rafmacalaba/datause-encoder-data huggingface dataset:
That information was available at rounds 1, 8 and 12.

Questions about sexual behaviors and AIDS related practices have been included in the medical questionnaires since round 4 (11/1992 — 9/1993) and have been repeated in several subsequent rounds. They generally include information about the number of sexual partners, the age at fi rst sexual intercourse, condom use and visits to the voluntary counselling and testing centers.

To understand the evolution of the HIV prevalence over time and across education and age groups, it is important to have in mind the history of the HIV/AIDS epidemic in Uganda. The fi rst case of AIDS was diagnosed in 1982. In 1986, the fi rst prevention efforts on a national level were launched. The health education messages in Uganda have been mostly carried by governmental and nongovernmental sources and in the media (private local radio station and newspapers) (Kamali and others 2003). The arrival of the MRC project in the surveyed area in November 1989 might have increased the AIDS awareness in the population followed in the General Population Cohort.<sup>8</sup> However, except for the distribution of condoms and the provision of voluntary counselling and testing for HIV, the GPC is an observational cohort, with no speci fi c interventions. A comparison of continuously (from 1989) and newly (from 2001) surveyed villages has demonstrated the generalizability of the results and the absence of an Hawthorne effect (Mbulaiteye and others 2002b).

One logical question is whether the intensity of the exposure to the information campaigns varies with schooling. HIV/AIDS speci fi c education programs in schools were only introduced in 1996, as a pilot program for a later nationwide application (Kinsman and others 2001). Given that the analysis in this paper is based on individuals aged 18 and above, only few individuals could have been in school when these sex education programs were in place and be included in the different samples. This fact ensures that there is no systematic differences in the treatment received by educated and less educated individuals.<sup>9</sup> Actually, the existence of a yearly survey on AIDS,

> combination of both (generally mud or wattle wall with iron sheets roof) or hard materials only (walls in bricks and iron sheet roof).

> 8 Another channel trough which individuals become aware of the epidemic might simply be that, as mortality starts to take its toll, people see their friends and relatives die. However, to obtain the speci fi c knowledge about the transmission modes and the way to avoid AIDS, information campaigns are crucial.

> 9 This point is further reinforced by the fact that during the pilot study, from 1996 to 1998, 95 percent of the schools taught the module “basic information about HIV/AIDS”, 83 percent of schools taught the module “responsible behavior: delaying sex”, but only 33 percent of schools taught “responsible behavior: protected sex”(Kinsman and others 2001). Teachers were reluctant to teach speci fi cs about condom use because they were afraid to be seen as ”immoral”.

8
# **Annex 3. Detailed Transport Questions in LSMS Surveys (12 Countries in LSMS Collection)**

|**Survey Question**|**Albania**|**Burkina Faso**|**Cambodia**|**Ethiopia**|**Iraq**|**Malawi**|**Mali**|**Nepal**|**Niger**|**Nigeria**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_I. Education_**<br><br>d f d f|||||||||||||
|1.<br>Main moe o transport to an rom<br>education facility|X|||X|X|X||X||X|X|X|
|2.<br>Time taken to get to school by this<br>means of transport|X|||X|X|X||X||X|X|X|
|3.<br>Education cost on transportation|X||X|||X||X|X|X||X|
|4.<br>Transportation difficulty as main<br>reason for non-attendance in|||||||||||||
|education/missingthe classes|||||||||||||
|**_II. Health_**|||||||||||||
|1.<br>Main mode of transport to and from<br>health facility|||||X|||X|||||
|2.<br>Distance to the nearest health facility||||||||X||||X<br>(2018/19<br>only)|
|3.<br>Time taken to travel from the<br>household to the nearest health<br>facility|||||X|||||X|||
|4.<br>Health cost on transportation|X||X|||||X||X||X<br>(2018/19<br>only)|
|5.<br>Transportation difficulty or high<br>transportation cost as the main reason<br>for not consulting healthcare during||||||||X||||X|
|<br>illness|||||||||||||
|**_III. Employment_**|||||||||||||
|1.<br>Main mode of transport to and from<br>work|||||X||||||||
|2.<br>Distance to the workplace|||||||||||||
|3.<br>Time taken to reach the workplace|||||X||||||||
|4.<br>Transportation cost to work|||||||||||||
|5.<br>Transportation problem as the main<br>reason for not working or not looking|||||||||||||
|for work|||||||||||||
|**_IV. Food security_**|||||||||||||
|Not able to reach the market due to<br>high transportation costs as the main<br>reason for food insecurity in the past<br>12 months||X||X||X|||X|X|X|X|

36
# **3.4 Canada (CAN)**

Canada’s antidumping process has a two-track determination of injury and dumping. Dumping determinations are handled by the Anti-Dumping and Countervailing Directorate of the Canada Border Services Agency (CBSA), while injury determinations are made by the Canadian International Trade Tribunal (CITT). Both bodies initiate their own investigations, each followed by preliminary and final decisions. Note however that this system was not used prior to mid-2000. Prior to 2000 the CBSA was known as the CCRA (Canada Customs and Revenue Agency) and the CITT did not initiate a separate investigation in the preliminary phase. The CCRA would briefly discuss whether injury was evident if they found preliminary evidence of dumping. Then the CITT would begin a separate injury investigation only if the case proceeded to the final phase. Alternatively, if an interested party (such as a company named in the case) referred the preliminary issue of injury to the CITT, the tribunal would hold a preliminary investigation.

A number of data sources were used to collect the Canadian information. The primary resources were the websites of the CBSA and the CITT. If possible, any discrepancies in the data (such as a difference in a date listed in two separate sources) were decided in favor of the information found in the actual case files on these sites. However, for pre-1995 CCRA cases, hard copies were used because the site did not have coverage of that era. They were provided by the Anti-Dumping and Countervailing Directorate and only covered final determinations. Therefore, information on preliminary CCRA cases - before 1995 was gathered from three sources: the CBSA Historical Listing (http://www.cbsa <u>asfc.gc.ca/sima/historic-e.html), a list of CCRA decision dates provided by the Directorate, and the</u> _Canada Gazette_ microfiche collection at Harvard University. Similarly, CITT cases before 1988 were not available online, and information on these was gathered from the Canada Gazette microfiche.

The additional data collected for Canada beyond that described in section 2 is detailed in the following table: 19
Table 5: Interaction results from baseline specification using institutions with EU‐wide input‐output coefficients

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TFP Acker|berg_et al_.‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.203||||||
||(0.158)||||||
|Overallt‐2* Inst|‐0.123*||||||
||(0.053)||||||
|Entryt‐2||0.024|||‐0.018|0.091|
|||(0.876)|||(0.901)|(0.532)|
|Entryt‐2* Inst||‐0.127**|||‐0.059|‐0.023|
|||(0.015)|||(0.316)|(0.698)|
|Conductt‐2|||‐0.093||‐0.132|‐0.134|
||||(0.227)||(0.123)|(0.116)|
|Conductt‐2* Inst|||‐0.125*||‐0.077|‐0.064|
||||(0.063)||(0.349)|(0.430)|
|FDI Overallt‐2||||‐0.057||‐0.130**|
|||||(0.294)||(0.012)|
|FDI Overallt‐2* Inst||||‐0.229***||‐0.367***|
|||||(0.001)||(0.000)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4966253|5413942|5046786|5818544|4966253|4965903|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

33
<!-- Start of picture text --> Exports of goods and services 30%<br>(constant 2000 USD)<br>1,200<br>Exports (% of Global total)<br>25%<br>1,000<br>Annual % growth<br>20%<br>800<br>15%<br>600<br>10%<br>400<br>200 5%<br>0 0%<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Source: World Bank GDF and WDI central database Sep 2009<br>Billions<br><!-- End of picture text -->

# **Terms and Definitions**

As we begin our discussion, some clarifications on the terms and definitions would be helpful. In particular, we need to differentiate between the various types of economic zones and industrial clusters.

# Special Economic Zones

_Special economic zone_ is a generic term that covers recent variants of the traditional commercial zones. The basic concept of a special economic zone includes several specific characteristics: (a) it is a geographically delimited area, usually physically secured; (b) it has a single management or administration; (c) it offers benefits based on physical location within the zone; and (d) it has a separate customs area (dutyfree benefits) and streamlined procedures (World Bank 2009). In addition, an SEZ normally operates under more liberal economic laws than those typically prevailing in the country. SEZs confer two main types of benefit, which explain in part their popularity: “direct” economic benefits such as employment generation and foreign exchange earnings; and the more elusive “indirect” economic benefits, which are summarized in table 1.1.

Table 1.1 Potential Benefits Derived from SEZs

||Direct<br>benefits|Indirect benefits|
|---|---|---|
|Foreign Exchange earnings|||

4
Except in Burkina Faso, marriage does not seem to protect women against HIV either. Those women who get married younger face the same (high) risk of contracting HIV as women who get married later. It is therefore important that this group of women not be ignored in prevention efforts and policies.

The diverging results across the five countries may reflect cultural differences or different levels of the epidemic in each country. HIV prevalence is higher in Cameroon and Tanzania, the only two countries in which in marriage appears to be a risk factor for some groups of women, than in Burkina Faso, the only country in which marriage appears to be protective.

# **_Discordant Couples_**

Recent research on discordant couples (couples in which only one partner is HIV– positive) in five countries—Burkina Faso, Cameroon, Ghana, Kenya, and Tanzania— yields two findings that challenge conventional notions about HIV transmission (de Walque 2007). First, in at least two-thirds of HIV–positive couples (couples with at least one HIV positive partner), only one partner is HIV–positive. Second, in many such couples only the woman is positive. These findings have very important implications for HIV prevention policies. This section extends the work of de Walque to include several new DHS surveys that included HIV testing (Côte d’Ivoire, Ethiopia, Guinea, Lesotho, Malawi, Niger, Rwanda, Senegal, and Zimbabwe).

A pervasive, if unstated, belief is that males are by and large responsible for spreading the infection among married and cohabiting couples (see UNAIDS, UNFPA, and UNIFEM 2004). HIV prevention policies should take into account the fact that partners who are not yet HIV–positive are an important target group and that women are almost as likely to transmit the infection to their uninfected partners as men are.

In nine out of 13 countries studied, less than one-third of couples directly affected by HIV are concordant positive (both partners are HIV–positive) (Table 4).<sup>9</sup> The figure is 42 percent in Malawi, 44 percent in Rwanda, 53 percent in Zimbabwe, and 59 percent in

> 9 In Senegal the number of infected couples is too low to perform a meaningful statistical analysis.

14
wheelers, 5 percent from three wheelers and 4 percent from taxis. Combining this information with other model parameters such as fuel efficiencies and emission factors, they estimate that the DM resulted in a reduction of 6,545 tons of CO. Guttikunda and Calorie (2012) estimate the total CO emissions in Delhi to be 1.52 million tons in 2010. If we assume the same total emissions for 2011 as well, then we arrive at a 0.43 percent reduction in CO due to the DM. This is much lower compared to our estimate of 34 percent. We refrain from commenting on this huge difference beyond making the following observations: i) We identify the localized reduction in CO at a major traffic intersection (ITO in Central Delhi) over the three year period 2004-2006, while Doll and Balaban's estimate is for the whole of Delhi for the year 2011.

ii) The two methodologies are very different and each has some limitations. While our RD identification strategy is not robust to the presence of sporadic and mobile sources of pollution, our data are actual measurements on pollution and weather obtained from monitoring stations located in Delhi. Their method relies on estimating travel sector parameters using data from multiple studies, and sometimes relying on estimates for other cities (e.g. their occupancy rates for cars is taken from average vehicle occupancy for Asian countries). Their method does not account for dynamic feedback effects such as improvement in car fuel efficiencies leading to greater use of cars. Their estimates would also change if actual fuel efficiencies and emission factors are different from what they use in their model.

iii) While our estimate is many times larger than Doll and Balaban's, it is comparable with the estimate in Chen and Whalley, 2012 who use similar identification strategy. Chen and Whalley find that the opening of the Taipei metro resulted in a 15 percent reduction in CO. We discuss their results in greater detail in section V.

70
# _Simulating the Impact of “Optimal” Geographic Targeting_

Simulating the impact of the “optimal” targeting scheme is a bit more complicated. Following Kanbur (1987) we want to equalize the following expression across the poorest locations of a country: which is _z_ times the poverty gap in location _c_ , after every person in the location has received a transfer _ac. Fc(y)_ is the average of the R simulated expenditure distributions of _c_ . The function ( _x_ )<sup>_+_</sup> gives the ‘positive part’ of its argument, i.e. ( _x)_<sup>_+_</sup> _=x,_ if _x_ is positive, otherwise 0. Transfers _ac_ (which must be nonnegative) add up to a given budget _S_ : where _Nc_ is the population size of location _c_ . After transfers there is a group of locations all sharing the same (maximum) poverty gap rate in the country. These are the only locations receiving transfers. We describe in the Appendix how this problem is solved given that we are working with a database of incomes for every household in the population census.

Table 1 presents the basic results from our simulations. There are a number of conclusions to be drawn. First, the availability of disaggregated data on poverty can help to improve on a uniform lump-sum transfer across the entire population. Targeting transfers to poor localities, in accordance with the optimization scheme outlined above, yields lower values of the national FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire population. Second, the more disaggregated the poverty map, the greater the improvement over the uniform lump-sum transfer. Traditionally, household survey data in Morocco are generally able, at best, to provide estimates of poverty at the regional level. The simulations here suggest that with estimates of poverty at the province, commune and district 12
approach that directly uses the 3-region average IO coefficients, although the differences are small in our particular model aggregation.

10. The aggregate model only has N(NM+M<sup>2</sup> +5M) variables and N(3M+N+5) constraints, while the extended model has (N<sup>2</sup> M + NHM)(M+1) variables and N(M<sup>2</sup> +NM+N+5) constraints. This is a much larger model, having NM<sup>2</sup> (N-1) + NM(HM-5) more variables and MN(M+N-3) additional constraints.

11. A random normal distortion of the ‘true’ trade data by an average of 400-percent was produced in the previous section to simulate a well designed but poorly sampled transportation survey of annual commodity flows.

12. Comprehensive studies by West (1990) and Lahr (2001) consider how to identify and use superior data in a regional accounting system context.

13. For example, by allowing both regional technical coefficients and intra/inter-regional flows to adjust, the optimal solution to the cross-entropy or quadratic formulations in section 2 must be jointly solved.

26
28 Table **11:** Composition of CEE-5 Industrial Exports to the EC, by ITA groups, 1988-92

|**Bulgaria**||||||||||
|---|---|---|---|---|---|---|---|---|---|
||1988|2.5|0.2|1 0.9|14.9|9.6|0.1|0.0|61.7|
||lass|2.1|0,4|12.6|15.7|14.2|0.0|0.0|55.0|
||1s90|1.4|0.3|13.6|19.1|17.3|0.3|0.0|47.9|
||1991|1.4|0.1|16.3|20.9|11.4|0.1|0.0|49.6|
||1992|0.9|0.0|14.0|29.5|8.0|0.2|0.0|47.4|
|FC<br>k ............<br>**FCSK**|.................................|...............................|..................................|.<br>.....................|....................|. ._||||
||1989|0.6|0.0|24.4|13.2|12.6|0.1|2.8|46.4|
||1989|0.7|0.0|25.3|12.3|13.6|0.1|2.7|45.3|
||1990|0.6|0.1|24.2|13.6|14.2|0.0|3.0|44.4|
||1991|1.1|0.1|26.5|13.8|10.5|0.0|3.0|45.2|
|.... 9.X............................|1992<br>......................................|1.3<br>.....................................|0.0<br>.......................................|25.9<br>.|13.7|10.4|0.0<br><br>|2.3|48.4|
|<br>Hungary|||||||......<br>_ ......|||
||1958|1.0|0.9|26.1|22.9|6.9|0.1|0.0|42.1|
||1989|1.5|1.0|26.7|21.5|6.6|0.2|0.0|43.6|
||1990|1.0|0.9|24.9|21.6|6.7|0.1|0.1|44.7|
||1991|0.7|0.5|24.3|21.4|4.2|0.1|0.0|48.9|
||1992|0.5|0.3|23.7|21.8|3.6|0.0|0.0|50.1|
|**Poland**||||||||||
||1998|0.2|0.3|22.4|13.8|4.2|9.6|3.5|46.2|
||1989|0.3|0.2|22.9|13.7|6.0|8.7|3.2|45.0|
||1990|0.6|0.4|23.7|15.2|6.4|0.0|3.9|44.9|
||1991|0.4|0.8|23.6|17.6|4.5|4.3|4.6|44.2|
||1992|0.7|0.7|24.4|19.0|4.3|3.7|3.5|43.6|
|..............<br>**Romanla**|............................|...........................|,..,,**,,,.,**,**.**,,<br>, .. ........................|..........|, , .........................|.|.. ....|||
||1988|0.1|6.1|26.7|19.9|4.2|0.0|0.0|45.1|
||1989|0.2|5.2|23.1|19.3|4.0|0.0|0.0|48.2|
||1990|0.1|2.7|28.0|26.4|4.4|0.0|0.0|38.5|
||1991|0.0<br>|1.6|31.4|28.2|3.6|0.0|0.0|35.3|
||* 1992|0.0|0.4|30.4|39.0|6.7|0.0|0.0|23.5|

Sources: Calculated from data in the United Nations COMTRADE data base and the Interim Trade Agreements.
children<sup>9</sup> were aged 6-20 months, the first follow-up conducted in 2006/7, when they were between 4 and 6 years old, and the last round in 2009/10, when they were between 7 and 8 years of age. The attrition rate between the three rounds of data collection is approximately 4 percent, which is low by international standards (Outes-Leon et al 2011).<sup>10</sup> Of the 2,000 index children in the baseline round, we focus our analysis on the sample of 1,095 of them who were present in the first three waves for whom data on maternal mental health and PPVT scores are available. We present below tests for differences in some characteristics between the included and excluded samples.

# **_B. Measures of a dimension of Child Development_**

We use PPVT scores (Dunn et al. 1986) as the measure of early vocabulary skills, a strong predictor of later cognitive ability, including writing and reading skills, schooling and labor market outcomes later in life (e.g., Cunha and Heckman 2007; Case and Paxson 2008; Schady 2011; Powell and Diamond 2012). The PPVT measures receptive vocabulary; children are shown slides, each of which has four pictures, and are asked to identify the picture that corresponds to objects or actions named by the test administrator.<sup>11</sup> The test continues until the child has made six mistakes in the last eight slides. The number and the level of difficulty of questions differ according to children’s age (see Cueto et al. 2009). We therefore construct age-specific z-scores by subtracting the month-of-age-specific mean of the raw score and dividing by the month-of-agespecific standard deviation. PPVT scores are available in the second and third rounds of the YL survey, i.e., when children were 4-6 and 7-8 years.

# **_C. Measures of maternal mental health_**

The explanatory variable is constructed using information on maternal common mental disorders from the Self Reporting Questionnaire 20 items (SRQ20), a screening (case-finding) tool included in the YL survey. This tool is recommended by the World Health Organization and has acceptable levels of reliability and validity in developing countries. The SRQ20 consists of 20 yes/no questions with a reference period of the previous 30 days. It is not diagnostic and cannot separate out anxiety from depression, but to the extent that these conditions are closely related and both

> 9 Within each household, YL surveyed an index child who was born in 2000-2001 and is followed from infancy until they reach their mid-teens. More details of this survey are presented in Bendini and Dinarte (2020). 10 A detailed analysis of the attrition bias between rounds 1 and 2 of the Young Lives sample strongly indicates that attrition is highly unlikely to bias research inferences. (Outes-Leon and Dercon, 2008)

> 11 Children do not need to name the objects or actions or be able to read or write them. It is just an object identification or association process.

7