rafmacalaba/datause-encoder-data huggingface dataset:
Croke et al. (2016) effects). Where the WHO recommends multiple annual MDA (areas with prevalence above 50%), the average estimated weight gain is 0.182 kg (CI: 0.070,0.293; p=0.001; random effects).

The implied average effect of MDA deworming on infected children in the full sample (calculated by dividing estimated impact by worm prevalence for each study and applying a random effects model) is 0.301 kg.<sup>5</sup> This average effect likely conceals substantial heterogeneity. Light infections are often asymptomatic, and only between 2 and 16 percent of the population experience moderate to severe intensity infections in the studies in our sample that report this information,<sup>6</sup> so implied effects in the subpopulation of those with moderate to severe intensity infections are likely much larger. For more general context on the implied average effect size of 0.301 kg, the difference in weight gain for boys at the 25th versus at the 50th percentile of the weight-for-age distribution between ages 2 and 3 is 0.2 kg (World Health Organization Multicentre Growth Reference Study Group, 2006).<sup>7</sup> Moreover, this gain comes at modest cost compared to some other common interventions. The implied weight gain per U.S. dollar of expenditure is 0.22 kg, assuming

> 5 The implied average effect on infected children in the subsamples of studies conducted in areas of 20% and 50% prevalence are similar, at 0.249 and 0.276 respectively. The confidence intervals overlap with that of the full sample.

> 6 In particular, Joseph et al. (2015) reports 15% prevalence of any helminthic infection among preschool aged children at baseline in Peru, with only 1.8% of children exhibiting moderate or heavy infections, while Miguel and Kremer (2004) report hookworm, whipworm, and roundworm prevalences of 77%, 42%, and 55%, with 15%, 16%, and 10% prevalence of moderate to severe infection respectively. Moreover, Miguel and Kremer (2004) use thresholds that are lower than the WHO thresholds used by Joseph et al. (2015) for moderate to severe intensity infections, suggesting that the comparable prevalence of moderate-severe infections in the Miguel and Kremer (2004) sample is lower than the figures presented here.

> 7 According to WHO growth charts, a boy at the 25th percentile of the weight for age distribution grows 1.9 kg between age 2 and age 3, while a boy at the 50th percentile grows 2.1 kg over that year. Note that the difference in weight gain for boys at the 25th versus at the 50th percentile of the weight-for-age distribution between ages 3 and 4 is also 0.2 kg (World Health Organization Multicentre Growth Reference Study Group, 2006). In our full sample, the median duration of follow up at which weight gain is measured is 1 year.

6
97. Straub, S. and D. Martimort. 2005. “Infrastructure privatization and changes in corruption patterns: The roots of public discontent.” _Journal of Development Economics_ . 90(1): 69-84.

98. Straub, S. 2008. "Infrastructure and Development: A Critical Appraisal of the Macro-level Literature," ESE Discussion Papers 178, Edinburgh School of Economics, University of Edinburgh.

99. Sturzenegger, F., and M. Tommasi. 1998. _The Political Economy of the Reform_ . Cambridge, Mass.:The MIT Press.

100. Thatcher, M. 2005. “The third force? Independent regulatory agencies and elected politicians in Europe.” _Governance_ , 18(3): 347-373.

101. Tremolet, S. 2005. “Mali Case Study.” KfW /TRC Economics.

102. Vaglisasindi, M. 2009. “Improving the Performance of Infrastructure State-owned Enterprises.” Processed. World Bank, Washington, DC.

103. Valero, V. 2010. “Government Opportunism in Public-Private Partnerships.” Processed.

104. Wieland, B. 2006. “Special Interest Groups and the 4<sup>th</sup> Best Transport Pricing. CNI-WP 2006-13.

105. Willig, R. D. 1994. “Public versus Regulated Private Enterprise.” Proceedings of the World Bank Annual Conference on Development Economics 1993. Washington, DC: World Bank.

106. World Bank. 2009. “Enhancing Planning and Control to Increase Efficiency of Public Spending." Public Expenditure Review, Report No. 44350-CV.

107. ---------.2010. “Peru Natural Gas Study.” 108. World Bank/Foster and Briceno-Garmendia. 2010). _Africa’s Infrastructure: A Time for Transformation_ . Washington, DC: World Bank.

36
untouched or converted into limited liability shareholding firms in which the state continues to own the majority of shares and/or to exercise control rights. Thus there are now effectively two classes of state firms: those with some outside shareholders and others that remain pure SOEs. In theory, the former are closer to the threshold of the market system and are subject to governance from supervisory boards or boards of directors and hence more likely to be accountable to shareholders, to maximize shareholder value, and therefore, to take their cues from the market with respect to management, structure, strategy, and technology. In practice, there is a lot of variance with the average shareholding MLSOE not having evolved very far from an unreformed SOE but with some reformed MLSOEs such as CIMC, Lenovo, Huawei, and Haier appearing to be blazing new trails and registering large gains in performance.<sup>17</sup> From the very start, the reform of state enterprises has been hamstrung by the social functions performed by SOEs for their workers. Comprising the core element of the urban labor force, a declining subset of the employees of the SOE sector rely on their work unit for housing, schooling, medical care, and pension benefits. Reforms that would rationalize the structure and employment of SOEs, focus their activities or force widespread closure of unviable enterprises have proven to be politically unpalatable because the social consequences—unemployment, loss of benefits—and the direct fiscal costs of compensating and/or retraining workers are high.<sup>18</sup> Moreover, the state sector employees still wield considerable political clout and radical measures to terminate a longstanding social compact would be problematic socially as well as politically. Thus a slow phasing in of enterprise reforms, that whittles down state owned industry, has been supplemented by the attempts underway since the late 1980s to create a national pension system,

> 17 The profit of CIMC increased by 120% for the first 6 moths of 2005 compared to the same period a year ago (CIMC Semi-Annual Report 2005).  Huawei’s profit increased 63% from 2003 to 2004 (Huawei Techologies Annual Report 2004).

> 18 Retraining, while it has helped some workers find employment mainly in urban services industries, often does not enable the displaced workers obtain jobs that pay as well as the ones lost.

17
slopes downwards. This impact was strong enough to result in the expansion of the gender wage gap at the mean between 2013 and 2015.

Figure 2. The gender wage gap across the wage distribution.

<!-- Start of picture text --> 2004 2005 2006 2007<br>2008 2009 2010 2011<br>2012 2013 2014 2015<br>0 5 10 0 5 10 0 5 10 0 5 10<br>deciles<br>Graphs by year<br>.6<br>.4<br>.2<br>.6<br>.4<br>decilegap .2<br>.6<br>.4<br>.2<br><!-- End of picture text --> These observations suggest that gender wage gap movements contain a strong cyclical element, due to the considerable industrial and occupational segregation. Similarly, for the period before 2012, Khitarishvili (2016) attributes the considerable size of the gender wage gap to the high degree of industrial and occupational segregation. This motivates the focus of the present paper to examine and quantify the role that segregation, in terms of occupations and sectors, has played in the movement of the gender wage gap in Georgia between 2004 and 2015.

# **III. The state of industrial and occupational segregation in Georgia**

In Georgia, women’s employment patterns by industries and occupations remain highly concentrated. As many as 47.2 percent of female workers in 2015 were employed in only three sectors: health, education, and culture, and 86.4 percent of them were employed in white-collar occupations. This contrasts with men’s employment composition, which is much more evenly distributed across different sectors and occupations. Industrial and occupational segregation are typically among the main explanatory factors underlying the gender wage gap (references). But, to what extent is it the case in Georgia? We explore this question by first examining in greater detail the degree of industrial and occupational segregation in Georgia during 2004 – 2015 and then by quantifying its contribution to the gap.

9
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The Port of Luanda is notorious for long delays and poor performance relative to other ports in Africa. Container dwell times, at 12 days, are twice as long as Durban, Africa’s best-performing port, and are rivaled only by ports in Mozambique that perform slightly worse. Truck cycle time, at 14 hours, is over twice as long as other southern African ports. Crane productivity is less than half that of other southern African ports.

Port-handling charges in Angola are among the highest in Africa. The container cargo-handling charge is almost five times what is charged at the Port of Mombasa (Kenya) and 25 percent higher than Durban (South Africa). Bulk cargo-handling charges are also on the higher end of what is observed in African ports.

Deterred by the lengthy delays and high prices, Angolan traffic is increasingly using the Port of Walvis Bay in Namibia as the main gateway to the sea. Walvis Bay is located 2,100 km to the south of Luanda, yet improvements in road and rail infrastructure linking the two cities have made the port more accessible to the Angolan market. The fact that port users increasingly prefer this long-distance road journey and its associated border-crossing delays is testimony to the severity of the problems facing the Port of Luanda.

Efforts that are now under way should help to ease the congestion problems at the Port of Luanda. A dry port has been developed at Viana, about 30 km inland from the port, connected by road and rail. The Viana dry port is in the process of being further expanded at a cost of about $70 million. A $136 million contract was signed in 2009 to improve road access to the port and to reclaim additional land for development around the port area. Further, a new container port is also being planned for a 2,400 hectare site, at Barra do Dande, north of Luanda, which is in the process of being cleared of land mines (Nathan 2010).

**Table 11. Benchmarking port performance**

|**Performance**|Cape Town|Durban|East London|Port Elizabeth|Walvis Bay|Luanda|Beira|Maputo|Mombasa|Dar es Salaam|Tema|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Container dwell time—average (days)|6|4|7|6|8|12|20|22|5|7|25|
|Truck time for receipt and delivery of cargo (hours)|5|5|2|5|3|14|7|4|5|5|8|
|Container crane productivity (container per hour)|18|15|8|15||7|10|11|10|20|13|
|**Prices**||||||||||||
|Container-cargo-handling charge ($ per TEU)|258|258|258|258|110|320|125|155|68|275|168|
|General-cargo-handling charge ($ per tonne)||8|8|8|15|9|7|6|7|14|10|

_Source:_ AICD Ports Database 2008. _Note:_ TEU = 20-foot equivalent unit.

25
higher employment loss than male workers. Other CGE-based simulations implemented in other contexts confirm such negative employment effects among females (Escalante and Maisonnave, 2021, in Bolivia; Chitiga et al., 2021 in South Africa). In addition, these studies suggest that, having lost their jobs, female workers are less likely to return to work than male workers. Moreover, by using a difference-in-differences framework to estimate the COVID-19 effects on the employment of Indian workers, Deshpande (2020) observes that, while employment among men recovered by August 2020, employment among women remained 9.5 percentage points lower than that of their male counterparts in August 2020, relative to the pre-pandemic period.

On the other hand, other studies seem to indicate that female employment has fared relatively better than that of males during the onset of the pandemic. Deshpande (2020) notes that the COVID-19 pandemic initially caused a 20-percentage-point reduction in the gender employment gap in the Indian labor market, mainly through greater employment reductions among the male population. Similar results have been observed in Ethiopia (Hossain, 2021), <mark>Türkiye</mark> (İlkkaracan and Memiş, 2021), Central America<sup>11</sup> (Webster, Khorana and Pastore, 2021) and, most notably, in Indonesia (Asian Development Bank, 2021). Some studies cite women’s disproportionate pre-pandemic concentration in occupations with a high suitability to workingfrom-home (WFH) as the most likely factor in explaining women’s relative employment increase during the onset of the pandemic (Montenovo et al., 2021; Mongey and Weinberg, 2020). In developing countries, employment among females is sustained by the overall transition into “lower quality” employment during the onset of the pandemic, such as more informal work (Asian Development Bank, 2021), indicating that the relative increase in female employment is not an entirely good story.

> 11 El Salvador, Guatemala, Honduras, and Nicaragua. See Webster et al. (2021) for more details.

11
reliably produce shorter interview times than time diaries. As such, current iterations of the WEAI continue to utilize the time diary format.

The ultimate decision of which method to use, however, requires consideration of factors beyond simply which method is most feasible. Researchers much consider which method makes the most sense for their research question. One component of this decision is the level of accuracy required to answer the research question. For instance, understanding how individual time allocation patterns evolve over time would require greater accuracy than comparing average time allocation patterns across gender at a single point in time. Hence, in the next section, we consider the relative accuracy of stylized questions and time diaries.

# **4.2. ACCURACY**

One of the fundamental goals of time use research is to establish patterns of individual time allocation. Which activities take up the most time in a person’s day? How much time does a person save by using a new water collection system? Does participation in a particular agricultural activity reduce women’s time available for the care and feeding of young children? Investigating questions like these requires accurate information about the amount of time people spend on different activities. As the previous discussion has noted, however, the complexities involved in answering time use questions mean that respondents’ own estimates of how they spend their time may not be accurate and, moreover, that the extent of these inaccuracies may vary depending on the activity in question and survey method.

In this section, we compare time use estimates obtained from the same individuals using two different methods—time diaries and stylized questions and attempt to explain these differences in terms of several potential factors. As before, the data come from the WEAI 2.0 pilot survey in Bangladesh and Uganda.

Admittedly, our method is imperfect; the WEAI pilot survey was not specifically designed with this type of analysis in mind. The primary problem owes to differences in recall periods. The time diary asks about the previous 24 hours; the stylized questions ask about the past seven days. While the average 15
48 Table 9: Correcting for Misreporting in Residential Burglary

|Pr|obitResultsusin|g<br>N|egativeBinom|ialRegressio|nResultsusi|ng|
|---|---|---|---|---|---|---|
|Ho|useholdreporting<br>Burglaries|of|Adjusted|Burglaries||Unadjusted<br>Burglanes|
|InequalityinPolice|0.20|0.88|0.34<br>|0.35<br>|0.33<br>|0.55<br>|
|Station|**(0.19)**|(0*09)t*|(0.08)**|(0.08)**|(0.08)**|(0.08)**|
|MeanExpenditurein|0.22||0.77|0.85|0.77|0.92|
|PoliceStation|(0.13)||(0.04)**|(0.05)**|(0.05)**|(0.05)**|
|UnemploymentIn|-0.42|||0.24|0.19|-0.04|
|<br>CriminalCatchmentArea|(0.16)**|||(0.06)**|(0.06)**|(0.06)|
|RichestJurisdictionIn|-0.14||||0.31|0.20|
|CriminalCatchmentArea|(0.21)||||(0.08)**|(0.07)**|
|ProvinceDummies|||||||
||Eastern Cape|-0.43|0.29|0.10|0.10|-0.02|
|||(0.10)**|(0.09)**|(0.11)|(0.10)|(0.10)|
|NorthernCape||-1.00<br>(0.13)**|-0.29<br>(0.11)**|-0.41<br>(0.12)**|-0.41<br>(0.12)*|-0.30<br>(0.11)**|
|FS||074|013|004|002|012|
|reetate||-.<br>(0.12)**|.<br>(0.11)|.<br>(0.11)|.<br>(0.11)|.<br>(0.11)|
|KlNl||021|033|014|015|013|
|wazuu-ata||-.<br>(0.10)*|.<br>(0.09)**|.<br>(0.11)|.<br>(0.10)|.<br>(0.10)|
|**Northwest Prov**<br>Northwest Province|**ince**|~-0.17<br>(0.12)|**0.65**<br>(0.11)**|**0.51**<br>(0.12)**|0.53<br>(0.11)**|-0.07<br>(0.11)|
|**Gauteng**||0.72<br>(0.11)**|0.38<br>(0.10)*|0.27<br>(0.10)**|0.37<br>(0.10)**|0.31<br>(0.10)**|
|**Mpumalanga**||-0.02<br>(0.12)|0.54<br>(0.10)**|0.39<br>(0.11)**|0.40<br>(0.11)**|0.31<br>(0.11)*^|
|**NorthernProvince**||-0.30<br>(0.12)*|0.35<br>(0.11)**|0.14<br>(0.12)|0.17<br>(0.12)|-0.17<br>(0.12)|
|Population||0.85<br>(0.02)**|0.81<br>(0.02)**|0.80<br>(0.02)**|0.80<br>(0.02)**|0.82<br>(0.02)**|
|Constant|-1.46<br>(0.72)*|-1.94<br>(0.22)**|-7.39<br>(0.33)**|-7.38<br>(0.33)**|-6.98<br>(0.34)**|-8.69<br>(0.33)**|
|PseudoR<sup>2</sup>|0.077|0.071|0.095|0.096|0.098|0.119|
|Observations|627|1066|1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; ** significant at 1%. Probit regressions are at the household level using data on reporting of residential burglary to the police from the Victims of Crime Survey. Adjust number of burglaries in a police station jurisdiction is the number of actual burglaries reported divided by the probability of a burglary being reported in that jurisdiction.
_God, visiting the iniquity of the fathers on the children, and on the third and the fourth generations of those who hate Me_ ” versus “ _Fathers shall not be put to death for their sons, nor shall sons be put to death for their fathers; everyone shall be put to death for his own sin_ .” Of course the above example with parents and children takes us back to other examples in Cohen (1989), which highlighted difficulties in Dworkin’s (1981a, 1981b) attempts to introduce the notion of responsibility into egalitarian philosophy by drawing a distinction between preferences and resources. If preferences are themselves determined by resources, say parental resources, then a clean separation may not be possible, certainly empirically and perhaps even conceptually. These difficulties are the subject of a large literature and debate. Key perspectives in this, from Roemer (1998), Barry (2005), and Swift (2005), are summarized by Jusot, Tubeuf and Trannoy (2013) as follows: “ _In what we call Barry’s view, circumstances are past variables and efforts are the variables which reflect the free will of the present generation. In Roemer’s view, the vector of circumstances includes all past variables, and the descendant’s efforts must be cleaned from any contamination coming from circumstances. In Swift’s view, the vector of circumstances only includes past variables, which have no consequences on children’s efforts. In other terms, the vector of circumstances must be cleaned from any correlation with [children’s] efforts_ .” (p1473) The differences between these views are only one part of a large literature on a range of issues in equality of opportunity<sup>1</sup> which reflect basic ethical disagreements that are unlikely to be resolved easily, and in any case need to be supplemented by the case of extreme outcomes discussed above, and how our moral intuitions in such extreme cases translate in turn to less extreme conditions.

# **3. Implementation in a Policy Context: Income Inequality**

The ethical disagreements on basic concepts notwithstanding, implementation of measures of (in)equality of opportunity has proceeded apace. The burgeoning literature has provided a plethora of measures and applications to data sets, from early attempts by Roemer and his associates (for example Roemer et. al. 2003), to more recent applications in specific contexts like health (for example, Jusot, Tubeuf and Trannoy, 2013). Thus the partitioning of influences on outcomes into legitimate and illegitimate has now been undertaken empirically in a large number of contributions across many countries.

Of particular interest is a line of analysis established by the work of de Barros et al. (2009). In part this is because it has become popular throughout the World Bank (spreading

> 1 A recent selection would include Fleurbaey and Peragine (2013), Ooghe, E., Schokkaert, E. and Van de Gae (2007) and Peragine (2004).

6
||Table 8(Continue<br>|d)<br>|||
|---|---|---|---|---|
|Dependent Variable:|Fallow Fiel|ds (% of Total)|Fallo|w Field †|
||(1)|(2)|(3)|(4)|
|**_Soil Characteristics_**|||||
|Nutrient Constraint †||0.0228***<br>(0.0087)||0.0199***<br>(0.0076)|
|Oxygen Constraint †||-0.0028||-0.0149**|
|||(0.0068)||(0.0068)|
|Toxicity Constraint †||-0.0084<br>(0.0149)||0.1218***<br>(0.0291)|
|**Observations**|1,861|1,861|13,020|13,020|
|**R-Squared**|0.001|0.093|0.000|0.023|
|Note: Clustered Standard Errors at EA (Enumerati<br>*** p<0.01, ** p<0.05, * p<0.1<br>† Denotes a Dummy Variable|on Area) Level in Parentheses.||||

32
The results confirm that registration requirements for SPS reasons (the so-called sanitary registries) are a measure affecting trade in Guatemala. As shown in Figure 3, there are only a few other countries that apply these types of measures as broadly as Guatemala. Other regulations, such as labeling and packaging requirements are also imposed on these goods. The impact of SPS measures varies greatly across these four products. According to our estimation, the average increase in the Guatemalan domestic price of beef due to SPS measures is equivalent to an ad-valorem tariff of 68.4%. This seems consistent with the perception of the Guatemalan beef market as quite closed to regional exporters, especially trade from Nicaragua. The AVE equivalents for the SPS measures facing bread and pastry, chicken meat and dairy products are 51.4%, 22.0% and 5.0% respectively.

# **b. Household Expenditure on Selected Products**

We now explore the implications of our results for the cost of living across the income distribution in Guatemala. To do this, we combine our estimated AVEs with household expenditure data from the 2011 Guatemalan Household Survey (HHS) and calculate the budget share of these products by quintiles of the income distribution. Results are presented in Table 8. As expected, given the essential nature of these items, the percentage of total income spent on them decreases with household income. Therefore, any policy aimed at streamlining NTMs would mainly benefit the poorest sector of the population in Guatemala.

**Table 8. Guatemala: Households’ Budget Shares of Selected Products (%)** **<u>(By Income Quintiles).</u>**

|**Products**|**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|
|---|---|---|---|---|---|
|Beef|1.18|0.90|0.71|0.57|0.35|
|Bread and Pastries|1.04|0.96|0.85|0.69|0.38|
|Chicken Meat|1.30|1.03|0.83|0.70|0.46|
|Dairy Products|0.59|0.50|0.41|0.36|0.24|
|Total|4.11|3.39|2.80|2.31|1.43|

Note: This table shows households’ budget shares of the selected products across the quintiles of income distribution. Budget shares are the percentage of total annual income by household spent on the consumption of each good. Each type of product is mapped to a group of goods in the HHS as follows: Beef ( _carne de cerdo con y sin hueso and carne de res sin y con hueso_ ), bread and pastries ( _pan de rodaja, pan dulce, pan frances_ ), chicken meat ( _carne de pollo o gallina, viceras de pollo o gallina_ ), and dairy products ( _leche liquida, queso fresco o duro_ ). Source: Authors’ computation using the Guatemala 2011 HHS.

# **c. Measuring the Impact of Streamlining SPS Measures on Poverty**

We now look at the hypothetical cases of the total and partial removal of SPS measures on our set of selected products and analyze the impact of these changes on poverty rates in Guatemala. We follow Porto (2010) in this analysis. Using the 2011 Guatemalan Household Survey, we calculate the head count ratio (HC), defined as the fraction of the population with an income below the poverty line _z_ . That is, 24