rafmacalaba/data-use-mentions-extended huggingface dataset:
# **VI.C DiD Estimates of Program Roll-out**

Our second identification strategy, based on the sequential roll-out of the policy, provides additional evidence and on larger differences in exposure to the program than the experimental variation discussed so far. At the same time, the data employed in this part contain direct measures of food security and vulnerability which add another layer to our welfare analysis. Further to that, we can also test for the effect of PROSPERA on later dependency on governmental programs, this would be a test of a successful policy able to graduate its beneficiaries out of social programs. Our approach here is to produce DiD estimates similarly to Parker and Vogl (2018), but with INEGI’s 2015 intercensus data.<sup>19</sup> _Figure 2_ contains the main estimates of this strategy. Each sub-graph shows the _τ_ estimates from equation (5), which correspond to difference-in-difference estimates. As one moves to the right through the X-axis you increase the exposure to PROSPERA (calculated by comparing 1998 to different delays to enroll) for a given cohort with respect to the cohort that should not be benefited by the program (17-22 year old individuals in 2001).

The results show positive effects on education, particularly for the cohort with the youngest members, which increases their schooling attainment between 0.5 and 1.5 additional years. For the other cohorts, the program involves an increase around 0.5 years of schooling. Regarding income, positive and significant effects are found: household per capita income increases between 2% and 8%, with largest effects for the cohort of those aged 6-10 in 2001. Positive effects are also found for a composite index of house characteristics and durable assets. When analyzing asset by asset, the largest effects are found for type of floor, washer, and cellphone. The largest effects are again found for the youngest cohort. To test for dependency on government aid, or graduation from social programs, we use, as dependent variable, a
In contrast to the rest of the literature, the high frequency of the data allows us to focus on the short-run dynamics during and right after the disaster. The results show that local-level aggregate activity declined for some months but then recovered quickly and even overshot within the first year.

# **3.2 Natural Disasters and Household Finances**

Amid developing countries’ high vulnerability to natural disasters, an important concern is their impact on rural households. Many papers focus on Southeast Asia and South Asia, two particularly vulnerable regions in terms of natural disasters.<sup>10</sup> Bui et al. (2014) and Arouri, Nguyen, and Youssef (2015), for example, find that natural disasters reduced household expenditure and income in Vietnam in the 2000s. Lohmann and Lechtenfeld (2015) show that droughts deteriorated health conditions and increased the share of health expenditure in rural Vietnam between 2007 and 2013. Karim (2018) finds that recurrent flood risks negatively affect the income and non-food expenditure of agricultural households. In line with these findings, Kurosaki (2015) shows that floods significantly reduce the consumption of rural households in Pakistan. In contrast, Bandyopadhyay and Skoufias (2015) find that proneness to floods does not affect the consumption of rural households in Bangladesh, but rainfall variability in non-flood-prone subdistricts has a negative effect. This strand of the literature highlights access to microcredit, internal remittances, education, social allowances, and land ownership as potential mitigating factors.<sup>11</sup> Case studies of specific disasters contribute to a better understanding of their impact on household finances and consumption. Akter and Mallick (2013), for example, find that while poor households in Bangladesh suffered greater damage during Cyclone Aila than others due to low-quality housing and distant cyclone shelters, they experienced higher income

> 10They tend to use cross-sectional or longitudinal data from primary surveys or nationally representative household surveys such as the Vietnam Household Living Standards Survey, the Household Income and Expenditure Survey of Bangladesh, or the
‐0.0385*|
|(3)|Capital goods (transp.equipment)|‐0.0318|‐0.0278|‐0.0394|‐0.0402|‐0.0296|‐0.0369|
|(4)|Durable consumer goods|‐0.00777|‐0.0150|‐0.0136|‐0.0170|‐0.0217|‐0.0156|
|(5)|Non‐durable consumer goods|‐0.0258*|‐0.0309**|‐0.0225**|‐0.0359**|‐0.0397***|‐0.0277**|
|(6)|Backward GVC participation (intermediate goods<br>and services embodied in exports)|‐0.0244|‐0.0386**|‐0.0398*|‐0.0228|‐0.0370*|‐0.0381|

_Sources_ : International Monetary Fund World Economic Outlook, World Bank Global Economic Monitor, International Monetary Fund International Financial Statistics, World Trade Organization, World Input Output Database (WIOD), <u>www.PolicyUncertainty.com,</u> Baker et al. (2016), and authors’ calculations. See Table 1 for details. Notes: Heteroscedasticityrobust standard errors corrected for clustering at country level. * p<0.10; ** p<0.05; *** p<0.01. Panel covers 18 countries. Period

> 5 The intensity of impact may, of course, differ across sectors depending on the importance of up‐front investments, but we have little data on these differences.

10
vectors, which are influenced by hygienic practices); (3) nutrient deficiency (calories, protein, micronutrients, affected by feeding practices); (4) injuries (accidents and intentional injury, which are affected by care); (5) personal illness control (preventive measures and curative treatment, affected by health care practices). All social and economic determinants must operate through the proximate determinants to affect child health and survival.

In the basic microeconomic model of the family, the overall wellbeing of the household depends on the consumption of goods, services and leisure, and on the health of family members. Households maximize their utility function under several constraints: a time constraint for each household member, a budget constraint for the entire household, and a health production function. The health production function relates the health of child to his F-ast health status and the proximate determinants, as outlined in the Mosley-Chen framework. It is a complex relationship that cannot be captured easily by regression analysis based on cross-sectional survey data. Only a series of measurements over time can provide sufficient information to allow reliable judgements on the relative importance of different proximate determinants on growth attainment at the individual level. Health production functions are therefore best estimated with longitudinal data.<sup>7</sup> The estimation of health production functions is further complicated by the fact that proximate determinants are subject to individual/household choice. Correct estimation of their causal effect relies on controls for simultaneity bias that require information not available in most demographic and health surveys.

# **5.2 Reduced-Form Estimation**

The exogenous variables in the niicroeconomic model are the same as the socioeconomic variables in the Mosley-Chen franework, and the choice variables that appear as covariates in the health production function are the same as the Mosley-Chen proximate determinants (Schultz 1984). The economic model of the family can be solved to yield a reduced-form equation for health outcomes in which child anthropometry depends only on exogenous individual, household and community characteristics:

# z; = h(Ci
, Country Policy and Institutional Assessment (CPIA), and World Economic Forum (WEF). Most macroeconomic data (general government debt-to-GDP ratio, exchange rate, inflation, foreign reserves as a share of GDP, real GDP growth, per capita GDP, net real ODA received, age dependency ratio, net FDI inflows, foreign trade-to-GDP ratio, current account balance, and total natural resource rents as a share of GDP) are from the World Bank’s World Development Indicators (WDI). Data on financial crises are from Laeven and Valencia (2020). Capital account openness data are sourced from the most recent update of Chinn and Ito (2006). Summary statistics for the variables used in the estimations are reported in Annex Table A2.

On average, EMDEs exhibit higher sovereign risk compared to advanced economies. However, there is significant heterogeneity within both country groups (Figure 3.1). Sovereign risk in lowincome countries is significantly higher than in middle-income and high-income countries. Among EMDEs, except for the majority of high-income ones, sovereign ratings generally fall within the speculative rating range (10-21 on our combined scale). If we exclude the Middle East and North Africa (MNA), all EMDE regions have average sovereign ratings above 10, indicating elevated risk (Figure 3.2). In recent years, sovereign risk has increased in all EMDE groups.<sup>23</sup>

> 22 This averaging procedure does not generate any source of bias because the correlation between the ratings of the three agencies is well above 90 percent.

> 23 We measure sovereign risk with yearly average. Note that we follow Combes et al. (2021) and use the log of CDS spreads. Taking logs helps in downplaying the role of outliers in the heavily skewed distribution of spreads and in addressing heteroscedasticity issues in the CDS spreads data.

16
where is a scalar quantity used to simulate an energy price.

# _6.6. Numerical scenarios_

Given the cumulative change in a variable with respect to the year 2001, we use the market-clearing condition to derive a counterfactual equilibrium world price for each crop for the various shocks for each year. We do so for four different alternative scenarios which either differ in the assumed range for elasticities used in calibration of supply and demand functions, or differ in the specification of the demand for food/feed (whether GDP per capita is explicitly represented in demand) or differ in parameters of the inventory demand function. Given the challenge of estimating a point estimate for the various elasticities, as well as the inventory parameters, we simulated these alternative scenarios to determine the robustness of our results.

The first scenario, which we henceforth refer to as the _baseline scenario,_ is one in which we use the range of price and income elasticities reported in the literature, namely, that mentioned in the USDA’s database of elasticities and in the FAPRI database. Under this scenario, the parameters for the inventory demand function are those that we estimate ourselves using the specification of Carter et al. [14]. As mentioned earlier, we perform 100 simulations of this scenario for the various shocks for each crop and for each time period but report the mean value of these outcomes.

In the second scenario, the _inelastic scenario_ , we assume a narrower range for elasticities, which is on average more inelastic compared to the baseline scenario and follows Gardner [24]. This scenario further differs from the baseline in that we employ a demand specification that does not include income. The reason for excluding income is that some of the elasticities reported in the literature were based on models that did not include income.

38
., Koolwal, G., & Moylan, H. (2021a). LSMS+ Program in Sub-Saharan Africa: Findings from Individual-Level Data Collection on Labor and Asset Ownership. Washington, D.C.: World Bank Group.

- Hasanbasri, A., Kilic, T., Koolwal, G., & Moylan, H. (2021b). LSMS+ Program in Cambodia: Findings from Individual-Level Data Collection on Labor and Asset Ownership. Washington, D.C.: World Bank Group.

20
In no case was there a significant fall in spending on durable goods between 2008 and 2009, and in some cases there were increases, such as for purchases of automobiles/vans/pickups. This is confirmed by the data in the bottom panel of Table 7, which reports on the stock of cars, vans, and motorbikes owned by households. The number of such assets held per household generally rose in 2009 relative to 2008. As a general rule, any drop in spending occurred between 2007 and 2008, not later.

The consumption of non-durables was also higher, or at least not lower, in 2009 than in 2008, and this includes items such as clothing, meat, fish, and prepared food. There is one major exception, and this is the consumption of home-produced “grains and cereal productions”, in practice mainly rice. Although _purchases_ of cereals rose substantially in 2009, this was offset by a clear drop in own-consumption, although the mean value of the fall in cereals consumption between 2008 and 2009 – at about 14.4 baht per person per month – was small compared to the overall rise in the value of consumption per capita over the same period, which we estimate at 363 baht per person per month.<sup>2</sup> Rice production per capita hardly changed between 2007 (405 kilos of milled rice per capita) and 2008 (404 kg.) and actually rose in 2009 (412 kg.), although it should be noted that the bulk of the harvest arrives in November and is consumed the following year. We surmise that consumption smoothing, to the extent that it occurred, did not occur at the expense of investment in durable goods or schooling, but more probably through lower saving.

**Table 7.  Measuring the Impact of the 2008-09 Recession on Real Expenditure, by Main Expenditure Heading**

||Real<br>expend/|Chan|ge in real exp<br>compared to|enditure/c<br>
0.991|0.930|0.453|0.228|0.015|0.733|0.540|
|MSE-opt. bandwidth|23.1|19.4|17.0|22.6|20.5|23.9|28.5|19.4|18.7|11.5|
|Effective obs.|266|239|216|262|249|276|316|240|234|158|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is: in columns 1 through 3, the latitude, longitude, and altitude of the village, respectively; in columns 4 and 5, a dummy variable equal to 1 if the village is located in a coastal area or a forest area, respectively; in column 6, a dummy equal to 1 if agriculture is the main economic activity in the village; and in columns 7 through 10, a dummy equal to 1 if rice, corn, rubber, or palm oil is the main economic activity in the village, respectively. All dependent variables are measured in the 2021 wave of the Podes survey.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

**Table A.3:** Balance Checks on Sampling of Citizens

||Listed byvillage offcial|Listed byBPD member|Degree of Connection|
|---|---|---|---|
||(1)|(2)|(3)|
|New village head|-0.006<br>(0.022)|-0.025<br>(0.020)|-0.213<br>(0.251
., Allen, E., & Kadiyala, S. (2023). Intrahousehold power inequalities and cooperation: Unpacking household responses to nutrition-sensitive agriculture interventions in rural India. _Maternal Child Nutrition_ , 19(3): e13503. <u>https://doi.org/10.1111/mcn.13503.</u> Hassen, H., Ali, J., Gebreyesus, S., Endris, S., Temesgen, A. (2020). National incidence, prevalence and disability-adjusted life years (DALYs) of common micronutrient deficiencies in Ethiopia from 1990 to 2017: Estimates from the global burden of disease study. _Global Health Action_ , 13 (1): 1776507, DOI: 10.1080/16549716.2020.1776507.

Isler, J., Sawadogo, N.H, Harling, G., Barnighausen, T., Adam, M., Sie, A. & McMahon, S.A. (2020). ‘If he 17
enhance national sample surveys. Annex 3 shows empowerment themes and strategies in selected Poverty Reduction Strategy Papers, and national-level empowerment indicators are presented in annex 4. A preliminary survey module comprises annex 5.

# 1. A Framework for Understanding and Measuring Empowerment

If a person or group is empowered, they possess the capacity to make _effective_ choices; that is, to translate their choices into desired actions and outcomes. In a five-country study on measuring empowerment currently overseen by the World Bank’s empowerment team, it is assumed that degrees of empowerment can be measured. As figure 1 illustrates, this capacity to make an effective choice is primarily influenced by two sets of factors: agency and opportunity structure.<sup>4</sup> Agency is defined as an actor’s ability to make meaningful choices; that is, the actor is able to envisage options and make a choice.<sup>5</sup> Opportunity structure is defined as the formal and informal contexts within which actors operate. Working together, these factors give rise to different degrees of empowerment. For example, a farmer in India chose to take out a Bank loan to finance a lift irrigation system, but the process for obtaining the loan required that he—an illiterate person—complete 20 forms, offer all his land as collateral, and obtain a lawyer to verify that he owned title to the land. The farmer’s choice was well informed and economically viable, but the opportunity structure—in this case the regulations concerning procurement—was an obstacle in his ability to make his choice effective.<sup>6</sup> Figure 1. The Relationship between Outcomes and Correlates of Empowerment <!-- Start of picture text --> A g e n c y<br>D e g re e o f D e v e lo p m e n t<br>E m p o w e rm e n t O u tc o m e s<br>O p
The marginal effects (table 7) show that a one unit marginal increase in per capita GDP growth is significantly and inversely related to the probability of a country being far from target in all MDGs excluding primary completion and gender parity in secondary education. Conversely, a one unit increase in GDP per capita growth significantly raises the probability of a country being on target by at least 0.05, holding other variables at their mean, for primary completion, gender parity in secondary education, and access to safe water and sanitation. In addition, CPIA scores appear to have significant marginal effects, at average values, on the probabilities of being on target (positive signs) and/or far from target (negative signs) for several health-related MDGs (hunger, child mortality and maternal mortality). For several development goals, the predicted probability of a country being close to target is significantly and inversely related to changes in per capita growth and the CPIA index ( that is, higher growth may reduce the probability of being close to the target). This does not imply that high growth is correlated with poor performance. Rather, countries with relatively high growth may be on track to meeting the goals, instead of off-track but close to the target.

**Table 7. Multinomial logit estimates: marginal effects (baseline representation)**

||**MDG.1a**|**MDG.1c**|**MDG.2a**|**MDG.3a**<br>**(primary)**|**MDG.3a**<br>**(secondary)**|**MDG.4a**|**MDG.5a**|**MDG.7a**<br>**(water)**|**MDG.7a**<br>**(sanitation)**|
|---|---|---|---
/sup><sup>_c_given</sup> by _yc_<sup>_∗_= (</sup><sup>_y_</sup> _c_<sup>_∗_</sup> 1<sup>_, . . . , y_</sup> _c,N_<sup>_∗_</sup> _c_<sup>)</sup><sup>_T_,whichisusedtocomputethetargetindicator</sup><sup>_τ ∗_</sup> _c_<sup>=</sup><sup>_f_(</sup><sup>_y_</sup> _c_<sup>_∗_).Unfortunately,the</sup> Census EB estimator is not implemented in the sae R package. One possible approach for applying this software when survey units cannot be identified in the census would be to append the survey data to the available census data, which would lead to a different approximation of the EB estimator. A drawback of this approach is that, in this case, the total size of the area is then _Nc_ + _nc ≥ Nc_ .

The EB estimator _τ_ ˆ _c_<sup>_EB_</sup> approximates the so called “best” predictor of _τc_ , which is the optimal predictor in the sense of minimizing the MSE under the model. This MSE of an estimator _τ_ ˆ _c_ is defined as the expectation of the squared prediction error under the considered model, MSE(ˆ _τc_ ) = _E_ (ˆ _τc − τc_ )<sup>2</sup> . ˆ The best predictor is given by the conditional expectation _τc_<sup>_B_</sup> = _E_ ( _τc|yc,s_ ; _θ_ ), where the expectation is with respect to the conditional distribution of the non-sampled data _yc,r_ given the sample data _yc,s_ , determined by the nested error model. Under normality of the random terms in the model, the conditional distribution
respondents. Suffering households on the other hand are likely to be lacking in basic food and shelter and report to be under physical pain, stress, worry and sadness. They carry more than double the disease burden as “thriving” respondents within each country.

Using data from Gallup for Nepal, we find that 90 percent of Nepalis fall in the suffering or struggling categories. In contrast, only 10 percent of the population actually falls in the thriving category (Figure 10). Juxtaposing this with the earlier result on the size of the middle class, it appears that even though there is a nascent middle class building up in Nepal, roughly half of this group is fundamentally insecure about the prospects of realizing continued improvements in its living standards.

**Figure 10: The Proportion of Nepalis Struggling, Suffering and Thriving**

<!-- Start of picture text --> 0.8<br>0.7<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>Suffering Struggling Thriving<br><!-- End of picture text --> Source: World Bank staff estimates based on data from Gallup World Survey. The numbers presented are country averages for the 2007-2014 period.

In the qualitative survey carried out for this analysis, respondents were asked about what they saw as key characteristics of the middle class in Nepal. In both in-depth interviews and focus group discussions, an overwhelming majority of respondents/participants associated being in the middle class with being engaged in relatively secure professions such as government jobs, teaching, medical and engineering professions, and other forms of private sector salaried employment. This association was reinforced by widespread recognition among respondents that economic prospects of those in this group hinge more critically on professional skills and labor
# **B Additional Results: Tables and Figures**

Table B.1: Effect of Years of Schooling on Number of Children Born – Replication of Table 2: Anderson-Rubin Weak IV Robust Confidence Sets

||Census<br>Census<br>+ DHS<br>Only|DHS<br>Only|
|---|---|---|
||(1)<br>(2)|(3)|
||Coefcient on<br>�<br>Years of Sch|oolingizy|
|Weak IV Robust 95%<br>Confdence Set|-0.676, -0.275<br>-0.576, -0.285<br>[0.000]<br>[0.000]|-1.567, -0.265<br>[0.003]|
|First Stage F-Statistic|14.80<br>37.62|5.93|
|Number of Clusters|32<br>32|30|
|N|83,005<br>69,083|13,922|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007 and the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : IV = instrumental variables. The dependent variable is number of children born. � Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Column 1 combines data from the Ethiopian census (2007) and the DHS (2005, 2011, and 2016); each data source is used separately in columns 2 and 3. Standard errors are