rafmacalaba/datause-encoder-data huggingface dataset:
# 2. Study Background

<mark>We summarize in this section the collaborative initiative between the</mark> members of the CLCI, CWG, and the WB that <mark>led to this working paper. The accompanying policy note</mark> (Jovanovic et al., 2021) <mark>discusses the policy initiative and lessons learned from the engagement, while the d</mark> esk review <mark>paper (Phadera, et al., 2022) documents the theoretical soundness and analytical procedure of the PPMT.</mark> <mark>More than 6 million Iraqis were forcibly displaced from their homes because of the conflict with ISIS that began in 2014. With the de-escalation of the crises and gradual return to normalcy, some 4.8 million displaced persons have returned, with more than 1.2 million remaining internally displaced as of July 2021.</mark><sup>6</sup> <mark>As Iraq begins its long recovery journey, some essential services like health, education, and social protection remain out of reach of many displaced and returnees (IOM, 2019). Re</mark> integration into the community have been difficult for many returnees with absence of livelihoods and services, lack of social cohesion, and security concerns. Access toa relevant social protection network remains a challenge for both the IDPs and the returnees due to obstacles such as complicated registration procedures, lack of documentation and inadequate assessment capacity of the authorities.

<mark>The situation resulted in the g</mark> overnment and international community's commitments to support IDPs and returnees through long-term and large-scale plans that go beyond humanitarian assistance. From the onset of the Iraqi crises, cash assistance to Iraqi IDPs has been chiefly championed by two coordination humanitarian bodies - the CWG and CLCI, through their signature cash transfer program - the MPCA. More than 1 million IDPs in <mark>Iraq</mark> have benefited from the MPCA, and the total value of the MPCA has exceeded $114 million (Jovanovic et al., 2021). This value, although substantial, would not be sufficient to meet the needs of all displaced persons in Iraq. Policy makers are also concerned about the well-being of IDPs with the reduction and coming cessation of this humanitarian aid.

<mark>On the other hand, the Iraqi state assists its citizens, including some IDPs, through the</mark> Public Distribution System (PDS) of food and the social safety net (SSN) that provides unconditional cash transfers to poor households. About <mark>1.36 million households</mark> hav <mark>e benefited from the SSN</mark> (Jovanovic et al., 2021). As the number of beneficiaries is significantly lower than the total population of poor in Iraq, there are ethical reasons for expanding the program <mark>. Given Iraq’s tight fiscal space, expanding the SSN, however, is a challenge. Currently there are more than 700,000 households, some of which are already verified as eligible, on the waitlist for the assistance. Lack of a social registry is another obstacle the program faces.</mark>

> 6 IOM DTM for Iraq. See link

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||- Morbidity<br>- Mortality<br>Economic<br>- Time savings|- Local (preservation of forest reserves, better soil fertility)<br>- Global (CO2, CH4emissions)<br>- Black carbon<br>Economic<br>- Profit|
|---|---|---|
||- Fuel/fuel cost savings<br>Others|- Market development<br>- Carbon finance<br>Others|
||- Cleanliness<br>- Aesthetic gains|- Skill development<br>- Job creation|
||- Social status gain<br>- Savingfertilizers(biogas)|- Community engagement|
|Costs|Cost of ICS<br>Fuel cost|Market intervention<br>- Subsidies|
||Maintenance cost|- Fuel cost|
||Others|- Program costs<br>Trainings|
|||Monitoringandqualitycontrol|

Many of the studies identify health benefits, especially associated with smoke and safety, and other environmental benefits, from choosing modern fuels and adopting ICS. For example, using cost benefit analysis (CBA), WHO (2006b) finds that it is potentially beneficial for human health as well as for local and global environment to invest in modern fuels and ICS. Using similar CBA framework in Kenya, Sudan and Nepal, Malla et al. (2011) find that there is a direct health benefit from improved cooking system interventions due to reduced treatment costs and in time savings due to fewer days spent ill or having to care for sick child. Habermehl (2007, 2008) finds that environmental benefits including preservation of forest reserves and benefit to CO2 and CH4 reduction from ICS program in Uganda and Malawi were significant. However, Madubansi and Shackleton (2007) find that most of the households in the villages of Bushbuckridge region of South Africa, who receive part of the electricity free, still rely heavily on fuelwood for cooking. The study also finds that number of households purchasing fuelwood had increased most likely due to increased fuelwood scarcity in the local areas as reflected by increased fuelwood collection times and changes in fuelwood species preferences. Asaduzzaman et al. (2010) in Bangladesh and Garica-Frapolli et al. (2010) in rural Mexico find that switching to modern cooking fuels and ICS lead to minimizing health risks associated with HAP. However, it is not always the case, as Mobarak et al. (2012) finds that women did not consider indoor air pollution a high priority for adopting ICS.

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included in the Chile data set). Those who perceive high school quality are less inactive, and are older when they begin working but are more likely to not complete secondary school. This particularly emerges for girls. It is also important for explaining responsible attitudes toward alcohol. Surprisingly, the link between school quality and sexual behaviors is not strong; it is positively correlated to safe sexual practices for males only, despite the large literature that identifies school connectedness as a protective factor in sexual behavior decisions (see Blum et al 2002 for the US, Ohene and Blum 2005 for the Caribbean, and World Bank 2007 for Brazil). Since the Mexico data do not directly measure school connectedness, though, we should not necessarily dismiss this factor.

Age and spirituality are the only individual factors that consistently emerged in the regression analysis. Age plays a part in explaining the variance in all seven behaviors considered. It is negatively correlated with all the behaviors in Chile and with all behaviors except “older age at first job” for males in Mexico. This confirms the evidence found elsewhere that risky behaviors and negative outcomes increase with age (Bagby and Cunningham 2007).

Young people who identify themselves as being more spiritual also have safer sexual behaviors and participate in more activities. This variable emerges as highly significant for explaining the variance in these behaviors in Chile and Mexico, but it is correlated with younger labor force entry in Mexico. Young Mexican women are particularly influenced by spirituality. The exact role played by spirituality is not clear. One hypothesis is that spirituality in itself is not important, but the connection with a group – whether parents who accompany a young person to church or the church community itself – may be protective (Blum and Irelend 2004). However, the Mexican data allow us to control for church attendance and connectedness with parents and spirituality still emerge as a key explanatory factor. Alternatively, the actual teachings of the religion may be a guide for daily behavior. Or, as posited in World Bank (2007), those who believe that there is a higher being may have less feelings of hopelessness, and thus less risky behavior, than do those who feel that they are not being monitored and judged.

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during a 32-week post-incentive period. A recent systematic review of randomized controlled trials of treatments for obesity (Paul-Ebhohimhen and Avenell, 2007) showed no significant effect of the use of financial incentives on weight loss or maintenance at 12 months and 18 months. However, further sub-analysis indicated that large transfers (greater than 1.2% of personal disposable income) had greater impact, as did rewards for behavioral change rather than weight loss, per se, and rewards based on group performance rather than individual results.

The CM literature, overall, offers useful insights into aspects of the conditionality that appear to elicit the desired behavior change. This is an important area of inquiry that has not been sufficiently explored within CCT programs. However, unlike CCT programs, studies of CM have remained largely experimental and have not been brought to scale (Petry, 2000; Kane et al., 2004). Furthermore, the small sample sizes of study groups – most typically involving groups of 20 to 100, and rarely more than 500 – have made it difficult to detect effects that are statistically significant, much less estimate effect sizes accurately. Also, factorial designs with several treatment arms are common which – in combination with already small sample sizes – has led to even more constraints on power (Kane et al., 2004).

# **Theoretical Pathways for Incentive Effects on Risky Sexual Behaviors**

As indicated in the above literature review, there are a variety of theoretical pathways via which incentives could influence risky sexual behaviors. In the STI domain, such behaviors might include sexual behavior (abstinence, fewer partners, less risky partners, condom use, pressure spouse/partner to reduce risky behaviors) as well as testing and treatment behaviors (regular STI testing, STI treatment, and encouraging partner to do the same). In this section we focus on behavioral changes induced particularly by incentives such as those employed in the RESPECT study: cash rewards conditional on testing negative for STIs.

# **_Neoclassical Price Effect_**

Neoclassical economics predicts that the incentives will influence behavior in part via a price effect. Conditioning the reward on STI status increases the implicit ―price‖ of 11
Table 5 below shows results with revised indices after exploratory factor analysis.<sup>5</sup> We first show the relationship between the revised indices and three outcome variables- women’s labor force participation women’s willingness to work, and whether women have looked for work. The results predict that a one standard deviation increase in the index including social empirical, normative, and interpersonal expectations<sup>6</sup> is linked to a 1.4 to 1.7 times increase in the odds of women working (Columns 1 and 2). Given that female labor force participation in our sample is about 30%, this represents a fairly large increase. For non-working women, the index does not increase the odds of wanting to work, but does predict a 1.3 times increase in looking for work (Columns 3 and 4). Columns 5 and 6 show how the four components are linked to female work status for both women and men. For the full sample, working status is correlated with social empirical expectations, as well as interpersonal and personal beliefs. Columns 7 and 8 show that for non-working women and the partners of non-working women, personal beliefs appear to be the main driver of wanting to work and having looked for work, rather than social or interpersonal expectations.

**Table 5: Individual level regression of revised indices after factor analysis**

|Sample|(1)<br>Full<br>Sample|(2)<br>Full<br>Sample|(3)<br>Non‐working<br>women|(4)<br>Non‐working<br>women|(5)<br>Full<br>Sample|(6)<br>Full<br>Sample|(7)<br>Non‐working<br>women|(8)<br>Non‐working<br>women|
|---|---|---|---|---|---|---|---|---|
|Dependent|Women’s<br>work|Women’s<br>work||Looked for|Women’s<br>work|Women’s<br>work||Looked for|
|Variable|status|status|Want to work|work|status|status|Want to work|work|
|Norms index|1.679***|1.444***|1.137|1.276*|||||
||(0.0912)|(0.0936)|(0.0968)|(0.153)|||||
|SE index|||||1.315***|1.267***|1.005|0.988|
||||||(0.0696)|(0.0777)|(0.0988)|(0.130)|
|SN index|||||1.031|0.972|0.854|0.945|
||||||(0.0606)|(0.0705)|(0.0774)|(0.111)|
|PB index|||||1.459***|1.373***|1.533***|1.450*|
||||||(0.0914)|(0.0965)|(0.159)|(0.219)|
|IH index|||||1.242***|1.146*|1.094|1.230|
||||||(0.0804)|(0.0767)|(0.0988)|(0.160)|
|Controls|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Observations|2,007|2,004|745|746|2,005|2,002|745|746|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from Logistic regressions. Control variables include age, years of education, marital status, number of children, dummy variables for Amman and Zarqa and if male counterpart works. The standardized indices includes items after factor analysis, as specified in Annex 2, Table 2.8.

> 5 All items as shown in Annex 2 Table 2.8. A standardized index is created with items across all the themes that load onto the first 4 or 5 factors for each of social empirical, social normative, personal beliefs, and interpersonal expectations, but ensuring that the item is represented in each of these components to be consistent with theory.

> 6 Excluding personal belief items. See Annex 2 Tale 2.10 for regressions of an index including all 4 components.

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choice at the individual-level using multivariate probit models. Gender differences in productivity are analyzed in section 5. A final section concludes and discusses policy implications.

# **2. RELATED LITERATURE AND COUNTRY CONTEXT**

# **(a) Related Literature**

At the individual-level, women’s labor allocation is primarily determined by the opportunity cost of working relative to earnings in productive employment, “unearned” income, preferences for different types of employment (which may be dictated by cultural norms and religious beliefs<sup>3</sup> ), as well as other household members’ characteristics and labor allocation. The opportunity cost of working is inter alia determined by the presence of children in the household and returns to working, which in turn depend on women’s human capital and the income-earnings opportunities available to them. Literature from developed countries furthermore suggests that entrepreneurship is often intergenerationally transmitted; children of entrepreneurs are significantly more likely to become entrepreneurs themselves (Parker, 2008, 2009).

Studies of gender differences in entrepreneurship in developing countries are scarce.

Existing studies are predominantly based on the World Bank’s Enterprise Surveys and typically find that female entrepreneurship is inversely correlated with firm size;<sup>4</sup> firms run by female entrepreneurs are smaller in terms of employees, sales and capital stock. However, gender differences in total factor productivity, profitability and capital-intensity become insignificant once firm characteristics are controlled for (Bardasi and Sabbarwal, 2009), except for the very smallest firms (Bruhm, 2009).<sup>5</sup> Thus, gender differences manifest themselves primarily in terms of scale, rather than differences in profitability, technology or capital intensity. However, HallwardDriemeier and Aterido (2009) point out that it matters how a female firm is defined; using definitions based on decision making authority, rather than (partial) participation in ownership as is 4
not appear to be the main problem this population faces, and as such, eliminating worm infections is less likely to have dramatic impacts on stature.

# **5.1 Heterogeneity and mechanisms**

To help untangle the mechanisms behind this large effect on cognition, I consider different subpopulations in Table 3. I begin in the first column by repeating the specification shown in earlier tables, for reference. No matter what the mechanism, one might expect the spillovers to be larger within a household where older siblings receive treatment at school than in a household without such older siblings. Respondents were generally not certain of the ages of their older siblings, but as a simple rule, I consider those with at least three older siblings attending the same primary school to have had a sibling in school at the time of the deworming campaign. When the sample is restricted to this group, shown in column 2, the effect size nearly doubles.

This raises the question of whether there are any spillovers for children who did not have siblings in the primary school that participated in deworming. If so, an epidemiological mechanism is supported; if not, a behavioral or financial within-household mechanism might be more plausible. Again, because of the imprecision of responses, I consider only respondents who did not have any older siblings attending the relevant primary school as the subsample best suited to answer this question; estimates are shown in column 3. The effect is similar in magnitude to that of the full sample, and while for Raven’s Matrices it is statistically significant, it is not for the first principal component of all cognitive measures. With this, evidence leans in favor of an epidemiological mechanism: fewer worms in the community mean fewer infections in early childhood for these respondents.

To further explore the sibling sample in column 2, I divide that group into those who had more female than male older siblings at the same primary school in column 4, and vice-versa in column 5. Sample size is quite small at this point, and standard errors widen, but it appears that the benefit is largest for those with older sisters at the primary school rather than older brothers. This may reflect the relative frequencies with which girls and zero, even after accounting for province fixed effects. Thus, calculations based on DHS data do partially corroborate the stunting rates I observe.

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provides a significant counterexample to the findings in the law, institutions, finance and growth literature. Despite its poor legal and financial systems and a corrupt and autocratic government, China has (in Purchasing Power Parity (PPP) terms) one of the largest and fastest growing economies. Moreover, AQQ document that alternative financing channels and informal governance mechanisms have substituted for formal channels and mechanisms to support corporate as well as overall economic growth in China.<sup>4</sup> In this paper, we examine the second largest emerging economy in the world, India. At the end of 2005, with a population of almost 1.1 billion (second largest behind China), India already has the world’s _fourth_ largest economy measured in PPP terms (Table 1-A). During the period 1990 to 2005, India’s GDP (in PPP terms) had an annual growth rate of 7.9%, second highest among the world’s largest economies. With India’s English common-law origin, the legal protection of investors provided by the law is one of the strongest in the world. However, widespread corruption within India’s legal system and government significantly weakens legal protection in practice. We find that formal external financing for firms has been dominated by non-market sources of funds in India, while listed firms behave more like those from countries with weak investor protection. Our evidence, including results based on a survey of small- and medium-scale (private) firms, demonstrates that alternative financing channels provide the most important source of funds. Our survey evidence also shows that entrepreneurs and investors rely more on informal governance mechanisms, such as those based on reputation, trust and relationships, than formal mechanisms to resolve disputes, overcome corruption and finance corporate growth.

Our results, along with the findings of AQQ (2005) on China, illustrate that alternative financing channels and informal governance mechanisms, rather than legal protection and political

> 4 The literature on economies of transition (from Socialist, central planning systems to market-based economies) also documents the important role of alternative mechanisms in promoting the development of markets and institutions (e.g., McMillan, 1997; McMillan and Woodruff, 2002).

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parental education (34 percent), and the lowest in the case of average parental schooling (24 percent), with an average bias of 29 _._ 7 percent.<sup>20</sup> A 30 percent bias on an average vindicates the unease among the researchers and editors of journals that the available household surveys in developing countries may not be particularly helpful in understanding the magnitude of intergenerational persistence in economic status.

We now turn to the IGC estimates for Bangladesh reported in columns 4 (full sample) and 5 (coresident sample) of Table 1. The estimated IGCs for three different indicators of parental education are reported in the top panel and the implied biases are reported at the bottom. The evidence is strikingly different; the estimate of IGC from the coresident sample is much closer to that from the full sample, and this is true for all three different indicators of parental education (top panel). The average bias in the IGC estimates is 8 _._ 7 percent which is less than one-third of the average bias in the IGRC estimates (29 _._ 7 percent). The _highest_ magnitude of bias is 11 percent in the case of IGC which is less than half of the _lowest_ bias found in the IGRC estimates (24 percent).

# **Evidence from India**

Table 2 reports estimates of IGRC and IGC from India data for three different indicators of parental education (father’s schooling, mother’s schooling, and average schooling of mother and father). The difference between the IGRC estimates from the coresident and full samples in the case of India are smaller in magnitude compared to the estimates from Bangladesh (compare top panel of Table 1 to that of Table 2). The average bias is about 17 _._ 6 percent. While the extent of bias is not as dramatic as in the Bangladesh data, the evidence still indicates that the coresident sample selection causes substantial downward bias in the IGRC estimates. The relatively lower selection bias in the India estimates reflects the fact that the proportion of coresident children is higher in India compared to Bangladesh (61 percent in India and about 40 percent in Bangladesh).

The IGC estimates in columns (4) and (5) in Table 2 show that the tuncation bias in 20It is the simple average of the three bias estimates in the bottom panel.

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