rafmacalaba/data-use-mentions-extended huggingface dataset:
# 2. Literature review on links between child labor and educational outcomes

The literature mostly suggests that child work has a negative effect on educational achievement (Bezerra, Kassouf and Arends-Kuenning 2009; Edmonds 2007; Ray 2003). In Brazil, Bezerra and co-authors (2009) study the impact of child labor on school achievement (grades of different subjects) looking at the effects of work intensity and different types of work, either within or outside the home. They find that children and adolescents who do not work perform better at school than those who work. In addition, intensity seems to matter as the educational performance of children working fewer than two hours a day is not affected whereas that of children working more than two hours a day is. When comparing child work within and outside the home, working outside the household significantly negatively affects test scores more than working within the household.

Similarly, when looking beyond the simple act of being enrolled and focusing on learning outcomes, the consensus is that there is a negative relationship between child labor and educational attainment (Emerson, Ponczek and Souza 2014; Heady 2003; Akabayashi and Psacharopoulos 1999). Using panel data from Brazil, Emerson and colleagues show that child work, while still at school, has a strong adverse effect on test scores in math and Portuguese; the average effect on proficiency scores of transitioning to work while in school is equivalent to between one-quarter and an entire year of learning (Emerson, Ponczek and Souza 2014).

At the same time, some research studies report little or no relationship between enrollment and child labor, suggesting that the two activities are not mutually exclusive (No, Sam and Hirakaw 2012; Ravallion and Wodon 2000). In their study on primary school dropout in Cambodia, No and co-authors find that child labor is not a major predictor of whether or not child workers will attend school, but family and school factors are. Putnick and Bornstein (2015), analyzing data from almost 200,000 families with children aged between 7 and 14
first- or second-generation unit root tests for this. First-generation tests are appropriate under cross-sectional independence; otherwise, secondgeneration tests are appropriate. We determine which generation to use via Pesaran’s (2004) CD test. As one might expect with macroeconomic variables, cross-sectional independence is rejected across the board for all variables, as is clear in table 3. So, we test the order of integration of the variables using the Cross-sectional Augmented Dickey Fuller (CADF) test developed in Pesaran (2003), and Newey and West’s (1994) rule-of-thumb for the number of lags included (4(T/100)^(2/9)≈3). For the CADF test, the results reject the null hypothesis of stationarity for all variables with the smallest p- value being 0.473, associated with PM2.5 readings (table 4). Therefore, none of the variables can be made stationary using up to 3 lags of the variable, showing that the variables are nonstationary. Given the results of the preliminary tests, we decide to use Pesaran’s (2006) CCE estimator, which is robust to CD and nonstationary data while allowing for slope heterogeneity, to estimate the model coefficients for all three fuels.

**Table 3. CD test results Table 4. CADF test results**

||**p-value**||**p-value**|
|---|---|---|---|
|**PM2.5**|0.000|**PM2.5**|0.473|
|**Gasoline****_p_**|0.000|**Gasoline****_p_ **|0.631|
|**Gasoline****_q_**|0.000|**Gasoline****_q_ **|1.000|
|**Coal****_p_**|
in** ambitious in its designs to protect the Mongolia's **Informal** Sector Mongolian worker. It seeks to ensure healthy and safe working conditions, The informal sector surveys provide some indication of the degree healthy and safe working conditions, to which informal sector participants are covered by the health and fair remuneration for an employee's social insurance systems. The percentage of informals not covered skills, spells out valid conditions for by either scheme ranges from 36% for the moonlighting-friendly firing, and requires that employers taxicab activity, to 55% for the informals that populate the streets retain a worker's post when a worker and outdoor markets. The lack of coverage for such large fraction leaves for government service, takes of a large segment of society could have important implications for regular annual leave, or goes on public policy, as it could be an indication that contributions to the strike. The _Labor Law_ is also quite health and social insurance funds may fall below current specific about the official workweek projections. However, pessimism on this score can be tempered (46 hours), the length of an ordinary with the knowledge that informals demonstrate some willingness workday (8 hours, 7 hours if at to make voluntary contributions to the health and social insurance night), and the minimum period of funds. Roughly half of the taxicab drivers, kiosks operators, and rest between two consecutive work street informals, who are not otherwise employed, make voluntary contributions to one of the schemes. Even at the outdoor markets, days (12 hours). Employers are 37% of the informals whose sole source of income is their generally forbidden from requiring informal occupation, make voluntary contributions to one or the overtime from their workers; when other schemes. overtime is performed, it is remunerated with double-pay. The Box 4 _Labor Law_ mandates eight holidays _Source:_ Author's informal sector surveys. per year, and Sunday is legislated as a public day of rest. With few exceptions, employers are forbidden from compelling
0.003)|
|**Environmental damages**|-0.036**|
||(0.016)|
|**Financial account**|0.308***|
||(0.013)|
|**Constant**|5.057***|
||(0.169)|
|<br>**Observations**|40,573|
|**Number ofgroups**|226|
|**Standard errors inparentheses**||
|*****p<0.01, **p<0.05, *p<0.1**||

_Source: World Bank estimates based on EHPM (2019). The number of observations is driven by missing variables in the variable indicating the highest grade obtained. For all other variables, we replace missing values with zeros to avoid a selective deletion of these individuals from the model estimation. See Annex 6 for the details._

# 5.1. Vulnerability to Poverty: Recent Estimate and Drivers

**Our estimates show that vulnerability to poverty in El Salvador in 2019 was slightly higher than poverty, with 24 percent of households being vulnerable; vulnerability is mainly driven by riskinduced vulnerability** . The overall vulnerability rate in El Salvador in 2019 was 23.7 percent and, therefore, slightly higher than the poverty rate.<sup>15</sup> The vulnerability rate was higher in rural areas with 41.2 percent and only 13.5 percent in urban areas (see Figure 1). In addition, risk-induced vulnerability played a more significant role in overall vulnerability in El Salvador, mainly driven by urban areas (see Figure 2). The ratio of risk-induced to poverty-induced vulnerability was 1.7 in total, 1.4 in rural areas, and 2.7 in urban areas. These results suggest that vulnerability in the country is mainly driven by climate and environmental shocks
destructive fishing practices, such as the use of poisons in closed creeks or canals; increased water pollution; reduction in the freshwater flow of the river system; and intrusion of salinity. Significant threats from human actions are likely to continue in the future, and the stress on fisheries in the region may be further aggravated by climate change. Among climate-related threats fisheries in this region will face,<sup>1</sup> one of the greatest challenges will be increased aquatic salinity from sea level rise and climate-induced changes in temperature, rainfall and riverine flows from the Himalayas (Dasgupta et al., 2014; Gain, Uddin and Sana, 2008). These changes will adversely affect many fish species, with significant impacts on their reproductive cycles, reproductive capacities, suitable spawning areas, feeding, breeding, and longitudinal migration. Fishing communities are among the poorest of the poor in Bangladesh, so understanding these impacts is critical for ensuring the future sustainability of fishing-dependent households.

Within the southwest coastal region, Sundarbans ecosystem supports a wealth of fish diversity,<sup>2</sup> provides a refuge for fish from predators, and serves as a nursery for the larvae and juveniles of 90 percent of commercial fish and 35 percent of all fish in the Bay of Bengal (USAID 2010). In 2008, Gain, Uddin and Sana studied the impact of river salinity on fish diversity in the southwest coastal region near the Sundarbans. Their research area included highly saline conditions in Paikgacha _upazila,_ Khulna district, and moderately saline conditions in Rampal _upazila,_ Bagerhat district. The researchers analyzed river salinity data monitored by the Bangladesh Water Development Board (BWDB) for the Sibsa river in Paikgacha and the Passur river in Rampal, and found a significant increase in salinity from 1975 to 2004. After surveying

> 1 Other threats include increased water temperature, changes in cyclonic storm patterns, change in surge heights.

> 2 According to IUCN, water bodies in the Sundarbans (ruvers, streams and canals) covering 1
care remains unclear, even after additional efforts were undertaken to increase health funding and provide for a more efficient distribution of drugs and other medical supplies.

In order to address public health priorities in a cost-effective and equitable manner, a substantial share of resources should be allocated to the lowest level of the healthcare system, i.e. first-tier institutions. However, in practice just over a third of health expenditures are allocated to primary healthcare (see Table 4). Expenditure data show that total public health spending is estimated at 2 percent of GDP, well below the expenditure share of many other countries in Sub-Saharan Africa.<sup>9</sup> Between 2006 and 2009,

> 9 Shares of public health care spending over GDP in the region are 9.3% in Sierra Leone, 3.9% in Namibia, 3.8% in Chad, 3.6% in Malawi, 3.5% in Niger, 3.4% in South Africa, 3.2% in Senegal. Nigeria and Ethiopia, at 2.1%, have similar shares than Zambia. Kenya, at 1.4%, has a lower rate.  See World Bank, Data Indicators, 2012b.

11
lower and higher saturation groups respectively. In control transformers, no study households were treated. In lower-saturation transformers, 60% of study households were treated. In higher-saturation transformers, 80% of study households were treated. As mentioned in the sampling discussion above, 20 percent of the households in each transformer were sampled into the study. Thus, the first stage randomization results in approximately 12% to 16% of all households within treated transformers being assigned to treatment.

In the second stage, a total of 1,000 study households within these 110 transformers were randomized into either control or treatment status. This resulted in 457 and 543 households in each group respectively. By design, as depicted in Figure 2, treated households are found only in treated transformers, in the proportions set by each transformer’s treatment intensity. Control households, however, can be in either control or treated transformers. The two stages of randomization also induced spatial heterogeneity in the location of the treated households, leading to variation in the proximity to and number of treated neighbors.

# **3.3 Intervention and household survey**

In the spring of 2013, all study households were visited and invited to participate in a baseline survey regarding electricity use. Their treatment status assignment was not disclosed at the time of the baseline survey. All households were given 150 Kyrgyz soms (approximately 3.26 USD) to compensate them for their time, after finishing the survey.<sup>31</sup> After the baseline survey, baseline interaction with the control households was complete. In contrast, households randomly assigned to the treatment group were offered the opportunity to purchase up to 4 CFLs at a highly subsidized, randomly-drawn price, via a willingness to pay experiment.<sup>32</sup> The set of possible prices was _{_ 0, 5, 10, 15, 20 _}_ . The market price for CFLs was a minimum of 100 KGS, so treated households were paying a maximum of 20
Table 2 summarizes child demographic characteristics and socio-economic status by living arrangement using the child sample, i.e. those who are 25 and older and with at least one parent over 60. Among the 2,667 children, 465 (17.4 percent) are living with their parents and co-residence is highly related to many child characteristics. The average age of those who co-reside is 39.1, significantly less than the mean of 42.5 for those who are non-resident. Daughters are less likely to live with parents and oldest sons and especially youngest sons are more likely to live with their parents. Furthermore, education is also associated with co-residence, but the pattern varies by level of education: adult children with low education (those who are illiterate and those with primary school education) and high education (college and above) are not likely to live with parents, but those with intermediate levels (middle school) are significantly more likely to live with parents.

# **4. Patterns of Transfers**

Respondents are asked, in the family module of the CHARLS survey, about the amount and frequency of non-time transfers, and including financial transfers and in-kind transfers (in the form of goods) received from and given to each child. Financial transfers involve giving money, helping pay bills, such as medical care or insurance, schooling, and down payment for a home or rent. These transfers are further divided into regular and non-regular financial transfers. Regular transfers were paid on a regular basis such as monthly payments. Irregular transfers occurred around such events as a festival or 14
0.575***|0.561***|0.561***|
||(8.69)|(9.11)|(8.69)|(8.69)|
|Wheat|0.464***|0.481***|0.464***|0.464***|
||(7.04)|(7.41)|(7.04)|(7.04)|
|Sorghum|-0.076|-0.060|-0.076|-0.076|
||(1.15)|(0.90)|(1.16)|(1.16)|
|Pulses|0.215***|0.239***|0.215***|0.215***|
||(2.96)|(2.98)|(2.96)|(2.96)|
|Number of observations|3743|3743|3743|3743|
|Number of households|635|635|635|635|
|_R_<sup>2</sup>|0.113|0.113|0.113|0.113|
|Sargan/Hansen J Statistic|14.599|14.948|14.596|14.596|
|Sargan/Hansen J Chi2|0.147|0.134|0.148|0.148|
|Anderson-Rubin Statistic|||14.630|14.630|
|Anderson-Rubin Chi2|||0.146|0.146|
|Anderson/Kleibergen-Paap Statistic|107.203|46.157|107.203|107.203|
|<br>Anderson/Kleibergen-Paap Chi2|0.000|0.000|0.000|0.000|
sup>6</sup> The Gender Gap Index (GGI) developed by the World Economic Forum since 2006 gives similar results; in 2011 Brazil was ranked 82 out of 135 countries, with a score of 0.668–compared to 0.654 in 2006.<sup>7</sup>

> 5There are also considerable gender differences across regions. For instance, wage gaps tend to be lower in Rio de Janeiro than in Sao Paulo; the largest gaps are found in the Northeast. Note that these disparities are not unique to Brazil; see Ñopo (2012).

> 6On the GII index, see http://hdr.undp.org/en/statistics/gii/. The GII is a composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment and the labour market. It varies between zero (when women and men fare equally) and one (when men or women fare poorly compared to the other in all dimensions). The health dimension is measured by two indicators: maternal mortality ratio and the adolescent fertility rate. The empowerment dimension is also measured by two indicators: the share of parliamentary seats held by each sex and by secondary and higher education attainment levels. The labour dimension is measured by women’s participation in the work force.

> 7On the GGI index, see http://www.weforum.org/. The GGI is designed to measure gender-based gaps in access to resources and opportunities in individual countries. The highest possible score is 1 (equality) and the lowest possible score is 0 (inequality). Beneria and Permanyer (2010) criticize the GGI for only capturing inequality in certain aspects of women’s lives therefore making it an incomplete measure of gender inequality. Their calculations suggest that the levels of gender inequality are mostly

6
|General Government|0.37|1.00||
|---|---|---|---|
||(0.000)|||
|Public Sector|0.20|0.71|1.00|
||(0.000)|(0.000)||
|**b) Cross section in**|**2005**<br>|||
||Central Government|General Government|Public Sector|
|Central Government|1.00|||
|General Government|0.41|1.00||
||(0.02)|||
|Public Sector|0.40|0.81|1.00|
||(0.05)|(0.00)||
|**c) Cross section o**|**f country average values**<br>|||
||Central Government|General Government|Public Sector|
|Central Government|1.00|||
|General Government|0.50|1.00||
||(0.00)|||
|Public Sector|0.048|0.50|1.00|
||(0.74)|(0.00)||

Source: calculations based on ILO Stats.

Figure 5: Number of countries with data per year (max in 2006, 2005, and 2008) 8
<sup>6</sup> Therefore, if the welfare distribution changes, it should be good practice, whenever possible, to re-estimate the poverty line accordingly. Trivially, if rents are included in the welfare aggregate, but the poverty line is left unchanged, poverty will decrease. When both the poverty line and the original welfare distribution increase as a consequence of including rents in the welfare aggregate, the effect on poverty becomes unpredictable, unless the poverty line is relative, and rents are modeled following scenario (ii). In this case, given that the poverty indices used in this analysis satisfy scale invariance, poverty will not change.

The third indicator of well-being taken into account in this paper is shared prosperity. In 2013, the World Bank adopted two new targets: ending extreme poverty by 2030 and promoting shared prosperity. The emphasis on reducing extreme poverty is in line with the commitment to eliminate extreme poverty enshrined in the first target of the Sustainable Development Goals, which were adopted by the United Nations in 2015 (United Nations 2015). Shared prosperity focuses on the less well-off more broadly. It is defined as the annualized growth rate of the average welfare of the bottom 40 percent of the population between two years and , where stands for the number of years between two data surveys. yy yy+ ττ Let us define the average welfare aggregate at timeττ , for the bottom 40 percent of the population as<sup>,</sup> Monitoring shared prosperity is challenging, as it requires building comparable welfare aggregates over time. For our purposes, the three scenarios we introduced before are here increased to nine, as summarized in Table 1, depending on the prevalent rent-imputation scenario in each one of the observed years.

> 5 Blades (2009), for example, shows that the share of housing on total consumption is positively correlated to GDP per-capita. However, there is still no evidence that the share of housing on total consumption is correlated to the distribution of the welfare aggregate in developing countries
for Malawi, 2010; Oyekale & Gedio, 2012), and the strong link between maize production and food security (World Bank, 2007). Households’ mean consumption per capita in 2013 was approximately USD 396 while mean income per capita was USD 188.<sup>4</sup> As has been amply noted elsewhere, income per capita figures are often lower than consumption per capita figures, largely thought due to under‐reporting of some income sources. Consumption is traditionally considered to be a good indicator of the individuals’ capability to achieve a certain living standard and is usually better captured than income in household surveys, but we include income to evaluate whether different explanatory factors are associated with vulnerability to income poverty. The three variables are related primarily through crop production, since part of income is comprised of the value of crop production, and part of consumption is comprised of consumption from own production. The correlation amongst the three ranges from .48 and .57, with the highest correlation between maize harvest and income per capita.

# **_Well‐being threshold_**

To identify the poor, we use the 2010 MWK 37,002 consumption per capita per year threshold identified by the National Statistical Office as the poverty line (NSO, 2012). Their calculation is based on the cost of a basket of food that provides sufficient daily calorie intake plus an allowance for basic non‐food needs (NSO, 2012). Corrected for 132% inflation between 2010 and 2013, the poverty threshold then becomes 2013 MWK 85,845. We apply this same poverty line for both consumption and income. Because income per capita is much lower than consumption per capita, this results in far more households being below the poverty line when using the income per capita figures at the consumption poverty line. Thus, we generate an income poverty line that results in the same proportion of poor households as under consumption per capita. For maize harvest, we take the median level of maize harvest across both 2010 and
we can safely assume the Tobias-Li data do not support this prior at all, as it puts no mass whatsoever on areas with large values for the marginal likelihood _ly_ ( _g_ ).

> 18 For the hyper- _g_ prior we find (as do Feldkircher and Zeugner, 2009) that the choice between _a_ = 3 and _a_ = 4 makes little difference. As a consequence, we would also expect the hyper- _g/n_ prior with _a_ = 4 to perform very similarly.

29
inequality encompass a broader definition of inequality that correlates, for example, with poverty or unemployment, as well as with fairness or social mobility, own situation and ideology. In other words, perceptions depend on the objective situation of both, the country and the individual, but they are also influenced by the fairness of the process through which objective inequality is generated and by individuals’ own views of what constitute a fair society, i.e., their ideology. We argue that perceptions, a key determinant of demand for redistribution, are formed in a much more complex way than typically assumed. In concrete, we find that inequality perceptions depend on the context: uncertainties in the labor market (unemployment), actual inequality, poverty, and government expenditures on education. Inequality perceptions however depend not

> 1 There are some actual issues that justify economists’ reluctance: in most surveys, people do not have incentives of revealing their genuine beliefs, and they are confronted by social pressure to say the socially acceptable thing. In his paper where he proposes to use subjective data, Manski (2004) notes that “[...] economists have been deeply skeptical of subjective statements; they often assert that one should believe only what people do, not what they say. As a result, the profession for many years enforced something of a prohibition on the collection of subjective data” (p. 1337).

2