rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10975

# **Abstract**

This paper reports on the prevalence of three facets of mental health—depression, anxiety, and parenting stress—among mothers of children ages 0–6 years in Khyber Pakhtunkhwa, Pakistan. Data from mother-child dyads were analyzed to examine differences in maternal mental health and early childhood development outcomes by maternal educational attainment, urban versus rural setting, and refugee versus non-refugee status. The analysis finds a higher prevalence of self-reported mental health concerns among refugee, less-educated, and rural mothers relative to non-refugee, more-educated, and urban mothers. Maternal mental health concerns are significantly associated with lower levels of early childhood development. This paper also analyzes how exposure to stressors such as food insecurity, financial insecurity, being impacted by flooding, community crime, discrimination, and domestic violence exacerbate both maternal mental health and child outcomes. The regression analyses indicate a significant and negative compounding interaction of maternal depression, anxiety, and parenting stress on early childhood development for younger (0–3 years) and older (3–6 years) children, even after controlling for stressors and other covariates. Policy improvements are needed that focus on at-risk communities, providing mental health services and reducing exposure to stressors within communities and households.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ahasan1@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
_Mdhod_ S were produced to take into account differences among 24 world regions in age structure within these age groups. This was not necessary for age groups 0 and 14, for which therefore only world equations are available. The derivatlon of these equations is explained in Hakulinen and others (1986a), and they are further described in Annex A.

These equations were used to predict mortality rates by cause wherever repoited data were not available. The reported data themselves were adjusted proportionally where necessary to provide the overall mortality levels estimated by the Bank.

New regression equations were estimated for mortality rates from 21 specific causes, using the rate for the major cause under wllch each falls as the predictor. These 21 causes include the 19 listed in the second part of Table 1 and two 'other' subcategories, under infectious and parasitic diseases and under other causes, estimates for which were obtained as residuals from the major causes. No 'other' subcategory is needed under circulatory system diseases because the subcateores are already comprehensive. The regression procedure is explained in Annex A.

As was done for the major causes, the equations were first used to estimate mortality rates by cause, age, sex, and country, and for each age-sex group in each country, rates were adjusted proportionally if necessary to provide the expected totals. Deaths from ill-defined causes were not reclassified under other categories, but estimates were made of the maximum possible increase in each category from such reclassification. Annex A provides details.

Some of the equations were clearly less robust than those for the major causes. They appear especially unreliable for various tropical diseases prevalent In regions that are poorly represented in the WHO database. Experts on partcur diseases were shown the regression estimates and asked about appropriate adjustments. The responses varied from acceptance of the estimates to detailed recommendations for revision, based on survey evidence of cases and case-fatality rates and on individual judgments. Since the recommendations sometimes appeared considerably at variance with reported deaths and regression estimates, they are presented and discussed separately.

# Results

The demographic background and projecied trends in overall morlity will be described, and then estimates of current mortality by canse will be presented. Next, trends in the major causes of death will be considered, and patterns of causes of death by age and sex will be discussed.

## _lhe demographkc background_

Population estimates and vital rates for each of the six regions, drawn from the Bank projections, are provided in Annex B. As will be shown, demographic variables contribute to important dhanges in cause of death dist butions. For instance, the population in deveilping countries over 45 is projected to more than double between 1985 and 2015, rising from 17 to 24 percent of the population. Causes of death, which
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## Table 5

# Persistence of policies and growth performance

Cross-section Correlation | Cross-Section Correlation between 1960s and 1970s between 1970s and 1980s

<!-- Start of picture text --> Per capita Growth .26 32<br>Black market premium 62 45<br>Inflation .40 41<br>Labor force growth -70 79<br>Government Consumption/GDP 76 64<br>Government Education Spending/GDP 75 74<br>Government Investment Spending/GDP 49<br>Real interest rates 48 32<br>Trade orientation’ 56<br><!-- End of picture text -->

- Comparing 1973-85 with 1963-73, based on 1987 WDR List of four categories: 1. strongly outward oriented

2. moderately outward oriented 3. moderately inward oriented 4. strongly inward oriented However, government investment, trade orientation, inflation, the black market premium, and real interest rates are not very persistent (although still more persistent than growh).” We will examine the ability of policies to explain the low growth persistence by examining the standard errors of pooled time-series cross-section regressions that use decennial averages for growth rates and policy variables.

Random error. A stochastic growth model, such as that of Rebelo (1991a), implies that growth will be given by a component dependent on policies and a stochastic term reflecting random shocks (such as those to the rate of return to capital). The policy component itself is less S2Most of these "policy" measures contain endogenous elements. However, it seems plausible that cross-country variation in, for example, inflation, black market premia, and real interest rates is mainly due to policy choices on money creation, exchange rate controls, and interest rate controls, respectively.
Policy Research Working Paper 6024

# **Abstract**

This paper investigates the impact of increases in wheat flour prices on household food security using unique nationally-representative data collected in Afghanistan from 2007 to 2008. It uses a new estimator, the Unconditional Quantile Regression estimator, based on influence functions, to examine the marginal effects of price increases at different locations on the distributions of several food security measures. The estimates reveal that the negative marginal effect of a price increase on food consumption is two and a half times larger for households that can afford to cut the value of food consumption (75<sup>th</sup> quantile) than for households at the bottom (25<sup>th</sup> quantile) of the food-consumption distribution. Similarly, households with diets high in calories reduce intake substantially, but those at the bottom of the calorie distribution (25<sup>th</sup> quantile) make very small changes in intake as a result of the price increases. In contrast, households at the bottom of the dietary diversity distribution make the largest adjustments in the quality of their diets, since such households often live at subsistence levels and cannot make large cuts in caloric intake without suffering serious health consequences. These results provide empirical evidence that when faced with staple-food price increases, food-insecure households sacrifice quality (diversity) in order to protect calories. The large differences in behavioral responses of households that lie at the top and bottom of these distributions suggest that policy analyses relying solely on ordinary least squares estimates may be misleading.

This paper is a product of the Economic Policy and Poverty Sector, South Asia Region. 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 authors may be contacted at adsouza@ers.usda.gov and djolliffe@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
# **<u>2. Context and Experiment Design</u>**

The experiment was carried out in collaboration with the Pakistan Poverty Alleviation Fund (PPAF), the National Rural Support Program (NRSP), and the World Bank. ECI, a local firm that specializes in capacity building activities for micro entrepreneurs, designed the business training modules, trained NRSP staff and was a key partner during all phases of field implementation. Baseline and follow up data were collected by Research Consultants (RCons).

PPAF is an apex institution created in 2000 with World Bank funding that provides capacity building and funding to numerous partner microfinance institutions and NGOs. More than half of its funds, however, go to the Rural Support Programs of which NRSP is by far the largest.<sup>6</sup> PPAF funding has allowed NRSP to grow rapidly over the past decade. It is now present in 51 districts and is considered the second largest microcredit provider in Pakistan.

NRSP provides uncollateralized microloans to individual clients who are required to become members of a community organization (CO). COs typically have between 5 and 30 members that meet regularly and contribute towards individual and group savings. Meetings are also used to make loan payments. Besides credit, NRSP offers training in vocational skills and provides up to 80 percent financing for infrastructure projects in the village. Due to budgetary constraints, only 8.2 percent of the members in our sample had received training prior to the baseline. NRSP records indicate that over the past decade, it has organized more than a million poor households into a network of more than 100,000 COs across the country. In principle, all loans have a joint liability clause at the CO level, but it is seldom enforced. In practice, new loans are often issued to members who belong to a CO with overdue loans.<sup>7</sup> NRSP has three main credit products: a single installment loan for agricultural inputs (fertilizer, seeds, etc) with maturity of 6 to 12 months; an enterprise loan of up to

> 6 Established in 1991, NRSP is modeled after the Aga Khan Rural Support Program, established in the early 1980s as a not-for-profit rural development organization. NRSP, along with Khushali Bank and Kashf Foundation, accounts for approximately 70 percent of the sector’s active clients according to MicroWatch, 2008. In 2010, NRSP also obtained a microfinance bank license and began operating as a bank in March 2011, after the study had concluded.

> 7 Borrowers are required to find two guarantors, who can be members of the same CO. NRSP appears to use guarantors as a means of exerting peer pressure, rather than enforcing repayment from them.

6
and have stronger GVC participation of natural-resource intensive sectors. Importantly, countries with stronger institutional quality have a comparative advantage in exports and in GVC participation of contract-intensive sectors. Importantly, conditioning on countries’ factor endowments, trade policies play an important role for GVC participation and exports. Sectors that face lower tari¤s in their destination markets or lower tari¤s on intermediate inputs exhibit stronger GVC participation and exports.

To compare the strength of di¤erent determinants, Figure 2 shows the standardized beta coe¢ cients corresponding to the estimates in Table 8. Hypothetical examples are useful to provide an economic interpretation to the coe¢ cients. If Ghana increased its skilled labor share (7.5 percent in 2012) to the cross-country median (20 percent), its backward GVC participation level and exports would grow by 42 percent, and its forward GVC participation level would grow by 39 percent at the sample mean of sectoral skill intensity. If Mozambique improved its rule of law index to the cross-country median, its forward GVC participation and export levels would grow by 32 percent, and its backward GVC participation would grow by 29 percent at the sample mean of sectoral contractual intensity.

Two robustness checks are performed on the cross-country cross-sector analysis. First, we add country-sector level FDI to vector Z. Given that, to the best of our knowledge, country-sector-year level FDI in‡ows data are not available for a wide range of countries, we use FDI announcement data from the fDi Markets Database for the period 2003-2015 as a proxy for FDI in‡ows, following the approach of Hallward-Driemeier and Nayyar (2019). Speci...cally, we cumulate the number of cross-border green...eld FDI projects announced from 2003 onwards for each country-sector. The estimates show that FDI has a positive and signi...cant marginal impact on country-sector GVC participation levels and on gross exports. Second, we show that countries with a higher internet penetration exhibit signi...cantly higher GVC participation and gross exports in sectors that use technology more intensively. These results are presented in Tables A7 and A8 of the Appendix.

Overall the results from this di¤erence-in-di¤erences framework based on the crosscountry cross-sector regressions are consistent with the previous results based on IV crosscountry regressions, highlighting the importance of endowments, institutional quality, tari¤ and FDI, and connectivity in determining GVC participation.

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# **Table 20: Cross-country covariates associated with deposit account penetration**

This table summarizes the results of OLS regressions of the (logarithm of) number of accounts per thousand adults in commercial banks on different country characteristics. Each column in each panel represents the result of one regression. The definition of each variable is shown in Appendix 1. Robust standard errors are in parentheses. ***= significant at the 1% level; ** = significant at the 5% level; * = significant at the 10% level.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.68***|0.66***|0.75***|0.63***|0.62***|0.40***|0.21*|0.69***|0.63***|0.66***|0.42***|0.39***|
||(0.06)|(0.06)|(0.08)|(0.06)|(0.06)|(0.12)|(0.10)|(0.11)|(0.06)|(0.06)|(0.08)|(0.09)|
|Population Density (log)||0.15*|0.35***|0.14|0.16*|0.13*|0.13*|0.16|0.15*|0.14|0.14**|-0.30*|
|||(0.08)|(0.10)|(0.08)|(0.08)|(0.07)|(0.07)|(0.12)|(0.08)|(0.08)|(0.06)|(0.15)|
|Gini|||0.00<br>(0.01)||||||||||
|Inflation||||-0.02***<br>(0.01)|||||||||
|Deposit Insurance|||||0.34**<br>(0.16)||||||||
|Electricity consumption||||||0.25**<br>(0.13)|||||||
|Phone lines|||||||0.47***<br>(0.11)||||||
|Road density||||||||-0.06<br>(0.13)|||||
|Absence of violence|||||||||0.09<br>(0.10)||||
|Offshore||||||||||0.20<br>(0.31)|||
|Branches per adult (log)<br>|||||||||||0.45***<br>(0.14)||
|Branches per km<sup>2</sup>(log)||||||||||||0.44***<br>(0.13)|
|Constant|0.90|0.37|-1.13|0.81|0.49|0.75|3.05***|0.29|0.63|0.44|1.33**|3.78***|
||(0.54)|(0.69)|(1.34)|(0.71)|(0.66)|(0.75)|(0.80)|(1.02)|(0.69)|(0.72)|(0.59)|(1.08)|
|N<br>|85|85|38|81|84|72|84|59|85|85|83|83|
|R<sup>2</sup>|0.64|0.67|0.67|0.70|0.68|0.64|0.75|0.71|0.67|0.67|0.72|0.72|

54
et al., 2016). For example, Gibbs et al. (2013); Bitler et al. (2014); Kline and Walters (2016) use data from the large US Head Start public preschool program and find that the initial cognitive impact fades by the first grade, even though the long-term impacts of the program are positive and significant (Bailey et al., 2021). Some studies offer a solution to this puzzle considering the process through which skills form and develop and highlight the importance of skill formation as a multi-skill dynamic process in which different skills complement each other (Heckman et al., 2010; Conti et al., 2016; Garcıa and Heckman, 2014). We find evidence against medium-term fadeout by showing that an ECD program with the characteristics and components of DICIPE can positively impact medium-term outcomes such as children’s primary school performance and cognitive skills, at least in communities under highly deprived conditions.

The paper is organized as follows. Section 2 describes the intervention. Sections 3 and 4 provide details on the sampling, randomization, and data collection for the study. Section 5 describes our estimation and inference methods. Section 6 shows our main results on the effectiveness of the program in improving school-age outcomes, our heterogeneity analysis, and intra-household spillovers. Section 7 shows robustness checks. Finally, Section 8 concludes.

# **2 The DICIPE Program: Components and Implementation**

The Mozambique government launched the Early Childhood Development Program (DICIPE) as a national strategy aimed at expanding access to quality Early Childhood Development (ECD) services for children aged 3 to 5 years in rural communities in ten districts in five provinces where no preschool services existed before.<sup>5</sup> A relevant novelty of the DICIPE program is that its components and delivery mode offer the potential for scalability and sustainability, especially when government implementation capacity is limited.

> 5 The program was designed following a successfully tested community-based ECD pilot run by Savethe-Children in the Gaza province of Mozambique. Martinez et al. (2012) evaluates this pilot and finds that the intervention participants were more likely to enroll in primary education at the right age and performed better on measures of school-readiness at age 6.

6
(with the exception of upper arm circumference in the baseline) tended to be lower than the population mean using the WHO standard (i.e., have negative _z_ -scores). All of these measures decreased between the baseline and the endline, reflecting the aging of the cohorts and the growth faltering that occurs during the first two years of life (Victora et al., 2010). There are no statistically significant differences in the anthropometric measures between the treatment and control groups.

# _8.9 Child Development_

Both the baseline and endline surveys included an “Ages and Stages” questionnaire, which measures child development across five domains: communication, gross motor skills, fine motor skills, problem solving, and personal-social development. At the baseline this questionnaire was administered to parents or caregivers of children ages 3 – 24 months. In the endline survey it was extended to children up to 60 months of age. This report focuses on the communication, motor skills, and personal-social development. These measures are of interest because a child’s health status might affect his or her development. The questions asked about each child were selected according to the child’s age in months. To make a comparison, the child development index for each skill is standardized by calculating _z_ -scores.<sup>19</sup> Table 8b shows greater improvement in communication (significant at the 10 percent level) for children in treatment communities.

To summarize, a comparison of raw outcomes in treatment and control communities shows greater toilet construction in treatment communities and declines in open defecation. In line with this, there is evidence of decreases in diarrhea prevalence in treatment communities, relative to control communities. The prevalence of mucus or blood in the stool has also decreased. Health outcomes further along the causal chain appear to have been largely unaffected by the TSSM program.

# **9. ESTIMATION STRATEGY AND RESULTS**

In this section, we estimate the “intention-to-treat” (ITT) of program impact. The ITT approach estimates the program impact from a comparison of those communities to which the program has been offered, regardless of whether they actually receive it, with control communities. The ITT allows us to determine a program’s average impact on the population it is targeting and is often used in the context of randomized controlled trials (Duflo et al., 2008). The ITT estimator is computed by comparing the mean outcome for the treatment group to that for the control group, which can be estimated using the following regression equation:

> deviation of weight for children of a given gender and height. The WHO standards use an international reference population.

> 19 _Z_ -scores standardize the measures so they have a mean of zero and a standard deviation of 1. The different measures are thus expressed in the same scale and so can be compared across different age groups.

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Table 7 Costs of **Some** of Brazil's Nutrition Programs. **1978-80**

|**Proexaw**NaMe<br>PNS Model A|Descrintio<br>Food As %<br>Total<br>Cost<br>Food distribution<br>25|Unit Cost<br>(cruzeiro<br>per<br>kgi|UnitCost<br>C<br>(cruzeiro<br>per<br>000cals)|ost/Beneficiary/Year<br>(cruzeiro)|
|---|---|---|---|---|
|<br>PNSModel B|<br><br>via health posts<br>73|10- 27|2.8- 8.0|595*|
|PCA<br>PINSModel A|Distribution of<br>86<br>special weaning foods<br>Food subsid<br>91|100<br>37|22-26<br>|1,460*|
|<br>PINS Model B<br>PINS Model C|y<br> <br>experiment<br>66<br>67|<br>33<br>83|10<br>9<br>|*|
|<br>PINS Model D|<br>80|<br>22|23<br>|29|
|<br>PINS Average|<br>--|<br>31|6<br>9||

Source: Musgrove (19X9). Conversion to cruzeiro/kg by author using data in Musgrove (1989). The PNS program distributes food via health posts. Model A includes the cost of complementary health services, B excludes them. The PCA distributes food via community centres. The PINS operated a subsidy experiment, Model A had a 60% subsidy, B a 45% subsidy, but participants had to undertake regular monitoring to be eligible. Version C had a 45% subsidy, Version D a 30% subsidy.

*Note, these data are not standardized by size of ration. PCA provides a smaller quantity of food.