rafmacalaba/datause-encoder-data huggingface dataset:
database. Some results are also presented to illustrate the potential relevance of in-house services in GVCs.

# **_The patterns of services in GVCs_**

_The share of services in value added trade is large and increasing_ Multiple studies have found that the share of services in trade in value added is both large (significantly larger than the share of services in gross trade) and increasing (OECD, WTO, and World Bank Group 2014). While services as a share of total world gross exports have remained around 20% since 1980, in valueadded terms they have increased from below 30% to more than 40% (compare figures 2.a and 2.b). For the case of Asia, this pattern holds by country as well, with no major differences between developed or developing, high-technology or low-technology, or high-wage or low-wage countries (Baldwin, Forslid, and Ito 2015).

**Figure 2.a Gross exports of goods and services as a percentage of total world gross exports**

<!-- Start of picture text --> 82 80 79<br>18 20 21<br>1980 1995 2009<br>Goods Services<br><!-- End of picture text -->

**Figure 2.b Value-added exports of goods and services as a percentage of total world valueadded exports**

<!-- Start of picture text --> 69<br>61<br>57<br>43<br>39<br>31<br>1980 1995 2009<br>Goods Services<br><!-- End of picture text --> Source: Authors’ calculations based on Johnson and Noguera (2016).

_The increasing share of services in value-added trade was driven by services embodied in exports_ Now take a closer look at the recent evolution of services value added in exports, decomposed into direct and indirect domestic value added and indirect foreign value added (figure 3). While direct exported value added shows a notable increase from 1995–2011, more than 65% of the growth of services value added in exports was due to an increase in services embodied in other exports. Both domestic and foreign embodied services grew, but the foreign services value-added component grew the most over the period.

9
Figure 6: Brazil: 2002–2005–2008 Individual Opportunity Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100<br>% population<br>Individual OGIC 2002−2005<br>Individual OGIC 2005−2008<br>10<br>5<br>yearly % growth 0<br>−5<br><!-- End of picture text --> _Authors’ calculation from PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)_ Although both curves lie almost always above zero, growth is outstanding in the second period. In fact, it is possible to unambiguously order the two growth processes because the difference between the GIC coordinates in the two periods is always statistically significant (see Table 5 ). The redistributive impact of the two processes is very similar. The respective curves are both neatly decreasing, demonstrating that growth acts by alleviating outcome inequality.

We now proceed in the evaluation of the Brazilian growth by endorsing an opportunity-egalitarian perspective. The individual OGICs for the two growth episodes are reported in Figure 6.

One feature stands out. For the 2002–05 growth episode, although the GIC lies almost always above zero, the individual OGIC is positive only for half of the smoothed distribution. This conflict indicates that although the majority of households experience positive growth, the extent of the losses borne by the richest 15% is substantial in determining the change in the value of the opportunity sets. This effect is plausible whenever the richest households are not concentrated only in the richest type; that is, income quantiles and types are not perfectly correlated, as for the case of Brazil during 2002–05.

30
Figure 1: Changes in average ad valorem trucking costs in Canada.

<!-- Start of picture text --> 1990 1995 2000 2005 2010<br>Year<br>.038<br>.036<br>.034<br>Mean transportation cost (500 km, truck)<br>.032<br><!-- End of picture text --> first decreasing – due, essentially, to reductions in labor costs at constant fuel prices – and then increasing – due, essentially, to increasing fuel prices at constant labor costs. They range from about 3.8% of the value of the shipment in the early nineties, to about 3.2% in the mid-nineties, with an average value of 3.4% (see Table 3). These figures are fairly close to the average ad valorem rates of 5% reported by Glaeser and Kohlhase (2004, p.206) using 2002 us data. As in their case, there is significant cross-industry variation in ad valorem trucking rates in our data. Table 11 in the Supplementary Appendix S.3 provides additional information on the highest and lowest ad valorem transport costs from 1992 to 2008. The highest ad valorem transport costs are for industries with low value to weight ratios (e.g., Cement Manufacturing and Breweries), with an average ad valorem transport cost across the top 10 industries in 2008 of 14%. The lowest are in industries with high value to weight ratios (e.g., Computer and Peripheral Equipment Manufacturing, and Medical Equipment and Supplies Manufacturing), with an average ad valorem transport cost in 2008 across the bottom 10 industries of 0.4%.

# **4 Empirical approach**

We now discuss the empirical model that we estimate and the different identification concerns that we need to address.

## **.1 Model 4**

The patterns documented in Section 3.1 reveal that there is a clear trend toward the geographic dispersion of industries. As further documented in Section 3.2, transport costs have 11
# **_5.2 Improving the enabling environment for financial inclusion_**

Beyond regulatory interventions, some of the barriers to account ownership highlighted by the unbanked require investments in government-enabled infrastructure to help increase financial inclusion. One example is official government-issued identification, which the barriers data discussed above shows is a significant reason why many unbanked adults do not have an account, as it is almost always required to open any type of account, whether with a financial institution or with a mobile money provider. Biometric identification, such as a fingerprint-based identification, can increase account ownership (as seen in India, discussed above), as well as significantly increase loan repayment rates for borrowers with the highest predicted default risk (Giné, et al., 2012).

Identification is not the only documentation barrier people face, however. Within the sample of adults who report documentation as a barrier, for example, almost half do have an ID. Government identification does not always fully satisfy KYC (“know your customer”) requirements defined by a country’s regulator, however. People may also need documentation such as a utility bill with a home address, which may be difficult to produce.

Access to a mobile phone is another example in which expanding infrastructure and access could facilitate broader inclusion. As mentioned in the section on barriers, the second most common reason adults in Sub-Saharan Africa name for not having a mobile money account was the lack of a mobile phone. The Gallup World Poll data on mobile phone ownership finds that in SubSaharan Africa, 44 percent of the 166 million unbanked do not have a mobile phone. Likewise in South Asia, 44 percent of the 432 million unbanked adults in the region do not have a mobile phone. Access to mobile phones could overcome the barriers of physical distance and the high cost of banking fees that unbanked adults say prevent them from accessing financial services. This could be especially beneficial for expanding access for women, who are much less likely than men to own a mobile phone in Sub-Saharan Africa (11 percentage points less) and in South Asia (22 percentage points less).

# **6. Building financial capability is critical for safe ownership and usage**

Beyond the external barriers to access, there are also social and personal barriers that people face, including their financial confidence. In developing economies, 64 percent of unbanked 18
qualitative and quantitative elements; each can contain specific sub-criteria, but their sum must be 100 and each must be weighted between 40 and 60 percent.

**Box 2.  Bidding variables in different auctions.** Most general principles but only few rigid criteria of the auction mechanism are defined by law. For this reason, regional authorities are empowered to shape the _bando di gara_ (call for concession) according to their own specific requirements. We will compare two calls and show the differences in the criteria introduced and the relative weights given to each. The two calls are for the same type of contract (build-operate-transfer, _BOT_ ) in two close regions (Lombardy and Veneto) and at similar times (2007 and 2006 respectively). Although the total investment considered by the concession is high in both cases, there is a substantial difference in degree (780 million and 2.5 billion euros) and an even more surprising difference in the bidding variables and their weights.

Table 1: Bidding variables and relative weights in the two auctions<sup>11</sup>

|**Lombardy**<br>**BOT**|**Sub-criteria**|**Points**|**Veneto BOT**|**Sub-criteria**|**Points**|
|---|---|---|---|---|---|
|**Qualitative**<br>**elements**||40|**Qualitative**<br>**elements**||40|
|**Quantitative**<br>**elements**||60||Quality of raw material and<br>material duration|(15)|
||Price|(35)||Environmental and aesthetic<br>characteristics|(10)|
||Building time|(15)||Project management|(10)|
||Amount of<br>initial private<br>financing|(10)||User safety characteristics|(5)|
||||**Quantitative**<br>**elements**||60|
|||||Duration of free-of-charge<br>access for residents in the<br>local area|(10)|
|||||Building time|(8)|
|||||Amount of public subsidy in<br>the capital account|(21)|
|||||Amount of public subsidy in<br>the current account|(21)|

11 The two auctions are classified as 2007/S 137-168844 for Lombardy and 2006/S 205-218224 for Veneto. The calls are available at the TED (Tenders electronic daily) supplement of the Official Journal of the European Union at the following webpage http://ted.europa.eu/ 5
This vertical distance, between the blue and the orange lines in Figure 4, is the magnitude of the correlation we are focusing on. And this distance is (almost) equivalent to moving from decile 5 to decile 4 on the blue line. In other words, the loss of income experienced by moving down one decile is how much is ‘worth’, in monetary terms, the loss of non-monetary benefits incurred by moving from a high to a low spending district. They are worth the same because, in this context, both have the same impact on the change in the perception of being poor.<sup>7</sup> Some caveats are in order. A district-level measure is far from granular given the size of the average district in India. Since the CPHS lumps all social or religious expenditures, the proxy measure does not allow us to describe the specific community-level goods to which households may have contributed, nor does it allow us to distinguish contributions to community-level goods from spending on individual-specific goods. Consequently, our analysis is more exploratory than confirmatory, and the estimate in Column (2) of Table 2 is best regarded as a lower-bound of the relationship between the supply of community-level goods and perceptions of relative income.

# **5. Discussion**

We have documented findings from the first large-scale survey of beliefs about inequality in India, noting the importance of perceptions of relative income in shaping support for reducing inequality. Despite sizeable differences between where people believe their household stands on the income distribution versus its actual position, the average respondent’s support for reducing inequality remained unresponsive to information on their household’s position. Building on Dasgupta and Kanbur (2007), we argue that households may be less likely to respond to information on the income distribution based solely on household income, simply

> 7 In fact, as shown in the figure, the two vertical distances, the one linked to moving down one decile and the other of switching districts, are not exactly the same, but for the sake of the argument the main text dismisses this difference.

17
be the same economy). “Vertical specialization” captures the value of the back-and-forth links when the subject economy is in the position of economy 2 (the exporter of a good incorporating imported intermediate inputs), while “vertical specialization 1” captures the value of vertical specialization when the subject economy is in the position of economy 1 (the exporter of intermediate goods to an economy that uses them in the production of the export good).

Vertical specialization is defined as: where _k_ and _i_ denote the economy and the good, respectively.

Vertical specialization 1 is defined as: where _j_ is the destination of economy _k_ ’s exports.

10 Much of the component trade in Hong Kong (China) relates to production fragmentation–based activities in China.

11 Machinery comprises all commodities classified under code 7 in the standard international trade classifications (SITC); see http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=14.

12 See, for example, Ando and Kimura (2003); Athukorala and Yamashita (2005).

13 On the impacts of FDI on trade, see, for example, Urata (2001); Kawai and Urata (1998, 2004). 14 See Jefferson et al. (2003).

15 See Eichengreen, Rhee, and Tong (2004).

16 We follow Hausman, Hwang, and Rodrik (2005) and create an index of the average real wage (as measured by per capita GDP at purchasing power parity) associated with exporters in a given industry. The index is created at the world level and is defined as follows: where _k_ denotes the industry, _j_ denotes the country, and _GDPPC_ is per capita GDP at purchasing power parity. _Exportsjk_ is exports of country _j_ in industry _k_ , and _EXPORTSj_ is total exports of country _j_ . Thus, the weight on GDPPC is a country’s RCA (that is, the share of its export basket in a product over the sum of the export shares of all countries). Export weights alone would place too much weight on large exporters of _k_ for whom _k_ might still be a small portion of overall exports. We calculate _PRODY_ for each 4-digit standard international trade classification (SITC) industry using average bilateral trade and average GDPPC using purchasing power parity data from 2000–04.

17 The market share approach introduces two problems. First, it is possible that China is increasing its market share at the expense of domestic producers, but _not_ displacing other exporters. As a result, the export market _shares_ of other exporters will decline, by definition, but there will not necessarily be an economic loss imposed on them. Second, the use of a relatively aggregate export category may overstate displacement if exports are actually in very different subcategories. For example, assume China primarily 32
# **Figure A.4 Marginal Probability of Receiving Ordinal IEG Outcome Ratings in Countries, by Share of Programmatic Political Parties: PSM versus Non-PSM Projects**

<!-- Start of picture text --> IEG Outcome Rating [ordinal]<br>Highly Unsatisfactory Unsatisfactory Moderately Unsatisfactory Moderately Satisfactory Satisfactory Highly Satisfactory<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties DPI Share of Programm atic Parties<br>.5 .5 .5 .5 .5 .5<br>.4 .4 .4 .4 .4 .4<br>Pr(Iegoutnum==1) .3.2 Pr(Iegoutnum==2) .3.2 Pr(Iegoutnum==3).3.2 Pr(Iegoutnum==4) .3.2 Pr(Iegoutnum==5) .3.2 Pr(Iegoutnum==6).3.2<br>.1 .1 .1 .1 .1 .1<br> with 95% Confidence Intervals<br>Predictive Margins of Public Sector Projects<br>0 0 0 0 0 0<br>.1 .1 .1 .1 .1 .1<br>.05 .05 .05 .05 .05 .05<br>0 0 0 0 0 0<br>-.05 -.05 -.05 -.05 -.05 -.05<br>Contrasts of Pr(Iegoutnum==1) Contrasts of Pr(Iegoutnum==2) Contrasts of Pr(Iegoutnum==3) Contrasts of Pr(Iegoutnum==4) Contrasts of Pr(Iegoutnum==5) Contrasts of Pr(Iegoutnum==6)<br>-.1 -.1 -.1 -.1 -.1 -.1<br>with 95% Confidence Intervals<br>-.15 -.15 -.15 -.15 -.15 -.15<br>Contrast of Predictive Margins of Public Sector Projects<br><!-- End of picture text --> 57
The table shows that the emigration rate from Zambia is lower than the African average but comparable to the average in Southern Africa. A case study of Zambia may, therefore, help understand migration issues in Southern Africa and our findings may also be relevant to other parts of the world.<sup>1</sup> It is well known that overall migration rates do not convey the full picture of the possible impact of migration and the _structure_ of migration is critical. The first element of the structure is the composition of migrants by their skill or the level of education. Table 2 provides a snapshot of this.

|**Table2: Emigration rates**<br> **Migrants as % of all educ**|**by education in**<br>**ated (natives plus m**<br>Primaryeducated|**2000 for SAD**<br>**igrants) in eac**<br>Secondary<br>educated|**C countries:**<br>**h category**<br>Tertiary<br>educated|
|---|---|---|---|
|Angola|2.10|3.40|25.60|
|Botswana|0.10|0.80|2.10|
|Congo,Dem. Rep.|0.10|0.50|7.90|
|Lesotho|0.00|0.10|2.4|
|Malawi|0.00|0.80|9.40|
|Mozambique|0.50|5.80|42.00|
|Namibia|0.10|0.20|3.40|
|South Africa|0.40|0.50|5.40|
|Swaziland|0.20|0.20|5.80|
|Tanzania|0.10|1.00|15.80|
|**Zambia**|**0.10**|**0.30**|**10.00**|
|Zimbabwe<br>Source: Docquier & Marfouk (2004).|0.20|0.70|7.60|

The table shows that tertiary educated Zambians living abroad equal 10% of all tertiary educated Zambians in the country and abroad. This rate is not too high when compared to other African countries although it is higher than many countries outside the African continent. For comparison, the corresponding rate stood at 6.2% (Northern Africa), 13.3% (Central Africa), 26.7% (Western Africa), 18.4% (Eastern Africa) and 5.3% (Southern Africa). For the South-Central Asia region, it equaled 5.1% and 4.3% for the Eastern Asia region.

> 1 The argument holds when we look at the unskilled and skilled emigration rates separately. For skilled workers, Zambia’s emigration rate is comparable to those of Malawi, Zimbabwe, Swaziland, South Africa, Cote d’Ivoire, Congo (D.R). For unskilled workers, Namibia, Tanzania, Congo (D.R), Ethiopia, Kenya, Madagascar show roughly similar rates of emigration as Zambia.

7
# Empowering Adolescent Girls through Safe Spaces and Accompanying Measures in Côte d’Ivoire<sup>∗</sup>

Othmane Boulhane Claire Boxho Désiré Kanga Estelle Koussoubé Léa Rouanet Originally published in the Policy Research Working Paper Series in _March 2024_ . This version is updated in _May 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

_Keywords_ : Safe spaces; Adolescent girls; Husbands’ clubs; Income-generating activities; Randomized experiment _JEL codes_ : J12, J13, J16

> ∗Boulhane: World Bank, oboulhane@worldbank.org. Boxho: World Bank, cboxho@worldbank.org. Kanga: International Monetary Fund, dkanga@imf.org. Koussoubé: World Bank, mkoussoube@worldbank.org. Rouanet: World Bank, lrouanet@worldbank.org. This paper greatly benefited from comments by Markus Goldstein, Michael O’Sullivan, Margareta Norris Harrit, Christophe Lemière, Kara Adamon, and Bastien Michel. We also want to thank participants at the Africa Gender Innovation Lab seminar, the CSAE conference 2023, the ICDE conference 2023, the NEUDC conference 2023, the PAA conference 2024, the IFPRI Seminar, the Nantes University Economics Departement, and the Paris Nanterre University EconomiX conference for their valuable insights. Special thanks go to Ioana Botea for her significant contribution during the early stages of this project. This research would not have been possible without the support of the World Bank’s Health, Nutrition and Population Global Practice, the SWEDD task team as well as the SWEDD Project Implementation Unit. We are also thankful to the implementing partners and the data collection firms. This paper is a product of the Gender Innovation Lab, within the Office of the Africa Region Chief Economist. We gratefully acknowledge financial support from Global Affairs Canada under the Gender Innovation and Regional Learning (GIRL) initiative, as well as the World Bank’s Umbrella Facility for Gender Equality (UFGE). The UFGE is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment through experimentation and knowledge creation to help governments and the private sector focus policy and programs on scalable solutions with sustainable outcomes. The UFGE is supported with generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, the Bill and Melinda Gates Foundation, and the Wellspring Philanthropic Fund. The study was preregistered in the 3ie RIDIE registry: `https://ridie.3ieimpact.org/index.php?r=search/detailView&id=653` . The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank Group and the IMF, their Board of Executive Directors, or the governments they represent. All errors and omissions are our own.
relatively low elasticity, as intersectoral spillovers are muted, if not nugatory. The evidence, however, is inconsistent with this strong form of segmentation.

Existing studies of the relationship between agricultural commodity prices and rural wages are based on aggregate time series data from countries that were effectively autarkic in the main food staple (pre-1980s Bangladesh in Boyce and Ravallion, 1991, and Rashid, 2002; the Philippines in Lasco et al., 2008), thus raising serious endogeneity concerns. A closely related and much larger literature based on micro-data considers the labor market effects of trade liberalization (see Goldberg and Pavcnik, 2007, for a review).<sup>7</sup> My estimation strategy follows the “differential exposure approach” employed in studies of the local wage impacts of tariff reform (most recently in Topalova, 2010, McCaig, 2011, and Kovak, 2011). Instead of considering the interaction between changes in industry protection rates and local industry composition (as in these papers), I exploit the huge variation across Indian districts in the crop composition of agricultural production coupled with differences in the magnitude of wholesale price changes across crops. Of course, price changes observed in local domestic markets cannot be treated as exogenous and must be instrumented for.

In rural India, the elastic rural wage response to changes in agriculture’s terms of trade has striking distributional implications. Higher food prices, rather than reducing the welfare of the rural poor as indicated by the conventional approach, which ignores wage impacts, would actually benefit both rich and poor alike, even though the latter are typically not net sellers of food.<sup>8</sup> In the next section, I sketch the theoretical framework and develop my empirical testing strategy. Section 3 discusses the econometric issues and the estimates. Section 4 derives and implements the welfare elasticity formulae, paying particular attention to the role of India’s vast Public Distribution System, which rations food staples to the poor. The price-

> 7Topolava (2010) and Marchand (2012), among others, consider the reduced-form wage and poverty impacts of India’s broad-based trade liberalization of the 1990s. However, these studies do not isolate the effects of a change in the relative price of food.

> 8To be sure, the increase in rural wages may _lag_ the increase in consumer prices, and so the conventional analysis may be more appropriate for the very short run. This paper does not speak to the timing issue.

3