rafmacalaba/datause-encoder-data huggingface dataset:
grammatical gender and surveys recent research on its impacts. Section 3 provides an overview of our data sources, including the data we have compiled on the grammatical structure of more than 4,000 languages. Section 4 presents our cross-country analysis; Section 5 presents individual-level, within-country analysis; Section 6 discusses causality; and Section 7 concludes.

# **2 Grammatical Gender**

Many languages partition the set of all nouns into mutually exclusive categories. Membership in these categories, which are typically referred to as either _genders_ or _noun classes_ (Corbett 1991, Aikhenvald 2003), can be manifest in several ways. First, members of a noun class may be semantically related. For example, Tamil has three noun classes: nouns representing male humans and gods, nouns representing female humans and goddesses, and all other nouns (Corbett 1991, Krishnamurti 2001). However, such strictly semantic systems of noun classification (where a word’s meaning always determines its categorization) are relatively rare. In many languages, members of a class are linked by morphology rather than semantics. For example, members of the KI-/VI- class in Swahili often begin with _ki-_ in the singular and _vi-_ in the plural — e.g. “chair” is _kiti_ and “chairs” is _viti_ . However, though semantic and morphological regularities are a common characteristic of noun classes, they are not required.

Instead, membership in a specific noun class is defined based on agreement: class must be reflected in the conjugation of associated words within the noun phrase or predicate in grammatically correct speech (Aikhenvald 2003).<sup>2</sup> In Swahili, for example, the noun class

> 2There is some debate among linguists as to whether agreement rules that do not involve elements of the noun phrase or the predicate can form the basis of a noun class system — specifically, linguists disagree as to whether requiring “anaphoric agrement” between nouns and associated pronouns constitutes a system of grammatical gender (Corbett 1991, Aikhenvald 2003). Corbett (1991) argues that there is no fundamental distinction between pronominal agreement and other forms of grammatical agreement; he consequently classifies languages that (only) require pronominal agreement (e.g. English) as gender languages in his work (Corbett 2013a, Corbett 2013b, Corbett 2013c). Aikhenvald (2003) agrees that there is no fundamental distinction between pronominal agreement and other forms of grammatical concordance, but advocates the use of the traditional definition of grammatical gender to avoid confusion. She also suggests restricting the use of the term “grammatical gender” to systems of noun classification involving

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off. Since 1996 onwards, an equal number of private schools were set up in rural and urban areas every year and in 1999, there were _8,000 new_ private schools setup in Pakistan, almost half of which were in rural areas.

The dramatic increase in the setting up of private schools translated directly into a greater enrollment share for the private sector. All four main provinces registered consistent increases in the share of private schooling in total enrollment between 1991 and 2001, although their specific experiences varied: the largest increases were in Punjab and NWFP (from 15 to 30 percent and 4 to 17 percent respectively) while the growth in Sindh (16 to 21 percent) and Balochistan (4 to 6 percent) was more muted.<sup>15</sup> The enrollment share of the private sector increased both in urban and rural areas, and for both the rich and the poor. Between 1991 and 2001, for the poorest decile of per-capita expenditure in rural areas, the share of private schools increased from 0 to 6 percent; for the richest rural deciles the share jumped from 12 to 38 percent. Urban areas reported equally high growths, although from a higher initial level. For the poorest deciles, the equivalent increase was from 9 to 18 percent and for the richest from 52 to 85 percent. By the end of the nineties, nearly all rich Pakistani children in urban areas, almost a third of the richer rural children and close to 10 percent of children in the poorest deciles nationally were studying in private schools.

Figure 2 examines this change using growth enrollment curves. We computed the growth in private and public enrollment for every decile based on household consumption expenditure data from the PIHS in 1991 and 2001. The horizontal axis in the figure shows the relevant decile in 1991 and 2001 where 1 is the poorest and 10 the richest. The vertical axis shows the _growth_ in enrollment, defined as ((Enrollment2001 – Enrollment1991)/Enrollment1991) for the relevant decile. Nationally, decadal enrollment growth rates in the private sector were higher among the poor (close to 300 percent) and lower among the rich (150 percent) and _middle-income groups_ in rural areas registered the highest growth rates (close to 400 percent). There was a decline in public school enrollment growth rates, both across urban and rural areas and across the rich and the poor, although these declines were more marked for the rich.

The 1990s saw the single biggest shift in the structure of educational delivery in Pakistan and contrary to popular belief, religious schooling played no role in this structural shift. Secular

> 15 All enrollment comparisons between 1991 and 2001 are based on enrollment and household consumption expenditure data from the PIHS in 1991 and 2001.

9
The Impact of Water Supply Variability on Treaty Cooperation between International Bilateral River Basin Riparian States<sup>1</sup> Ariel Dinar, University of California, Riverside (adinar@ucr.edu) Brian Blankespoor, World Bank, Washington DC (bblankespoor@worldbank.org) Shlomi Dinar, Florida International University, Miami (dinars@fiu.edu) Pradeep Kurukulasuriya, United Nations Development Program, New York (pradeep.kurukulasuriya@undp.org)

> 1 The work leading to this paper was supported by the Knowledge for Change Project (KCP), Development Economics, World Bank.  This paper is a product of the project “The long term sustainability of hydropower-based energy generation transboundary agreements within changing climate scenarios” conducted in collaboration with a team from Oregon State University.  We thank GRDC for providing us access to its basin runoff dataset. The views expressed in this paper are those of the authors and should not be attributed to the World Bank or UNDP. We benefitted from many constructive comments provided by participants of the workshop Water and Security, New Orleans February 16, participants of a seminar at UC San Diego, February 22, 2010, and participants of a seminar School of Natural Resources, University of Nebraska, Lincoln, March 3, 2010.
The regressions of the first difference of Latin America on the first difference of debt portfolio flows to Asia (with and without the trend variable) are more significant on the basis of their F-test. These results are shown in the last two columns of Table 10. In both these cases the coefficient for D(Asia) was found to be negative and statistically significant at the 5% level. This implies that, even in the case of debt portfolio flows, the change in these flows to LAC are inversely related to changes in debt portfolio flow to Asia. Specially, if debt portfolio flows to Asia increase between two quarters of any year, those to Latin American emerging markets will be expected to decrease. This further supports the "portfolio adjustment" hypothesis in the case of emerging markets. The presence of the trend variable or the South Asia dummy did not lead to any improvement in this result.

Since the panel series of kv&1 of debt portfolio flows to Latin America w-as found to be non-stationary, i.e., with the presence of a unit root, the additional step of testing for cointegration between Latin America and Asia levels of debt portfolio flows was undertaken. The residuals from the level regression was tested for the presence of a unit root. The Dickey-Fuller statistic (Table I 1) was computed and compared to the Mackitmon critical values at _5_ and 10% level of significance. The null hypothesis for the presence of a unit root (Ho: p = 0 for the residuals regression) could not be rejected implying that the error terms were non-stationary. That is, the integration of order 1, I(l), dominates integration of order zero, I(0) in the series of residuals. This showed that the levels of debt portfolio flows for LAC were not cointegrated with the levels of flows to Asian emerging markets. The levels regression would yield spurious results. Hence, one should base conclusions on the basis of the difference regressions (that were found to be stationary).

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But then along came the new economic geography, which provided both a strong theoretical argument for a relationship between structural transformation and rural-urban inequality (economies of scale and agglomeration, see World Bank, 2009 for a discussion as applied to low income countries). While analysis of ―Asian tiger‖ economies showed that absolute poverty can decrease along with structural transformation, the rising inequality which usually accompanies agglomeration and urbanization makes the problem more complex (World Bank, 2009). Measures need to be taken to manage this transition in lagging areas to avoid an unequal growth which excludes large swaths of the population.

At the same time that the new economic geography brought a theoretical and empirical justification to the questions of growth and transformation, and inequality and poverty reduction, another set of analysis showed that too much inequality is actually toxic for growth and structural transformation (World Bank, 2005). While no absolute level has been identified as a maximum, experience from a number of countries indicates that growth problems occur at the high end of the usual income distribution measures. Explanations for this type of growth slowdown include the political difficulty of implementing economic policies which would create broad-based growth when so much of the wealth is held by a small group, and the effect of inequality traps on savings, investment and market institutions.<sup>4</sup> Timmer, in a recent paper, argues that a process of structural transformation which results in big productivity gaps between the agricultural sector and the non-agricultural sector will

> 4See Acemoglu and Robinson, (2005), and World Bank (2005) for literature reviews.

7
We deflate GDP and all capital flows to 1996 USD and express them in billions.<sup>28</sup> Additionally, we construct accompanying stock measures of external debt by sector. Here, we rely first on the IIP data as the main source. When this is missing after the internal fill, we rely on QEDS data on external debt by sector. We fill any remaining observations with our BIS estimates.

A detailed description of the datasets and our construction of the estimates to fill missing data can be found in Appendix A.3. Here, we briefly illustrate the validity of our approach. To gauge how well our estimates capture the true inflows, we undertake a counterfactual exercise. We take a sample of countries where BOP data by sector is non-missing over 2006q12013q4. Then we compare this data to our estimates done for this period as if the BOP data were missing. Then, for each country group, we plot the aggregate flows for each sector and capital flow type using non-missing BOP data, and our constructed estimates. Figures A4 and A5 in Appendix A.3 report these plots for both other investment debt flows and portfolio debt for each sector. The match is close and to the of our flows pretty speaks quality constructed estimates to fill missing data over the entire sample. The correlation between the two series for total debt inflows is over 86 percent. On the whole, our filled series capture most of the volume and variation of inflows for most countries and allow us to extend substantially the coverage of our dataset.

There are few important details to note. We remove exceptional financing flows to banks and corporates, within portfolio debt and other investment debt, and reassign them to the central bank. Exceptional financing captures financial flows made or fostered by the authorities for balance of payments needs. Thus, they can be seen as a substitute for reserves or

> 28Quarterly GDP data is from Datastream and national sources. We deflate series using US CPI from FRED.

15
**Figure 5. Employment-to-population ratio, % of population aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**Informality remained high in recent years and was more common among youth, males, the less educated and residents in rural areas.** As depicted in Figure 6 Panel a, 42.4 percent of overall employment was informal in 2018.<sup>16</sup> Among young individuals (aged 15-24), the percentage was 56.5. Informality was also negatively correlated with educational attainment, with larger increases observed among workers with less than primary school.

**Figure 6. Informality rate, % of employed population aged 15-64, 2012-2018**

**a. By age and gender b. By educational level**

<!-- Start of picture text --> 80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20<br>20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS._

> 16 We define informality in Section 2. The estimates can vary depending on the definition; as a sensitivity check, we reestimate the informality rate defining _informally employed worker_ as an employee with no written contract, self-employed in firms with fewer than 5 workers and self-employed in non-professional occupations or unpaid family workers. Using this alternative definition, the informality rate is slightly lower: 35.6 percent compared to 42.4 percent in 2018.

13
flows. For each capital flow type, sector, and country group, the table shows the percentage of observations in our balanced panel that come from the raw BOP data, from our internal filling procedure, and from our filling from external data sources. Generally speaking, developing countries, central banks, and portfolio debt tend to have less data available in the original BOP. Our internal filling procedure makes a large difference for the coverage of central banks, but otherwise does not provide many more observations for portfolio debt and/or developing countries. Our external filling procedure, on the other hand, makes a large difference, especially for the quarterly data, where it fills 25-40 percent of observations for EM and 75-90 percent of observations for developing countries that were missing under portfolio debt. In the case of other investment debt, only 11 percent of observations are filled for EM, but for developing countries 40-50 percent of observations are filled. A sizable number of observations are filled by external data also for advanced economies: 20-30 percent for portfolio debt observations, and 15-18 percent of other investment debt.

Our filling exercise has a dramatic impact on the time and country coverage of the inflow data. A balanced sample requires that portfolio debt and other investment debt not be missing for any of the 4 sectors in any period. With 8 components required to be non-missing in each the that at least one is is With no period, probability missing high. adjustments to the BOP data, we have 0 countries in our sample (12 in the annual data). After our internal BOP fill, our sample of countries increases to 10 (16 in the annual data). After incorporating the IIP, BIS, and QEDS data, our balanced sample increases to 85 countries (89 in the annual data). Given the advantages of a balanced country sample for cross-section and panel regression analysis, the impact of our data filling on sample size can be very 18
The impact of climate change is estimated using the future baseline income and population. The impact of climate change is equal to the future tropical cyclone damage with a warmer climate minus the future tropical cyclone damage with the current climate. Using the minimum pressure damage model, the estimated impact of climate change on tropical storm damages ranges from $28 to $68 billion USD/yr (0.005 to 0.012 percent of GWP) by 2100. This represents an increase of between 50 percent and 122 percent over future baseline levels. Climate change is expected to double the damages from tropical cyclones by 2100 by $54 billion USD/yr. The findings confirm the results of earlier tropical cyclone studies that relied on cruder methods.

The damages would not evenly spread across the planet. Because tropical cyclones in the North Atlantic and North West Pacific Oceans consistently increase in intensity with warming, North America and eastern Asia are likely to have the largest and most consistent impacts. The average additional impact in Asia is predicted to be $21 billion of damages and the average additional impact in North America is predictedt to be $30 billion of damage. The average impact on the rest of the world is predicted to be just $3 billion because the remaining continents see both harmful and beneficial impacts depending on the climate model. A beneficial impact is simply a reduction in existing tropical cyclone damages. Even controlling for GDP, North America and eastern Asia are predicted to bear the highest damages per unit of GDP. However, the most vulnerable countries are predicted to be relatively small islands especially in the Caribbean.

The results reveal that the damages from tropical cyclones are quite skewed. Even with the current climate, the 10 percent worst storms (measured by damage) account for 90 percent of the total damage. The 1 percent worst storms in the world account for 58 percent of the damages. With warming, these powerful storms get even more harmful. In a warmer climate, the 10 percent worst storms are predicted to account for 93 percent of the total damages and the 1 percent worst storms are predicted to account for 64 percent of the total damages. Because these large storms explain so much of the story and are very rare, it is very difficult to rely on observations of tropical storms over time to detect a trend. The large storms are so rare so that it may take several centuries of observations to see whether there is a change in their frequency.

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I POLICY RESEARCH WORKING PAPER 2880

# Abstract

Alva, Murrugarra, and Paci examine the impact of ethnic segmentation in education on educational outcomes. Between 1991 and the late 1990s, the Albanian Kosovar population received education services in an informal system parallel to the official one. Using the 2000 Kosovo LSMS Survey data, the authors exploit cohort differences in exposure to the parallel system to estimate its effects among Albanian youth. The first (untreated) cohort includes individuals who entered secondary education before 1991 when the "parallel" education system was initiated. The second (treated) cohort includes individuals who entered secondary school in the last ten years under the ethnically segmented education system. To disentangle the effects of the changing system and economic environment, and the changes in the characteristics of the population, a Oaxaca-type decomposition is used.

The results suggest that the past decade of ethnic tension has claimed a substantial toll on the educational outcomes of young male Albanian Kosovars. In addition to declines in enrollment rates in secondary education, those who are enrolled are expected to complete one less year of education. However, secondary school enrollment for girls increased during the parallel system, but with a sharp decline in the expected number of years completed.

This paper-a product of the Human Development Sector Unit, Europe and Central Asia Region-is part of a larger effort in the region to examine poverty and social service delivery issues. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Theresa Bebli, room H7-114, telephone 202-473-9690, fax 202-477-0574, email address tbebli@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at salva@unc.edu, emurrugarra@worldbank.org, or ppaci@worldbank.org. August 2002. (31 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers cany the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessanly represent the view of the World Bank, its Executive Directors, or the countries they represent._

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