rafmacalaba/datause-encoder-data huggingface dataset:
# **SUPPLEMENTAL APPENDIX**

Excluding the rural population: the impact of public expenditure on child

# malnutrition in Peru

|**Contents**|
|---|
|**S1 Peruvian Mining Context 3**|
|**S2 Conceptual Framework 10**|
|S2.1 The Model................................................................................................10|
|S2.2 Complementarities between public and private assets ............................................11|
|**S3 Data Sources 13**|
|S3.1 The Design of the Young Lives Study...............................................................13|
|**S4 Validity Checks 16**|
|S4.1 Instrument’s validity.....................................................................................16|
|S4.2 Changes in the composition of regional public expenditure.......................................18|
|S4.3 Assessing the instrument’s strength for the selected spending categories........................21|
|**S5 Alternative Interpretations of the Results 22**|
|S5.1 Differences in public spending quality between urban and rural areas..........................22|
|S5.2 Exclusions at work.......................................................................................24|
|S5.2.1 The impact of public spending according to child resilience..........................24|
|S5.2.2 The impact of public spending according to child’s initial nutrition status.........25|
|S5.2.3 The impact of public spending according to maternal mother tongue...............25|
|<br>S5.2.4 The impact of public spending according to household wealth level................26|
|**References 27**|

1
5 TABLE 2: GRAIN PRODUCTION AND POPULATION GROWTH, 1970-82

|Region|Grain<br>production|Population|Grain<br>production<br>per capita|
|---|---|---|---|
|World|2.3|1.8|0.5|
|Industrial economies|2.5|0.7|1.6|
|East Africa|0.8|3.0|-2.2|
|West Africa|1.9|2.7|-0.8|
|East Asia and Pacific|3.5|1.7|1.8|
|South Asia|2.7|2.4|0.3|
|Latin America|3.2|2.4|0.8|

Source: Poverty and Hunger. 1986. Washington, D.C.: World Bank.
# **Namibia**

||**Prevalence amon**|**g females 15-24(%)**|**Spec**<br>**Incidence in**|**trum estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**adults 15-49**<br>**(%)**|**Antiretroviral coverage**<br>**(%)**|
|**1990**|-|-|0.49|-|
|**1991**|-|-|0.72|-|
|**1992**|-|-|1.02|-|
|**1993**|-|-|1.39|-|
|**1994**|-|-|1.78|-|
|**1995**|-|-|2.15|-|
|**1996**|-|-|2.44|-|
|**1997**|-|-|2.64|-|
|**1998**|-|-|2.70|-|
|**1999**|-|-|2.64|-|
|**2000**|-|-|2.50|-|
|**2001**|-|-|2.29|-|
|**2002**|-|-|2.03|-|
|**2003**|-|-|1.78|-|
|**2004**|15.2|-|1.53|22|
|**2005**|-|-|1.31|62|
|**2006**|14.2|-|1.12|64|
|**2007**|-|-|0.96|88|
|**2008**|10.6|-|0.88|-|
|**2009**|-|-|0.81|78|
|**2010**|10.3|-|0.78|90|
|**2011**|-|-|0.82|>95|
|**2012**|-|-|0.81|90|

ANC data source: Namibia UNGASS Report 2010

||**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trialpopulation**|**Region**|
|---|---|---|---|---|
|Aulagnieretal.,PLoS One2011|2.4|2007-2009|Adultgeneralpopulation|Windhoek|
|ALPHA network study|||||

# **Nigeria**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.22|-|
|**1991**|-|-|0.26|-|
|**1992**|-|-|0.30|-|
|**1993**|-|-|0.34|-|
|**1994**|-|-|0.39|-|
|**1995**|-|-|0.43|-|
|**1996**|-|-|0.48|-|
|**1997**|-|-|0.51|-|
|**1998**|-|-|0.53|-|
|**1999**|-|-|0.56|-|
|**2000**|-|-|0.55|-|
|**2001**|6.0|-|0.54|-|
|**2002**|-|-|0.51|-|
|**2003**|5.3|-|0.49|-|
|**2004**|-|-|0.45|2|
|**2005**|4.3|-|0.40|6|
|**2006**|-|-|0.37|13|
|**2007**|-|-|0.34|26|
|**2008**|4.2|-|0.31|-|
|**2009**|-|-|0.28|25|
|**2010**|4.1|-|0.27|27|
|**2011**|-|-|0.26|30|
|**2012**|-|-|0.23|32|

<u>ANC data source: 2010 Nigeria National HIV Sero-prevalence Sentinel Survey</u> 59
compared to one having only up to primary education. While females are marginally less likely (3 percent) to own a formal account or have formal savings, the probability of obtaining credit is 15 percent less likely for them compared to males. This is in line with findings by Fay and Williams (1993), and Alibhai et al. (2019) which indicate the credit bias faced by women. Women are 11 percent less likely than men to use digital cards. Thus, being an educated and formally employed man is more favorable in using formal finance.

# **4.2. Factors That Drive Informal Financial Inclusion**

Expanding further, the model was used to examine determinants of informal savings and credit. Table 3 shows the results comparing both formal and informal use of finance as well as aggregate use of same.

Table 3. Determinants of informal finance

||Formal<br>Savings|Informal<br>savings|Saved in the<br>past 12<br>months|Formal<br>Credit|Informal<br>credit|Obtained a<br>loan in the<br>past 12<br>months|
|---|---|---|---|---|---|---|
|Female|-0.0355***|0.0576***|0.0318**|-0.1515***|0.1167***|0.0191|
||[0.0128]|[0.0221]|[0.0148]|[0.0210]|[0.0193]|[0.0146]|
|age 35-54|0.0028|-0.0021|-0.0633***|0.0402*|0.024|0.0450**|
||[0.0151]|[0.0254]|[0.0180]|[0.0238]|[0.0214]|[0.0181]|
|age 55-64|0.0149|-0.0573*|-0.0630***|0.0051|0.0268|-0.1162***|
||[0.0185]|[0.0343]|[0.0236]|[0.0347]|[0.0311]|[0.0235]|
|age 65 & above|-0.0713**|-0.0688*|-0.0958***|-0.0536|0.0104|-0.3053***|
||[0.0301]|[0.0413]|[0.0272]|[0.0524]|[0.0485]|[0.0240]|
|Secondary education|0.0920***|-0.0490*|0.1048***|0.1737***|-0.1414***|0.0262|
||[0.0176]|[0.0255]|[0.0161]|[0.0233]|[0.0191]|[0.0160]|
|Tertiary education|0.1329***|0.028|0.2329***|0.3255***|-0.3293***|0.2095***|
||[0.0244]|[0.0530]|[0.0402]|[0.0501]|[0.0497]|[0.0374]|
|Formal employment|0.0238*|-0.028|0.0614***|0.1246***|-0.0655***|0.0562***|
||[0.0132]|[0.0228]|[0.0159]|[0.0223]|[0.0200]|[0.0155]|
|Observations|2179|2,179|4,791|2268|2,268|4,791|
|Pseudo R2|0.0558|0.0068|0.0215|0.0646|0.0562|0.0514|
|Loglikelihood|-637.62312|-1453.6806|-3226.32|-1482.1384|-1275.603|-3145.495|
|Predicted probability<br>(atmeanvalues)|0.9054|0.41395|0.4504|0.4943|0.73226|0.47723|

17
Assume there are two time periods in a young child’s life: period 1 begins with conception and ends when the child enters primary school, while period 2 covers the primary school years. Further suppose that a child’s academic knowledge in period 2, _Yi_ 2 _,_ is a function ( _f_ ( _._ )) of several factors, in addition to an unobserved componet _μi_ : the stock of health prior to entering school ( _HSi_ 1); parental health investment in the child prior to primary school ( _HIi_ 1), such as nutritional intake and vaccinations; the effort the student devotes to school related activities ( _SEi_ 2); the quality of the primary school ( _Qi_ 2); parental investments in the child during school ( _PIi_ 2).

For each of the inputs described in eq. 1 there is well established evidence on the relationship with learning. Because of the self-productivity of human capital skills (Heckman and Cunha, 2007), child health before entering school is an important determinants of the health stock later in life and recent evidence shows that it a significant impact on learning outcomes.<sup>18</sup> Parental health investments might compensate or reinforce gaps in children’s endowments (Becker and Tomes, 1976), since parents might decide to invest more either on children who are in worse health in order to minimize the gap with other siblings or on those who are in better health in order to maximize the overall return. Zinc and iron supplementation in early years has been found to be beneficial for cognitive outcomes (e.g. Powell et al., 2005, Feyrer et al., 2017). Student effort, either in the form of class participation or time spent on home assignments, can contribute to improved student performance. We do not have reliable measures of time spent by the students in those activities. We do, however, have measures of child labor, that has been shown to have detrimental effects on both education attainments and learning (Ravallion and Wodon, 2000, Beegle et al., 2009, Heady, 2003) School quality - a broad term that refers, among others, to the quality of teachers (Rockoff, 2004, Chetty et al., 2014) principals (Roland G. Fryer, 2017), peers (Duflo et al., 2011b), class size (Angrist and Lavy, 1999) - is a key determinant of learning outcomes. Finally, a growing body of literature shows that parental time investments play a key role in improving child cognitive and socioemotional skills in the early years (Attanasio et al., 2018, Agostinelli and Sorrenti, 2018).

Our data allow us to test the impact of cash and in-kind transfers on each of these inputs

> 18Figlio et al. (2014), using a sample of siblings in Florida, find that birthweight has a positive constant effect on test scores throughout the entire academic life. Bharadwaj et al. (2012) exploit school and birth records from Chile and Norway to find that low-weight children who receive extra medical care at birth have higher test scores.

16
Tables 5 and 6 consider consumption mobility over the longer intervals of 1993/94– 2011/12 and 1987/88–2011/12, in an effort to enquire into longer-term welfare transitions.<sup>18</sup> A striking observation from these tables is that although poverty declined markedly over this entire period, a very significant percentage did not experience mobility out of poverty. About 43 percent of the poor in 1993/94 were still poor in 2011/12 (Table 5, panels A and B). Reading down the first column of Table 5 reveals that of the 25.5 percent of the population that was poor in 2011/12, a remarkable three-quarters (76 percent) had been poor in 1993/94. The corresponding figures for the 1987/88–2011/12 interval in Table 6 are very similar.

Thus, while mobility has risen in India, with growing numbers of the poor transitioning upwards into the category of the vulnerable (and some even graduating to secure status), those who are poor in 2011/12 are largely comprised of the long-term or chronically poor. This picture accords with a narrative of poverty decline accompanying accelerating economic growth in India, but with the poor increasingly comprising the structural long-term poor, who have been non-participants in the growth process. It is important to note that this picture is far from inevitable: one could also have imagined a growth process involving a great deal of churn, in which households escaped and fell back into their respective consumption classes, and where the poor in any one year were largely comprised of previously vulnerable and secure households. A potential concern emerging from the patterns we observe is that further poverty reduction will become increasingly difficult to achieve through a general growth process that fails to address the structural factors that prevent the chronically poor from escaping poverty.

Before concluding this subsection, we revisit briefly the finding, already noted by Dang and Lanjouw (2018), of mobility changes within the 2004/05–2011/12 interval. Tables A.3 and

> 18 Synthetic panels constructed over longer intervals are generally more tentative, as time-invariant regressors in the prediction models are less readily available, and the models accordingly have less explanatory power. Cruces et al. (2015) indicate, however, that they may not perform too badly; in their validation of the synthetic panel method with long-term panel data from Chile, they find that welfare transition estimates remain quite accurate.

22
25 TABLE 11.4 NUMBER Of VENTURE-BACKED ACOUISITIONS AND IPOs in tho U.S. ...........................................

.......................

||1981|1982|1983|1984|198S|1986|1987|1988|
|---|---|---|---|---|---|---|---|---|
||.....|.....<br>.|.....<br>|.....<br>.<br>|......<br>||.....<br>.<br>|......<br>...............................<br>|
|Acquisitions|32<br>|40<br>|49<br>|**86**<br>|101<br>|120<br>|140<br>|135<br>|
|IPOs|68|27|121|53|4?|M|81|36|

Source: Venture Capitat Journal,<sup>**May**1989.</sup>
**Figure 7: Location of commercial sorghum farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2014<br>yield6<br>2 - 12<br>13 - 17<br>18 - 22<br>23 - 28<br>29 - 83<br>Region boundary<br>Buffer around Sorghum farm (14)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 32
estimated to have increased price differences in northern states by about 6 percent, on average. Although not quantified here, other restrictions to trade within the country in the form of formal and informal tolls that are charged in check-points throughout the country to users of the road network are also likely to increase segmentation.

The welfare gains from increased market integration via road construction are large. Upgrading all roads across states to primary roads would yield a first-order increase in total consumption by 28 to 57 percent for the average household. This increase would yield a reduction in poverty from the rate of 51.7 percent in 2009 to between 46.9 and 42.8 percent. These estimates have to be interpreted as conservative as they do not take into account the second-order effects of road construction, coming from the increased trade following due to the better road connectivity.

Our analysis has also provided a welfare-based comparison of the improvement of various roads, which can be helpful in guiding road infrastructure investments. The available data constrains our ability to make accurate estimations, but we believe this type of analysis could be replicated elsewhere to help prioritize investments on the basis of poverty reduction objectives. The findings also confirm the importance of connecting markets in order to reduce the exposure to shocks in other markets and thus the potential price volatility.

Road infrastructure is not the only policy to improve market integration. Our results also show that price differentials in the price of diesel also play an important role in maintaining high price gaps between towns. In addition, while the paper has not analyzed the issue, market intermediaries can be important drivers of price gaps and can appropriate welfare gains from lower prices (Atkin and Donaldson, 2014).

# **References**

Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). New Trade Models, Same Old Gains? _American Economic Review_ , 102(1): 94–130.

Atkin, D. and D. Donaldson (2014).Who’s getting globalized? Intranational trade costs and world price pass-through, mimeo.

Brenton, P., A. Portugal-Perez and J. Regolo (2013), “Food Prices, Road Infrastructure and Border Effects in Central and Eastern Africa”, World Bank, mimeo.

Cali, M. and G. Varela (2013) “Shutting the border: The effects on poverty in South Sudan”, World Bank mimeo.

Deaton, A. and J. Muellbauer (1980). “An Almost Ideal Demand System”, _American Economic Review_ , vol. 70, No. 3, pp. 312-326.

Donaldson, D. (2014). Railroads of the Raj: Estimating the Impact of Transportation Infrastructure, _American Economic Review_ .

28
**Table 3. Odds ratios from logit “Model 2a” of extended formality in EcC 2012/13**

|**Ent. size (one worker omit.)**||
|---|---|
|2|1.640***|
||(0.157)|
|3-4|2.005***|
||(0.254)|
|5-24|4.945***|
||(0.617)|
|**Industry (wholesale/retail omit.)**||
|Manufacturing and related trades|0.697***|
||(0.056)|
|Construction|4.965**|
||(3.018)|
|Transportation and storage|1.579|
||(0.572)|
|Accommodation and food service|0.764**|
||(0.070)|
|Various professional acts.|0.492***|
||(0.038)|
|Other service|0.748***|
||(0.054)|
|**Region (Greater Cairo omit.)**||
|Alexandria and Suez Canal|2.146***|
||(0.168)|
|Lower Egypt|2.052***|
||(0.139)|
|Upper Egypt|3.085***|
||(0.341)|

25
health hazards common in indigenous households: i) the use of combustible fuels for cooking and ii) lack of adequate plumbing and sewage systems. In the absence of a ventilation system the first contributes to the high level of respiratory infections and the second to diarrhea and other intestinal problems.

The majority of indigenous peoples live in rural areas and their living conditions are worse than those of other rural dwellers. Health agents argue that infant mortality is about 30 percent among indigenous people. Despite important improvements at the national and provincial levels in recognizing indigenous peoples’ rights and land regularization, there are few other effective policy actions aimed at improving their livelihood. At the national level, four programs specifically target indigenous people, the largest of which is _Desarrollo de Comunidades Indígenas_ (Development of Indigenous Communities), which is aimed at community development and natural resource management in indigenous communities. The pilot project is implemented in Misiones, Neuquén, Jujuy, Río Negro, Salta, and Tucumán. Finally, indigenous people are also reached by social protection and rural development programs such as PSA (Box 4), ProHuerta, Jefas, and pensions. However, coverage information for indigenous peoples is not available. Since rural dwellers are undercovered, it seems likely that indigenous people also are. Field visits to Chaco and Mendoza confirmed this hypothesis.

Unfortunately the census micro data is not publicly available, therefore no disaggregate analysis or comparison between indigenous and nonindigenous peoples are feasible to undertaken for this study.

Two issues, targeting and institutional arrangements, arise in the implementation of the poverty alleviation strategy. Targeting seeks to reduce costs by limiting benefits only to the desired beneficiary group. Clearly, for targeting to be efficient the administrative mechanism must not be so costly that it offsets savings from excluding the nonneedy. In Argentina, targeting can be effective if it is based on individual or family characteristics.

The seriousness of poverty in rural Argentina calls for the active participation of all resources, including NGOs. According to _Organizaciones de la Comunidad_ (CENOC) 2,615 civil society organizations are operating in rural areas in Argentina: 38 percent are located in the Pampeana region, 25 percent in the Northwest, 14 percent in the Northeast, 12 percent in the Cuyo region, and 11 percent in Patagonia. NGOs in rural Argentina cover different fields, such as _Fundación Solidaridad_ which assists groups of rural dwellers in improving their production and livelihood, and _Fundapaz_ that provides technical assistance and economic support to small farmers.

42
that we draw on not just psychology, but also from sociology and anthropology to determine a situation’s civic trust in authority (Hoff and Stiglitz, 2016). Alternatively, diminished trust in the legitimacy or integrity of the enforcing entity may dissuade public assistance, deepening both that entity’s inability to confront violence as well as public reluctance to assist (Rossmo, 2018) . The emergence of online language metadata enables scope to measure sentiment about related topics, the circumstances that affect them, and how sentiment about topics affects both sentiment about law enforcement and incidents of violence. Measuring the relationship of online language sentiment about violence-relevant topics has already been shown to not only relate to change in violence, but to also enable significant accuracy in predicting its change (Mahony et al., 2019).

# **_Contexts where state control of territory is contested_**

In the future, capacity may emerge to forecast when, for example, criminal networks or the state are consolidating or ceding control over communities to one another and how strong a shift in control over territory might be.<sup>8</sup> Emerging data sources, such as geolocated online language data may assist in indicating change in phenomena such as which entity exercises effective authority over specific territory.

Once we have identified the mechanisms associated with decline in criminal conduct, we can use available data to identify the most efficient use of resources to advance that decline. For example, because more _certain_ (not longer) punishment is more heavily associated with decline in youth violent conduct than severity of sentence, redirecting money away from incarceration and prisons and toward (evidentially-informed) community policing constitutes a more efficient allocation of scarce resources to reduce youth violence (Lee and McCrary, 2005). This is known in the criminological literature as justice reinvestment. Using data procured from existing World Bank and other justice sector analytical products may assist in maximizing identification of the specific elements of community policing capacity that demand prioritized support. Identifying armed groups’ deterrence or incentives when designing justice interventions may be even more complex. To consider prioritization of policy in such circumstances is to consider that, firstly, criminal organizations’ control of

> 8 Our understanding of local context informs the degree of control over local civic life (criminal consolidation) and the nature of relations between non-state actors and the state (proximity between armed actors and the state). Where civic groups play important roles in mediating relations with the state, an opening to employ those groups as sensitizing agents exists. Where closer relations between the state and the armed actors exist, the state should take the lead in sensitizing the armed actors as to the intended focus of punitive state action. See: Enrique Desmond Arias. (2013). The impacts of differential armed dominance of politics in Rio de Janeiro, Brazil. Studies in Comparative International Development. 48:3, 263-284, p. 265.

12