rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 12:** Intergenerational Mobility and Literacy of the Old Generation

<!-- Start of picture text --> Bolivia Uruguay UruguayTrinidad and TobagoJamaicaJamaica Brazil IGM Down = .242 -.255 lit_par (.0127) (.0215)<br>Costa Rica MexicoPeruPeru Costa RicaTrinidad and TJamaicaChileArgentinaTrinidad and TobagoMexic Uruguay Trinidad and Tobago UruguayUruguay Ecuador o Trinidad and TobagobagoArgentinaChileArgentinaSurinameCuba Cuba BrazilHaiti Haiti<br>Saint LuciaBrazilGuatemalaHondurasHondurasParaguaySaint LuciaHondurasBoliviaMexicoBrazilBoliviaParaguayDominican RepublicHaiti Guatemala HaitiGuatemalaCosta RicaVenezuelaPanamaColombiaEl Salvador Ecuador PanamaUruguayEcuadorHondurasBNicaraguaParaguayazil Nicaragua Costa RicaVenezuelaNicaraguaMexicoPeruArgentinaEl SalvadorEl SalvadChileBolivia Colombia PanamaEcuadorCChile o Paraguaylombiar BoliviaVenezuelaArgentinaBrazilDominican RepublicEcuadorPanamaColombiaColombiaDominican RepublicVenezuelaPanamaCosta RicaPanama Saint Lucia Guatemala GuatemalaParaguayBrazilGuatemalaNicaraguaHondurasHondurasParaguaySaint LuciaHondurasBoliviaMexicoBrazilBoliviaParaguayDominican Republic Guatemala HaitiGuatemalaCosta RicaVenezuelaPanamaColombiaEl Salvador Ecuador PanamaUruguayHondurasBrazilNicaraguaParaguay Nicaragua Costa RicaCosta RicaVenezuelaNicaraguaMexicoPeruArgentinaEl SalvadorEl SalvadorChileBolivia Colombia PanamaEcuadorColombiaChileParaguayBoliviaVenezuelaArgentinaBrazilDominican RepublicMexicoEcuadorPanamaPeruPeruColombiaColombiaBoliviaCosta RicaTrinidad and TobagoDominican RepublicJamaicaChileArgentinaTrinidad nd TobagoVenezuelaMexico Uruguay PanamaTrinidCosta Rica UruguayUruguay Ecuador a d and TobagoTrinidad and TobagoPanamaArgentinaChileTrinidad and TobagoArgentinaJamaicaSurinameCubaJamaicaCuba<br>GuatemalaParaguayNicaragua Ecuador<br>Brazil Guatemala Uruguay<br>BrazilHaiti IGM UP = .165 + .7996 lit_par (.0211) (.0358) Uruguay<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Share literate old Share literate old<br> 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>1<br>.4<br>.8<br>.3<br>.6<br>.2<br>Upward IGM.4 Downward IGM .1<br>.2 0<br>0 -.1<br><!-- End of picture text -->

**(a)** Upward mobility **(b)** Downward mobility

Notes: These estimates are computed by birth decade cohort of the children.

mobility at regional level (see Equation 3) run country by country: where _eicroyt_ is a dummy variable equal to 1 if the completed educational level of the old generation observed for individual _i_ from country _c_ region r is at least primary. Similar to before, _γo_<sup>_b_and</sup><sup>_γ_</sup> _y_<sup>_b_are birth-decade fixed effects for parents and children, and</sup><sup>_θt_a census year</sup> fixed effect. In other words, _δcr_ estimates the share of “parents” who complete primary by region netting out cohort and census year effects.

I find a strong positive (negative) correlation between upward (downward) mobility and literacy of the old generation (see Figure A18). This suggests the existence of a high level of inertia, confirming the findings of Alesina et al. (2021). Similar patterns are found at the country-birth cohort (see Figure 12).

27
|•<br>•<br>•<br>•<br>•<br>•|remuneration for men and women<br>Legislation exists to ensure non-<br>discrimination in respect of<br>employment and occupation<br>Legislation exists to ensure protection<br>of children and adolescents<br>Legislation exists to abolish forced<br>labor (3, 4)<br>Legislation exists protecting the right<br>to organize and bargain collectively<br>(4)<br>Core labor standards are implemented<br>through regulatory frameworks (3)<br>Institutional framework exists for<br>government – employer – trade union<br>partnerships|choose their employment options<br>•Percentage of people from identified caste able to<br>choose their employment options<br>•Percentage of households with no rigidly defined and<br>inflexible roles for household members<br>•Percentage of households with equal workloads for<br>adult members<br>•Percentage of total workforce working as bonded<br>labor (4)<br>•Percentage of school-age children working to<br>contribute to household income (4)|collected using the national<br>survey or in-depth research<br>techniques.|
|---|---|---|---|

70
Policy Research Working Paper 6869

# **Abstract**

Strong boy-bias and its consequences for young and unborn girls have been widely documented for Asia. This paper considers a country in Sub-Saharan Africa and finds that parental gender preferences do affect fertility behavior and shape traditional social institutions with negative effects on adult women’s health and well-being. Using individual-level data for Nigeria, the paper shows that, compared to women with first-born sons, women with first-born daughters have (and desire) more children and are less likely to use contraceptives. Women with daughters among earlier-born children are also more likely to have shorter birth intervals, a behavior medically known to increase the risk of child and maternal mortality. Moreover, they are more likely to end up in a polygynous union, to be divorced, and to be head of the household. The preference for sons is also supported by child fostering patterns in which daughters are substitutes for foster girls, while the same does not hold for sons and foster boys. These results can partly explain excess female mortality among adult women in Sub-Saharan Africa.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at amilazzo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure I1: Unconditional quantile regression of height-for-age z-scores on food security measures, limited** **<u>controls (Uganda)</u>**

<!-- Start of picture text --> 1-SSEXR FCS<br>.05 .15 .25 .35 .45 .55 .65 .75 .05 .15 .25 .35 .45 .55 .65 .75<br>Quintile Quintile<br>95% Confidence Interval O 95% Confidence Interval O<br>RIF-OLS RIF-OLS<br>DDS Per Capita Consumption<br>.05 .15 .25 .35 .45 .55 .65 .75<br>.05 .15 .25 .35 .45 .55 .65 .75 Quintile<br>Quintile<br>95% Confidence Interval O<br>95% Confidence Interval O<br>RIF-OLS<br>RIF-OLS<br>.4 .4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.4 -.2<br>.4 .4<br>.3<br>.2<br>.2<br>0<br>.1<br>-.2 0<br>-.1<br>-.4<br><!-- End of picture text --> 113
the interactions between (t-r) with firm ownership shares (especially with the foreign share) are more significant. We find stronger evidence for the evasion by SOEs probably because SOEs are more familiar with the domestic policies and are better connected to government officials, and hence can do better than foreign firms to take advantage of the policy loopholes in China. The results from the previous country-product level and transaction level analyses are also robust to the alternative liberal definition of trading firms, but are not shown in tables to save space. This is to be expected given the small difference between the conservative and liberal definitions of trading firms as reported in Section 4.2.

Second, we consider some additional variables that may also explain the variations in indirect export share. As discussed in the literature, trading firms can play a role in verifying product quality for buyers. Therefore, different product quality heterogeneity across industries can affect the choice of exporting modes. Following Tang and Zhang (2012), we examine both the horizontal differentiation and vertical differentiation, together with the tax evasion motivation. For the horizontal differentiation, we use the elasticity of substitution estimated by Broda and Weinstein (2006). For vertical differentiation, we use R&D and advertising intensity of each industry constructed from the NBS industrial census data as in Tang and Zhang (2012).<sup>34</sup> In Table 5(2), we add the two variables to regressions based on the full sample, and subsamples for differentiated and non-differentiated products respectively. Although the number of observations is greatly reduced owing to missing data in the two additional variables, our main finding remains robust: (t-r) is positive and significant not only for differentiated products but also in the full sample, but not significant for non-differentiated products. The coefficient of the measure of elasticity of substitution (sigma) is never statistically significant at the 10% level. Advertising and R&D intensity, when significant at the 10% level, has a negative effect on indirect export share. This is consistent with the argument in Tang and Zhang (2012). We do not include these variables in our baseline regressions to avoid dropping a large number of observations due to missing data.

Third, we examine some alternative stories which can also be consistent with our results. China has used the rebate rates frequently as an industrial policy to promote the

> 34 This census covers all of the SOEs and all of the other types of firms with sales above 5 million RMB. First, we calculate the ratio of advertising and R&D expenditure to sales for each firm (Ad-R&D-Sales). Then, we take the average of this ratio for each Chinese Industry Code (CIC). Finally, we concord CIC to ISIC Revision 3 and then to HS6 and HS8.

25
Second, we develop indices of environmental threats. For air pollution, our measure of problem scale is attributable daly's (disability-adjusted life-year losses from health damage). Our estimates come from recent collaborative research by the World Bank and the World Health Organization (Pandey, et al., 2003). For water pollution, our measure of problem scale is preventable deaths from unsafe water and poor sanitation. The country estimates have been produced by recent research at the World Bank (Wang, et al., 2003). Quantitative studies of poverty-environment links on fragile lands are less advanced, but policymakers and researchers agree that people are particularly vulnerable in such areas. For this reason, our measure of problem scale is the total rural population living on fragile lands. To estimate the affected population, we apply GIS (Geographic Information Systems) techniques to spatial overlays of demographic, topographical, climatic and natural resource information.

Third, we develop country estimates of project success probability. Our information source is a database of over 3,000 project outcome ratings maintained by the Bank's Operations Evaluation Department (OED). For each country, we use the proportion of projects judged satisfactory by OED as an estimate of success probability.

We use our environmental threat measures and success probabilities to compute optimal country investment shares on two bases: Problem scale unadjusted for project success probability, and problem scale adjusted by the OED ratings. To obtain country allocations, we multiply the investment shares by the Environment Department's most recent estimates of total investments for pollution control and natural resource conservation since 1990. We obtain overall optimal allocations by summing the optimal allocations for reducing air pollution, water pollution, and threats to fragile lands.

3
# TABLE 8

# The demand for tasks and exports Only exporters (log employment)

|||exp|ort inten|sity||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|A) Skilled and Unskilled Labor||||||
|log highly-skilled|0.37**|0.26**|0.25*|0.24*|0.25*|
||(0.170)|(0.128)|(0.130)|(0.131)|(0.131)|
|log skilled|0.30**|0.19*|0.18*|0.17*|0.18*|
||(0.146)|(0.100)|(0.100)|(0.101)|(0.102)|
|log unskilled|-0.09|-0.24|-0.23|-0.23|-0.24|
||(0.144)|(0.152)|(0.150)|(0.150)|(0.150)|
|B) Production and Non-Production Labor||||||
|log production|0.22*|0.04|0.04**|0.04**|0.04**|
||(0.121)|(0.024)|(0.022)|(0.022)|(0.022)|
|log non-production|-0.01|-0.09*|-0.09*|-0.10*|-0.10*|
||(0.079)|(0.053)|(0.053)|(0.053)|(0.052)|
|C) Tasks||||||
|log managers|-0.17|-0.23*|-0.22*|-0.23*|-0.22*|
||(0.145)|(0.131)|(0.130)|(0.128)|(0.128)|
|log engineers|0.47**|0.35**|0.35**|0.34**|0.35**|
||(0.208)|(0.164)|(0.166)|(0.168)|(0.168)|
|log services|0.06|-0.03|-0.04|-0.05|-0.05|
||(0.117)|(0.097)|(0.098)|(0.097)|(0.097)|
|log blue-collar|-0.08|-0.25|-0.23|-0.23|-0.23|
||(0.158)|(0.169)|(0.168)|(0.168)|(0.168)|
|log maintenance|-0.01|-0.04|-0.03|-0.03|-0.04|
||(0.130)|(0.132)|(0.140)|(0.140)|(0.140)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales) only for exporting firms (the intensive margin). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

22
# **Annex A - The Variation and Dispersion of Annuity Prices**<sup>39</sup>

This annex discusses the variation and dispersion of annuity prices. It focuses on annuity quotations that were obtained from the website of the UK Financial Services Authority (UK FSA). The website has been created in order to increase transparency in the annuity market and strengthen competition. It is part of the so-called Open Market Option (OMO) that aims to improve the marketing and selling of annuities, lower the dispersion of annuity prices and protect retiring workers from the potentially distorting and harmful influence of brokers.

It is noteworthy that despite the creation of the website and the promotion of the Open Market Option, considerable dispersion continues to be observed among annuity quotations. No information is available on the dispersion of prices in actual annuity contracts. Using quotations rather than contracts increases the extent of reported dispersion.

The following 3 tables report data on internet quotations obtained on July 22, 2009. They relate to immediate annuities payable monthly in advance for a capital premium of GBP 100,000.<sup>40</sup> The first set covers a single life annuity for a non-smoker male aged 65 with no guaranteed period of payment (GPP). The second set allows for a 10 year GPP. And the third set covers a joint life annuity with a 10-year GPP and a 67 percent annuity for a surviving spouse aged 60. Only 8 companies offered quotations for such annuities in the Open Market Option and of these only 5 offered quotations for inflation-linked annuities. The majority of life insurance companies operating in the UK offer only annuities to workers who have maintained accumulation accounts with them.

The data show that even under the OMO there is significant dispersion in annuity prices. This ranges between 21 and 25 percent for fixed nominal annuities. It is higher at between 25 and 30 percent for 3 percent escalating annuities but narrows to 14-15 percent for fixed real annuities. The latter is the result of one company quoting higher fixed real annuities than 3% escalating ones, implying that this company expects inflation over the period of the annuity to be below 3 percent. For the same reason, the coefficients of variation at close to 6 percent are smaller for fixed real annuities than for nominal annuities. They reach 11 percent for escalating joint-life annuities.

> 39 Extracted from Rocha and Vittas (2009).

> 40 This short annex has not examined the dispersion of annuity quotations for other, smaller or larger, amounts of capital premiums.

60
households’ economic position. Second, some of the most important cash transfer programs in Brazil are conditional on having children and have specific rules for them such as Bolsa Família and Salário Família. At the same time, children demand investments from the households' budget, but cannot readily generate labor income. Therefore, it is not ex-ante clear what the net effect on household economic position is. Third, elderly members often do not generate labor income, but may be recipients of contributory pensions or eligible to compensatory income in the form of non-contributory pensions. This latter case includes programs like BPC and rural pensions, for example. Hence, the presence of elderly may reduce the likelihood of a household to be in poverty.

**Table 4. Categorization of households by headship, presence of children and presence of elderly**

|**Family composition**|**Presence of children**|**Presence of elderly**|**Proportion of the**<br>**population (%)**|
|---|---|---|---|
|Single male|No child|No elderly|4.3|
|Single male|No child|At least one elderly|1.5|
|Single male|At least one child|No elderly|1.5|
|Single male|At least one child|At least one elderly|0.2|
|Single female|No child|No elderly|6.9|
|Single female|No child|At least one elderly|3.4|
|Single female|At least one child|No elderly|10.0|
|Single female|At least one child|At least one elderly|0.7|
|Couple|No child|No elderly|20.1|
|Couple|No child|At least one elderly|6.7|
|Couple|At least one child|No elderly|43.4|
|Couple|At least one child|At least one elderly|1.5|

Source: authors’ estimations based on PNAD-C 2019. Family composition is determined by whether there is at least one household member that declared to be the spouse of the household head, while the presence of children and elderly is determined by the presence of individuals aged 0-15 (who are neither the household head nor the spouse) and 60 or more, respectively.

Figure 13 shows the poverty rates considering both the pre- and post-fiscal income aggregates for the twelve household categories of interest. It also presents the change in poverty rates. The first six bars represent households with at least one child, while the last six represent those with no child. Among the former, the presence of elderly is concentrated in the second, third, fourth bar, while among the latter it is concentrated in the seventh, eighth, and nineth bar. There seems to exist four distinct groups in terms of the change in poverty: (i) households with elderly but no children (orange bars), (ii) households with both elderly and children (green bars), (iii) households with children but no elderly (grey bars), and (iv) households with no children and no elderly (blue bars). The presence of elderly – regardless of headship structure – makes the household better off after fiscal policies and transfers, while the presence of children seems to have the opposite effect. Figure 14 shows that results that create population groups based on a _proxy_ for economic agency rather than headship structure, considering the highest-earning individual as the household main breadwinner, are qualitatively similar.

30
# **Assessing the Odds of Achieving the MDGs**

Delfin S. Go and José Alejandro Quijada<sup>1</sup> JEL classification: O11, O43, C25 Key words: Millennium Development Goals, growth, institutions

> 1 The World Bank.  The paper is part of the background analysis undertaken for the _Global Monitoring Report 2011,_ a joint undertaking of the World Bank and the IMF _._ We thank the following for comments and suggestions at various stages of the study -  Shantayanan Devarajan, Ann Harrison, Aart C. Kraay, Brad McDonald, Catherine Patillo, Ritva S. Reinikka, Luis Serven, William Shaw, Hans Timmer, and Lucio Vinas de Souza. Views expressed are those of the authors and do not necessarily reflect those of the World Bank or its affiliated organizations.
14 How will this affect overall composition of infrastructure stocks? It will not change dramatically, but continue along the trend established since the 60s of a gradual shift towards telecommunications and power, and away from transport, with road dominating transport more and more.

Figure 2. Changing infrastructure stocks per capita, developing countries 1960-2010 <!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>-- Elecric Generation Capacity .UTelepthone Mainlines -&- Paved Roads -Rail Routh Length -MobiIe<br><!-- End of picture text -->

**Table 9: The composition of infrastructure stocks in 2010**

||Developingcountries<br>2000|Developing countries<br>2010|
|---|---|---|
|Power|41%|39%|
|Roads|35%|32%|
|Telephone(fixed)|3%|5%|
|Telephone(mobile)|3%|8%|
|Waterand Sanitation|11%|10%|
|Rail|7%|_5%_|
||100%|100%|
|Totalvalue(US$Billion)|6,162|8,089|
<!-- Start of picture text --> Agr, Forestry, Fisheries<br>Beverages and Tobacco Products<br>Chemical, Rubber, Plastic Products<br>Construction<br>Dwellings<br>Electricity, Gas, Water<br>Energy Extraction<br>Ferrous Metals<br>Leather Products<br>Machinery and Equipment nec<br>Manufactures nec<br>Metal Products<br>Metals nec<br>Mineral Products nec<br>Minerals nec<br>Other Private Services<br>Paper Products, Publishing<br>Processed Foods<br>PubAdmin/Defence/Health/Educat<br>Textiles<br>Trade and Transport Services<br>Transport Equipment<br>Wearing Apparel<br>Wood Products<br>0 20 40 60 80<br>Total LVAX share (%)<br>ZAF COL CHL PER THA TUR<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

Minerals have a relatively high share of labor in exports even vis-à-vis other sectors. Exports of the minerals sector in South Africa are the most labor intensive among any of its comparators. In Figure 6 we observed that labor value added contained in South Africa’s minerals exports was primarily direct labor content with weak backward linkages, and in Figure 14 we assess whether this over performance is due to direct value added or indirect value added via backward linkages. Figure 14 repeats Figure 13 except for the direct LVAX shares across sectors and countries. The direct LVAX share continues to be above all other countries for minerals as well as for ferrous metals. In other sectors, however, the direct LVAX share is no longer higher than other non-BRIC comparators, implying that South Africa’s high LVAX share relative to other countries is due to backward linkages, rather than the direct content. Adding this result to the below finding that the number of jobs supported by exports of minerals is limited suggests exports support high wages in the minerals sector (around US$18,000 per average worker per year if dividing the direct LVAX in minerals by the direct number of jobs in minerals).

23
<!-- Start of picture text --> HB poverty<br>0.00400% Sf<br>0.003004 : p [<br>VON N<br>0.00100°4) | /<br>0.000005 955 17201740 T 760 77a0 1800 7820 1840 TB60 jes0 1800 7320 1840 7560 7380 2000<br><!-- End of picture text -->