rafmacalaba/datause-encoder-data huggingface dataset:
Of the 816 children under five, 341 (42 percent) are stunted. Among the stunted children, 62 percent are exposed to E. coli in their drinking water, while 36 percent are stunted without any exposure. Annex Figure 3 shows that stunting rates are largely driven by E. coli contamination at the household point and not wealth, i.e., the difference in stunting rates between the bottom 40 and top 60 segments of the population is small.

Annex Figure 4 compares stunting rates and contamination in different age groups. Stunting is found to be most prevalent in the 24-60 month group, which includes children who are more likely to drink contaminated water than younger children who are exclusively breastfed. In addition, children in that age group are mobile and develop mouthing behavior that expose them to more pathogens (George et al 2015, Tulve 2002).

# **4.2     Fecal contamination effects on nutrition**

Table 2 presents results from specification (1) which examines the likelihood of stunting in different age groups when exposed to E. coli contamination at the household point. In the 6-23 month group, contamination does not have a significant correlation with stunting. This could be because children at this age group are more likely to be exclusively breastfeeding. In this category, households with flooring that is made of earth and mother’s education have the strongest association with stunting. In both (1.1a) and (1.2a), a child with a mother who completed secondary education is 27 percentage points less likely to be stunted compared to a child whose mother has no education. Children in this age group from households whose floor is made of earth are 26 percentage points more likely to be stunted. Children in this age group would have begun crawling and could have been exposed to contaminated soil within the household. As mentioned earlier, children in this age group are also known to inadvertently consume contaminated soil (e.g. geophagia) which in turn lead to higher risk of stunting (George et al 2015).

In the 24-60 month group, E. coli increases the likelihood of stunting by 13 percentage points, only when it is at a high risk level (1.2d). The significance of contamination in this age category can be attributed to the fact that children aged 24-60 months are more likely to drink water. Variables that have significant and negative associations with stunting include breastfeeding and being in the topmost wealth quintile. A child between 24 and 60 months who has been breastfed is over 27 10
12 ITTSI – and was utilized to collect data for the full sample of at-scale PD programs, while the ITTSI was applied to the three or four largest PD programs per country.

# **Applying the ITTSI to Evaluated PD Programs**

# _Search Strategy_

We searched the existing literature on in-service teacher PD in low- and middle-income countries to identify a sample of PD programs that had been evaluated in terms of the impact they have on student learning. The resulting sample would serve first to inform a review of what we know to date about the effectiveness of different kinds of in-service teacher PD in those settings. Secondly, we used this sample of studies to inform the design of our survey instrument – by including questions about relevant characteristics either reported or noticeably omitted by studies – and to pilot the instrument in interviews with the program implementers.

Our inclusion criteria for the search were impact evaluations – i.e., studies that sought to establish the causal impact of a program through use of a counterfactual, including experimental and quasi-experimental studies – of primary education interventions in low- and middle-income countries that (a) focused primarily on in-service teacher training or included this as a major component of a broader program, and (b) reported impacts of the program on student test scores in math, language, or science. We included both published and unpublished papers and do not explicitly restrict by year of authorship.

In order to identify papers fulfilling the above criteria, we searched 10 meta-databases through EBSCOhost: the Education Resources Information Center (ERIC), Academic Search Complete, Business Source Complete, Econlit with Full Text, Education Full Text (H.W.Wilson), Education Index Retrospective: 1929-1983, Education Source, Educational Administration
> **TABLE 4: Calibrated values of the model’s elasticities by income.**  _f_  1,   0.25.

|**Income**<br>|**Wage**<br>RMB/hr|**Income**<br>RMB/year|**Housing**<br>**Cost share**|**Average elasti**<br>**with**|**city of choice p**<br>**respect to own**<br>|**robability**<br>|
|---|---|---|---|---|---|---|
|**quintile**|_f_<br>_w_|_f_<sup>_y_</sup>|_f_<sup>_b_</sup>|**Monetary cost**<br>_fg_<br>|**Travel time**<br>_fG_<br>|**Rent**<br>_fR_<br>|
|1|6.61|20,814|0.50|-0.0266|-1.3592|-0.4113|
|2|8.47|37,242|0.45|-0.0133|-1.7959|-0.3817|
|3|10.56|49,009|0.40|-0.1200|-2.2573|-0.3420|
|4|12.66|61,145|0.35|-0.0061|-2.4747|-0.2737|
|5|23.59|115,510|0.30|-0.0035|-4.8413|-0.2463|

# **4. Lock-in effects of road expansion**

As explained in the Introduction, the purpose of the article is to quantify the “lock-in effects” of expanding road transportation. However, a clear definition of “lock-in effects” needs to be developed first. To do so, we first describe the following procedure of comparative equilibrium simulations which illustrates the lock-in concept, and then we generalize to the definition.

1. _Base run_ : We compute a base equilibrium, representative of the 2005 conditions. In fact, from the calibration of the model described in section 3, the base equilibrium replicates exactly the observed 2005 data.

2. _Transit improvement on base_ : The transit travel time in the core zone is reduced by a certain percentage (ignoring the cost of doing so) and the changes induced by this policy are calculated. In particular, we focus on changes from the base in: (i) traffic speed; (ii) housing supply in the periphery; (iii) aggregate VKT (car kilometers traveled); (iv) aggregate fuel consumed by cars; (v) aggregate carbon emissions from cars; (vi) aggregate rents; (vii) expected utilities by income quintile.

3. _Road improvement on base_ : The base highway capacity in the peripheral zone is increased by a certain percentage (ignoring the cost of doing so) and the changes in (i) to (vii) (see 2 above) are calculated relative to the base.

4. _Transit improvement in conjunction with road improvement on base_ : The road capacity improvement in the periphery (step 2) and the transit travel time reduction (step 3) are introduced together. The transit travel time in the core is reduced by the same percentage as in step 2, and the road capacity is increased by the same percentage as in step 3, and the changes in (i)-(vii) induced by this combined policy relative to the base are calculated.

16
accept government’s right to make people pay taxes. These results suggest that the more effective and fair the government, the more likely that its population will accept the government’s right to make people pay taxes, my indicator for legitimating beliefs. With the available African data, I cannot identify how respondents’ beliefs about taxation translate into actual compliance behavior.

# **7.1.1 Control Variables**

Although I control for several socio-demographic variables, very few are significant at the p _<_ 0.05 level. Gender, age, ownership of a radio and television, use of the internet, and employment status are not significant at the p _<_ 0.05 level. Respondents who live in an urban rather than a rural area are more likely to be willing to defer to the tax department (p _<_ 0.05), as are respondents who use a mobile phone every day (p _<_ 0.05). Respondents who have experience with paying property taxes or fees (p _<_ 0.001) are also more likely to be willing to defer to the tax department. Yet, there is no association between experience with paying income taxes and the willingness to defer to the tax department. Although including random intercepts for ethnicity improves the model’s fit, the intra-class correlation for ethnicity is quite small. Ethnic affiliation only explains 1 percent of the variation in deference to the tax department.

Another step in the quantitative analysis is to test whether including country-level indicators improves the model’s fit beyond individual socioeconomic characteristics and perceptions of government effectiveness and fairness. I estimate coefficients for each of the following indicators for 2007: GDP per capita; population size; government consumption as a share of GDP; Freedom Houses measures of civil liberties and political rights; and World Bank Governance Indicators, government effectiveness and corruption. Not one of these indicators is significant at the p _<_ 0.05 level when I include the variables in the equation together as a group or when I include them separately. The coefficient estimates for the individual-level explanatory variables barely change when I include these country-level indicators.

The most likely explanation for why I am not finding any robust countrylevel effects is that there is more variation in attitudes within countries than between countries. The intra-class correlation coefficient for the country level is only 0.03. Thus, only 3 percent of the variation in deference to the tax department can be attributed to country-level factors. By contrast, 20
Page 3 Bennett 1982; Birdsall and others 1983; King and Evenson 1983; Collier 1987). A detailed survey in Nepal, for example, reveals that women contribute about half of the family's full income, counting subsistence production (Acharya and Bennett 1982). In general, women work longer hours than men, rising first, often eating after others have finished, and resting less (see box) (Buvinic and others 1983). The poorer the household, the larger the share of total output likely to be provided through the unrecognized work of women. "When this omission is replicated in low income countries, where income-in-kind can be a substantial share of personal income, a serious distortion is introduced that systematically affects one's perception of women's contribution to the economy and permeates the entire quantitative record of modern economic growth" (Schultz 1989).

BOX: 1 Rural Women's Work Day "An Indian agricultural worker's day is typical. She rises at 4 a.m. She cleans the house, washes clothes, prepares the meal for her husband and children, and leaves for the fields at 8 a.m. She works there until 6 p.m., in the meanwhile nursing the small children she took with her. On her way home she collects fuelwood and, if necessary, drinking water. She cooks the evening meal, cares for the children, and tends to the animals. At about 10 p.m. she goes to bed. On such a day, she might earn less than two rupees." Source: Dankelman and Davidson 1988 An African woman farmer's day has been described thus: "She rises before dawn and walks to tne fields. In the busy seasons, she spends some nine to ten hours hoeing, planting, weeding, or harvesting. She brings food and fuel home from the farm, walks long distances for water carrying a pot which may weigh 20 kilograms or more, grinds and pounds grains, cleans the house, cooks while nursing her infant, washes the dishes and clothes, minds the children, and generally cares for the household. She processes and stores food and markets excess produce, often walking long distances with heavy loads in difficult terrain. She must also attend to the family's social obligations such as weddings and funerals. She may have to provide fully for herself and her children. During much of the year she may labor for 15 or 16 hours each day and she works this way until the day she delivers her baby, frequently resuming work within a day or two of delivery." Source: United Nations Economic Commission for Africa Women's Programme, "Women of Africa -- Today and Tomorrow," Addis Ababa, 1975
proposal tabled April 15, 2010 [Euractiv 2010]).<sup>18</sup> As a result, this note uses the term of carbon tariffs, as noted in the introduction.

Carbon tariffs have a feature which is undesirable from _both_ a climate change and trade perspectives: they may distort the ranking among foreign competitors in the importing market in ways that reflect only loosely climate change concerns, but that may be costly from the point of view of the relative efficiencies of the world economies. (In sharp contrast, the carbon border taxes analyzed in the previous section do not modify the ranking among foreign products since their level is independent from the country’s origin.) Carbon tariffs may make imports from the so-called “non-comparable” countries more expensive than imports from other countries even if the latter are only marginally less carbon-intensive than the former. While gains in terms of carbon emissions may be small, losses in terms of efficiency may be high. Such a possibility is likely all the more because the carbon tariffs currently proposed clearly target emerging (relatively efficient) economies.

Carbon tariffs reflect a serious misunderstanding of basic facts in climate change and trade. They assume that the targeted “non-conform” emerging economies are exporting carbon-intensive products more massively than developed (“conform”) countries. Table 3 shows that a small share of the U.S. and EC imports of carbon-intensive goods come from the emerging economies. This is largely because most exports from the emerging economies still consist in products or activities that are not much carbon-intensive, such as clothing, shoes, assembling parts, etc.

**Table 3.  EC and U.S. imports of carbon intensive products, 2007**

||EC impo|rts from|U.S. imp|orts from|
|---|---|---|---|---|
||China|India|China|India|
|Aluminum|1.5|0.3|6.1|0.5|
|Cement|18.4|4.7|18.1|8.9|
|Chemicals|1.5|0.6|4.9|1.8|
|Copper|1.3|0.2|4.9|0.2|
|Glass|6.3|0.4|25.1|0.4|
|Paper|2.0|0.1|12.4|0.3|
|Steel|5.8|1.4|13.8|3.4|
|Woodpulp|0.2|0.0|0.2|0.0|

Source: Kommerskollegium [2009].

Note: The figures show the imports from China and India as a percentage of total EC and U.S. imports.

As a result, carbon tariffs are unlikely to achieve their intended goal to “convince” the emerging economies to join a world climate change policy. The same could be said _a fortiori_

> 18 Interestingly, the supporters of the proposal say that “carbon tariffs” should respect WTO rules.  Germany criticized this idea as “eco-imperialism” while popular support for such carbon tariffs does not seem to exist [Carasco 2010].

20
to monitor short-term trends and dynamics. The survey has been run in parallel with the EIA and has covered the same plants. The EIM panel is available for the period 1994-2004 covering 205 clases. The principal difference with EIA is its periodicity (being this monthly instead of yearly) and its data content. The EIM contains the following revenue-related variables: total production, net sales and export sales. Plants are asked to report both values and quantities for each product produced, therefore an implicit product unit value can be calculated.

Particularly valuable for our purpose is the fact that the EIM collects information at the plant-product level. Information on 3,396 unique products is included in the survey.<sup>2</sup> Each clase contains a list of possible products, which was developed in 1993 and remained unchanged during the entire period under observation. For instance, the clase of distilled alcoholic beverages (identified by the CMAP code 313014) lists 13 products: gin, vodka, whisky, liquors, coffee liquors, liquor “habanero”, “rompope”, prepared cocktails, prepared from agave, brandy, rum, table wine, alcohol extract for liquor preparation. The clase of small electrical appliances contains 29 products, including vacuum cleaners, coffee makers, toasters, toaster oven, 110 volt heaters and 220 volt heaters (within each group of heaters the classification distinguishes between heaters of different sizes: less than 25 liters, 25-60 liters, 60-120 liters, more than 60 liters). These examples illustrate the narrowness of product definitions and the richness of micro-level information available in our dataset.

We aggregate monthly EIM data into annual observations. Because the information is collected at the plant level, we also aggregate the information at the firm level using specific multi-plant firm identifiers.<sup>3</sup> In the case of multi-plant firms with different plants producing the same product, the figures are aggregated into one product.<sup>4</sup> Nominal values are converted into real terms using price deflators provided by Banco de Mexico for intermediate inputs, investment, wages, etc. The details of data cleaning procedures are described in Iacovone (2008). We supplement the EIM data with information on other aspects of firm operations available from EIA, including investment in physical assets, investment in R&D and investment in technology acquisition.

The number of firms in the sample varies between 5,495 in 1994 and 3,883 in 2003 (see

> 2For comparison, the study by Bernard, Redding, and Schott (2006b) using US data is based on approximately 1,800 product codes.

> 3This information is normally not part of the EIM, however in 2005 a special study was done and the data identifying the multi-plant firms can be linked to the year 2003.

> 4As most Mexican firms have only a single plant, conducting the analysis at the plant-level would lead to similar results. In fact in the EIM there are 5,265 single plants firms and 473 multi-plants ones, with the latter owning in average 3 plants each.

6
This version: 15 Feb 2024 7 Our objective is to contribute to this literature by taking a bottom-up approach and directly analyzing the voices of Malaysian citizens. The transcripts of focus group discussions (FGDs) we conducted provided answers to five broad questions related to (a) intergenerational mobility, (b) cost of living, (c) the roles of government and the private sector, (d) aspirations, and (e) preferences for relative versus absolute income. This qualitative assessment of living standards covered three geographic areas of Malaysia – the wealthy and urbanized suburbs of Kuala Lumpur in the state of Selangor, the relatively poor states of Terengganu in east coast of peninsula Malaysia and Sabah located in the island of Borneo in East Malaysia. In ethnically diverse Selangor we also spoke to all three dominant ethnic groups - Bumiputeras, Chinese and Indians.<sup>4</sup> Our paper is motivated both directly and indirectly by Martin Ravallion. We build on his work on poverty and inequality in Malaysia (Ravallion, 2020a and 2020b), his work on measuring the relationship between growth, income distribution and poverty (e.g. Ravallion and Chen, 2003), and on his recognition that well-being may be driven by non-economic, subjective, factors (Ravallion and Lokshin, 2000, Pradhan and Ravallion, 2000, Ravallion, 2012). More directly, Martin suggested to us that we include a question for our focus groups on their preference for absolute versus relative income (which he had used in his Georgetown classes). We analyze the responses to

> 4 Official documents and daily discourse in Malaysia generally refer to these three groups as distinct races. In this paper we opt for the terms “ethnicities” or “ethnic groups” for the same categorization. Bumiputeras comprise Malays and other indigenous ethnic groups.

7
Figure<sup>1</sup> Iminigrants<sup>Admitted: Flcal Years 1900-87</sup> Thousands ! 1,200 <!-- Start of picture text --> A<br>_<br>750 600<br>UEAi<br>.Ad<br>I.E us<br>1980 1987<br>1970<br>1960<br>1950<br>1940<br>1930<br>1920<br>-.1100 1910<br>Service, 1988a:xviii.<br>and Naturalization<br>,,-2 - Lmigration m<br><!-- End of picture text -->
Figure **3.** 132-city **Price** Index for **All Food** <!-- Start of picture text --> dec91 =100 for both state and market<br><!-- End of picture text --> <!-- Start of picture text --> 2500<br>2000<br>1500-<br>1 000_<br>500 --<br><!-- End of picture text -->

> DEC91 02i04 02i25 04jO7 04i28 **05i19 061i6 07iO7** 07i28 **08i18 DATE**

> - - MARKET PRICE STATE PRICE
# **I. Introduction**

The demographic and socioeconomic patterns of prevalence and incidence of HIV/AIDS in Sub-Saharan Africa should shape the programs and policies to combat the epidemic. A large body of literature examines the relation in Africa between demographic and socioeconomics variables on the one hand and HIV infection on the other (for reviews of this literature, see Wojcicki 2005, Glick 2007, and Hargreaves and Glynn 2002). Among the issues addressed are the associations between HIV status and variables including education status, income (wealth or poverty), residency, age, marriage, and empowerment. With new and expanded datasets, researchers are increasingly able both to measure HIV status and to collect detailed socioeconomic variables for the same individuals in population-based samples, thus enabling moredetailed analyses than were previously feasible.<sup>1</sup> Moreover, with these data sources, we can focus on underlying factors which are more distal to HIV status (e.g. education, marriage) in addition to proximate factors or behavioral risk (e.g. sexual behavior such as condom use).

This paper aims to look more closely at the methods and unusual evidence from the existing body of work on the link between demographic factors, socioeconomic status and HIV/AIDS. Our objective is not to conduct a review of the array of empirical work looking at the pattern of HIV for different socioeconomic and demographic groups. Rather, we discuss the methodological challenges for such analysis and focus on some of the more controversial evidence about these patterns. The first section discusses some of the methodological issues that confront empirical studies of the relation between socioeconomic status and HIV.<sup>2</sup> The second section discusses some of the misconceptions about HIV/AIDS in Sub-Saharan Africa that persist in the literature and popular media despite accumulating evidence to the contrary. The last section provides some concluding remarks.

> 1 This paper focuses more on the population-based household surveys which now provide both HIV status and SES indicators. We are not suggesting that demographic surveillance sites (DSS) are not still crucial for assessing HIV prevalence across demographic groups. They are, however, generally lacking detailed socioeconomic characteristics of the DSS population.

> 2 In a similar approach, Beegle and De Weerdt (2008) examine the methodological issues involved in the study of the impact of HIV on socioeconomic outcomes.

2
Is Escaping the Fiscal Pro-Cyclicality Trap Possible? Evidence from the Middle East and North Africa by **Željko Bogetić**<sup>∗</sup> **and Dominik Naeher**<sup>+</sup> _Keywords:_ Fiscal policy, Business cycle, Procyclicality, Countercyclicality, MENA _JEL codes:_ E32, E62, H20, O23

> ∗ World Bank Group, Washington, DC, United States. E-mail: zbogetic@worldbank.org.

> + University of Goettingen, Goettingen, Germany. E-mail: dnaeher@uni-goettingen.de.