rafmacalaba/datause-encoder-data huggingface dataset:
Himalayas, cyclone-induced surge intensification,<sup>16</sup> and SLR are likely to be the most significant effects of climate change. In particular, salinity ingress is likely to be more severe in the future for two reasons: a) freshwater flows from rivers in the Himalayas are predicted to decrease during the dry season and b) the sea level will gradually rise. In the following, we present a model and simulation results for river salinity during the dry season (December to May) in the southwest coastal region of Bangladesh for 2050.

# _Modeling Scenarios_

_Baseline:_ In order to model the impact of climate change on salinity, we defined the baseline scenario (current conditions) of salinity using historical (measured) data on the maximum river salinity for the dry season of 2011–2012 (December 2011 to May 2012). The data are from the database of the Institute of Water Modeling in Bangladesh.

_Climate Change:_ Overall, climate change uncertainties in the Ganges basin are extreme. Regarding the greater Himalayan region, the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) pointed out that available data were insufficient to support a credible analysis (IPCC 2007a). Uncertainties are compounded by the inability so far to construct a credible methodology for modeling predictions of how monsoon patterns might change, in particular with regard to the relationship between climate and hydrology (World Bank 2013).

In recent years, attempts have been made to downscale temperature, rainfall, and runoff for the Ganges Basin using all the Global Circulation Models (GCMs) recognized by IPCC AR4. A number of studies have been conducted on glacier melt, snow accumulation in the Himalayas, SLR, and the possibility of cyclone intensification in the Bay of Bengal. Our study draws on the existing literature to outline the climate change scenarios for coastal Bangladesh.

> 16 Although Bangladesh is a global tropical cyclone hotspot, storm surge-induced river salinity is less of a concern.  Cyclone-induced river salinity in coastal Bangladesh is generally short-term and washed away with the advent of monsoon.

16
estimates with a degree of caution, this is suggestive evidence that African countries may have been more successful in reducing poverty than currently thought.

The paper is organized as follows. Section II explains why CPIs in Sub-Saharan Africa may be biased and outlines the Engel curve method. Section III describes our empirical methodology, while section IV describes the data sources. Section V shows estimates of the CPI bias and conducts several robustness checks. Section VI assesses the implications of CPI bias for measured poverty trends. Section VII concludes.

# **II. Literature review**

# _CPI bias in Sub-Saharan Africa_

The CPI measures the rate at which the prices of a specific basket of goods and services change from month to month. To compute the CPI, NSOs require price and quantity data for a variety of goods and services. Virtually all NSOs in Sub-Saharan Africa run regular monthly consumer price data collection programs, which form the basis for computing the CPI. In addition, estimating a weighted average of price changes relative to a base period requires data on consumed quantities as budget shares. These quantity data typically come from nationally representative household budget surveys (HBS), which most NSOs field in irregular intervals (see Beegle et al 2016).<sup>2</sup> Most NSOs use a fixed-base Laspeyres-type index and staged aggregation approach to compute the CPI (see United Nations 2009). In a first step, individual price quotations are combined to elementary aggregates that represent broad goods categories purchased by consumers in a specific locality and type of outlet. In the second step, elementary aggregates are combined to commoditygroup indexes (for example, food and beverages, apparel, transport) and ultimately the all-item CPI using weights (budget shares) estimated from the HBS for a base period.

It is well-known that Laspeyres indexes tend to overstate changes in the cost of living. This is because they hold quantities fixed in the base period and disregard consumer substitution behavior

> 2 The term HBS is used to denote national household surveys collecting consumption data, this includes a range of surveys that go by different labels, e.g. Living Standards Survey, Income, Expenditure and Consumption Survey, etc.

4
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28
# **1 Introduction**

The HIV/AIDS epidemic is the greatest challenge Lesotho is facing. According to UNAIDS, Lesotho is the country with the third highest HIV prevalence rate in the world, after Swaziland and Botswana. In 2005, the estimated adult HIV prevalence was 23.2 percent, equal to 270,000 people infected with HIV (UNAIDS, 2006). Access to anti-retroviral treatment (ART) is progressing at a rapid pace in Lesotho. According to statistics provided by the Ministry of Health and Social Welfare in Lesotho in July 2007, out of an estimated 56,000 people in need of ART, 24,700 had received ART treatment (compared to 8,000 in September 2005) (Lesotho Governement, 2007). However, prevention remains a key strategy in the fight against the HIV/AIDS pandemic. Moreover, most HIV prevention programs in Africa provide general HIV/AIDS information on only the average risk population, without taking into account the fact that some categories are more at risk of being infected than others.

This paper analyzes the socioeconomic determinants of HIV/AIDS and related sexual behaviors in Lesotho, both at the individual level and the level of the couple. Understanding the determinants of HIV infection and of sexual related habits is crucial for targeting prevention. The present paper sheds light on the key categories in the society that policy makers should target in Lesotho in order to prevent HIV/AIDS.

The socioeconomic profile of the HIV/AIDS epidemic in Africa has been analyzed in the epidemiological literature and, to a lesser extent, in the economics literature. Few of these studies have used nationally representative samples. Datasets which include the results of individual HIV tests are generally drawn from surveillance data taken from pregnant women attending ante-natal care clinics (Fylkesnes and others, 1997; Kilian and others, 1999) or from high risk groups (Nagot and others, 2002). A further limitation of this literature is that most of these data sets have only a limited number of socio-demographic variables and most of them cannot claim to be representative. More recently, Clark and Vencatachellum (2003) used a nationally representative sample from South Africa. Fylkesnes and others (2001) compare results from surveillance data among pregnant women and from population 2
# **References:**

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# **Table 5: 2006 DHS sample of women 15 to 49 by marital status**

|**Marital status**|**Urban **|**Rural **|**Total **|
|---|---|---|---|
|**Single**|865|880|1,745|
||16.97%|9.20%|11.85%|
|**Married once**|3,324|7,173|10,497|
||61.59%|75.49%|70.74%|
|**Married, previously widowed**|110|320|430|
||2.41%|3.67%|3.24%|
|**Married, previously divorced**|567|830|1,397|
||13.35%|9.46%|10.79%|
|**Currently widowed**|98|127|225|
||1.84%|1.27%|1.47%|
|**Currently divorced**|179|110|289|
||3.83%|0.91%|1.91%|
|**Total**|5,143|9,440|14,583|
||100%|100%|100%|

Note: Sample size and population percentages. Percentages are population weighted. Source: 2006 Mali DHS.

35
create an inventory of teacher practices that are commonly evaluated.<sup>2</sup> The team then built upon this list to include behaviors from international classroom observation tools used in developing countries.<sup>3</sup> Based on this preliminary analysis, the team created an inventory of three areas and 43 elements.<sup>4</sup> Second, the development team hosted a working group of 22 education experts and practitioners to help further reduce and prioritize elements for the _Teach_ framework. Participants were asked to indicate whether any elements were missing from the inventory, to rank the elements and areas by relevance, and to identify elements they characterized as unobservable. This process reduced the framework to 25 elements. Then the development team reviewed the theoretical and empirical evidence from developing countries to further eliminate elements from the framework. This process resulted in a downsized framework of 14 elements.

These 14 elements comprised the first working version of the tool, which aimed to capture both quality and frequency of teaching practices as measured by each element.<sup>5</sup> This preliminary tool was piloted in person in Pakistan and Uruguay and using classroom video footage in Afghanistan, China, Pakistan, the Philippines, Tanzania, Uruguay, and Vietnam. From these pilots, it became apparent that observers struggled to code reliably when they had to simultaneously capture the frequency and quality of teaching practices for each element. In response, the development team revised the structure of the tool to address this challenge as well as other errors and logical inconsistencies. This process resulted in a tool that comprised 10 elements.

Next, the development team convened a technical advisory panel, including Lindsay Brown, Pam Grossman, Heather Hill, Andrew Ragatz, Sara Rimm-Kaufman, Erica Woolway, and Nick Yoder, to provide written feedback on the tool. These comments were compiled and addressed as part of a one-day technical workshop. During the workshop, the

> 2 The _Teach_ framework built upon the inventory created by Gill and others (2016), who conducted a content analysis of the differences in dimensions of instructional practice of five commonly used classroom observation tools comparing the behaviors they measure with the extent to which they predict student learning. The tools included CLASS, FFT, PLATO, Mathematical Quality of Instruction, and UTeach Observational Protocol. The content, predictive power, and potential bias of these instruments were also analyzed as part of this preliminary framework.

> 3 These included OPERA, SCOPE, SDI, Stallings, and TIPPS.

> 4 Elements refer to groups of multiple, similar behaviors that aim to capture teaching practices related to positive learning outcomes.

> 5 For example, the tool aimed to capture not just the quality with which a teacher checked for understanding (adjusting the lesson, prompting students to determine their level of understanding, etc.), but the frequency with which the teacher checked for understanding in each lesson.

7
**Table 4. Actual Sample Selection (original and substitutes by cohort)**

||**Selected**||**Substitut**|**es**|**Total**|**% vs 1,500**|
|---|---|---|---|---|---|---|
|Graduation Year Cohort|<br>M|S|T|Z|346|23.1|
|1991|268|53|20|5|704|49.9|
|1992|560|96|42|6|349|23.3|
|1993|286|46|15|2|1,399|93.3|
|Total|1,114|195|77|13|||
|Cumulative percentage|74.3%|87.3%|92.4%|93.3%|93.3%||

_Source:_ CONALEP (1999).

The second CONALEP Graduate Tracer Study (CONALEP 1999) was conducted between May and June of 1998 on the basis of a random sample of individuals who graduated between June 1993 and June 1997. The sample is representative of geographical regions, all 29 careers and all cohorts. The difference between the actual sample of 5,574 individuals and the planned sample of 10,000 was due to exogenous factors such as changes in address (3,590 cases); addresses that belonged to different states (651 cases); differences between the number of graduates officially registered and those found in the administrative records (229 cases), and technical careers that had never been offered (7 cases). CONALEP (1998) extensively reviews the sample frame of the second CONALEP Graduate Survey as described by LEVANTA, the consultant firm which designed the sample process. The distribution of the 1998 CONALEP survey was as follows. The table shows that the response rate is high.

**Table 5. Distribution of the 1998 Sample by Cohort**

|**Cohort**|**Interviewe**<br>**Number**|**d Graduates**<br>**%**|**% Completed**<br>**Interviews**|
|---|---|---|---|
|90-93|779|14.0|59.0|
|91-94|951|17.1|72.0|
|92-95|1,127|20.2|85.4|
|93-96|1,268|22.7|96.1|
|94-97|1,449|26.0|109.86|
|Total|5,574|100.0|84.5|

_Source:_ LEVANTA C.

**6** This value, as listed in CONALEP data sets, appears to exceed 100 percent because the number of responding graduates exceeded the goal number.

23
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT|Mustapha Benmaamar|
|---|---|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT|Mustapha Benmaamar|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT|Mustapha Benmaamar|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT|Mustapha Benmaamar|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT|Mustapha Benmaamar|
|PSAC for Decentralization|Edgardo Mosqueira Medina|
|PSAC for Decentralization|Edgardo Mosqueira Medina|
|PSAC for Decentralization|Edgardo Mosqueira Medina|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|ParticipatoryManagement of Protected Areas|Gabriela Arcos|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|Chile: Life-longLearningand TrainingProject|Dena Ringold|
|E-Business for Small Business Development Project|James C. Hanna|
|E-Business for Small Business Development Project|James C. Hanna|
|WESTERN ALTIPLANO NATURAL RESOURCES MANAGEMENT PR|OJECJames W. Smyle|
|WESTERN ALTIPLANO NATURAL RESOURCES MANAGEMENT PR|OJECJames W. Smyle|
|CapacityBuildingof the Legal Profession|Nightingale Rukuba-Ngaiza|
|CapacityBuildingof the Legal Profession|Nightingale Rukuba-Ngaiza|
|CapacityBuildingof the Legal Profession|Nightingale Rukuba-Ngaiza|
|Provincial Reform Loan Project - Cordoba|Fernando Rojas|
|Provincial Reform Loan Project - Cordoba|Fernando Rojas|
|Provincial Reform Loan Project - Cordoba|Fernando Rojas|
|Provincial Reform Loan Project - Cordoba|Fernando Rojas|
|Provincial Reform Loan Project - Cordoba|Fernando Rojas|
<u>Table A16. Relationship between socioemotional skills and schooling in Cambodia</u>

||<br>(1)<br>Conscientiousness|<br>(2)<br>Grit|<br>(3)<br>Openness to experience|(4)<br>Extraversion|(5)<br>Agreeableness|(6)<br>Emotional stability|
|---|---|---|---|---|---|---|
|Years of schooling|0.044***|0.000|0.044***|0.027*|0.035**|0.017|
||(0.014)|(0.014)|(0.013)|(0.014)|(0.015)|(0.014)|
|Respondent age|0.018*|0.012|-0.012|-0.004|0.009|-0.018*|
||(0.010)|(0.010)|(0.011)|(0.010)|(0.010)|(0.010)|
|Respondent is female|-0.151***|-0.094**|-0.067|0.067*|0.063|-0.025|
||(0.040)|(0.044)|(0.043)|(0.039)|(0.041)|(0.043)|
|Household head can read (2011)|0.193|0.236**|0.197*|0.195*|0.264*|0.029|
||(0.126)|(0.110)|(0.118)|(0.118)|(0.137)|(0.104)|
|Household head can write (2011)|-0.125|-0.255**|-0.088|-0.180|-0.264*|0.025|
||(0.128)|(0.111)|(0.121)|(0.116)|(0.136)|(0.103)|
|HH SES in 2008|-0.027|0.018|-0.005|0.004|-0.041|0.003|
||(0.026)|(0.028)|(0.028)|(0.029)|(0.029)|(0.034)|
|Constant|4.904***|7.022***|5.119***|3.897***|5.755***|4.994***|
||(0.230)|(0.229)|(0.228)|(0.247)|(0.225)|(0.221)|
|Observations|3,278|3,274|3,279|3,278|3,279|3,281|
|Romano-Wolf p-values - Years of schooling|0.004|1.000|0.004|0.024|0.004|0.147|
|Adjusted R-squared|0.027|0.031|0.027|0.019|0.007|0.002|
|Sample|All|All|All|All|All|All|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|No|No|No|No|No|No|
|||6.28 (|||||
|Test Yrs of school = Yrs of school (1) - Chi2 (p-value)|NA|<br>0.01)|0.00 ( 1.00)|1.03 ( 0.31)|0.31 ( 0.58)|1.97 ( 0.16)|
|Test Yrs of school = Yrs of school (2) - Chi2 (p-value)|6.28 ( 0.01)|NA<br>6.23 (|6.23 ( 0.01)|2.11 ( 0.15)|3.44 ( 0.06)|0.84 ( 0.36)|
|Test Yrs of school = Yrs of school (3) - Chi2 (p-value)|0.00 ( 1.00)|0.01)<br>211|NA|1.11 ( 0.29)|0.27 ( 0.60)|2.31 ( 0.13)|
|||. (|||||
|Test Yrs of school = Yrs of school (4) - Chi2 (p-value)|1.03 ( 0.31)|<br>0.15)<br>3.44 (|1.11 ( 0.29)|NA|0.26 ( 0.61)|0.23 ( 0.63)|
|Test Yrs of school = Yrs of school (5) - Chi2 (p-value)|0.31 ( 0.58)|0.06)<br>0.84 (|0.27 ( 0.60)|0.26 ( 0.61)|NA|0.79 ( 0.37)|
|Test Yrs of school = Yrs of school(6)- Chi2(p-value)|1.97(0.16)|0.36)|2.31(0.13)|0.23(0.63)|0.79(0.37)|NA|

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

_Notes_ . This table reports estimates of the relationships between socioemotional skills and schooling in Cambodia. All regressions control for village fixed effects. Standard errors are in parentheses (clustered at the village level).

100
number in the cohort relative to the total population as our measure of the relative cohort supply. Figure 3 shows the path of average wages across birth cohorts after netting out the survey fixed effect.<sup>11</sup> Starting with the oldest cohorts, average earnings rise over birth cohorts until the mid1950s when the average earnings begin to decline. The reversal is due to the declining age of the more recent birth cohorts, illustrating that we will need to control for position in the life cycle to remove the effects of age on lifetime earnings. As we demonstrate in the next section, using quadratic terms in age of the cohort or using cohort-specific fixed effects serves to correct for the age effect on earnings.

# **5. Empirical Specification**

The theory suggests that the reduced-form equation for completed schooling and earnings will depend on conditions known at the time of birth, plus changes to those variables conditioned on information orthogonal to the variables known at birth. We specify the equations for completed years of schooling j and log earnings j ) for cohort _j_ , country _c_ , and survey ρρ ρρ year _t_ as SS ln(YY j 0 j j j j j , (26) PP ρρ LL RR ρρ MM ρρ PPLL jj SSjjρρ ρρ SS = γγj + γγ0 LLEE + γγj RRUURR + γγj MMAALLEE + γγj LLEE + aaj + θθ+ ∑ + εε j + PP JJ ρρ LL RR ρρ MM ρρ PPLL pp=1 pp ρρ jj YYjjρρ Ln(YY j ) = aa. + ββ LLEE + ββ RRUURR + ββ MMAALLEE + ββ LLEE ββ ZZ + aa + θθ (27) ρρ ωω The focus on years of schooling is a matter of convenience in that we know that human capital investment will rise in every period _t_ as life expectancy for cohort _j_ , j , rises, but schooling is the most readily observable and consistent measure of human capital investment LLEE

> 11 <mark>The survey fixed effects give us an estimate of average wages net of fixed factors such as the home currency; the time unit used for wages, whether hourly, weekly, monthly or annually; country-specific fixed effects; and inflation. The wages are presented on a logarithmic scale.</mark>

16