and 2010. It is organized in four sections. In Section A, we provide an overview of the region’s experience with education attainment during 19702010. In Section B, we measure the performance of 15 MENA countries against countries that had similar levels of education stocks in 1970 and show that most MENA countries have done better than their comparators in increasing education stocks over time. In Section C, we compare across geographical regions using population weighted averages. We find that the regional picture is consistent with that suggested by the country-specific experience in that MENA has performed better than other developing country regions. Finally, in Section D, we attempt to cast more light on the sources of MENA’s better performance through econometric analysis. We explore the extent to which such factors as income growth, public spending on education, opportunity costs, food sufficiency, urbanization and initial endowments of education have affected the growth of education among developing countries. We find that initial endowments and public spending are the two most important determinants of rising education stocks and contribute the most to MENA’s edge over other developing regions. We also find that the number of additional years of education obtained per dollar spent was low in MENA compared to most other regions.

# **A. Rising Education Attainment in MENA**

Barro and Lee (2013) have recently conducted another valuable exercise to improve global estimates of years of schooling completed by adults above the age of 15. They use observations from censuses, surveys, and enrollment rate sources to establish a consistent time series for the period 1950-2010. The Barro-Lee data set is convenient for tracking the evolution of education attainment in individual MENA countries and conducting international comparisons.

Figure 1 below shows how the stock of education has evolved in 15 MENA countries for which we have data for the period 1970-2010.<sup>1</sup> Highlights include the following:

- There was great variation among MENA countries in 1970 with regard to the stock of education, ranging from a low of 0.06 years per person in Yemen to a high of 3.8 years in Qatar.

- All 15 MENA countries experienced an increase in education attainment over time.  However, some did better than others.   This inter-country variation in performance suggests that country circumstances, institutions and policies played a role in enhancing education achievement.

- Variation in education stock increased over time.  The gap between the country with the lowest education stock in 2010 (Yemen, with 3.7 years) and the highest (Bahrain, with 9.8 years) was higher than the lowest-highest gap in 1970.

> 1 The 15 countries are listed in Figure 1.  The Barro-Lee dataset does not include data for Oman, Lebanon and Djibouti, countries which are classified as part of the Middle East and North Africa Region in World Bank databases.

2
# 3.1 Seclusion practices and mobility constraints

Seclusion of women (purdah) is practiced widely and in varying degrees throughout rural Pakistan. As they enter puberty, girls usually experience increased enforcement of purdah norms and attendant restrictions on mobility and social interaction. Limits on mobility for young unmarried girls include prohibitions on traveling unaccompanied or the need to obtain permission to do so from a male family member. These mobility constraints stem from a desire to maintain family honor (izzat), which, in a conservative society, is intimately bound up with female reputation and hence behavior, whether real or perceived. Unchaperoned travel outside the immediate confines of the village and, especially, the settlement can invite damage to a girl’s reputation.

Among households living in close proximity with long standing familial, caste, or patronage ties, as would typically be the case within a settlement, there would tend to be less harassment of each others womenfolk than of those from other settlements. Support for this claim is found in the PRHS-II, where all married women age 15-40 were asked about their perception of safety within and outside their own settlement as well as their use of any type of veil or purdah in public.<sup>4</sup> While 80% of women report feeling “safe” alone inside their own settlement, only 27% report feeling safe alone outside of it. Similarly, only 14% of women report practicing full purdah (coverage of whole body including face) out in public within their own settlement compared to 31% outside their settlement. Thus, settlement boundaries appear to matter as far as female reputational risk is concerned.

Other data also point to mobility restrictions as a key constraint on schooling for girls. The nationally representative Pakistan Integrated Household Survey (PIHS) of 2001-02 shows that enrollment decisions for girls in rural areas are more responsive than those of boys to proximity to school (see World Bank, 2005), but this analysis does not account for settlement boundary-crossing. More suggestive evidence comes from the 2001-02 PRHS-I, in which parents of children never enrolled were asked the main reason for the child’s non-enrollment. For boys, far and away the most important reason is economic ("school too expensive" cited by 43% of parents), but the picture is quite different for girls. While economic motives still dominate (32% reported "school too expensive"), social constraints become an important consideration. Respondents were much more likely to report that they did not "approve" of

> 4Veiling or purdah in this context is the use of a large shawl (chador ) to cover the head and the body, and sometimes also part of the face.

5
We address possible omitted variable bias by controlling for a number of variables. First, we control for all time-invariant country specific factors using country dummies (country fixed effects) and time specific global factors using a dummy variable that equals 1 for the latest round of the survey and 0 for the earlier round ( _Time_ ). Second, we control for time varying country level variables that are known to be correlated with trade openness including (log of) GDP per capita, PPP adjusted (constant 2005 international dollars) taken from World Development Indicators (WDI), World Bank ( _Income_ ); the weighted average of tariff rates (percent) across all products taken from WDI (Tariff rate); and the Heritage Foundation’s measure of trade openness - _Trade Freedom index_ . We note that in order to expand coverage of tariff rates and the trade freedom index, we take the average values of these variables over 2 or 3 years (depending on data availability) prior to the survey years for each country. Third, we control for time-varying firm-level variables from ES that may be correlated with _Import Cost_ as well as _Imports_ . These include (log of) total number of permanent employees (firm-size), (log of) age of the firm, a dummy equal to 1 if the firm was inspected during the year prior to the survey and 0 otherwise ( _Inspected_ ), and the average level of severity on a 0-4 scale (higher values imply greater severity) of the following constraints to the current operations of the firm: tax rates, tax administration, obtaining licenses and permits and corruption.

# **3.  Results**

Baseline results are presented in Table 1. Column 1 provides a parsimonious OLS estimation of the relationship between imports and the costs to import with country fixed effects and the time variable. The estimated coefficient value of _Import Cost_ equals -0.016, significant at less than 1 percent level. Adding the controls listed above does not change the qualitative nature of the 4
within-group inequality of each locality, respectively. In addition, the contribution of each locality's inequality to overall inequality of Ghana was presented, which adds up to 100 percent (see Appendix B for definition). In general, the within-group inequality among the three localities explain most of overall inequality _(E(a)w_ in the last row of Table 2a almost equal _E(o)_ in Table 1).

Several additional findings are worth noting. First, in spite of 17 percent of decrease in mean expenditure per capita in Accra from 1988 to 1992, Accra still enjoyed the highest average living standard in the country in 1992. Second, in Accra not only mean expenditure has decreased, but also inequality has worsened. In contrast, in Other Cities and Rural Areas, mean expenditure has increased and inequality at the lower end of distribution has decreased, implying a pro-poor growth pattern. Third, contributions to the inequality at the lower end of distribution has increased for Accra, but decreased for Other Cities between 1988 and 1992. In short, the economic decline in Accra hurts the low-income population most, while the economic growth in Other Cities and Rural Areas benefits the low-income population most.

Table 2a. Ghana: Within-group inequality and contribution to overall inequality by locality, GLSS1(1988) and GLSS3(1992)

|_Measure_||_E(0)_|_%_||_E(1)_|||_E(2)_|%|
|---|---|---|---|---|---|---|---|---|---|
||1988|1992|_Change_|1988|1992|_Change_|1988|1992|_Change_|
|**Accra**||||||||||
|Mean expenditure<br> <br>|313962|260418|-17.1|||||||
|_E(a)j_<br>_-_i|18.5'|**_21.4_**|_15.7_|20.9|23.6.|-<br>_12.9-_|29.9|33.4|_11.7_|
|Weighted|1.5|1.8|20.0|2.7|2.3|-14.8|6.2|4.0|-35.5|
|Contribution(°/v)<br>|,<br>7.9|_9.3_|_20.3_|_13.4_|_11.5_|_-14.2_|_21.8_|_13.5_|_-38.1_|
|**Other**Cities||||||||||
|Mean expenditure<br>|206162|224783|_9.0_|||||||
|_E(a)j_-:20.2||18.9|-6.4<br>-|21.6|20.2|_-6.5-_|:-29.7|26.9|-9.4|
|Weighted|5.2|4.7|-9.6|5.8|5.3|-8.6|8.3|7.3|-12.0|
|Contribution**(%/o)-**|_26.8:_|_25.6-_|_-4.5_|_28.4._|_25.8._|_-9.2_|_29.1_|_24.7_|_-15.-_|
|**Rural**||||||||||
|Mean expenditure|180677|205771|13.9|||||||
|_E(a),_<br>_-_|19.4|17.'9|-7.7|19'.9:|20.0|5|**25.7**|29.9|_16 3_|
|Weighted|12.7|11.6|-8.7|11.9|12.8|7.6|14.0|18.3|30.5|
|Contribution(%/)|_65.3:_|_64.9_|_-0.6_|_58.2_|_62.7_|_:7.7_|_49.41_|_61.7_|_25.7_|
|**Ghana**||||||||||
|E(at).|19.5|18.4|_-5.6_|20.5|20.4|_-0.5_|28.6|29.7|_3.8_|

*Mean expenditure is in 1992 Accra Cedis. A negative percent change indicates an improvement in inequality, see Appendix B for details.

4The Entropy class measures are additive with the weight _E(cc) = y{(J(Y) J )a( n )'l-a}E(a) *,=l nt(y) n_ where ai =0,1, and 2, j is the sub-group of the population and 4(y) is the mean expenditure per capita.

5
8 individual developing country studies spanning the three key regions of Asia (where nearly two-thirds of the world’s poor live), Sub-Saharan Africa and Latin America.

# **2. ANALYTICAL FRAMEWORK**

In order to adequately capture poverty and inequality effects of price-distorting policies, careful consideration must be given to its impacts on household income and expenditure. Many farm households in developing countries rely on the farm enterprise for virtually all of their income, and in the world’s poorest countries the share of national poverty concentrated in such households is large. The fact that the poorest households in the poorest countries are concentrated in agriculture means those households are likely to benefit from farm producer price increases engendered by trade policy reform, other things equal. However, the outcome is not certain because poor households also spend the majority of their income on staple foods (Cranfield _et al._ 2003), so if food prices rise as a consequence of reform then this adverse effect on their household expenditure may more than offset the beneficial effect on them of higher earnings. The urban poor also would be adversely affected by a rise in consumer prices of staple food. However, it is possible that a trade reform that induced a rise in food prices may also raise the demand for unskilled labor (according to the relative factor intensities of production in the economy’s expanding sectors), which – depending on how intersectorally mobile is labor – could raise the income of poor households more than it raises the price of their consumption bundle.

The approach adopted in the present study to operationalize the above theory is a variant on the path-breaking approach pioneered by Hertel and Winters (2005, 2006) in their study of the poverty consequences of a prospective Doha round agreement under the WTO. The present study contrasts with that earlier one in three respects. First, here the focus is on
<!-- Start of picture text --> US$/ton<br>300 Wheat daily prices for 2070,CME Nearby contract,<br>Announcementof the export ban by Russia| August 5) ~<br>270<br>240<br>210 -<br>Weather elated problems began unfolding ~,<br>150<br>150<br>Jan-10 0 Feb-70 Mar-70 Apr-710 May-170 Jun-10 Jul-10 Aug-10 Sep-10<br>Source: Chicago Mercantile Exchange<br><!-- End of picture text -->
discourage employment growth, while competition from the informal sector is associated with lower labor productivity. These survey results are in line with previous evidence showing that financial constraints can restrain the ability of domestic firms to export and invest in innovation, especially for SMEs (Pietrovito and Pozzolo, 2021; Gorodnichenko and Schnitzer, 2013).

Table 1. Business environment barriers and their association with trade, innovation and firm’s performance.

||(1)<br>Trader|(2)<br>Innovator|(3)<br>Annual<br>employment<br>growth (%)|(4)<br>Labour<br>productivity|
|---|---|---|---|---|
|Inadequately educated workforce|0.024|1.871|1.673|-0.041|
||(0.040)|(4.501)|(1.101)|(0.091)|
|Access to finance|0.056**|-2.056|-1.524|-0.175|
||(0.027)|(2.808)|(0.971)|(0.126)|
|Competitors in the informal sector|0.043|-2.574|0.008|-0.263***|
||(0.028)|(2.990)|(0.979)|(0.092)|
|Customs and trade regulation|0.095***|-0.077|1.058|-0.140|
||(0.027)|(3.500)|(1.054)|(0.094)|
|Transport infrastructure|0.018|0.465|-0.254|-0.083|
||(0.030)|(3.500)|(1.178)|(0.107)|
|Tax rate|-0.000|-4.408|0.139|-0.132|
||(0.028)|(2.750)|(0.926)|(0.088)|
|Tax administration|0.012|-1.956|-0.975|-0.138|
||(0.035)|(3.482)|(0.958)|(0.110)|
|Business license|0.023|2.298|0.030|-0.170|
||(0.035)|(3.939)|(1.246)|(0.120)|
|Political instability|0.016|-0.045|-2.275**|-0.058|
||(0.028)|(2.852)|(0.913)|(0.089)|
|Corruption|-0.026|-8.082***|-0.566|-0.094|
||(0.028)|(2.718)|(0.889)|(0.089)|
|Courts|-0.068|-5.353|-1.297|-0.153|
||(0.043)|(4.411)|(1.326)|(0.148)|
|Observations|5,532|5,297|5,363|5,362|
|R-squared|0.200|0.108|0.097|0.262|

_Note:_ Marginal effects from Probit regressions and OLS regressions using sampling weights. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The explanatory variables in the regression are business environment barriers, for example the share of firms that report an inadequately educated workforce to be a major or severe obstacle to operations. Other firm characteristics included in the regression but not reported in the table include: country, industry, a binary variable whether the firm is foreign-owned (defined as defined as those with foreign capital share of more than 10%), and firm size.

The presence of barriers and obstacles to trade, either through non-tariff or tariff measures, can reduce overall trading activity and volumes, both for importers and exporters.<sup>7</sup> Customs and trade regulations appear to be more severe barriers for firms in MENA than in peer countries, in particular for GVC participants (Figure A2). Firms in MENA also report that it takes more days to clear customs to import or export goods than in other countries (Table A1). This is worrisome because the efficiency

> 7 Non-tariff barriers may include standards, licensing, packaging, and labeling requirements required by the countries to which MENA firms export.

8
page 19 the variable schedule. Thus the variable schedule makes a somewhat more significant improvement of about 2.6%. Again, the best fixed schedule represents an improvement of about 10% - 20% over suboptimal schedules.

It is conjectured that in more realistic flow models the variable allocation system will result in improvements on the order of 10% relative to the best fixed allocation. This conjecture will be tested as part of a future project.

# **6. Conclusions**

The objective of this paper has been to indicate how to assess whether stable water-sharing agreements in international river basins are possible, and to identify and describe, insofar as possible., any stable agreements that do exist. A method for determining stable allocations was developed and applied to some simple two- and three-country models. For stability, and to maximize welfare over the long term, this method depends on the operation of new systems that will make current flow data available to each riparian, permitting each to learn the others' withdrawal rates. These systems will also provide accurate annual flow volume predictions, making feasible annual adjustments in allocations (and payments). Using some simple examples, it was shown that an adjustable allocation scheme that reflects total flow volume results in improved total welfare, relative to the best fixed scheme.

But there are other reasons, beyond this increase in total welfare, for recommending a system that adjusts the allocation according to the total annual flow volume. The best fixed allocation may be difficult to determine, not just because of the stochastic nature of the flow, but because long term trends may be changing the flow distribution. As well, demands for water may change with time, and the particular allocation that maximizes welfare may depend on the location and shape of the demand curves. And, as noted in the Introduction, a more fundamental problem is that an allocation system that is far out of line relative to the supply of and demands for water may be challenged by one or more riparians, leading to regional tensions and, possibly, war.

Future research will include the application of these ideas to an existing international river basin, using more realistic models of the flow and the riparians' water demands. It is hoped that the these methods will permit full utilization of current flow level data, which is about to become available. This important information, if efficiently utilized, may be crucial to the social and economic development of many countries around the world.
and 2005. We collect stock names, amount invested in each stock by each fund, and country of origin of these stocks.<sup>17</sup> One difficulty in constructing the holdings database is that mutual funds report their asset allocation separately over time, that is, their holdings are not linked and do not tend to have identifiers across reports. Therefore, each security needs to be identified at each point in time. This is not a simple task as stock identifiers are rarely available and, when they are, they are not always unique. We match these holdings across mutual funds over time based on the country of origin (when available) and the stock name for each security holding. We can then determine whether the same stocks appear in different mutual fund portfolios across and within fund families.

Table 1 describes the data. We collect data on 8,420 fund-year portfolio holdings over the period 1991 to 2005, covering 499 mutual fund families (companies) and a total of 1,904 funds. Each mutual fund family has on average four different mutual funds. Some families sell the same portfolio to investors under different names depending on their fee structure and minimum investment requirements. In this paper, we consider these different funds (with identical portfolios) only once; i.e., we do not treat them as separate funds as Morningstar does.<sup>18</sup> The total number of asset-level observations in our dataset is 1,359,750, counting each stock-level allocation across all funds over time.

The U.S. mutual fund industry is organized by splitting funds according to their investment scope (Figure 1). In particular, funds are classified into five categories: world funds, foreign funds, emerging market funds, regional funds, and country funds. Regional funds are divided into: Asia (and Pacific) funds, Europe funds, and Latin America (and the Caribbean) funds.<sup>19</sup> World funds invest all over the world including the U.S., while foreign funds invest around the world excluding the U.S. Emerging market funds invest only in emerging market assets.<sup>20</sup> Regional and country funds invest only in a particular

> 17 We classify holdings according to the country of origin of the company issuing the stock, independent of whether these assets are traded in domestic or international markets.

> 18 For example, Fidelity Advisors Funds contain the following Latin America funds with the same portfolio: Fidelity Advisors Latin America A, Fidelity Advisors Latin America B, and Fidelity Latin America T.

> 19 Asia funds can actually invest in countries located in both the Asian and the Pacific regions. Latin America funds can also invest in countries in the Caribbean. Some Europe funds also tend to invest in countries in Africa, such as South Africa.

> 20 Emerging markets are typically middle-income countries. However, these funds might also invest a small proportion of their portfolios in low-income countries.

11