rafmacalaba/datause-encoder-data huggingface dataset:
However, there is a general lack of up-to-date evidence on child poverty and specifically so on children in Nigeria. Even irrespective of age groups, the latest detailed poverty assessment for Nigeria was published in 2016 (see World Bank, 2016), using data up until 2013. This is, apart from survey specific reports, the latest systematic analysis of poverty indicators available for Nigeria and does not provide detailed child poverty estimates. The current, post-economic crisis poverty situation may though well be different, as also the pattern of child versus adult poverty in its different dimensions.

The objective of this study is therefore threefold: It aims at providing a) an evidence base of the extent and heterogeneity of monetary and multi-dimensional poverty of children and households with children throughout the country and how these types of poverty overlap; b) insights to what extent poverty appears to be an intergenerational concern, coinciding with the children’s parents’ level of education; and c) insights to whether poverty is chronic or transitory over the years these survey spans. Study results may provide an evidence base for policy makers and development partners to design anti-poverty policies and targeted interventions under circumstances with limited resources.

This study will proceed with a review of the existing literature on poverty measurement, in particular child poverty in Nigeria. Thereafter the methodology and datasets used in this analysis will be discussed, followed by the presentation of results. The final section concludes.

# 2. Literature Review on Defining (Child) Poverty and Poverty Measurement

## 2.1 General

Poverty has by the World Bank and others traditionally been measured using income in relation to a certain poverty line threshold, and in the absence of income data, consumption and expenditure data has been used to estimate a welfare aggregate as a proxy for income (see Haughton and Khandker, 2009). While there had long been strands of literature arguing in favor of alternative and multi-dimensional approaches to poverty measurement, such as Amartya Sen’s (1987) capability approach, only around the change of the millennium there has been a surge in recognizing child poverty and poverty in general as multi-dimensional and measuring it more systematically (Abuda and Delamonica, 2018).

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that the impact of these shocks is the same for young boys and girls which suggests that there is no discrimination in the allocation of household resources towards girls. The negative impact of _contemporaneous_ rainfall shocks on child weight-for-height reveals the importance of the disease environment in determining health status in the short-run. This finding is reinforced by the positive and significant effect that rainfall shocks have on the incidence of diarrhea among children. Taking into consideration the fact that diarrhea is the leading cause of child malnutrition, and the second leading cause of death for young children,<sup>27</sup> our findings suggest that there is ample room for policy intervention in this area. Low cost public health interventions –like oral rehydration therapy- will certainly reduce the negative impacts that weather shocks have on short-term child malnutrition. In this sense, one of the contributions of this study was to show that having easy access to a health clinic significantly improves children’s weight-for-height. We also find some evidence, albeit weak, on the important mitigating effect that health interventions may have on child health after experiencing a weather shock.

It is also the case that above normal precipitation during the _last_ completed rainy season has a positive impact on child weight-for-height which suggests that the income effect associated with a higher than normal rainfall during the previous agricultural cycle is stronger than the negative effect of the disease environment. The importance of the income effect –taking place mainly through the positive effects of higher than normal rainfall on agricultural production- suggests that advances in weather prediction and monitoring techniques can be employed to develop early warning systems to alert rural communities as a means of reducing the risks of crop failure. In this sense, the work that the Nigerian Meteorological Agency has been doing in providing reliable planting dates forecast seems to be a step in the right direction (Nnoli et al. 2006).

Rainfall shocks also appear to have an effect on the longer-run measure of child nutritional status such as height-for-age. As is the case for weight-for-height, rainfall shocks during the most recent rainy season have a positive effect on the height-for-age Z-score suggesting an income effect stronger than the disease environment effect. However, the fact that it is only the shock from the last completed rainfall season (and not the shocks from earlier seasons) that has a significant effect across different ages, suggests that children are able to catch-up with their cohorts soon after the shock.

The analysis also reveals that the relationship between the contemporaneous rainfall deviations and weight-for-height is almost linear which suggests that any future moderate increase in rainfall

> 27 According to the World Health Organization (2002), in developing countries children under three years old experience on average three episodes of diarrhea every year, with each episode depriving children of the nutrition necessary for growth.

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countries results in the participation effect of agriculture on head count poverty being on average 1.6 times (-2.24/-1.41) larger than that of non-agriculture, while it is on average multiple times larger in the low-income countries.

# **6 The Relative Contribution of Agriculture and Non-Agriculture to Poverty Reduction – Evidence from the Recent Past**

From equations (4) and (6) we know that poverty reduction during a certain period can be decomposed into sectoral participation and growth effects. We estimated the participation effects of the different sectors and concluded that one percent of agricultural growth yields on average 2.2 times more poverty reduction than one percent growth in non-agriculture. While agriculture could potentially grow faster, it is likely to continue to grow at a slower pace than non-agriculture due to Engel’s Law. But the indirect effects of agricultural growth on non-agriculture are substantial and likely at least as large as the reverse feedback effects. Whether investments in agriculture in a particular country would generate faster or slower _overall_ economic growth than investments in non-agriculture is a priori not clear. This would depend on the structure of the economy and the governing institutional arrangements.<sup>28</sup> How these potentially counterbalancing forces (potentially slower overall growth from investments in agriculture against a much larger participation effect) play out, remains an empirical question. To shed more light on this, we explore how agriculture and non-agriculture contributed to poverty reduction over the past two decades in the countries in our PovCal sample. In particular, we revisit equation (6) and estimate the relative contribution of each sector to the (predicted) change in (US1$/day) poverty incidence in these countries. To do so, we apply the estimated coefficients from the pooled data set reported in Table 6 column B to the (share weighted) sectoral GDP growth rates in our PovCal sample. The poverty spells in our sample concern the 1980-2000 period, with about two thirds of the spells occurring in the 1990s. It is especially useful to examine

> 28 These two forces may further affect the country specific size of the participation effect as well, as will be discussed below.
In the Filipino labor force, females are on average better educated than males as those who are less educated tend to choose not to participate in the labor market (Table 1).<sup>2</sup> For example, 32 percent of females in the labor force have completed university education or above compared with only 14 percent of males; only 8 percent of females in the labor force have not completed elementary school education compared with 13 percent of males. As the unemployment rate and daily wage are similar between males and females, the fact that females are better educated suggests, to some extent, that females may in fact still face tougher conditions in the market compared with their male counterparts with similar education background.

**Table 1: Percentages of Labor Market Participants with Different Education by Gender**

|**Highest Degree**<br>||**Elementary**<br>**M**|**ale**||
|---|---|---|---|---|
|**Completed**|**No Gade**|<br>**School**|**High School**|**Some College**|
|2003|14.1|29.2|42.8|13.8|
|2004|13.8|29.5|43.0|13.6|
|2005|13.2|28.3|43.9|14.7|
|2006|12.7|28.3|44.2|14.8|
|2007|12.3|27.9|45.3|14.5|
|**2003-2007**|**13.2**|**28.6**|**43.9**|**14.3**|
|**Highest Degree**||**Fe**|**male**||
|<br>||**Elementar**|||
|**Completed**|**No Gade**|**y**<br>**School**|**High School**|**Some College**|
|2003|8.3|22.1|38.5|31.1|
|2004|8.0|21.3|38.7|31.9|
|2005|7.7|20.2|40.0|32.1|
|2006|6.8|19.8|40.9|32.4|
|2007|6.6|18.7|41.8|32.9|
|**2003-2007**|**7.5**|**20.4**|**40.0**|**32.1**|

_Source:_ Authors’ calculation based on LFS, 2003-2007 As in many other countries, the youth are better educated than the older generations (Table 2). For example, 49 percent of the labor force aged 15-25 has high school certificate compared with only 23 percent of those aged 55-64, although the difference might already be underestimated as some of the youth may still pursue higher schooling. The large difference in human capital between the older age group, for example 55-64, and the younger age group, for example 15-25 or 25-50, is consistent with achievements in education in the Philippines in the past decades. Less than one out of ten in the labor force younger than 50 has not completed elementary school education, compared to more than one out of five aged 50-65.

> 2 In addition to education, household environment, such as household income level, marital status, existence and number of child(ren) and age of child(ren), affects female worker’s participation in labor market.

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- A man should have the last word in household decision-making about finances. ( _last word_ )

- Housewives should be paid for their household labor. ( _pay women_ )

- Women should have as much opportunity as men to take a break from household responsibilities to spend time with friends. ( _leisure_ )

- Men who take on more household tasks to support women pursuing careers are not very manly. ( _manly_ )

Responses to these items constitute the personal attitudes (PA) indicators. I also ask whether respondents believe that (1) most Moroccan women, (2) most Moroccan men, and (3) “people in your neighborhood” currently strongly agree, agree, disagree, or strongly disagree with the same set of statements. Responses to these items constitute the perceived norms indicators. To avoid systematic question order effects, whether respondents were asked about personal attitudes or perceived norms first was randomized using Qualtrics’ built-in randomization, as was the order in which respondents were asked about their perceptions of each reference group, and the order in which the individual survey items were presented within each grouping.

For each respondent, I generate an aggregate personal attitudes score and separate perceived norms scores with respect to each reference group (men, women, and neighbors). First, I re-code responses to the items such that 0 = strongly agree and 3 = strongly disagree when the statement as written is a conservative viewpoint, and such that 0 = strongly disagree and 3 = strongly agree when the statement as written is an egalitarian viewpoint. Higher numerical values then represent more egalitarian viewpoints for all items. The scores are the mean responses to survey items after this re-coding.<sup>4</sup> For each respondent, I also calculate a pooled perceived norm for each individual item taking the average of respondents’ perceived norms of men, women, and neighbors. From this, I calculate an overall _pooled perceived norms score_ which is the average pooled perceived norm across all eight survey items. The mean values of these scores among male and female respondents, respectively, are visualized in Appendix Figures 6 to 8.

> 4. All questions were forced choice (respondents had to select an answer to continue the survey), so there are no missing values on the personal attitudes or perceived norms items.

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urban sector is systematically greater than the real wage rate paid in the informal sector. Consequently, unskilled urban workers will first seek employment in the private formal sector. The actual level of employment in that sector is determined according to equation (19). The remainder of the urban unskilled labor force, Uu, will thus seek employment in the informal economy, where there are no barriers to entry: From (6), the demand for labor in the informal sector is given by UI = _3px(Vj1/wI),_ where w, is the product wage defined as W,/PV1. Because the informal labor market clears continuously (Ud = U,), the equilibrium product wage is given by The urban unskilled labor supply, Uu, grows as a result of "natural" urban population growth and migration of unskilled labor from the rural economy, as discussed earlier. Moreover, a fraction of the urban unskilled population acquires skills _(SKL)_ and leaves the unskilled labor force to increase the supply of skilled labor in the economy. We make the additional assumption that individuals are born unskilled, and therefore natural urban population growth. (not resulting from migration or skills acquisition factors) is represented by urban unskilled population growth only, at the rate gu. Thus, the size of the urban unskilled labor supply evolves according to We treat the growth rate of the urban unskilled population endogenously in our framework. We do so by dwelling on the several studies have documented the existence of a negative association between population growth rates and income levels; fertility rates tend to fall as the level of income increases (see for instance Barro and Becker (1989), and Becker, Murphy and Tamura (1990) for the growth implications of this relationship). Specifically, we assume that the growth rate of the urban unskilled population is related to a distributed lag of current and past values of the ratio of average income in the urban economy for skilled and unskilled workers. Assuming that these lags follow a declining geometric pattern and using the Koyck transformation, 22
**Figure 8: Average annual real GDP per capita growth rate for Sub-Saharan African countries in different scenarios, 2011-2030 (percent)**

<!-- Start of picture text --> 6.5<br>6.0<br>5.5<br>5.0<br>4.5<br>4.0<br>3.5<br>Baseline Pipeline Education Improvement<br>Median Weighted Average<br><!-- End of picture text --> Note: The orange bars are the median real GDP per capita growth rates in the sample of SSA countries, with the error bars describing the interquartile range. The blue diamond is the GDP weighted average growth rate for the region. Source: Authors’ simulations The faster growth scenarios also have implications in terms of poverty and income distribution. The simulation results suggest that poverty headcount rates in the SSA region would decline from 51.8 percent in 2007 to 16.5 percent in 2030, under the _skilled pipeline_ scenario (Table 7). This reduction would be greater (12.9 percent in 2030) if educational attainment improvements are considered. Moreover, an increase in the share of skilled workers has implications in terms of income distribution (Figure 9). In the GIDD model the shift in distribution is mostly driven by three factors: i) growth convergence between African countries; ii) sluggish growth or reduction on wage skill premia due to labor supply falling faster than labor demand; and iii) unskilled workers migrating from the agricultural sector to manufacturing and services. These findings suggest that human capital may play a critical role in poverty and inequality outcomes related to the demographic dividend in Africa.

**Table 7: Poverty in Sub-Saharan Africa* under Alternative Scenarios at $1.25 a day** **<u>poverty line</u>**

||**Scenario**|**Poverty Headcount**<br>**(millions)**|**Poverty Headcount rate**<br>**(%)**|
|---|---|---|---|
|Base Year, 2007||356|51.8|
||Baseline|210|17.1|
|Hih Growth 2030|Skilled labor pipeline (hi)|206|16.7|
|g ,|Education Improvement (hic)|159|12.9|
||Age Structure Freeze|250|20.3|

Note: * The country coverage in GIDD is slightly smaller than the full set used by the World Bank’s PovcalNet tool for official estimates. Hence, the total populations and poverty headcount may differ from what would be obtained through PovcalNet. The African countries covered by the GIDD database are Angola, Burkina Faso, Botswana, Côte d'Ivoire, Cameroon, Cape Verde, Ethiopia, Gabon, Ghana, Gambia, Kenya, Madagascar, Mozambique, Mauritania, Mauritius, Malawi, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Swaziland, Chad, Togo, Tanzania, Uganda, South Africa, DR Congo, and Zambia. Source: Simulation results 24
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# _Quantifying the Leakage Effects of a Universalistic Transfer Program_

As Figure 4 shows, targeting programs based on a universalistic scheme would shift the function up by an amount equal to the poverty line Z. Table 8 is used for our counterfactual analysis. Two types of count,..: `actuals are performed. In the "ALL" case, the transfers are distributed to all households simultaneously. In the second type of counterfactual, each household in turn is targeted and receives a transfer shown in Table 8. I.. both cases, this transfer as in the tax incidence analysis uses a differential incidence concept where total real government revenues (and hence expenditures) are kept constant. In all the counterfactuals, all households, including those that receive the transfers are taxed.

Household h therefore receives an amount _rh(l-t,)_ where rh and /' are respectively the transfers received by and the average tax rate for group h.<sup>42</sup> The percentage change in utility (a measure of real income) and the poverty index resulting from the policy change are presented in Tables 9 and 10. Table 9 shows that in all the cases, the percentage increase in final real income is less than the percentage increase in base income due to the transfer. This is better reflected in Table 10 that translates the relative changes in real income in poverty measurements. The objective of eliminating poverty is evidently not reached.

42 By financing the transfer program with a tax imposed on all households including the beneficiaries of the transfers, one captures the same problem of identifying households in targeting programs viz., identifying households as taxpayers (or beneficiaries of tax exemptions). The financing scheme applied here therefore includes the leakages that arise from imposing a tax to finance a transfer program, and assumes that the government is unable to distinguish -- from a tax policy perspective -- those exempted from the scheme because they benefit from the program and those that should be taxed.
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Dang, Hai-Anh, Peter Lanjouw, Jill Luoto, and David McKenzie. (2014). “Using repeated cross-sections to explore movements into and out of poverty,” _Journal of Development Economics_ 107: 112-128.

Elbers, Chris, Jean O. Lanjouw, Peter Lanjouw. 2003. “Micro-Level Estimation of Poverty and Inequality”, _Econometrica_ , Vol. 71, No. 1 (Jan., 2003), pp. 355-364 Ferreira, Francisco H.G., Phillippe G. Leite, and Martin Ravallion. (2010). “Poverty reduction without economic growth? Explaining Brazil’s poverty dynamics, 1985-2004”, _Journal of Development Economics_ 93: 20-36.

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OECD Economic Surveys: Turkey 2014 Ravallion, Martin. (1993). “Poverty Alleviation Through Regional Targeting: A Case Study for Indonesia”, in _The Economics of Rural Organization: Theory, Practice, and Policy_ edited by Karla Hoff, Avishay Braverman, and Joseph E. Stiglitz.

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Ravallion, Martin, and Gaurav Datt. (2002). “Why has economic growth been more pro-poor in some states of India than others?”, _Journal of Development Economics_ 68:381-400.

Ravallion, Martin, and Shaohua Chen. (2007). “China’s (uneven) progress against poverty”, _Journal of Development Economics_ 82:1-42.

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