rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 – Uncompensated own-price elasticities (continue)

|**Authors**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Meyerhoefer et al.,**<br>**2007**|**Le, 2008**|**Mergenthaler et al.,**<br>**2009**|**Gibson and Kim,**<br>**2013**|**Gibson and Kim,**<br>**2013**|**Gibson and Kim,**<br>**2013**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Demand System**|AIDS|AIDS|AIDS|AIDS|AIDS|AIDS|LAIDS|LA/AIDS|AIDS|AIDS|AIDS|AIDS|
|**Price method**|MP|UV|Deaton|MP|UV|Deaton|MP|MP|CW|UM|SPM|UV|
|**VHLSS wave**|1992|1992|1992|1997|1997|1997|1997|2004|2005 (own survey)|2010|2010|2010|
|Rice|-0.84|-0.95|-0.83|-1.01|-0.89|-0.79|-0.38|-0.06|.|-0.27|-0.83|-0.60|
|Other cereals|.|.|.|.|.|.|.|.|.|.|.|.|
|Roots and tubers|.|.|.|.|.|.|.|.|.|.|.|.|
|Starch crops|.|.|.|.|.|.|-0.61|.|.|.|.|.|
|Protein crops|.|.|.|.|.|.|-0.74|.|.|.|.|.|
|Fruits|-0.92|-0.79|-0.86|-0.92|-0.82|-1.17|-0.91|.|-0.64|.|.|.|
|Vegetables|-1.07|-0.92|-0.90|-1.11|-0.94|-1.03|-0.91|.|-0.64|.|.|.|
|Pork|.|.|.|.|.|.|.|.|.|-0.26|-0.87|-0.68|
|Beef|.|.|.|.|.|.|.|.|.|-0.19|-0.93|-0.69|
|Chicken|.|.|.|.|.|.|.|.|.|-0.15|-0.70|-0.34|
|Fish|-1.06|-0.89|-1.49|-1.21|-0.86|-1.16|.|.|.|-0.13|-1.08|-0.82|
|Other meets and seafood|-0.76|-0.36|-0.65|-0.55|-0.29|-0.80|.|2.36|.|.|.|.|
|Eggs|.|.|.|.|.|.|.|.|.|.|.|.|
|Milk|.|.|.|.|.|.|.|.|.|.|.|.|
|Spice|-0.93|-0.79|-0.73|-0.87|-0.79|-0.68|.|.|.|.|.|.|
|Staples|-1.35|-0.97|-1.04|-2.07|-1.02|-1.28|.|.|.|.|.|.|
|Noodle|.|.|.|.|.|.|.|.|.|-0.11|-0.90|-0.44|
|Sugar|-0.59|-0.39|-0.61|-0.80|-0.88|-1.02|.|.|.|0.05|-1.06|-0.27|
|Dairy|-0.44|0.01|-0.72|-0.60|-0.22|-1.57|.|.|.|.|.|.|
|Fats and cooking oil|.|.|.|.|.|.|.|.|.|0.00|-0.75|-0.07|
|Cooking sauce|.|.|.|.|.|.|.|.|.|.|.|.|
|Other food|.|.|.|.|.|.|-0.68|-0.30|.|.|.|.|
|Food away from home|.|.|.|.|.|.|.|.|.|.|.|.|
|Drinks|.|.|.|.|.|.|.|.|.|.|.|.|
|Alcoholic drinks|.|.|.|.|.|.|.|.|.|.|.|.|
|Beer|.|.|.|.|.|.|.|.|.|.|.|.|
|Non food|.|.|.|.|.|.|-1.27|.|.|.|.|.|
|Leisure|.|.|.|.|.|.|-0.82|.|.|.|.|.|

10
**29**

# 9: Feed demand Sorghum

<!-- Start of picture text --> Trade liberalization vs Price Guarontee<br><!-- End of picture text --> <!-- Start of picture text --> 9.<br>8 /<br>7 /<br>4-<br>6/ > 6 j ~ ~~~//'<br>5 /<br>4<br>3i<br>I- - I I I<br>1?4 5 G 8 79 80 81 8 2 S3 84 85<br>(ears<br>= price guarantee + = trode lib<br><!-- End of picture text --> <!-- Start of picture text --> 10: Gov. supply adjustment Sorghum<br>Trade liberalization vs Price Guorantee<br>4-<br>3.5<br>'-.5<br>O.5<br>0 5~~~~~.<br>05 ..... .'~~.<br>is~~~~~~~~~~~er<br>-0.5/<br>1974 715 76 77 78 79 80 81 82 83 4 85<br>Years<br>- price guarontee + -trode fib.<br><!-- End of picture text -->
# **<mark>I. Introduction</mark>**

As part of its general strategy for Latin America and the Caribbean (LAC), in 2010, the World Bank (WB) conducted a study of HIV allocative efficiency in several Latin American countries. This study compared actual expenditures of the HIV/AIDS response with the estimated costs foreseen in the new national HIV and AIDS plan. The goal of this study was to determine whether differences between expenditures, resource allocations, and health needs existed—especially in regard to the groups affected by the epidemic.<sup>6</sup> In line with that regional effort and as a response to the interest of the Colombian Ministry of Social Protection (Ministerio de la Protección Social de Colombia, MPS), the current Study—entitled Evidence-based Implementation Efficiency Analysis of the National HIV/AIDS Response in Colombia—has now been completed. The Study seeks to examine how the national response has been implemented—whether it has been done according to the available evidence about the epidemic and as the response was originally planned. Special effort has been put into verifying that the prevention and care actions envisaged in the national AIDS program are reaching the priority populations.

More specifically, the Study analyzes three dimensions of efficacy: (i) programmatic, (ii) budgetary, and (iii) service delivery. It is structured as follows:

- After the Introduction, Section II summarizes the analytical framework applied and the activities developed for conducting the Study.

- Section III analyzes the state of the HIV/AIDS epidemic in Colombia—placing particular attention on contextual elements, the type of HIV epidemic, and the articulation of the national response.

- Section IV details the findings for each one of the dimensions of the Study— programmatic, budgetary, and service delivery.

- Section V summarizes the conclusions of the Study, and suggests a number of recommendations to improve the national response in Colombia.

> 6 Rodriguez-García R, Gaillard ME; Aran-Matero D. (2010) AIDS Resource Efficiency in Latin America. World Bank.

12
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|**Dimension**|**Indicators**|**Peru**|**South Africa**|
|---|---|---|---|
|||0.17***|<br>0.35***|
||Quality of employment|(0)|(0)|
||Access to water, sanitation,<br>|0.78***<br>|<br>0.6***<br>|
|**Social Inclusion**|electricity & internet|(0)|(0)|
||Level of education|0.76***<br>(0)|<br>0.46***<br>(0)|
||Medical attention|0.71***<br>(0)|<br>0.58***<br>(0)|
||Quality of housing|0.79***<br>(0)|<br>0.69***<br>(0)|
|**Resilience**|Posession of assets|-0.05***<br>(0)|<br>0.79***<br>(0)|
||Public assistance|0.86***<br>(0)|<br>0.39***<br>(0)|
||Capacity for saving|0.53***<br>(0)|<br>0.47***<br>(0)|
||Confidence in government|0.52***|<br>0.66***|
||institutions|(0.01)|(0)|
|**Social Cohesion**|Experience of discrimination|-0.19***<br>(0.01)|<br>0.12***<br>(0.01)|
|||0.19***|<br>0.35***|
||Perception of safety|(0.01)|(0)|
|||0.12***|<br>0.16***|
||Victim of crime|(0.01)|(0.01)|
||Civil participation|0.56***<br>(0)|<br>0.08***<br>(0.01)|
|**Process**|Satisfaction with democracy|0.42***<br>(0.01)|<br>0.74***<br>(0)|
|<br>**Legitimacy**|Government effectiveness|0.7***<br>(0.01)|<br>0.52***<br>(0)|
||Equality before the law (PE) /<br>Sifi ih h|0.27***|<br>0.52***|
||atsacton wt te way<br>corruption is combatted|(0.01)|(0)|
|**Number of obser**|**vations**|45881|2885|
|**Log-likelihood**||-966870.93|-61074.971|
|**LR test of model**|**vs. Saturated**|32107.30***|5222.04***|

The robustness analysis aims to assess the sensitivity of results to changes in parameters. We consider four scenarios: i) changes in the cross-dimensional threshold, ii) changes in the definition of indicators, iii) changes in weights, and iv) changes in the number of indicators per dimension, and analyze the changes using dominance analysis and rank correlation.

# **Scenario 1: Changes in the cross-dimensional threshold**

Figure 7 reports the incidence, intensity, and social sustainability rates for each possible cutoff point in Peru and South Africa. As expected, incidence rates decrease, intensity rates increase, and the Social Sustainability Index decreases as cutoff points become more stringent. That is, the higher is the number of indicators required to identify a person as experiencing multiple social gaps, the lower the incidence, while its intensity will increase. The robustness analysis also shows that such changes are not linear. In Peru, incidence declines slowly for cutoff points lower than 40 percent, declines more markedly up to a 60 percent cutoff point, and then stabilizes regardless of increases in cutoff points (Figure 7, panel A). Intensity in Peru also follows a nonlinear pattern. It increases slowly up to the 45 percent cutoff point to then increase markedly across the rest of the cutoff points. The resulting Social Sustainability Index rates in Peru follow a nonlinear pattern as 21
1.2 related literature 7

Stein & Daude (2002) and Daude & Stein (2007) use a broad set of “institutional” variables to find that regulatory burden is an important determinant of FDI location. They draw on a wide range of sources: i) World Governance Indicators (WGI) developed by Kaufmann et al. (2010); (ii) International Country Risk Guide (ICRG) variables on the risk of repudiation of contract, risk of expropriation, corruption, rule of law, and bureaucratic quality; (iii) La Porta et al. (1999)’s index of shareholder rights, and (iv) World Business Environment Surveys on taxes and regulations, policy instability, and corruption.

Bénassy-Quéré et al. (2007) use 2001 survey data from the French Finance Ministry to find that effective bureaucracy and low corruption, among others, attract FDI. Kinda (2010) brings to bear firm-level data to find that institutional problems, together with poor infrastructure and financing constraints discourage FDI in a sample of 77 developing countries. Mottaleb & Kalirajan (2010) use panel data from 68 low-income and lower-middle income developing countries to show that open countries that rely on trade, have large GDP, high growth rate, and are business-friendly tend to be more successful in attracting FDI.

Alfaro et al. (2008) explain the “Lucas Paradox” of inadequate capital flows from rich to poor countries. Despite rates of return being higher in countries where capital is scarce, they argue that poor countries do not receive investment from abroad because of institutional weaknesses. But there are exceptions. Fan et al. (2009) attribute record inflow of FDI into China in recent decades, in spite of the indifferent quality of the country’s institutions, to its stewardship of sustained economic growth.

While this literature confirms the salience of specific institutional variables, it suffers from limitations relating to model mis-specification and variable mismeasurement. The studies mentioned above do not address the problem of sample selection that arises when the pairing of FDI-sending and receiving countries does not occur randomly. They do not incorporate firm heterogeneity in a cross-national
<!-- Start of picture text --> ~ ae<br>—S<br>as % Me<br>0) ‘<br>| a<br>| = i da Saline content<br>; Se (gram0-2per liter)<br>Sacer A ll 6-16<br>aai Ml 2-6<br>aes \ J . iHl >3016-30<br>= {__]} Province boundary<br>Rivers, canals, sea<br><!-- End of picture text -->
**_Chart 5.9   Advanced degrees among present-day teachers in Anjouan and Ngazidja, by hiring period_**

<!-- Start of picture text --> 100% 91.46%<br>80% 72.39%<br>64.31%<br>60%<br>40%<br>20%<br>0%<br>Up to 2000 2001‐2005 2006‐2011<br><!-- End of picture text --> Source: Author database, 2011.

The data present a picture that shows a surprisingly high level of qualification among teachers in terms of their level of education, with over 77% of respondents holding an advanced degree, meaning a degree above the high school level. In fact, many of these respondents are over-qualified if they are primary school teachers, who are required to have only a high school diploma. Interestingly, as Chart 5.9 suggests, this trend has been increasing over time; over 91.46% of the staff of Commissions of Education who were recruited after 2005 had an advanced diploma, a substantial increase from 64.31% of those present-day teachers who were recruited in the period up to 2000, and 72.39% from 2001 to 2005. In other words, despite potential political motives for recruitment, the merit of the recruited teachers seems to be respected.

This data points to an important observation, that even if a system of political clientelism exists – as suggested by the correlation with the 2010 election, it is not conducted such that unqualified staff are hired in the public sector; rather, the rules on qualifications continue to be respected. The reasons for this outcome are explored in a later section.

**_Chart 5.10   Pedagogical training among present teachers in Anjouan and Ngazidja_**

<!-- Start of picture text --> 75.78%<br>80% 70.53%<br>59.07%<br>60%<br>40%<br>20%<br>0%<br>Up to 2000 2001‐2005 2006‐2011<br><!-- End of picture text --> Source: Author database, 2011.

The level of pedagogical training among respondents is less than the level of higher education, at 68.22%. Moreover, while the proportion of staff in the Commissions of Education who possess an advanced diploma has increased over time, the proportion who received pedagogical training has dropped over time. 75.78% of teachers who were recruited in 2000 and before have undergone some type of pedagogical training, a level that declined to 70.53% of teachers recruited in 2000 to 2005, and finally 59.07% of those recruited from 2006 to 2011. These increases in the hiring of teachers with university degrees also coincide with a large increase in the number of university graduates throughout Comoros. While updated official 15
Changes in the relative quantity of output/consumption between sectors i and j, _Y_<sup>_<u>Y</u>_</sup> _jt_<sup>_<u>it</u>_,isdetermined</sup> by: (1) difference in sectoral productivity growth rate and time, _σ_ ( _τi − τj_ ) _t_ ; and (2) difference in sectoral income elasticities and real income growth, ( _εi − εj_ ) ∆ _logYt_ .

**Proposition 2** _Relative price between sector i and j is inverse to their productivity levels. Sectors with higher productivity have lower relative prices._

Relative prices are only driven by supply-side forces in the form of differential rates of productivity growth. As agricultural product is set as the numeraire and agriculture sector has the highest productivity growth, prices for all other sectors will grow monotonously over time:

**Proposition 3** _For non-agricultural sectors, sectoral employment/expenditure/nominal output share monotonously increases with real income/welfare, and monotonously decreases with productivity._

_Employment/expenditure/nominal output share of agriculture monotonously decreases over time. For both manufacturing and high-skill tradable service sectors, employment share exhibits an inverted U- pattern over time. Share of low-skill, nontradable services rises monotonously._ The dynamics of sectoral employment share can be derived: In the agriculture sector, where _εA_ = _σ_ , the employment share decreases at a constant rate over time, as ∆ _logLAt_ = ( _σ −_ 1) _τA<_ 0.

For other sectors, the first item (income effect) is positive but decreasing over time, while the second term (price effect) is negative and constant. Given the sectoral income elasticity assumption in 4 and sectoral productivity growth assumption in 7, both manufacturing and high-skill service sectors exhibit 20
For the end line survey, instruments consisted of a household questionnaire and an individuallevel questionnaire administered to recent (two-week recall) fever cases. The household-level questionnaire recorded demographic, socio-economic and health characteristics, general health seeking behavior, knowledge on malaria and utilization of bed nets. The individual fever questionnaire collected information on treatment seeking behavior from the recent fever cases. In each study village, a full household listing was conducted from which 10 randomly selected households were interviewed for the household level information. The full household listing also included a listing of all recent fever cases (determined through 2-week recall) and 10 cases were randomly selected from each village and interviewed for individual-level information. For both surveys, interviews were recorded on paper forms and double-entered in CS Pro software (version 4.0) at a central location. Project level cost data were extracted from the financial reports and government level data from the registers at the health centers.

# **Statistical analysis**

The data were analyzed as an intention-to-treat analysis with treatment at the cluster (village) level. Balance across treatment arms in pre-intervention or fixed characteristics measured at endline but unaffected by the intervention were assessed through normalized mean differences and differences exceeding a threshold of 25% were considered significant [21]. Pair-wise t-tests of difference were also estimated. Differences in outcomes between intervention and control clusters were examined with logistic regression. Socio-economic status (SES) was calculated by a principal component analysis of key household characteristics and assets to create a wealth index [22]. Since no differences were found between unadjusted and adjusted odds ratios – i.e. results are unchanged if we adjust for the observable characteristics in Table 2 – we present 9
# **4.1.3 Degree of Norm Misperception across the World**

Figures 5 to 7 present maps of the degree of misperception across the globe with respect to the broad norm, the breadwinner norm, and the caregiver norm. These maps are color-coded to represent the varying degrees of misperception across different countries. Where darker shades indicate a greater disparity between personal beliefs and perceived community norms; and lighter shades suggest that norms are more closely aligned with actual beliefs.

In Figure 5, the map quantifies the misperception of the broad norm at the country level and presents the absolute value of the difference. We show a universal underestimation of support for gender equality. Figures 6 and 7 explore misperceptions around the gender role specific norms. Here, positive values signify a perception that society is more traditional or gender-stereotypical than personal beliefs, while negative values indicate the opposite. Darker shades of blue highlight countries where the breadwinner norm is believed to be more conservative or stereotypical in society than actual beliefs held by the Facebook sample. Light blue and yellow-green shades demonstrate a closer match between perceived norms and beliefs or a slight underestimation of the norm i.e. they think their community is more gender progressive or counter-stereotypical than actual beliefs.

Collectively, these figures reveal regional patterns where certain areas consistently show misperceptions across all norm constructs, while others display varying degrees of misperception depending on the norm in question. To discern whether misperceptions regarding gender norms are more prevalent among men or women, we delve into a comprehensive analysis in Appendix D. This examination leverages individual responses from our dataset to evaluate the discrepancies between perceived community norms and actual beliefs, dissected by gender and geographic region.

According to Table D1, the extent of misperception is substantial, with the frequency of under- and over-estimation eclipsing the instances of accurate assessments of community beliefs. It is important to note that when evaluating the level of misperception (i.e., whether an individual overestimates or underestimates a norm), we juxtapose an individual’s perception of their community norm against the aggregate beliefs within their country. The findings indicate that, on average, respondents perceive gender norms within their communities to be more traditional than what national averages indicate. Particularly, individuals in the Latin America and the Caribbean (LAC) and the Middle East and North Africa (MENA) regions exhibit the highest levels of misperception regarding the gender equality norm. Furthermore, the LAC region stands out with the most pronounced misperception concerning gender role norms.

Some of what we are labelling as “misperceptions” could in fact reflect sample selection whereby the respondents’ neighbors are not necessarily the same as the average Facebook 16
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