rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Hypothesis 2: Respondent selection effects</u>

Our second hypothesis relates to how respondents were chosen within phone survey households. The phone surveys typically interviewed one respondent per household per survey round who was selected to be knowledgeable across the different topics covered in the survey (the “main respondent”). Often this was the household head. As a result, respondent selection was purposive, not random, and overrepresented male, older, and more educated respondents relative to the general population (Brubaker et al. 2021) . These traits, and other unobservables, may correlate with vaccine uptake and bias vaccine coverage estimates. We thus posit our second hypothesis.

**Hypothesis 2:** Purposive (non-random) respondent selection within the household overrepresents individuals that are more likely to be vaccinated.

We test this hypothesis in two ways. First, we randomly selected a respondent alongside the purposively selected (“main”) respondent during one wave of the phone surveys in three countries (Burkina Faso, Malawi, Uganda). A respondent selected at random from among the members of the household is expected to be representative of the general population of eligible individuals, that is, all adults (15+) within households with access to a phone. Random selection of any eligible household member allows for the possibility that the purposively selected main respondent and the randomly selected respondent are the same individual. In this case, we conducted a single interview and count the observation both for the main respondent estimates and for the random respondent estimates.<sup>8</sup> Second, we asked the main respondent to report the vaccination status of all household members on their behalf (‘proxy reporting’). This gives us the following information within each household: (i) the vaccination status of the purposively selected respondent; (ii) the vaccination status of a randomly selected household member (possibly, but not necessarily a different member than in (i); (iii) the vaccination status of all household members as reported by the purposively selected respondent. We formulate the following three predictions.

**Prediction 2.1:** Respondent selection at random will significantly reduce estimated vaccine uptake compared to purposive selection.

**Prediction 2.2:** Eliciting the vaccination status of all household members (via proxy-reporting) will significantly reduce estimated vaccine uptake compared to purposive selection.

**Prediction 2.3:** Randomly selecting a household member to be interviewed or collecting (proxyreported) data on all household members will lead to statistically indistinguishable estimates of vaccine uptake.

> 8 In these cases, the same individual will thus appear twice in the data, once as purposively selected respondent and once as a randomly selected respondent.

10
will correctly capture the Gini coefficient of the population that excludes the selected top earners. For example, suppose that we choose to apply the methodology focusing on the top 0.1 percent of the population, so _S_<sup>_∗_</sup> in the formula above would be the share of income of the top 0.1 percent computed from tax records. Suppose also that the household survey is only representative of the bottom 95 percent of the population, because individuals above the 95th percentile do not correctly report their income. In that case, the _G_ computed from the household survey would not correspond to the true Gini coefficient of the population outside the selected group of top earners (in this case the bottom 99.9 percent), since the 95-99.9 percentiles are not covered at all in the household survey. The methodology would then suffer from omitting a nontrivial segment of the population from the analysis.

In this paper we build upon the existing methodologies (particularly, Atkinson 2007 and Alvaredo 2011) and propose a new methodology aimed to optimally combine the two data sources, accounting explicitly for the underreporting of the top of the income distribution in the household survey. The methodology is based on finding the optimal income threshold _b_ such that the following statements hold true: (1) the segment of the population with incomes below _b_ is well represented in the household survey data; (2) those with incomes above _b_ are captured in the tax records; and (3) _b_ minimizes the distortions attributed to household survey underreporting at the top of the income distribution. This paper shows that if _b_ can be found (possibly below the income level of the top 0.1 or 1 percent), the population’s full unconditional distribution of income can be estimated by combining the Gini coefficients of the population segments with incomes below _b_ (using household survey data) and above _b_ (using tax records). Since the underreporting distortions in the household survey are stronger for higher incomes, the optimal _b_ that satisfies these requirements is the lowest level of income that triggers mandatory tax filing. This is the key element of the proposed methodology. Note that this income threshold can be significantly below the 1 percent traditionally used in the existing literature.

The proposed methodology, therefore, solves the issue of arbitrarily choosing the top income group, instead relying on the optimal income threshold _b_ , which gives the maximum weight possible to tax records at the top of the income distribution. In addition, the 3
<!-- Start of picture text --> 4000<br>3500 --<br>3000- ___ . _-<br>2500<br>* 2000 .__ -__ _<br>*~1500 -- #<br>1000 _ ___ __ _ _ .<br>500<br>0<br>0 1000 2000 3000 4000<br>Outpatients - facility data<br><!-- End of picture text --> _Source: Lindelow, Reinikka, and Svensson (2001)._

# **_Quality_**

Health care quality is a vague and nebulous concept. Yet, however defined, it is a commonly held view that the quality of health services can have a big impact on health outcomes, health related behavior, and patient satisfaction. It is therefore not surprising that many facility surveys have tried to capture some dimensions of quality. In general, the need for the concept of "quality" stems from the considerable heterogeneity of health services, and from the fact that this heterogeneity is often difficult to observe and is not, in general, reflected in prices. For example, while health services differ along some observable dimensions-e.g., input availability, amenities, and clinical and inter-personal skills of health workers-other dimensions of this heterogeneity, such as clinical effort and competence, are harder to observe.

According to a commonly used definition, health care quality pertains to the management of a personal health problem by a health care practitioner (Donabedian 1980). Following previous literature, Donabedian distinguishes between a technical and an interpersonal domain of management, where the first refers to the application of the science of medicine and other health sciences, and the second the social-psychological interaction of the health care provider and the patient.25 In this framework, the basis for making assessment about the quality of technical care pertains largely to professional norms based on a body of scientific knowledge. It is more complicated to evaluate the quality of interpersonal care, but criteria must clearly relate to socially defined norms of acceptability and good practice. In other words, according to this view, quality must always be conceived with reference to some standards or norms, and is inherently a relative concept.

> **25** Donabedian underlnes that the two domains are interrelated and often difficult to distinguish. He also distmguishes the "amenities" offered by the health care provider as a possible element of quality, but considers this part of the

> interpersonal management of an illness episode.

13
Siougle, E., S. Dimelis, and C. Economidou (2019). Does ISO 9000 Certification Matter for Firm Performance? A Group Analysis of Greek Listed Companies. _International Journal of Production Economics_ 209: 2-11.

Starke, F., R.V. Eunni, N. Manoel Martins Dias Fouto, and C. Felisoni de Angelo (2012). Impact of ISO 9000 Certification on Firm Performance: Evidence from Brazil. _Management Research Review_ 35(10): 974-997.

Sun, H. (2000). Total Quality Management. ISO 9000 Certification and Performance Improvement. _International Journal of Quality & Reliability Management_ 17: 168–179.

Ullah, B. (2020). Signaling Value of Quality Certification: Financing Under Asymmetric Information. _Journal of Multinational Financial Management_ 55: 100629.

Ullah, B. (2022). The Impact of Quality Certification on SME Innovation and the Role of Institutions. _Research in International Business and Finance_ 62: 101748.

Van Reenen, J. (2011). Does Competition Raise Productivity through Improving Management Quality? _International Journal of Industrial Organization_ 29(3): 306–316.

Vu, K., and P. Glewwe (2022). Maternity Benefits Mandate and Women’s Choice of Work in Vietnam. _World Development_ 158: 105964.

Wen, H., N. Li, and Chien-Chiang Lee (2021). Energy Intensity of Manufacturing Enterprises Under Competitive Pressure from the Informal Sector: Evidence from Developing and Emerging Countries. _Energy Economics_ 104(December): 105613.

Wen, W., C. Forman, and S.L. Jarvenpaa (2022). The Effects of Technology Standards on Complementor Innovations: Evidence from the IETF. _Research Policy_ 51(6): 104518.

Yang, Z., P. Liu, and L. Luo (2023). Growing Exports Through ISO 9001 Quality Certification: Firm-level Evidence from Chinese Agri-food Sectors. _Food Policy_ 117: 102455.

36
_Figure 1: Officials prioritize improvements to school completion over improvements to learning for economic growth._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>MNG LAO PHL IDN VNM Non-EAP<br>MOE MOF All<br>increase<br> mprove test scores<br>instead of<br>Share of officials choosing<br>number of students finishing secondary<br><!-- End of picture text --> Source: CGD–World Bank survey, 2022. The figure aggregates the answers obtained for the question “For the economy to grow faster, the most important thing your country could do in education would be to: (1) Increase the number of children finishing secondary school, (2) improve test scores, (3) Do not know, (4) other: specify. The “other” replies were then identified as either linked to quality (test scores) or quantity (more secondary completion) by looking at individual responses.

# 3.2 Policy makers underestimate learning poverty by large margins in EAP

Policy makers in EAP underestimate learning poverty by a larger margin than officials from other countries. Learning poverty is defined as the percentage of children aged 10 who are unable to read and understand age-appropriate text. Senior education and finance officials in EAP underestimate learning poverty by an average of 31 percentage points. The gap between officials’ estimates and World Bank estimated learning poverty levels is larger in EAP countries than in non-EAP countries sampled. This is true for both the 2020 and 2022 survey waves. Non-EAP officials underestimate the learning poverty level by an average of 23 percentage points, while EAP officials underestimate it by 31 percentage points.

This underestimation of learning poverty is an overestimation of the reading and comprehension ability of children around the age of 10. In countries where this divergence between perception and actual data is large, for example in Mongolia, Vietnam, Indonesia, Lao PDR, and the Philippines, one might expect that officials would be less likely to pursue education policies and funding to support learning at the primary level than they would be if they had a more accurate understanding of the actual abilities of students. We find no significant link between income levels and the underestimation of learning levels, as evidenced by the low F-value of a simple OLS regression.

The fact that officials from all countries, on average, underestimate learning poverty indicates an important disconnect between how much learning officials believe is happening and how much learning is taking place. If officials do not have an accurate understanding of the magnitude of learning poverty, they may be less likely to take effective action to address the quality of learning in their systems. Recent increases in learning poverty due to school interruptions during the COVID-19 pandemic, coupled with 4
**Figure 3:** Random forest importance for the top 15 most important variables for ML prediction of levels of Rice. _Notes:_ 3/4 of the crop-cut sample is used. 5 validation cross-fold splits are used.

<!-- Start of picture text --> Population Center<br>rict of Residence<br>g planting season<br>rter average start<br>ean Temperature<br>nual Precipitation<br>Elevation<br>l planting season<br>y growing season<br>Excess salts<br>y growing season<br>Border Crossing<br>SR Yield<br>Slope<br>ousehold weights<br>0e+00 2e+08 4e+08 6e+08 8e+08<br><!-- End of picture text --> 28
- Besley, T., & Persson, T. (2009). The Origins of State Capacity: Property Rights, Taxation, and Politics. _American Economic Review_ , _99_ (4), 1218–1244. 1, 4, 6, 7, 28

- Besley, T., & Persson, T. (2011). Pillars of Prosperity: The Political Economics of Development Clusters. Economics Books, Princeton University Press. 1, 6, 7

- Bisin, A. (2020). A Comment on: “State Capacity, Reciprocity, and the Social Contract” by Timothy Besley. _Econometrica_ , _88_ (4), 1345–1349. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA18010. 4, 13

- Bloom, N., & Van Reenen, J. (2010). Human Resource Management and Productivity. Tech. Rep. w16019, National Bureau of Economic Research, Cambridge, MA. 13

- Bourguignon, F., & Gunning, J. W. (2018). Foreign Aid and Governance: a Survey. In _Handbook of Economic Development and Institutions_ . Princeton University Press. 5, 28

- Bourguignon, F., & Platteau, J.-P. (2015). The Hard Challenge of Aid Coordination. _World Development_ , _69_ (C), 86–97. Publisher: Elsevier. 5

- Bourguignon, F., & Sundberg, M. (2007). Aid Effectiveness – Opening the Black Box. _American Economic Review_ , _97_ (2), 316–321. 5

- Bowles, S. (2020). A Comment on: “State Capacity, Reciprocity, and the Social Contract” by Timothy Besley. _Econometrica_ , _88_ (4), 1337–1343. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA18037. 4, 13

- Brautigam, D. A., & Knack, S. (2004). Foreign Aid, Institutions, and Governance in Sub-Saharan Africa. _Economic Development and Cultural Change_ , _52_ (2), 255–85. Publisher: University of Chicago Press. 5

- Dal Bo, E., & Finan, F. (2016). At the Intersection: A Review of Institutions in Economic Development. 6, 28

- Dal Bó, E., Finan, F., & Rossi, M. A. (2013). Strengthening State Capabilities: The Role of Financial Incentives in the Call to Public Service. _The Quarterly Journal of Economics_ , _128_ (3), 1169–1218. 4, 5, 7

- Dal Bó, P., Foster, A., & Putterman, L. (2010). Institutions and behavior: Experimental evidence on the effects of democracy. _American Economic Review_ , _100_ (5), 2205–29. URL https://www.aeaweb.org/articles?id=10.1257/aer.100.5. 2205 6

- de Janvry, A., & Dethier, J.-J. (2012). The World Bank and Governance: The Bank’s Efforts to Help Developing Countries Build State Capacity. SSRN Scholarly Paper ID 2179406, Social Science Research Network, Rochester, NY. 1, 7

30
there is heterogeneity in the main source of income and reported changes by gender of head of households.

Table 6: Loss in income during COVID-19 from household’s income source

|Variables|(1)<br>Main<br>Income|(2)<br>Farm<br>Income|(3)<br>Business<br>Income|(4)<br>Wage<br>Income|
|---|---|---|---|---|
|Gender of household head (1=female hh,|-0.092|-0.281|0.681***|-|
|0=male hh)||||0.799**|
||(0.136)|(0.187)|(0.173)|(0.321)|
|Education level of household head|-0.152|-0.209|0.348|0.403|
|(1=primary)|||||
||(0.145)|(0.172)|(0.273)|(0.496)|
|Education level of household head|-0.224*|-0.393**|0.650***|0.389|
|(2=secondary)|||||
||(0.128)|(0.163)|(0.214)|(0.300)|
|Education level of household head|-0.122|0.265|0.647***|0.431*|
|(3=post-secondary and above)|||||
||(0.167)|(0.351)|(0.237)|(0.259)|
|Sector (urban=1, rural=0,)|0.134|0.166|-0.235|-0.503*|
||(0.084)|(0.121)|(0.144)|(0.283)|
|Household size|-0.002|0.009|-0.015|-0.033|
||(0.014)|(0.018)|(0.027)|(0.033)|
|Constant|0.477***|0.436***|0.693***|0.429|
||(0.123)|(0.149)|(0.263)|(0.343)|
|Observations|1,724|690|657|344|

Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10). Each column reports results from a single probit regression of a binary variable equal to 1 if the respondent’s household experienced loss in income and 0 otherwise. “main_inc_dwn” represented a loss in income from the main income source of the household. The columns show the dependent variables associated with income loss in farm income, business/enterprise income, and wage income, respectively, for households that reported these sources as their main income source prior to COVID-19.

Regression results presented in Table 6 show female-headed households are significantly more likely to have lost income from non-farm enterprise, and male-headed households are significantly more likely to have lost income from wage employment. Among the surveyed sample, only 15 percent of the femaleheaded households reported having earned income from wage employment by any of the household members, versus 22 percent in male-headed households. It is notable here that wage employment is far less common among women in Chad and in the agriculture sector. They work largely as unpaid laborers in family enterprises (Ngatia et al., 2021), where a reduction in wage income would not be captured. Wage employment among the female heads of households is even smaller; only 14 percent of the surveyed sample of female heads worked in salaried employment. Most of them (63 percent) worked on their own account (versus 47 percent of male heads).

Although income losses during the pandemic have been incurred in all income sources, we focus on nonfarm enterprise income as this is the main income source reported by the majority of female-headed households, and enterprise income in the service sector is the most affected by lockdown and other 13
money are likely to offer more credit to customers and also demand more credit, while male owned-firms do not. One potential explanation is that mobile money builds trust between womenowned businesses and suppliers and customers, providing opportunities to expand and invest. Alternatively, mobile money may provide liquidity to female-owned firms, allowing them to lend to customers. The provision of customer credit is more under the control of businesses than supplier credit, which is ultimately a decision by the supplying firm. Gosavi (2018) uncovered a positive relationship between mobile money and access to credit for a smaller sample of economies in East Africa.

This study builds on the emerging literature exploring mobile money adoption by businesses, and its benefits. This literature has typically focused on a single or a handful of economies, mainly in East Africa. Gosavi (2015) investigates the characteristics of businesses that use mobile money in Kenya, Tanzania, Uganda, and Zambia and explores the relationship between mobile money and access to finance for the same sample (Gosavi, 2018). A precursor to this study, Islam et al, (2018) find a positive relationship between mobile and investment for firms in Kenya, Uganda, and Tanzania. Beck et al (2018) model the relationship between the adoption of mobile money, entrepreneurial growth, and finance, calibrating their model to match firm-level data from Kenya. Bastian et al. (2018) experimentally explore the relationship between mobile money savings and the growth of female-owned micro-businesses in Tanzania and Indonesia. This study is the first to exhaustively explore the relationship between mobile money and investment for formal firms in 16 economies across Sub-Saharan Africa. It also provides important descriptive information for policy makers on adoption rates and the reasons for mobile money use by firms across Sub-Saharan Africa.

This study also relates to the literature on access to finance for women entrepreneurs, a topic of great interest given that financing obstacles hold back firms (Beck et al., 2005 and Ayyagari et al., 2008) and that discrimination in access to finance limits growth of women-led businesses and represents an obstacle for their investing opportunities (World Bank, 2011). The growing body of literature on this topic has focused mainly on gender-based discrimination in credit rationing and discouraged borrowers. Evidence is mixed as to whether female-owned firms are more financially constrained than male-owned firms in Sub-Saharan Africa (Asiedu et al., 2013; Aterido et al., 4
scenarios constructed to highlight the contribution of changes in inequality vs. mean incomes on poverty forecasts; thus, one should focus on the differences in projections generated under the different scenarios as opposed to the actual forecast levels. We set out to calculate: for each country _i_ , _t_ = _{T, T_ + 1 _, ...,_ 2030 _}_ , where _T_ denotes the latest survey year for each country. Then by region, we calculate average headcount poverty (weighted by country population): where _r_ denotes region, and _ωi_ denotes country _i_<sup>_′_</sup> _s_ population relative to the total population in the region.<sup>17</sup> Note, we could also project headcount poverty using an elasticity approach, e.g., _Hi,_ 2030 = _Hi,T_ +<sup>�2030</sup> _s_ =1<sup>_−T_</sup> ( _κy,i,s_ ̄ ∆ ̄ _ys_ + _κσ,i,s_ ∆ _σs_ ), but as discussed earlier, the elasticity approach only provides a first-order approximation for headcount poverty as changes in mean incomes and inequality are discrete.

To project headcount poverty, we need values for mean income and inequality out to 2030. As our baseline, we generate predictions for _y_ ̄ using data on real GDP per-capita from 1990-2022 from the Macro Poverty Outlook (MPO).<sup>18</sup> In particular, we estimate the following MA(2) regression for each country _i_ :<sup>19</sup> where log( _gdp_ _~~p~~ ci,t_ ) denotes the log of GDP per-capita in country _i_ in year _t_ . Using the coefficients estimated from these regressions, we then predict real GDP per-capita for 2023-2030 for each country. Then, using this GDP per-capita series (the MPO series up until 2022 plus our predicted series from 2023-2030), we calculate country specific growth rates in GDP per-capita out to 2030. These growth rates, combined with the latest observation of mean income in each country from PovcalNet, allow us to predict mean

> 17. For population measures, we use predicted country populations for 2022 generated by the Macro and Poverty Outlook (MPO). See `https://www.worldbank.org/en/publication/ macro-poverty-outlook#:~:text=Next-,Overview,Group%20and%20International%20Monetary% 20Fund.` . The predictions we use are from the vintage dated June 9, 2020. 18. Specifically, we use the vintage dated June 9, 2020. 19. We estimate an MA(2) model as opposed to the simpler log-linear specification so as to smooth out shocks when forecasting GDP per-capita. Note, the MPO projections take into account the anticipated COVID-19 shock.

23