rafmacalaba/data-use-mentions-extended huggingface dataset:
included in the available observations after which storm surges were modelled due to the limited period of the available records (Muis et al. 2016). In Vietnam, the GTSR dataset shows barely any flooding along the North and South Central coast which does not accurately reflect actual flooding experienced in these regions. This can likely be explained by the absence of wave setup in the GTSR approach in combination with a topography characterized by relatively steep slopes on this coastline section (Bunya et al. 2010).

For this report, a new set of coastal flood maps for Vietnam has been created that tries to reflect flood risk more accurately. It is based on a global digital elevation map with a resolution of 90 meters at the equator that has been corrected for several errors typically present in spaceborn elevation models (Yamazaki et al. 2017). In order to arrive at predictions for flood extent and depth, digital elevation data is combined with averaged provincial surge levels. For each province and each return period, an average surge level is calculated based on modeled frequency curves. These curves have been generated by statistically postprocessing the results from storm surge modeling with a wide range of possible typhoons (Ministry of Agriculture and Rural Development, 2012). The flood depth in the new coastal flood map is then calculated following s −d , in which s reflects the average storm surge level in province for return period , and d reflects the average altitude in grid cell of the digital elevation ii,tt jj ii,tt mm map used. Whenever s −d ≤0 the corresponding grid cell value is set to 0, indicating no flooding. jj ii tt mm jj The output has a raster format with a resolution of about 3′′ resolution (~90 m at the equator). Following ii,tt jj mm this simple methodology, flooding depth for the 28 coastal provinces are calculated for events with return periods of 5, 25, 50, 100, 250, and 500 years (figure 2.1.b)
POLICY RESEAR( H WORKING PAPER 2116

# Summary findings

RavalCion and Wodon try to determine whether children seln _to_ work in rural Bangladesh are caught in a poverty trap, with the extra incorne to poor families from child labor comiing at the expense of the chilcdren's longer-term prospects of escaping povei-ty through education.

The poverty trap argument depends on children's work being substitutable for schooling. Casual observations and the descriptive statistics available from surveys seem to offer littLe support for the argument.

To explore the questioni more deeply. Ravallion and Wodon use a targeted school stipend to identify how nmuch clhild labor substitutes for schooling. They find that Bangladesh's Food-for-Education program is a strong incentive tor school attendance. A stipend with a value considerably less than the mean child wage was enough to ensure nearly full school attendance among participants.

The enrollment subsidy also reduced the incidence of clhild labor, an effect that accounted for only a small proportion of the increase in school enrollment. The reduction in the incidence of child labor among boys (girls) represents about one-quarter (one-eighth) of the increase in their school enrollment rate. Parents are clearly substituting other uses of their children's time to secure the current income gain from access to the program, with modest impact on earnings from their children's work.

The authors' tests were limited. Work may well displace time for doing homework or attending afterschool tutorials, for example. Ravallion and Quentin were unable to identify such effects from the data available.

There may also be other welfare losses to children from work (such as exposure to an unsafe working environment) as well as welfare gains (such as skills learned from working that enhance returns to schooling).

But their results do lead them to question the seemingly common view that child labor is a major factor perpetuating poverty in Bangladesh by keeping children from poor families out of school.

This paper-a joint product of Poverty and Human Resources, Development Research Group, and Poverty Reduction and - Economic Management Sector Unit. Latin America and the
erfo a i () 8) ] 1 ic metric specification: indicates the nt’s gender. The model inc ts for grade, specifically comparing Ukrainian refugees to other newly arrived foreign students. This approach a ; iS iS 4.

class influences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on findings from the survey data to unpack and analyze how Ukrainian Ukrainian refugees were enrolled into Italian secondary schools. This figure constitutes 24% of the
is, all three targeting methods misclassify ultra-poor households as non-ultra-poor when those ultra-poor households are better-off, according to other observable characteristics not used in the targeting. Likewise, false positives (inclusion errors) tend to fare worse than true negatives across these same measures. The exact pattern of differences depends on the targeting method; for instance, asset-based targeting (first set of columns) tends to misclassify ultra-poor as non-ultra poor when they have assets (the difference of -2.21 is large), but errors are not systematically correlated with consumption (the difference of -0.19 is relatively small). The CDR-based method in particular tends to prioritize households that score low on these alternative measures of well-being. These patterns suggest that the CDR-based targeting method

> 7Similar analysis could also be performed using the balanced sample or the full sample; however, results would conflate differences caused by the targeting data (the current focus of Table 3) with the differences that arise from considering (or excluding) households without mobile phones (the current focus of Table 2).

12
Trade Facilitation..................................... 50|
|Annex E. Country Ranking by WMO Trade Facilitation Indicators.................................... 53|
|Annex F. TTFSE Performance Indicators ............................................................................ 55|
|Annex G. Trade Logistics Template..................................................................................... 57|
|Annex H. Sample of Infrastructure/Facilitation Indicators................................................... 59|
|Annex I. Summary of Main Data Sources...........................................
nity level. While we focus on this pre-specified analysis of neighbor impacts, the PAP contained several additional secondary or more exploratory analyses, including the estimation of ITT effects, the evaluation of the effects of the program using a continuous treatment variable (measured in either months of treatment and the total cash value of the transfer<sup>15</sup> ), the assessment of treatment effects on refugees’ economic convergence with their neighbors, and estimation of heterogeneous effects (based on various demographics, see the full set of pre-specified results in the Appendix).

## **4.5 Estimating the accuracy of forecasts**

To determine whether mean forecasts differed significantly from the estimated program impact, the analysis takes into account both the estimation error in the estimated program impact ( _β_<sup>�</sup> 1 and _θ_<sup>�</sup> 1 in the second-stage estimation equations above) as well as the observed range of forecasts, using 1000 simulated draws of each. For each draw, the difference between the two values is taken, yielding the distribution of the differences. Statistical significance is determined by inspecting whether zero lies within the 95% confidence interval of the distribution of this difference; if zero lies outside the interval, we reject the hypothesis that the mean forecast and estimated treatment effect are equal.

# **5 Housing Subsidy Program Impacts on Refugee Life Outcomes**

We first estimate program impacts on refugees pooling survey rounds, and then explore dynamic effects by rounds. As noted, we generally focus on results that both survive a multiple hypothesis testing adjustment and do not change sign when bounded to account for differential attrition.

> 15Since the implementing organization negotiated the length of the contract and the amount of reduced rent with each landlord, there is some non-random variation in both across treatment households.

20
UK, Bryant et al (2004) for New Zealand, Blanes-Cristobel (2003) in Spain, White (2007b) for Denmark and Hatzigeorgiou (2010) in Sweden. At the _country_ level, only three papers examine direct-links amongst groups of countries. Hatzigeorgiou (2009) examines a cross-section of 75 countries in 2000, while Felbermayr and Toubal (2008) implement a cross-section for the OECD in the same year. Felbermayr and Jung (2009) is the only paper to the knowledge of the author which implements a panel of countries (for 1990 and 2000).

Greater availability of disaggregated data has spurred ever more sophisticated empirical studies. Some focus upon the trade-migration nexus within a country, for example Combes et al (2005) for France. Yet another strand of the literature examines _states_ or _provinces_ trading with overseas country partners. Examples include Wagner et al (2002) for Canada, Co et al (2004), Bardhan and Guhathakurta (2005), Herander and Saavedra (2005), Dunlevy (2006) and Bandyopadhyay et al (2008) for the US and Peri and Requena (2010) for Spain.

> 4 These concentrations are modelled as the cross-products of the share of ethnic Chinese in each trading partner. 5 Migration data are notoriously weak and this focus can be explained by the paucity of the available migration data.

4
the PECS 2016/17 consumption dataset.

- (iv) Calculated indirect effects of fuel excises based on the Augmented IO Matrix for the West Bank and the Cost-Push Model (Inchauste and Jellema, 2018). The model applies the price shock of fuel excises (differentiated for gasoline, diesel, and kerosene) to the petroleum input sectors; output sectors that have used petroleum products as an input will carry indirect effects from fuel excises.

- (v) Calculated direct effects based on (estimated) quantities of gasoline, diesel and kerosene consumed by households in the West Bank and the fuel excise rates for each product. Quantities were estimated based on households’ self-reported expenditure in gasoline, diesel and kerosene divided by each products’ prices in 2017 (differentiate by region).<sup>54</sup> As explained above, Israeli fuel excise rates (2017) were applied for gasoline, diesel and kerosene quantities.

- (vi) Applied indirect effects of fuel excises in the PECS 2016/17 to all households’ products that have used petroleum as a production input.

# **4.5. Indirect subsidies**

## **4.5.1. Domestic electricity**

**Estimation of the electricity subsidies to the domestic consumers is based on the applicable tariff structure (Table 4-2) and estimated cost of supply.** There was no data available on the cost of electricity

> 51 See Inchauste and Jellema (2018), for an explanation of the methodology on indirect effects and the Cost-Push Model.

> 52 The 2017 (Israeli) excise rates used in the model were: gasoline (2.89 NIS/L), diesel (3.02 NIS/L) and kerosene (2.89 NIS/L). 53 The IO sector of “Petroleum products” was disaggregated between “gasoline”, “diesel” and “other oils” based on consumption shares calculated using the PECS 2016/17.

> 54 Fuel prices (2017) are
In the home good production sector, the efficiency parameter <sup></sup> is normalized at unity and the curvature of the home production function initially at <sup></sup> = 07, to capture rapidly decreasing marginal returns in terms of women’s time and infrastructure services. In the experiments reported in the next section, a smaller value of <sup></sup> = 012, corresponding to the value used by Kimura and Yasui (2010, Table 4) for a production function with labor only, will also be used to capture smaller decreases in marginal returns initially.

To estimate women’s time allocation, we use the 2009 PNAD data and the calculations in Ribeiro and Marinho (2012). They estimate that the time spent by women in household chores is 20.2 hours per week, and that the time spent in market work is 41.4 hours per week. To calculate total time available in a week, we subtract from raw time (7 · 24 = 168 hours) time spent sleeping (8 hours a day, or a total of 56 hours), time spent on personal care (2 hours a day, or 14 hours). Weekly time available is thus 98 hours. The time spent by women in home production can consequently be estimated as <sup></sup> = 20298 = 0206, whereas the time spent in market work can be estimated as <sup></sup> = 41498 = 0421.

Time spent by women to accumulate human capital in adulthood is estimated by using an estimate on the number of years devoted to human capital accumulation, divided by the length of the period in adulthood, 25; thus, <sup></sup> = 4625 = 0185. As discussed earlier
U.S., although the precise magnitudes of the relationships are yet to be pinned down, it is now widely agreed that increases in minimum wages do reduce employment opportunities, especially among teenagers. Most research has focussed on unintended consequences employment reductions. Very little research has addressed the question of whether minimum wage legislation is achieving its intended goal of reducing the incidence of poverty. The few for the U.S. that have considered this have found that minimum wage legislation has only a minor effect on the distribution of income. This finding is not surprising because not all low wage workers are members of low income families: e.g. teenagers. In other words, minimum wages more directly affect low wage workers, not low income families. Empirical evidence from Mexico based on data from formal sector manufacturing firms (Bell, 1994) shows virtually no disemployment effects for unskilled workers primarily because the minimum wages are set rather low relative to the distribution of wages. However, the use of household level data did suggest that the minimum wage had an important effect on the unskilled wage distribution in the informal sector, affecting in particular women and part-time workers.

It may be politically difficult to argue for elimination of minimum wage and countries face immense pressure to introduce or raise minimum wages as they develop. In the U.S., instead of elimination, people are arguing for youth sub-minimum or training wage. However, such sub-minimum wages for particular groups while retaining minimum wages for the rest may end up benefitting these groups relative to others, for example, in the U.S. teen employment at expense of adult.

38/ Mincer argues that under some circumstances, it may be rational for workers to remain unemployed for a while to search for jobs in the covered sector.

39/See Gill, Sedlacek and Nayar (forthcoming),
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Air Zimbabwe maintains regular service between Harare and London. Carriers based in Zimbabwe are not currently allowed to fly aircraft into the United States, however.

**Table 12. Benchmarking air transport indicators for Zimbabwe and select other countries**

|Country|Zimbabwe|Botswana|Zambia|Mozambique|Namibia|South Africa|
|---|---|---|---|---|---|---|
|**Traffic (2007)**|||||||
|Domestic seats (seats per year)|237,835|241,696|437,658|1,144,644|84,162|31,767,537|
|Seats for international travel within<br>Africa (seats per year)|1,109,986|435,708|1,459,766|582,836|877,812|6,314,557|
|Seats for intercontinental travel (seats<br>per year)|182,585|n.a.|113,217|91,637|242,736|7,707,063|
|Seats available per capita|0.118|0.357|0.168|0.087|0.574|0.954|
|Herfindahl-Hirschmann Index—air<br>transport market (%)|30.20|60.25|17.53|31.54|39.39|16.66|
|**Quality**|||||||
|Percent of seat-km in newer aircraft|71.4|100.0|63.8|57.0|79.0|83.8|
|Percent of seat-km in medium or<br>smaller aircraft|42.7|0.0|50.6|42.5|28.3|
Our main contribution in this paper is to shed novel light on index rebalancings – and especially on the behavior of investors – by combining transaction-level data from the Colombian stock market with episodes of additions into and deletions of Colombian stocks from MSCI indexes. Our dataset contains all the stock purchases and sales from the secondary market where these stocks are traded. Additionally, we observe the individual/institution involved in each transaction. To our knowledge, this is the first attempt to provide a complete picture on index rebalancing episodes by analyzing not only price changes, but also the actual trading behavior of the universe of investors in response to these episodes. Furthermore, our dataset allows us to measure also the demand elasticity for stocks using actual traded quantities, without having to make assumptions on funds’ behavior.

We find that inclusion into an international equity index leads to a median cumulative abnormal return, from the announcement to the implementation date, of 5.5%, which is line with recent studies of equity additions and deletions (Chang et al., 2014). We show that the price effect results from a large increase in the demand by foreign investors.<sup>4</sup> This group buys (sells) about 2.7% of the outstanding shares of stocks included into (removed from) the indexes, with most trades taking place on the effective day of the index recomposition rather than on the announcement day. Such portfolio rebalancings do not revert, at least in the month following the index redefinition. In addition to passive mutual funds and ETFs, most foreign investors – including active mutual funds, pension funds, investment firms, government agencies and sovereign wealth funds – also rebalance their portfolios in the direction of the index. The demand shock from such nonpassive investors is 1.7 times larger than the demand from purely passive funds. Foreign hedge funds, instead, tend to trade in the opposite direction of the index recomposition, possibly to speculate on the events. Yet, their trades are very small
ital, or sector. However, Ethiopian firms score particularly low on a measure of the quality of management. The most critical area is that related to the management of workers. In particular, the quality of selection, incentives and retention practices is lower than in all comparison countries. This suggests a need for reformed labor management practices. Further, given the scarcity of rigorous evidence on the merits of specific interventions, this highlights the need for more experimental work on labor management in Ethiopia.

What is particularly interesting is that the historical experience of other nations also points to the importance of developing the right labor institutions and practices to make industrialization possible and to distribute its gains. Scholars of both the industrial revolution in the UK (Thompson, 1967; Clark, 1994) and of mid 20th century industrialization in Asia and Latin America (Kohli, 2004) agree on this point. Research on early industrialization in Africa also points to labor management problems, in particular, retention (Elkan, 1971). Finally, newly available historical data has highlighted the important role of unions in securing a wider distribution of the gains from industrialization (Farber et al., 2018).

This paper is also related to and complements two recent studies by Gelb et al. (2016) and Gelb et al. (2017), which argue that high labor costs hold back industrialization in Africa. These studies identify Ethiopia as a country with relatively low labor costs. Our analysis further points out that this labor cost advantage is partly offset by low productivity.

The rest of this paper is organized as follows. Section 2 describes the data. I then present the comparison of productivity and input costs and discuss the determinants of Ethiopia’s low productivity in Section 3 and 4. Finally, in Section 5, I discuss evidence from recent studies of labor interventions. Section 6 concludes.

# **2 Data**

Most of the analysis is based on data from the _World Bank Enterprise Survey_ – an harmonised representative survey of firms covering about
moved from rural to urban settings and their displacement is part of an urbanization process that in many parts of the world is a mega-trend that is rarely reversed. The definition of an end to internal displacement is hence complex, as it typically requires return (which may be unlikely, for example where IDPs have moved from rural to urban settings or in situations where they have spent extended time in the same place), integration (which is difficult to define) or settlement elsewhere in the country. IDPs can also become refugees if they cross an international border. In countries such as Colombia, IDP status is not attached to an individual but to a family, and is hence transmitted across generations (to facilitate the provision of reparations and other entitlements), and there is an imperfect system for removing IDPs from the register if they have achieved a durable solution or when they die. In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015).<sup>33</sup> The absence of a clear and operational approach to defining the ‘end’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR’s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4.5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC’s aggregate
Figure 6. Decomposition of inter- and intra-sectoral components into the composition and structure effects.

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>-0.1<br>intra comp intra struc<br>inter comp inter struc<br><!-- End of picture text -->

# **Occupational segregation**

To provide another angle for examining the degree of gender-based segregation in the Georgian labor market, we examine the gap by skill characteristics focusing on low- and highskill blue- and white-collar occupations.

The BMZ decomposition by skill level reveals that gender differences in skill-based employment composition (inter-occupation component, or occupational segregation) during 2004 – 2015 lower the gap on average by 11 log points (Table 7). This implies that skill-based gender segregation favors women in that it should reduce rather than widen the gender wage gap, mainly due to the high concentration of women in high-skill white-collar occupations. This, in turn, means that the gap is solely due to the high degree of gender wage inequality within skill categories.

20