rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7864

# **Abstract**

Despite record economic growth for more than a decade, poverty has remained stubbornly high in Afghanistan, especially in the regions that suffered less from conflict. This paper aims to explain this paradox by combining a model of conflict intensity at the province level over period 2007–14 with a model of consumption at the household level in 2011. Provincial data show that higher levels of conflict were positively correlated with both a larger presence of troops (international and Afghan) and larger aid flows. Household data show that the negative impact of conflict on consumption was more than offset by the positive impact of aid and troops. According to the estimates, Afghan troops contributed more to poverty reduction than international troops, possibly because they spent more locally. The paper uses the estimated models to conduct an out-of-sample validation exercise, focusing on the transition initiated in 2014. The results should be interpreted with caution, as the quantitative models cannot account for strategic shifts in the insurgency and watershed political developments. But they suggest that the reduction in the number of international troops and declining foreign aid flows led to an increase in conflict intensity and a decline in consumption per capita, matching current trends.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Office of the Chief Economist, International Finance Corporation. 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 gacevedo@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
found that point-of-use water treatment is among the most effective WASH interventions to reduce diarrheal disease incidence (Fewtrell et al 2005; Clasen et al 2007; Wolf et al 2018). Further, a metaanalysis examined 14 studies on various WASH interventions (improved microbiological quality of drinking water, water quantity and supply, sanitation coverage, handwashing, and child feces disposal) and found that interventions with solar disinfection of water, provision of soap, and improvement of water quality) had some benefit on the linear growth of children under 5 (Dangour et al 2013). However, two recently published RCTs evaluating the effectiveness of chlorination tablets in rural Bangladesh and Kenya found that the water quality intervention had no impact on diarrheal disease incidence or anthropometric measures (Luby et al 2018; Null et al 2018). The conclusions of these studies do not necessarily imply that there is no link between contaminated water and nutrition, but more likely that the interventions did not effectively limit exposure to feces in a sustainable manner. Further research is needed in understanding the relationship between pathways and quantities of fecal exposure and nutritional status of children.

# 3. Methodology

## **3.1     Data**

We use cross-sectional data from the 2012-13 MICS survey for Bangladesh, a nationally representative survey with various indicators on household characteristics across urban and rural regions of Bangladesh. The 2012-2013 MICS is the first and latest national representative survey that provides water quality data on E. coli contamination in drinking water sources in Bangladesh. Globally, there are very few other data sets that examine microbial water quality indicators on a national scale. We particularly examine indicators on E. coli contamination in drinking water and childhood stunting. The independent variables include E. coli contamination at point-of-source (e.g. water quality results from the household’s main reported source of drinking water) and E. coli contamination at point-ofconsumption (water quality results from a cup of drinking water prepared by a household member). The dependent variable, childhood stunting, is based on linear growth measure of height-for-age z- scores that are two standard deviations below the WHO reference population.

## **3.2    Empirical Strategy**

This analysis uses a probit regression model to determine the probability of stunting when children are exposed to E. coli in drinking water at varying risk levels and different collection points (source and consumption).

6
residual normality. Finally, coefficient signs, significance, and size are robust (see Table A.11).

Lastly, we consider alternative measures of terms of trade and productivity differentials (see Table A.12). Real soybean prices are correctly signed and significant at the 10% level; however, no cointegrating relation is detected and moreover agriculture-to-nontradables productivity differentials loses significance (column 2). Aggregate productivity differentials, as measured by real GDP per capita vis-à-vis main trade partners, yields no cointegrating relation and takes the wrong sign (column 3). Sectoral productivity differentials perform better, once we exclude China (columns 1, 6-8).<sup>42</sup> We obtain the best specification to capture the Balassa-Samuelson effect uses agriculture relative to nontradables labor productivity. This reflects the importance of the agricultural sector as the main source of exports in Argentina. Results are robust to all three measures of the nontradables sector (columns 1, 7-8).

The annual analysis suggests the peso was overvalued by 15% on average in 2014 (see Appendix Figure A.5). The monthly analysis allows to shed light on the appreciation experienced by the peso during 2014, following the nominal devaluation that occurred at the beginning of the year. According to all specifications, the overvaluation in December 2014 exceeded the year average. This appreciation continued in 2015, leading to increased measures of overvaluation as of October.

The series of robustness checks support our assessment that the peso is strongly overvalued, as of October 2015.

<u>Table 4: Adj-REER Overvaluation in 2014 – Monthly Analysis</u><sup>43</sup>

|<br>**Adj-REER**|A|verage 201|4|D|<br>ecember 2|<br>014|<br>|<br>October 20|15|
|---|---|---|---|---|---|---|---|---|---|
|**Overvaluation(%)**|VEC|DOLS|FMOLS|VEC|DOLS|FMOLS|VEC|DOLS|FMOLS|
||||||No HP filt|er||||
|CPI/PPI<br>||||||||||
|Official CPI/PPI (upper bound)|-3|24|23|36|46|45|62|78|76|
|Adjusted CPI/PPI|0|12|11|22|28|28|49|58|57|
|Large adj. CPI/PPI (lower bound)|-4|-6|-6|0|0|0|26|25|25|
|IP-differential||||||||||
|Official IP|-|13|16|-|24|21|-|49|40|
|Adjusted IP|-|14|18|-|26|24|-|54|49|
||||||HP filter|||||
|CPI/PPI||||||||||
|Official CPI/PPI (upper bound)|6|22|21|20|39|38|46|69|67|
|Adjusted CPI/PPI|1|9|8|14|23|23|39|49|49|
|Large adj. CPI/PPI (lower bound)|-11|-12|-11|1|0|1|21|20|21|
|IP-differential||||||||||
|Official IP|-|8|9|-|23|22|-|48|46|
|Adjusted IP|-|10|12|-|24|25|-|49|49|

> 42 Including China, agriculture-nontradables productivity differentials is insignificant but correctly signed (column 4), whereas the sum of agriculture and industry (tradables) relative to nontradables is incorrectly signed (column 5). Moreover, both affect the terms of trade significance. 43 Estimated overvaluation for the VEC specifications using industrial production is not reported due to aforementioned evidence of model misspecification.

23
# **Introduction**

The returns to schooling in Argentina increased over the last decade. The overall rate of return to an additional year of schooling increased from 8.6 percent in 1992 to 11.4 percent in 2002 (Giovagnoli, Fiszbein and Patrinos 2005). This represents a 32 percent increase in 10 years, a large increase as compared to most countries (Psacharopoulos and Patrinos 2004).

The returns to primary schooling remained unchanged during this decade, as did the (already low) returns to incomplete secondary schooling (Table 1). The returns to complete secondary education increased, but not nearly by as much as did the returns to university education – complete or incomplete. While complete university education has a high rate of return, the rate of return for incomplete university education increased at a faster rate in the 1992-2002 period. For example, in 1992, the highest private returns were for primary schooling. By 2002, the highest returns were for complete university education, followed by incomplete university.<sup>1</sup> Table 1: Argentina: Private Rates <u>of Return to Schooling by Level</u>

|Education Level|1992|2002|
|---|---|---|
|Primary versus none|10.7|10.6|
|Secondary incomplete versus primary|6.8|6.7|
|Secondary versus primary|8.5|9.2|
|Incomplete university versus secondary|6.9|11.1|
|Complete University versus secondary|9.2|13.8|

Source: Giovagnoli, Fiszbein and Patrinos 2005

> 1 In April 2002, Argentina launched a social safety net program to alleviate the impact of rising unemployment due to the sharp worsening of the economic crisis.  The _Jefes de Hogar_ program provides a stipend of 150 Argentine _pesos_ a month to an unemployed head of a household in exchange for participation in four hours of work in community services, small construction or maintenance activities, or training, including finishing basic education, or as a temporary employee of a private company.  Program beneficiaries are considered as officially employed, so they are included in the analysis.  However, taking them out, which means a 7 percent reduction of the sample, mostly concentrated with less than secondary schooling, does not affect the results very much.  The overall returns to schooling increase slightly.  But the basic findings remain the same.  See Appendix for results without _Jefes_ participants.

- 2 -
# **Table 5. Robustness check, varying the window around the discontinuity**

||Dependent variable:<br>incumben|Share of votes for<br>t in 2006|
|---|---|---|
||Window of 30mm|Window of 20mm|
||(1)|(2)|
|Below Cutoff|6.516|6.874|
||(2.57)*|(2.72)**|
|Rain Deviation|0.116|0.142|
||(0.75)|(0.93)|
|Constant|52.325|48.997|
||(4.69)**|(4.46)**|
|Observations|810|766|
|R-squared|0.88|0.84|
|State controls|Yes|Yes|
|Controls at electoral section level|Yes|Yes|
|Controls at municipal level|Yes|Yes|
|Mean of dependent variable|49.21|53.28|

Robust standard errors in parentheses State controls are dummy variables for each state. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000.

* significant at 10%; ** significant at 5%; *** significant at 1%

Page **27** of **29**
Lastly, the annual growth rates for output in the initial steady state are set equal to 004 percent, based on Morocco’s average growth rate over the period 2006-13.

# 6 Mitigating Gender Bias in the Marketplace

As stated earlier, the focus of our analysis in this paper is the impact of anti-discrimination policies aimed at mitigating gender gaps in the labor market. In recent years such policies have been put in place in a number of countries (including Morocco, see United Nations (2016, Chapter 6)) but compliance with them has not been enforced as tightly as the law requires. Moreover, how these policies operate, and how they ultimately affect gender equality and economic growth have not been examined quantitatively. We begin with a discussion of our core experiment and then report some sensitivity analysis The next section will consider the case where policies aimed at mitigating gender bias in the marketplace are combined with other types of gender-based policies.

## 6.1 Core Experiment

Our core experiment involves a 4 percent autonomous increase in the gender bias parameter, , through a higher scale parameter 0, as a result of stronger enforcement of anti-discrimination laws against pay differences in the workplace.<sup>22</sup> The results are shown in Table 3. The first column in the table presents baseline values for a number of key variables, including not only the initial steady-values reported in Table 2 but also values for some additional indicators, such as the family-wide preference for current consumption and gender employment ratios. The second column shows deviations from the baseline and captures therefore the general equilibrium effects of anti-discrimination policy.

Intuitively, a reduction in the gender wage gap raises (at the initial level of the minimum wage) the female skilled-unskilled wage ratio. In turn, an increase in that ratio induces more women to invest in training, implying therefore that the supply of female skilled workers increases, whereas the supply of female unskilled workers falls. This increase in supply lowers skilled wages, which stimulates the demand for female skilled labor. Through gross complementarity, the demand for male skilled labor rises

> 22The magnitude of this increase is of course somewhat arbitrary but it is sufficient to illustrate our purpose. In all experiments, subjective employment probabilities are kept constant.

24
<mark>up low paid jobs in the informal service industry. This may imply that the children of these mothers had less exposure to their mothers’ care during a period when education and health services were deteriorating. This provides a lead for future research into the mechanisms that may explain why children of mothers who lived under a refugee crisis at an early age have worse nutritional outcomes as compared to other children. It should also be recognized that, as for other similar studies, our results can still be contaminated by some form of unobserved heterogeneity. For example, while we control for local and geographical proximity, macroeconomic effects may not operate homogeneously within clusters and may affect children cohorts differently.</mark>

# **7.** **<mark>Conclusions</mark>**

<mark>The main purpose of this paper was to empirically assess how mothers’ exposure to refugees during their early childhood (0-5 years old) translates into lower health outcomes of their offspring born some 15-20 years after the shock. We used the latest 2015/2016 Tanzania Demographic and Health Survey to study the intergenerational health impacts of mothers on the Height for Age Z score and the probability of stunting of their children born 15 years after the massive arrival of refugees. By exploiting spatial and temporal variation in mothers’ exposure to the refugee shock, we find that almost two decades later, children of mothers who were less than 5 years old at the time of the influx and living closer to the 13 main historical refugee camps are more likely to be stunted.</mark> <mark>As key mechanisms, our paper hypothesized that the positive economic growth generated by the refugee influx resulted in increased female labor market participation during the refugee crisis that, combined with deteriorating education and health services, has had adverse effects on child care, nutrition and morbidity. The poor nutritional status of children at the time of the crisis might potentially translate into lower anthropometrics of their own children some 15 years later. In an attempt to identify key mechanisms, we found an increased participation in the labor market of mothers who spent their first five years of life in the presence of refugees. This might explain lower anthropometrics of their own under five years old children in line with the microeconomic literature on the negative effects of maternal employment on young children’s development (see e.g. Baum II, 2003; G</mark> regg et al., 2005). <mark>We also found that those mothers who were between 0 and 5 years old at the time of the refugee shock are more likely to achieve fewer years of education today and are also less likely to own key assets such as land and house. In short, their underachievement and lower assets ownership have had a negative impact on their children’s health, as predicted by the early childhood development theory.</mark> <mark>If these findings are confirmed, they call for aid organizations to consider the impact of a refugee crisis on mothers and small children in the host community and possibly provide support to education and</mark> 22
The same is found for Mali in the 75 km bandwidth, except that here the border is farther away from Abidjan than from Bamako by approximately 300 km (top panel of table A.1). On both sides, as seen in figure 1, very few PSUs are found on the western part, which is underpopulated, probably due to the prevalence of parasitic diseases. Again, withdrawing the two or three most western PSUs (above longitude 7<sup>_◦_</sup> W) is innocuous. When enlarging the bandwidth to 100 km, additional Ivorian PSUs are found to lie relatively farther away from the border in the South. Their inclusion produces a significant “matching RD” discontinuity in rainfall. This leads us to prefer the narrower 75 km bandwidth.

Table 1: Geographical variables at the border of Cˆote d’Ivoire with Ghana

||Borde|r RD|Polyno|mial RD|Match|ing RD|
|---|---|---|---|---|---|---|
|Bandwidth in km:|50|75|50|75|50|75|
|Relative PSU weights<sup>_a_</sup>|-0.25|-0.14|-0.35*|-0.19|-0.06|-0.13|
||(0.20)|(0.18)|(0.18)|(0.18)|(0.41)|(0.30)|
|Rainfall (mm per day)<sup>_b_</sup>|+.028|+.012|-.008|-.011|-.011|-.005|
||(.020)|(.021)|(.006)|(.007)|(.006)|(.005)|
|Elevation (meters)|+25|+25|+20|+19|+0|-1|
||(37)|(25)|(32)|(23)|(22)|(16)|
|Dist. nearest river (km)|-1.5|-1.1|+4.0|+3.5|+0.1|-1.5|
||(8.9)|(6.6)|(6.4)|(5.3)|(5.4)|(4.0)|
|Dist. city 5,000+ 1960 (km)|+14.1|-3.7|-3.2|-8.3|+6.9|+0.2|
||(16.0)|(15.7)|(20.0)|(16.9)|(30.3)|(23.4)|
|Dist. city 5,000+ 1990 (km)|-6.3|-13.5|-18.1|-21.3|-5.9|-10.4|
||(11.0)|(8.8)|(11.9)|(12.8)|(15.1)|(11.9)|
|Pop. density 1990 (inh./km<sup>2</sup>)|-5.9|-17.5|+3.7|-17.7|-6.3|-4.4|
||(11.2)|(26.3)|(10.3)|(12.9)|(7.6)|(9.7)|
|Dist. capital city (km)|-131***|-136***|-150***|-153***|-137***|-140***|
||(14)|(14)|(13)|(13)|(22)|(19)|

_Source_ : Authors’ analysis based on data described in the text. _Coverage_ : PSUs in the bandwidth window (50 or 75 km from the corresponding border). _Notes_ : See equations (2), (3), and (4) for each estimator. For “Border RD” and “Matching RD”, the only control variable is latitude. a: Probabilistic weights with means standardized to one on each side of the border. Non-weighted estimates. b: Average over the 1984–2001 period. Positive numbers indicate differences in favor of Cˆote d’Ivoire. Standard errors in parentheses. ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 In both the Ghana and Mali cases, the Cˆote d’Ivoire side is slightly more urbanized. Although not significantly so, Ivorian localities lie closer to the regional 17
**Table 1. Human Development Opportunity Indicators for Egypt, 2000 and 2009**

|_Category_|_Label_|_Definition_|
|---|---|---|
||Complete primary education on<br>time **|Completion of below intermediate education on time<br>(primaryschooling)|
|Education|Complete secondary education on<br>time **|Completion of intermediate education on time (general<br>or technical secondary schooling).|
||School attendance, 9-15 **|Attended school, 9–15|
||Water **|Access to clean water without interruption,, 0–17|
||Sanitation **|Access to adequate sanitation, 0–17|
|Basic<br>housing|Lighting energy source **|Access to adequate lighting energy source, 0–17|
|<br>services|Cooking energy source **|Access to adequate cooking energy source, 0–17|
||Non-overcrowding, 0-5 **|Children under 5 in non-over-crowded homes|
||Telephone|Access to a telephone, 0–17|
||Assisted birth delivery*|Access to institutionally assisted birth delivery, 0-4|
|Early<br>|Post-natal care, 0-5*|Access to adequate post-natal care, 0-5|
|childhood|Prenatal care, 0-4*|Access to prenatal care for children under age 4|
||Immunization vaccines, 0-4*|Access to complete vaccinations, children under age 4|
||Non-wasting, 0-4 *|Adequate nutrition by weight-for-height measures for<br>children under age 4|
|Nutrition and<br>hunger|Non-stunting, 2-17*|Adequate nutrition by height-for-age measures for<br>children ages 2–17|
||Non-underweight, 0-17*|Adequate nutrition by weight-for-age measures for<br>children 0–17|

* Available from the EDHS 2000 and 2008 surveys. ** Available from the CAPMAS HIECS 2000 and 2008.

_Nutrition and Hunger._ This group contains three opportunities based on nutrition: non-wasting for children age 0-4, non-stunting for children age 2-17, and non-underweight for children age 0-17. The nutritional indicators measure correct diet and growth. The first indicator, non-wasting or “weight-for-height,” measures the nutrition of children age 0-4. The second, non-stunting, or “sizefor-age” indicator captures _chronic deficits_ in a child’s nutrition and health; it is a _long-term_ indicator. The third, non-underweight or “weight-for-age” indicator captures both short and longterm problems and reflects body mass in comparison to age. It is the most widely used indicator for the measurement of this kind of nutrition problem (WHO, 2007).

The specific measure for each child is compared with a distribution of the same variable for a “healthy” sample, following the WHO Child Growth Standards. For each individual indicator, z- scores are computed as the difference between the measure of the individual and the median of the reference population, divided by the standard deviation of the reference population. WHO classifications of malnutrition are applied: mild (z-score ≤ -1), moderate (z-score ≤ -2), and severe (z-score ≤ -3).<sup>15</sup> Access to the opportunity of nutrition is assigned to all children with _z-scores_ greater than _-1._

> 15 More specifically, the routines ANTHRO and ZANTHRO are applied with statistical software. See O’Donnell, Doorslaer, Wagstaff and Lindelow (2008) and WHO (2007).

9