rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Results and discussion**

# **4.1.  Fiscal incidence**

# **_Incidence across child multidimensional poverty_**

Taxes or public spending do not affect all children equally. Analyzing the fiscal incidence will allow us to differentiate the effects by multidimensional poverty, providing important insights into the potential role individual fiscal policies can play in reducing poverty and inequality for children.

Figure 2 compares the distribution of taxes and transfers as a percent of market income by child multidimensional deprivation counts. In all scenarios (all children, children by gender or location), direct taxes are progressive, i.e., their value relative to market income decreases with the average number of child deprivations. For example, direct taxes constitute 6% of market income among households of non-deprived children, while this is only 1% when a child experiences five or more deprivations. Indirect taxes, comprising of VAT and excise taxes, are regressive. There are also differences by gender. The share of both direct and indirect taxes relative to market income is slightly higher for girls (with one or two deprivations) than for boys in the same situation. However, the reverse is the case for children with four or five deprivations. Rural-urban differences also exist. In rural areas, we find that both direct and indirect taxes are low across multiple child deprivations, i.e., they are neither progressive nor regressive. For urban children, excise taxes are regressive.

Transfers are almost completely in the form of indirect in-kind transfers, with direct transfers accounting for only 0.3% (in the case of no deprivations) to 1.9% (in the case of five or more deprivations) of market income. Direct transfers are relatively equal across the various disaggregation groups, although they are slightly higher for urban children with four or more deprivations than similarly deprived children in rural areas. Primary education is the largest in-kind transfer and is progressive overall, constituting about 3.9% of market income of non-deprived children and rising to 13.2% for those with four deprivations. Though no differences exist between boys and girls, public spending on primary education is progressive in urban areas while neutral in rural areas. On the other hand, secondary education is regressive overall and in all disaggregation groups.

These findings are in line with recent studies in Kenya (Save the Children 2021) and Uganda (Cuesta, Jellema, and Ferrone 2021). We also find that health care is generally progressive among urban children and neutral among their rural counterparts.

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Figure 5: Employment and subjective ranking <!-- Start of picture text --> Probability of positioning in given steps of the economic ladder<br>Steps 1 and 2 Steps 5 and 6 Steps 9 and 10<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile of Consumption Decile of Consumption Decile of Consumption<br>has worked in the last 12 months has not<br>.6 .6 .6<br>.5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>Probability of positioning Probability of positioning Probability of positioning<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> Note: this graph plots the probability of respondents positioning themselves in different steps of the economic ladder by objective decile of consumption and employment status. The left panel plots the probability of choosing steps 1 and 2; the center panel plots the probability of choosing steps 5 and 6 and the right panel plots the probability of choosing steps 9 and 10.

the bottom steps for every objective consumption decile. However, the effect is larger in magnitude at the bottom of the objective distribution than at the top. For objectively poor respondents having a university degree is associated with a higher subjective ranking, whereas for objectively rich respondents having or not such degree makes a small difference. In this sense, the boost to self-rated economic welfare given by educational attainment appears to be concentrated among the poorest.

**The effect of beliefs and perceptions on subjective welfare** Beliefs and expectations also may play a role in individuals’ perception about their economic welfare. Figure 8 shows how individuals’ subjective ranking varies according to what they believe are the main reasons for success in the country they live in. Those that believe that intelligence, effort and hard work are the reason for success tend to position themselves in higher steps of the economic ladder than those who believe that political connections or breaking the law are the only way to succeed. A similar result, not shown here, holds for beliefs about the cause of need in society: those who think that laziness and lack of willpower are the cause of need tend to position themselves more in the middle steps and less in the lowest steps than those who think that injustice or lack of luck is the cause of need. Expectations of mobility are another important factor that affects perceptions of economic standing. LiTS includes two questions on past and future mobility: one question asks respondents their degree of agreement with the statement that "I have done better than my parents" and another questions asks their degree of agreement with "Children who are

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46 But, the structure of public health expenditures in 1985 suggests that the emphasls on curative care, based disproportionately in Abidjan hospital centers, has not shifzed. And immunization coverage showed no signs of improvement between 1980 and 1984. As subsidies to urban consumers have be.... curtailed, and real income in these settings has been declining, child health in the urban areas has deteriorated, particularly in the postneonatal period. This deterioration has disproportionately affected families in the top 40 percent of urban income distribution, mostly civil servants. The LSMS data suggest that families with no savings have been severely affected by these changes in child health. In the rural areas child health has not been significantly affected by the economic crisis or adjustment policies. This finding is consistent with the notion that when families are insulated from (or marginally affected by) public services and subsidies, they will be insulated also from changes in these services and subsidies during an adjustment process.

From a historical perspective, it would be surprising if economic crisis and adjustment, both of which imply falling real wages for some components of society, had no effect on child mortality in Sub-Saharan Africa. Countries of the region have not yet reached the levels of development and industrialization that now-developed countries had reached when the link between their economic and health indicators weakened. But the analysis reported here does not indicate an across the board increase in mortality. Rather, it shows a change in relative levels among groups that favors the rural nonpoor ar the expense of the urban poor, with little net effect at the national level. The 1980s have of course been a period of great expansion of child survival interventions, which would be expected to have their greatest effect in rural, poorly served areas. How much the effect of crisis and adjustment has been cancelled out by immunizations and Oral Rehydration Therapy it is impossible to tell.
21 reducing fertility and improving health, and the development of better policies and measures to support such programs (Annex 10). This focus provided the framework within which several emerging issues likely to be critical in shaping PHN programs were addressed: (a) the role of the private sector and NGOs in family planning and health delivery, (b) the need for high-quality he-lth data to assist in health research in Africa and elsewhere -- focusing on such issues as the burden of disease, high birth rates, and malnutrition, and (c) what to do about AIDS.

In response to the AIDS epidemic, the Bank became involved in collaborative analysis with WHO's Global Programme on ATJS (GPA) on several subjects: the direct and indirect costs and attendant economic implications of AIDS, the demographic impact of AIDS, and its implications for family planning and health programs. In addition, the Africa Region produced a strategy paper for dealing with AlDS in that region which was well received.

54. Health costs and financing are critical policy areas for the Bank and its borrowers although the key issues vary substantially by region and level of development. Studies are underway to examine the efficient use of hospital resources and to evaluate alternatives to high-technology hospital care. This work includes an AFTPN study, "Cost Recovery in the Health Care Sector: A Synopsis of Selected Country Studies in West Africa" and an ASTPN study, "The Financing of Social Services in Asia,' both of which began in FY88.

55. The main piece of nutrition research completed in FY88, Ngalr,u.rition: What Can Be Done? Lessons from World Bank ExDerience, shows that nutrition projects are feasible and effective and have produced important findings, a few of which refute conventional wisdom about nutrition and development. The successful Tamil Nadu nutrition project, for example, showed that targeting the program to 6-36 month old children in this case, is far more cost-effective than programs covering children of all ages. This lesson is transferable to other types of programs. The comprehensive and high-quality longitudinal data emanating from the Tamil Nadu project provide the basis for continued analysis.

56. Recent years have seen a weakening of the commitment of developing countries and donor agencies to endemic disease control and in many cases a resurgence of disease. Little empirical research has been done on the organization and management of major disease control programs. With the support of the Edna McConnell Clark Foundation, PLRHN initiated work on an "Inter-Country Comparative Study of the Organization and Management of Schistosomiasis and Other Tropical Disease Control Programs." This research project will help in the development of new approaches to the control of tropical diseases. Research will be carried out in Brazil, Egypt, Indonesia, Morocco, Zimbabwe, the Philippines and the Sudan.
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey which firms must choose whether to be formal, and if formal, whether to hire formal or informal workers. His counterfactual scenario with increased enforcement leads to a reduction in the number of informal firms, but results in welfare loss. This happens because more enforcement imposes entry costs on informal firms, reducing their number. Since informal firms hire mostly low-skilled labor, the wages of these workers are reduced due to lower demand, increasing the skill premium. Given these conflicting results, more research is needed to clarify the net effect of higher enforcement on wage inequality.

Declines in unionization have been pointed out as a possible factor behind the increasing inequality in the United States in the last decades. For instance, DiNardo et al. (1996) finds a significant role for de-unionization in explaining rising inequality in that country. Similarly, Firpo et al. (2018) find that de-unionization is associated with decreasing wage inequality at the lower tail of the wage distribution in the United States, whereas it increases wage dispersion in the upper tail. In Brazil, however, research on this topic is limited. A possible reason for this lack of studies is the distinct way in which unions are organized in Brazil. Until 2017, a constitutional mechanism called “union contribution” entitled unions with an annual and automatic transfer proportional to salaries. This legal structure provided large incentives for the creation of new unions with the exclusive aim of capturing this rent, not necessarily representing workers interests (Menezes-Filho et al., 2008). According to Messina and Silva (2017), labor union membership in Brazil is higher in the upper half of the income distribution, which may reflect this particular incentive structure.

Among the few exceptions in the Brazilian literature, Arbache (1999) looks directly to the effect of unions on wage premium and wage dispersion of semi-skilled formal male workers in the manufacturing sector. This restriction on the sample may potentially reduce concerns with selection, although no procedure is explicitly conducted aiming at correcting it. He finds that unions are associated with higher wages, although the premium is small. However, unions seem to increase wage dispersion, with wage dispersion among union members being around 11 percent higher than among non-union members. A possible explanation for this is that unions in Brazil do not promote standardization, that is, unions amplify the returns to various workers’ attributes instead of reducing them. Other reasons for such result is the larger dispersion of workers’ characteristics and the smaller bindingness of the minimum wage among unionized workers. Hence, given the reduction in union density reported by Messina and Silva (2017) of 22 to 17 percent, we should expect a decrease in inequality due to union participation. However, the limited number of studies and information on this topic prevent more thorough analysis of the impact of unions on wage inequality.

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Table 3: Impact on primary outcomes

|||(1)<br>SRH<br>behavior<br>(15yo+)|(2)<br>Economic<br>outcomes|
|---|---|---|---|
|T1|(safe spaces)|0.202***|0.051|
|||(0.076)|(0.092)|
|T1|+ Leaders|0.132|0.022|
|||(0.093)|(0.113)|
|T2|(safe spaces + IGA)|0.049|0.179**|
|||(0.084)|(0.090)|
|T2|+ Leaders|-0.011<br>(0.112)|0.240***<br>(0.089)|
|T3|(safe spaces + husbands’ clubs)|0.197**<br>(0.096)|0.293***<br>(0.081)|
|T3|+ Leaders|0.141**|0.271**|
|||(0.071)|(0.118)|
|T4|(control spillover)|0.079|0.074|
|||(0.066)|(0.071)|
|T1|= T1 + L|.481|.819|
|T2|= T2 + L|.625|.55|
|T3|= T3 + L|.579|.862|
|T1|= T2|.0965|.235|
|T2|= T3|.174|.255|
|T1|= T3|.965|.0189|
|T1|+ L = T2 + L|.276|.0844|
|T2|+ L = T3 + L|.188|.814|
|T1|+ L = T3 + L|.921|.102|
|N||2,855|3,003|
|Co|ntrol mean|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; SRH = sexual and reproductive health; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

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For country _U_ , all exported goods rely on domestic labor. Hence, the index developed by Hummels et al. (2001) would be equal to one for _U_ . The VAX ratio, however, is again lower than unity and is equal to country _D_ ’s VAX ratio. For country _U_ , value-added exports only account for the export of intermediate goods that are embodied in final consumption in _D_ , while gross exports also include the exports of intermediate goods that are embodied in the final goods that are re-exported back to _U_ . We show that this back-and-forth trade grows faster than other trade flows as trade costs decrease, which implies that the VAX ratio for country _U_ decreases as trade costs decrease.

We summarize these results on the trade cost elasticity and VAX ratio in the following Proposition:

**Proposition 4** _The effect of trade costs on trade is such that:_

_i) The elasticity of trade in final goods to trade costs is higher than without fragmentation; ii) This elasticity is larger when trade costs are smaller;_ _iii) The VAX ratio (value-added content of trade) decreases as trade costs decrease._ The results of Proposition 4 are intuitive and supported by recent empirical evidence. In particular, Johnson and Noguera (2013, 2012b) use multi-country input-output tables to show that the VAX ratio has decreased over the past decades and that the bilateral VAX ratio depends positively on bilateral trade costs.

**Gains from trade:** How does trade affect welfare in upstream and downstream countries? A key policy question is whether a country is affected differently depending on its position in international production chains. To examine this question, we derive an exact expression for the price index and the gains from trade relative to autarky and compare it to standard models without cross-border fragmentation of production. In particular, we use the formula developed by Arkolakis et al. (2012) as a benchmark.

For country _D_ , which tends to specialize downstream, the change in the real wage compared to autarky is: 1 where _PD_ = exp ��0<sup>log</sup><sup>_PD_(</sup><sup>_ω_)</sup><sup>_dω_</sup> � is the price index (the nominal wage _wD_ is normalized to unity in country _D_ ). The first term<sup><u>1</u></sup> _ξ_<sup>log (1</sup><sup>_−ω_</sup> _D_<sup>_∗_)correspondstotheArkolakisetal(2012)</sup> formula based on final demand trade: the log of the gains from trade are proportional to the log of the domestic content of consumption, where the proportionality coefficient is the inverse of the trade elasticity _ξ_ . For country _D_ , expression (45) indicates that the Arkolakis et al 30
# **3. Data sources**

As described in the methodology section, the 2009 poverty maps are based on two data sets. The first is the 15-percent sample of the Vietnam Population and Housing Census (VPHC). The 2009 VPHC was conducted by the General Statistics Office of Vietnam in April 2009, with (technical) support from the United Nations Population Fund (UNFP), and includes two modules. The first module is used to collect basic demographic and housing data for the whole population. Data include age, gender, race and education of individuals. The second module, which underpins our small area poverty estimates, contains more elaborate data. Individual data include demographics, education, employment, disability and migration. Household data include durable assets and housing conditions. The 15-percent sample is representative at the district level, and is selected on the basis of a cluster sampling technique, covering 3,692,042 households with 14,177,590 individuals (Central Population and Housing Census Steering Committee, 2009).

The second dataset is the 2010 Vietnam Household Living Standard Survey (VHLSS). The 2010 VHLSS is also conducted by GSO with technical support from the World Bank. It includes very detailed data on individuals, households and communes. Individual data consist of information on demographics, education, employment, health and migration. Householdlevel data include information on durables ownership, assets, production, income and expenditure, and participation in government programs. There are 9,402 households with 37,012 individuals covered in this data set. This 2010 VHLSS is representative for rural/urban areas and 6 geographic regions. Only 3 households are sampled from each commune. Thus, 9,402 households are sampled from 3,113 communes which belong to 686 districts. The 2010 VHLSS differs from earlier rounds (2002-2008) in terms of content as well as sampling design. The principal changes include shortening of the questionnaire, reframing of the consumption modules, and drawing of the sample from the 2009 instead of the 1999 Population Census.

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<u>Table 2 Demographic characteristics of teachers in the analytical sample</u>

|||<br>|<br>Teach<br>|<br>ers<br>Percent working|Percent on<br>||Other wage w<br>|orkers<br>|
|---|---|---|---|---|---|---|---|---|
|Country|Age|Percent<br>male|Percent<br>urban|<br>in private<br>schools|fixed term/<br>temporary<br>contracts|Age|Percent<br>male|Percent<br>urban|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Burkina Faso|35.1|57.3%|76.6%|20.4%|10.6%|33.0|73.1%|79.6%|
|Côte d'Ivoire|38.4|79.1%|76.1%|21.0%|41.7%|33.5|75.7%|80.1%|
|DR Congo|37.5|72.6%|50.6%|15.1%|40.0%|39.7|78.8%|81.2%|
|The Gambia|35.2|67.5%|67.5%|38.3%|N.A.|33.6|71.7%|79.6%|
|Ghana|35.0|64.6%|61.4%|17.9%|N.A.|35.9|70.6%|74.7%|
|Liberia|44.6|85.4%|42.1%|29.4%|N.A.|38.1|80.0%|58.4%|
|Malawi|38.2|59.5%|41.2%|21.9%|N.A.|34.7|79.2%|47.6%|
|Namibia|39.6|34.9%|48.7%|4.7%|14.9%|35.9|57.8%|78.2%|
|Niger|37.1|47.9%|90.6%|18.0%|29.1%|36.2|85.0%|78.2%|
|Nigeria|40.7|45.4%|45.4%|24.2%|N.A.|39.6|68.1%|54.2%|
|Senegal|37.1|76.7%|83.4%|9.1%|16.9%|35.6|64.6%|82.0%|
|Sierra Leone|41.8|63.6%|79.3%|25.5%|16.9%|38.8|69.0%|90.4%|
|Tanzania|38.6|43.9%|85.4%|19.3%|5.4%|37.5|70.7%|96.3%|
|Uganda|37.5|60.6%|29.4%|31.2%|40.1%|31.9|71.5%|48.8%|
|Zambia|35.3|49.6%|60.1%|12.1%|12.6%|33.1|73.1%|69.3%|
|**Average **|**38.1**|**60.6%**|**62.5%**|**20.5%**|**22.3%**|**35.8**|**72.6%**|**73.2%**|

Notes: Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other occupations; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. Teachers were considered working in private schools unless they reported government (at any level) or any public entity as their employer. Surveys in The Gambia, Ghana, Liberia, Malawi, and Nigeria did not report whether a worker was on a permanent or fixed term/temporary contract. Source: Surveys listed in Table 1.

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Table D.3: Within-firm Variance in Technology Sophistication and Firm Characteristics, Robustness

|VARIABLES|(1)<br>EXT<br>Var(ABF)|(2)<br>MOST<br>Var(ABF)|(3)<br>GAP<br>Var(ABF)|
|---|---|---|---|
|ABF MOST|0.35***|0.50***|0.13***|
||(0.02)|(0.02)|(0.02)|
|ABF MOST<sup>2</sup>|-0.30***|-0.37***|-0.14***|
||(0.02)|(0.01)|(0.02)|
|Vietnam|-0.06***|-0.07***|-0.03***|
||(0.00)|(0.00)|(0.00)|
|Senegal|0.01***|-0.04***|-0.04***|
||(0.00)|(0.00)|(0.01)|
|Manuf|0.03***|0.00|0.02***|
||(0.01)|(0.01)|(0.01)|
|SVC|0.04***|0.01|0.03***|
||(0.01)|(0.01)|(0.01)|
|Medium|0.01**|-0.00|0.00|
||(0.00)|(0.00)|(0.00)|
|Large|0.02***|0.02***|0.02***|
||(0.01)|(0.01)|(0.01)|
|Age 6 to 10|-0.01***|0.01**|-0.01***|
||(0.00)|(0.00)|(0.00)|
|Age 11 to 15|-0.01***|0.00|-0.02***|
||(0.00)|(0.00)|(0.00)|
|Age 16+|-0.01***|0.01*|-0.01***|
||(0.00)|(0.00)|(0.00)|
|MNCs|0.02***|0.00|-0.01|
||(0.01)|(0.01)|(0.01)|
|Exporter|0.01**|0.01**|0.02***|
||(0.00)|(0.00)|(0.00)|
|Constant|0.06***|0.03***|0.12***|
||(0.01)|(0.01)|(0.01)|
|Observations|3,893|3,893|3,135|
|R-squared|0.14|0.38|0.08|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Reference group for country, sector, size, and age categories is Brazil, Agriculture, Small, and Age 0 to 5, respectively.

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