rafmacalaba/data-use-mentions-extended huggingface dataset:
shows that other poor households were 4ppt more likely to report receiving transfers from ultra-poor in treated communities, corresponding to a 46% increase relative to the control group. The middle classes were 2.5ppt (52%) more likely to receive transfers from ultra-poor, while the corresponding effect was 1ppt (71%) for the wealthiest class. The remaining columns in Panel A2 shows no significant effects for other wealth classes. This implies that the increase in likelihood of transfers from ultra-poor households observed in column 1 did not crowd out non-ultra-poor households’ likelihood of receiving transfers from other wealth classes<sup>26</sup> The lower panel of Table 6 shows the effects on transfers given by non-ultra-poor households to different wealth classes. On average, the non-ultra-poor households were 3ppt more likely to report giving transfers to ultra-poor in their communities (a 35% increase relative to the control group). Columns 2 to 4 show that the treatment effects on likelihood of transfers to other wealth classes are negative but small in magnitude and imprecisely estimated. Panel B2 breaks down the effects by wealth class of the respondent’s household. Respondents from other poor and middle class households were 3ppt more likely to report giving transfers to ultra-poor households. While the corresponding effect for upper class households is also positive, it is imprecisely estimated.<sup>27</sup> The rest of the table shows that there were no significant spillover effects of the program on non-ultra-poor households’ likelihood to give transfers to households from other non-ultra-poor households.<sup>28</sup> In order to assess the long-run effects of the program, I use data from the third followup survey that was conducted in 2014, that is seven years after the baseline. At that point, 49% of control communities had also been treated by the program. Moreover, only the female respondents were surveyed and detailed network information was not collected. However, respondents from all wealth classes were asked whether they provided informal transfers to any of the ultra-poor households within
# **Appendix A: Constructing separate asset indices for urban and rural areas**

Tables A1 presents basic descriptive statistics for the ownership of selected assets in urban and rural areas of Azerbaijan. Given that there are marked differences in the extent of assets ownership between urban and rural areas, it could be argued that it is more appropriate to derive separate asset indices for each locality rather than a single index for the country as a whole. The drawback of doing so is that such an approach precludes any national analysis. However the advantage is that separate indices may provide better rankings of welfare _within_ urban and rural areas than a simple national index. Tables A2 and A3 therefore present the results of the PCA within each area and these weights are used to construct two separate assets indices, one for urban households and another for rural households.

**Table A1: Ownership of assets and household characteristics in urban and rural areas, Azerbaijan 1999 Census**

|||**_Urban_**|**_R_**|**_ural_**|
|---|---|---|---|---|
||Mean|Standard<br>Deviation|Mean|Standard<br>Deviation|
|1. Gas Line|0.792|0.405|0.310|0.462|
|2. Gas Cylinder|0.139|0.346|0.303|0.459|
|3. Electric Oven|0.188|0.390|0.321|0.467|
|4. Heating system (private, public)|0.110|0.313|0.063|0.244|
|5. Stove|0.171|0.376|0.681|0.465|
|6. Water|0.835|0.370|0.261|0.439|
|7. Hot water|0.
predicted income based on urban or rural location, which would have led us to overestimate downward mobility for rural households. Table A 2 in the Appendix provides the regression results of our income models for each survey year, based on the subsample of households with heads in the age range of 25– 55.

The synthetic panel method requires checks on the stability of the sample, as a means of assessing the assumption of a stable underlying population. We observe some differences in the distribution of explanatory variables between survey rounds, notably in the means of education variables. This seems to be driven by the age of household heads, with the mean age of heads in later rounds being lower

> 18 We are aware that the index is thus based on expenditure patterns at the very low end of the income distribution. However, this is the best we could do in the absence of spatially disaggregated consumer price data.

> 19 The first two bins are taken as base categories, since the first bin alone does not always contain a sufficient number of observations.

> 20 With the caveat that classification of a location as urban or rural may change over time, the cross-sectional data show that the proportion of the population that is living in rural areas is steadily decreasing: from an estimated 36 percent in 2004, it declined to an estimated 23 percent in 2016. Similarly, mean estimated household size decreased from 4.5 in 2004 to 4.0 in 2016.

14
septic sludge disposal plant, but its capacity is greatly exceeded. Most domestic wastewater is discharged into streets and gutters. The greater Cotonou metropolitan area, including Porto Novo, is home to more than 80 percent of the industries of Benin. In the absence of adequate waste-treatment facilities, industrial wastewater is also discharged into the open, into lagoons, or into the sea. In many cases it is left to filter into the groundwater without treatment (World Bank 2009d).

# Power

## **Achievements**

Benin has steadily increased access to power since the middle 1990s, particularly in urban areas. Electrification rates increased from 14.5 percent in 1996 (Demographic and Health Survey 1996) to 25 percent in 2009 (World Bank 2009b). While improvements in access are encouraging, overall access is below the average level of low-income countries, at 33 percent, and only half the level of middle-income countries, at 50 percent (table 11).

26
- 4 -

for measuring transport connectivity in different ways, while avoiding potential endogeneity caused by infrastructure placement. The primary data come from the Demographic and Health Surveys (DHS) carried out in Mozambique at two different points of time: 2011 and 2018. During this period, the government invested heavily in the road network, presumably resulting in significant improvement in connectivity. The paper exploits such variations observed over time to examine how people changed their behavior when they have a fever. To mitigate the potential bias caused by unobservables, household-, facility- and locationspecific characteristics are controlled.

The following sections are organized as follows: Section II describes some country background in the health and transport sector. Section III develops a methodology and summarizes our data. Section IV presents the main estimation results and discusses policy implications. Then, Section V concludes.

# **II. Country context**

In Mozambique, various health indicators have been improved in recent years. Life expectancy increased by nearly 9 years since 2001, from 48.8 to 57.6. Infant mortality, expressed as the number deaths per thousand live births, fell from 99.1 in 2003 to 68.1 in 2011 (World Bank, 2020). The share of children under 5 years old who slept under an insecticide-treated net (ITN) increased from 35 percent in 2011 to 73 percent in 2018 (DHS 2019). The number of malaria deaths declined from over 3,000 in 2014 to about 1,000 in 2018 ( **Figure 1** ).

Still, malaria remains one of the most important challenges to the country. Mozambique has one of the highest malaria death rates in the world. Importantly, while the number of deaths significantly declined, the total number of malaria cases reported has not reduced. According to the WHO statistics, the number of confirmed cases increased from 3.4 million in 2010 to 5.5 million in 2015 and 9.3 million in 2018. While antimalarials only cost a few U
and workers with college education (Figure 6a). The probability of job loss was higher for younger and older workers and lowest for prime-age workers (Figure 6b). Those working in manufacturing, commerce, and other services before the pandemic were significantly more likely to have stopped working than those in agriculture.<sup>11</sup> A greater share of urban workers lost jobs compared to rural workers, but this is the result of spatial differences in employment composition – when the effects of other factors like sector of 8 The countries included in the updated results for Bundervoet et al (2021) are from Sub-Saharan Africa (21 countries), Latin America and the Caribbean (13 countries), East Asia and the Pacific (8 countries), Middle East and North Africa (5 countries) and Europe and Central Asia (5 countries). Grouped by per capita GDP, the sample consists of 4 highincome, 12 upper-middle income, 21 lower-middle income, and 15 low-income countries. The data corresponds to the April 2021 vintage of the harmonized database. To assess the immediate impact of the crisis in a comparable way across countries, the authors select survey waves (between April and July 2020) that are no more than two months of the peak of the economic crisis, with the peak measured by Google mobility data when available or by policy index of social distancing measures, available from Oxford’s “Coronavirus Government Response Tracker” (OxCGRT).

9 This indicator is part of the standard Food Insecurity Experience Scale (FIES) and was included in the surveys for a subset of countries.

> 10 Kugler et al (2021), with sample of countries that may not exactly match those used by Bundervoet et al.

11 Having a school-age child in the household, which could affect labor force participation due to additional childcare responsibilities during school closures, did not significantly affect the likelihood of work stoppage. However, Cucagna and Romero (2021), using the same data source for three rounds of phone surveys
sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any loans from another household during the 12 months preceding the survey; in column 2 it is the monetary value of loans received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any loans to another household during the past 12 months; in column 4 it is the monetary value of all loans given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net loan receiver in the past 12 months; in column 6 it is the monetary value of loans received minus loans given by the respondent’s household in the past 12 months. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village informal loans.

8
Figure 5: Means 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Output Changes by Sector from Armenian-CISFTA—30,000 simulations.

<!-- Start of picture text --> 5<br> 4<br> 3<br> 2<br> 1<br> 0<br>-1<br> pipelinessport activities rier activitiesnsport<br>an u ra<br>nsport viiliary tra ax st and cooer land t h nkinga urance s<br>Electricity, gas and water supplyReal estate and business activitiesEducation Mining and quarrying ManufacturingTrAu Transport via railways POt Telecommunications Hotels and restaurants Air transport B In<br><!-- End of picture text --> Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles.

69
of human capital. The results of our analysis suggest that the answer to this question is not straightforward and more efforts are required along different dimensions. First, it is necessary to improve data availability. As stressed by the recent IMF report on engagement on social spending (IMF 2019), there is a need for a concerted strategy to improve coverage and availability of data. The report points to the improved availability of expenditure data by Function of Government (COFOG) as one of the possible useful avenues for moving forward. Advantages of COFOG data include: (i) they are based on already consolidated statistical concepts, so there would be no need for establishing new statistical standards or placing new reporting requirements on statistical authorities; and (ii) they would also be fully consistent with Government Finance Statistics and National Accounts concepts, and therefore would ensure consistency of the analysis with the broader understanding of countries’ economic developments (IMF 2019).

Second, in order to further tease out the relationships between spending and human capital outcomes, additional research is required. Until data quality are improved, analysis similar to the one conducted in this paper will continue to have weaknesses. As suggested throughout, cross-country analyses may mask key confounding factors and complicate the associations observed here. One way of strengthening the analysis would be through examination of spending and HCI data sub-nationally, and even more so by quasi-experimental work that could test the impact of exogenous variation in social sector spending on HCI outcomes. Another way would be through strengthening and harmonization of sector-specific or programspecific studies (e.g. Public Expenditure Reviews), which are likely to deliver much more robust evidence for examining the relationship between monetary inputs (government spending) and outcomes. Progress in these alternative research avenues may begin to help develop strategic guidance on the magnitude and nature of financing required to further human capital outcomes.

24
from Nepal and<br>Cameroon, analysis would isolate<br>Cameroon data only and/or consider the<br>transferability of data from Nepal to<br>Cameroon setting<br>**E**<br>|
||Deep Learning system to<br>analyze digital chest<br>radiographs<br>for TB-related<br>abnormalities|Type: Health Service User targeted. (WHO<br>Class 2.10: “laboratory and diagnostics<br>imaging management”) with predictive<br>analytics component.|
|**STEP 3. ESTABLISH**<br>|**THE LEVEL OF COMPLEXI**<br>|**TY**<br>|
|**Intervention**|High complexity|Depending on operation and interface the<br>intervention may represent low complexity<br>to the user|
|**Outcomes**|High complexity|For immediate diagnostic accuracy there is<br>low complexity. Improved TB management<br>and increased health is high complexity|
|**Causal pathway**|High complexity|**Table continued...**|
**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**emae**<br>**participatio**<br>**n**|**No female**<br>**participation**|**Female**<br>**participation**|**Female**<br>**participation**|**o emae**<br>**participati**<br>**on**|**emae**<br>**participatio**<br>**n**|**o emae**<br>**participati**<br>**on**|
|WBL index|-0.002***|0.002*|0.000|-0.000|0.000|0.000|0.000|-0.003**|-0.002**|-0.006***|0.002|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.002)|(0.003)|
|Firm size at start|-0.000|-0.000|-0.000|-0.000*|0.000|0.000**|-0.002|-0.000|-0.000*|0.000|-0.000|
||(0.000)|(0.000)|(0.
Randriamamonjy (2002) and Orr, Mwale and Saiti‐Chitsonga (2009). The seasonal gap measure is calculated as the difference between the highest and lowest seasonal factor: In statistical terms, this is a range. Although the estimates of the 12 seasonal factors themselves are unbiased, the range is a nonlinear function of these seasonal factors and so will potentially be biased. Bias arises if large irregular movements confound identification of the peak and trough months. This is more likely when samples are short. This bias would not occur if it is known a priori that the seasonal peak is in month _a_ , say, and the trough is in month _b_ since the difference _sa_  _sb_ is the unbiased difference between two unbiased statistics.

Monte Carlo results indicate that the dummy variable procedures to characterizing seasonality perform poorly for samples up to 20 years with irregular variations. They frequently suggest the existence of seasonality which is in effect absent in the data generating process (Table 1, columns 1‐2).<sup>7</sup> With no seasonality genuinely present and using 10 years of data, the average estimated seasonal gap is 10 percent using the dummy variables approach, while in effect it is zero.<sup>8</sup> In addition, these spurious seasonal factors purport to explain an average of 9.2 percent of the sample price variability (R<sup>2</sup> =0.0925). A more extended set of simulations, reported in Gilbert, Christiaensen and Kaminski (2015) indicates that large biases also persist in cases in which the seasonality is present, but not well‐defined. Series of 10 to 15 years of food price data are common in African samples. The series used here has 19 years of data.

This weakness of the common dummy variable approach to characterizing seasonality can be mitigated by using a more parsimonious, but more restrictive functional approach to characterizing seasonality, such as the trigonometric functions. Ghysels and Osborn (2001) provide a
# **4 Comparing alternative indicators of welfare at the spatial level**

As discussed above in recent years, growing attention has been given to derivation of alternative measures of household welfare. Expenditure and income data often suffer from a variety of measurement errors (Sahn and Stifel 2001), and are expensive to collect on regular basis. One practical alternative that has been growing in popularity is the use of data on the ownership of assets and dwelling characteristics to create an asset index.

Few studies have attempted to verify the extent to which the asset indicator being used is a good proxy for household consumption, the main reason being that such verification requires a data set that contains both metric measure of household consumption and the components of the asset index.

Montgomery et al. (2000) evaluated the performance of proxy measures in relation to consumption expenditures per adult, the latter being their preferred measure of living standards. They found that proxy variables were weak predicators of consumption per adult, with extremely low partial R<sup>2</sup> values. However, in subsequent analyses of fertility, child schooling and mortality, the proxy–based coefficient estimates compared favourably to those obtained using consumption, providing a generally reliable guide to sign and magnitude of the preferred estimates. Sahn and Stifel (2001) also found the correlation of their asset with household expenditure to be weak.

In contrast Filmer and Pritchett (2001), who validated their asset index using data from the Indonesian, Pakistani and Nepalese LSMS, concluded that the asset index had ‘reasonable coherence’ with current consumption expenditures and worked ‘as well or better, than traditional expenditure-based measures in predicting enrolment status’. They also note that their asset index is a better proxy for long-run household wealth than current per capita consumption.

20
2.432|0.0365|0.0113|0.0063<br>-|0.1711|0.0554|0.0264|
|__,ud, y, ac, ,B, for all<br>|210.995|0.587|0.577|0.727|2.177|0.0931|0.0433|0.0321<br>-|0.2724|0.1129|0.0677|
|**_EducationEndowment_**_Effects_<br>|393|54|0|_04|||0<br>6|0||||
|e only, for|339.753|0.594|0.650|0.740|2.485|0.0424|0.0136|0.0073|0.1593|0.0567-|0.0287|
|**[ id,**[Le for all<br>|353.248|0.571|0.584|0.688|2.320|0.0225|0.0078|0.0049|0.1131|0.0359|0.0173|
|1.te, pd, y, oc,3,B<br>for all|263.676|0.594|0.600|0.727|1.896|0.0735|0.0374|0.0296 =|0.2204|0.0913|0.0561|
|Source: Based on "Pesquisa Nacional p|or Amostra|de Domic|ilios" (PNA|D) of 1976 a|nd 1996.||||||.-|
constant aggregate level of land, labor, and capital employment reflecting the belief that the aggregate employment of factors is unaffected by the climate shocks that are affecting grain production.

The model is adjusted to distinguish between land classes with different biophysical characteristics, following the approach of Hertel et al. (2009). This distinguishes land by Agro-Ecological Zone (AEZ), based on the data of Lee et al. (2009) and Monfreda et al. (2009). The model is then

> 9 The data for domestic production and consumption in Tanzania is based on a 1992 input-output table that has subsequently been updated to be consistent using 2001 macro-economic and trade data. While a more recent input-output for Tanzania would be preferable, this was the most recent data made available during the construction of the GTAP Database, and has passed several consistency checks. The full details on the input-output contribution, the database construction process, and the consistency checks can be found in the documentation for Dimaranan (2006).

11
of utility associated with the initial price index _Pt_<sup>0and observed expenditures</sup> _E_ ( _Pt_<sup>0</sup><sup>_,_</sup> _U t_ )<sup>0</sup> . In counterfactual analysis we remove _τ_ ̃ _j,t_<sup>_l_in (16) on imports from the</sup> U.S. mainland, assuming no change in ROW or Puerto Rican domestic prices.<sup>44</sup> Given new values of the price index _Pt_<sup>1,wecalculateanupdatedvalueoftheex-</sup> penditure function _E_ ( _Pt_<sup>1</sup><sup>_,_</sup> _U t_ )<sup>1</sup> . The CV of the price change is calculated as We do this calculation for JA tariff-equivalent trade costs on U.S. imports, and U.S. tariffs on ROW imports. We also conduct the exercise for sub-components of final demand (Consumption, Investment, Government spending, etc.).

> 43We use trade shares and inferred trade costs from 2016. We inflate the older input-output data to produce estimates in 2016 dollars. Puerto Rican nominal GDP grew by a factor of 1.195 between 2006 and 2016.

> 44The assumption of perfectly elastic supply to the Puerto Rican market is reasonable for U.S. and ROW imports. Our calculations implicitly assume no change in the prices of Puerto Rican goods. Puerto Rican domestic prices might also be expected to fall with JA removal, since domestic suppliers would face greater competition from imports (and have access to cheaper imported inputs). Falling domestic prices would raise our estimate of CV. But these additional welfare gains would be partially offset by reduced incomes from reduced domestic sale revenue (absent any additional income gains arising through comparative advantage). We take the assumption of no net change in Puerto Rican prices as a reasonable approximation that facilitates transparent calculations