rafmacalaba/data-use-mentions-extended huggingface dataset:
occupations by their skill intensity and then use information on the occupational distribution of labor over time. In this case, skilled labor and less skilled labor have to be weighted by their respective relative labor productivity to account for differences in skills. For the case of Guatemala, similar to equation (14), this can be done by weighting labor inputs of different industries with the share that each type of labor occupies in total labor compensation.

However, it should be kept in mind that this kind of implicit differentiation of labor is a rather incomplete substitute for labor quality. It can only take into account some of the quality changes of labor input and does not allow the sources of the change to be identified. Moreover, the eight industry categories available from Banco de Guatemala (2003) statistics only apply to the _formal_ sector. As such, they ignore approximately 75 percent of the population working in the informal and rural economy. Placing less emphasis on educational improvements in _primary_ schooling—the working population of the IGSS is typically better educated than the population in the informal and rural sectors—the implicit labor quality index is biased downwards.<sup>19</sup>

**Figure 4. Guatemala: Comparison of Labor Quality Indices, 1970-2002** (in percent, relative to base year 1970)

<!-- Start of picture text --> 160<br>1970 = 100<br>140<br>120<br>100<br>80<br>60<br>70 75 80 85 90 95 00<br>Implicit labor quality index (formal sector) Labor quality index (hq)<br><!-- End of picture text --> _Source:_ Author’s calculations based on Banco de Guatemala (2003) data (implicit labor quality index), and human capital stock estimate for labor quality index ( _hq_ ).

> 19 This is because primary school enrollment has increased substantially over time (see Table 3).

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Finally, the PCA metric is intended as an overall assessment of facility performance, combining results across the variables and allowing for identification of high and low performing facilities. These differences follow some predictable patterns, such as private facilities scoring better than public facilities on average, but there remains wide variation within each country unexplained by our analysis. There may be facility factors that increase performance which our survey has not measured, such as the adeptness of management or the job satisfaction of health workers, and these results suggest a continued need for research into determinants of facility performance. Continued efforts to identify high-performing facilities, both in terms of outputs and health outcomes, can open opportunities for country governments to identify these positive deviants and to learn lessons on what enables their success.

# 5.2 Enhancing health system measurement

In 2018, the World Bank, in collaboration with the WHO and the Organisation for Economic Co-operation and Development (OECD), released a joint report, “Delivering quality health services”, highlighting the importance of delivering high quality health services as a global imperative within the context of achieving Universal Health Coverage by 2030.<sup>45</sup> This report acknowledges the dearth of actionable evidence on quality of service delivery, despite its critical role in initiating, sustaining and monitoring quality improvement processes. As the Service Delivery Indicators surveys are specifically developed with the aim of improving quality and accountability in public service delivery, these data can provide useful guidance to this end.

These lessons have already informed project design and supported reform in a number of countries. Along with the policy lessons learned, the 10-year experience of data collection efforts across a variety of settings offers valuable lessons on further enhancing the goals and methods employed for health system measurement, including future SDI surveys. Quality of service delivery is a multifaceted, complex process and no single survey can comprehensively measure all relevant aspects. By employing a novel methodology to measure provider ability and effort, SDI surveys go beyond most existing large-scale efforts that focus primarily on measuring inputs to
per function. This average is consistent across both general and sector-specific business functions. The distribution of _ANUMf,j_ reveals that 62.6% of functions use more than one technology, and 28.3% use at least three.

The vector of technologies used in a business function is relevant to two economic literatures. Schumpeterian models predict that when adopting a more sophisticated technology, an establishment will abandon the less sophisticated ones it was using. In models of technological leapfrogging, late adopters skip the less sophisticated technologies to directly use more sophisticated ones. Although FAT only provides cross-sectional data, it can be informative about the empirical support for these predictions. We explore the frequency of instances where establishments (i) completely skip or abandon less sophisticated technologies, (ii) use the least sophisticated technology despite having more advanced options, and (iii) create sophistication gaps by skipping some technologies in the business function.

Of the 37.4% of functions where only one technology is used, 52.8% use the least sophisticated technology. In the remaining 47.2%, the technology used is not the least sophisticated. Therefore, only in 18% (37.4% * 47.2%) of functions, establishments have fully skipped or abandoned all less sophisticated technologies. Conversely, in 70.4% of functions where multiple technologies are used, one of the technologies the establishments use is the least sophisticated technology in the grid. Finally, sophistication gaps are infrequent. Overall, they occur in 25% of business functions: 27% among GBFs and 17% among SSBFs. The GBFs where gaps are more frequent are payments (48%), business administration (34%), and sales (28%).

These observations show that establishments continue to use less sophisticated technologies even in business functions where more advanced technologies are available. FAT, therefore, does not support the predictions of Schumpeterian and leapfrogging models regarding the abandonment and skipping of less sophisticated technologies.

Additionally, the infrequency of sophistication
flooding are expected to have a lower price tag than equivalent properties not exposed to flooding. This price discount can be interpreted as the welfare cost of flood exposure, or in other words the willingness to pay to avoid flooding. Numerous studies which focus on high- or middle- income countries use hedonic price models to calculate the property price discount for location in an area exposed to flooding.<sup>1</sup> This study is one of very few analyses of the relationship between flooding risk and rents in low- and lower-middle income countries. Meta-analysis of 37 published papers and 364 point estimates shows that all of them focus high-income countries, mainly the United States (Beltr ́an et al., 2018). These metaregressions show that properties located in flood plains which suffer from inland flooding are 4.6 percent cheaper on average than a similar property not exposed to flood risk. However, the effects of flooding on property prices in the 37 papers studied by Beltr ́an et al. (2018) vary between a 75.5 discount to a 61 percent price premium. Thus, their study finds very high levels of heterogeneity between the effects of flooding on property prices even in an efficient housing market.

Examples of studies of the relationship between the housing market and flooding in upper-middleincome economies include Rabassa and Zoloa (2016), and Alvarez and Resosudarmo<sup> ́</sup> (2019) who study La Plata, Argentina, and Jakarta, Indonesia respectively. Rabassa and Zoloa (2016) estimate the benefits of a new hydraulic infrastructure based on estimation of the willingness to pay to avoid flood exposure using data on real estate prices and a flood risks map. They find that flood plain areas have a 3.5 percent price discount compared to other areas, and that this discount can be five times higher in areas of higher flood risk and less resilience. ́Alvarez and Resosudarmo (2019) measure the correlation between the water level of floods in Jakarta in 2007 and housing rental
53.7|
|9|15.4|12.9|13.9|29.4|12.5|23.1|

Source: SSB: Situation of Children, 1992.

**(c)** Access to **education** by **provinces.** The foregoing is substantiated by the differences in enrollment rates for primary, secondary and higher education by quintile of provinces in Table 2. The attendance rates at the primary level are, by and large, similar irrespective of poor or rich area, confirming the earlier observation from national data of universal entry at this level of education. But the enrollment-rate disparity between rich and poor provinces grows as students move on to secondary and higher levels. Such disparity is particularly acute among the 12-14 and 14 and older age groups-corresponding to post-primary education--than among the 7-11 age group. Urban-rural differentials are also pronounced, and this can be attributable to differences in income and quality. Primary education that corresponds to age range 7-11 is compulsory in China, but attendance is not universal in rural area particularly among the females. Urban children and those from high-income provinces are much more likely to attend secondary school (about 2.2 times) compared to children from rural area and low-income provinces. In the absence of compulsion in the subsequent age groups the gap in attendance rates widens steadily. Higher education is mostly received by households in richer provinces.
. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments.

_Variables:_ We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses:<sup>6</sup>

**Economic and life optimism** : We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are

> 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here.

> 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses.

> 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95% of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses.

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entrepreneurs in Ghana.<sup>8</sup> Specifically, before we take the respondents through any questions about their business performance, we ask them to give their best estimate of business profits for the most recent month after accounting for all business revenues and costs. Moreover, the question on recalled profits is shifted to the very beginning of the survey rather than after the _Money In_ section.

Table 4 presents the results of this comparative analysis. Consistent with the previous results, we find that the AAA measure leads to profit values that are significantly larger (with average AAA profits of 349 USD compared to average self-reported profits of 300 USD), with a p-value of 0.01 on a difference in means test. We also find a reduction in CV, from 0.68 in self-reported profits to 0.64 in AAA profits. Furthermore, we find 5% of the sample was unable to provide any response on self-reported profits. While this nonresponse rate is lower than what other studies have found (e.g. Bruhn and Zia, 2013), our analysis nevertheless confirms that the nonresponse falls to zero for the AAA measure.

Table 4 also compares AAA profits to calculated profits as in previous tables and finds similar pattern of results. Specifically, we find an adjustment factor of 63% between the two measures and a coefficient of variation that falls from 2.06 for calculated profits to 0.64 for AAA profits.

Finally, the Spearman and Pearson correlation coefficients in Table 4 show that self-reported profits and AAA profits are highly correlated with correlation coefficients ranging from 0.70-0.78. By contrast, the Pearson correlation coefficient between calculated profits and AAA profits is as low as 0.14.

# **3.4. Comparison with Administrative Data**

Thus far our results have established that the AAA performance measures are significantly larger and more precise (i.e. lower CV) than traditional estimates. However, are these estimates more accurate, i.e. more representative
life expectancy is higher in rich countries, longer schooling careers imply that individuals in those countries enter later in the labor market. Klenow and Rodr ́ıguez-Clare (1997b) and Caselli (2005) document that on balance this leads, if anything, to a higher average experience in poorer countries. Since the micro evidence suggests that experience is positively correlated with earnings, adding this component by assuming a common return across countries unavoidably leads to a smaller contribution of human capital to income differences.

Lagakos et al. (2016) argue however that returns to experience do vary across countries. The authors estimate the experience-wage profiles of US immigrants, and show that those

> 11I take the survival rate to 65 by gender from the World Development Indicators, and use the gender composition of the population to compute an average rate. All data refer to 2010.

> 12This is potentially problematic as educational attainment and health (or height) are likely to be positively correlated, and therefore the estimate of _β_ “ 0 _._ 10 from Mincerian regression without health controls might already partially reflect the effect of health. While the estimates Weil (2007) refers to for the calibration of _γ_ are conditional on educational attainment, he does not report the corresponding estimates for the education controls. However, reasonable changes to the value of _β_ do not massively affect the conclusions that follow.

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<!-- Start of picture text --> Year on year, by month (percent)<br>30 4 MMB Banks mortgage loans<br>25 4<br>—— Apartment purchase price index, Seoul<br>20<br>15<br>10 5<br>> linall |<br>2005.1 7 2006.1 7 2007.1 7 2008.1 7 2009.1 7 2010.1 7<br>s<br><!-- End of picture text -->
Figure 9: Change in Gini after fiscal policy (including in-kind transfers) <!-- Start of picture text --> 0<br>-5<br>LIC<br>-10<br>HIC UMIC LMIC<br>-15<br>-20<br>-25<br>High income Upper middle income Lower middle income Low<br>income<br>Point Change<br>Poland Croatia Panama Uruguay Chile Romania Namibia Argentina South Africa Costa Rica Brazil Mexico Iran Mexico Mexico Ecuador Belarus Colombia Colombia Venezuela Russia Albania Dominican Republic Paraguay Armenia Peru Peru Indonesia Guatemala Jordan Ukraine Tunisia Kenya El Salvador Bolivia Tanzania Nicaragua Egypt Ghana Comoros Honduras Sri Lanka Cambodia Burkina Faso Uganda Ethiopia<br><!-- End of picture text --> Source: CEQ and World Bank databases and World Bank calculations (see World Bank, 2022b). Note: HIC = high-income country, UMIC = upper middle-income country, LMIC = lower middle-income country, LIC = low-income country.

At the same time, poorer households pay more in tax than they receive in cash transfers; short-term poverty is slightly higher due to indirect taxation. Overall, the modelled fiscal system increases short-term poverty by 2.1 percentage points, as shown by the transition from _market income_ to _consumable income_ (Figure 10). The net transfer achieved through direct transfers and direct taxes on income are too small to change average household incomes, and thus poverty. However, the net transfer resulting after subsidies and indirect taxes on consumption is negative and large, thereby increasing poverty measured today. In other words, all households face higher prices on goods from indirect taxes. For poor and nearpoor households, the higher price reduces purchasing power, potentially reducing net expenditures below the poverty line. It is important to note that poorer households are only out-of-pocket in the short-term; this analysis does not include the economic benefits of health and education services which households will enjoy in the longer-term and which will help reduce poverty.

Figure 10
, from 9% to 29%. The accumulation of human capital during workers’ career appears to be an important source of cross-country differences in economic performance.

# **4.4 Development Accounting with Migration Data**

In this section I discuss further recent work using migration data to investigate cross-country differences in human capital. The premise of this approach is that when individuals migrate they bring the human capital accumulated in their country of origin, but then face a common technological and institutional environment in the new host country. Therefore, comparing earnings across migrants’ nationalities is informative on the cross-country variation in human capital, since it allows to keep constant the impact of other factors of production. Of course, these exercises need to take into account that migrants are not randomly selected

> 13Lagakos et al. (2016) emphasize that this approach ignores that life-cycle wage growth also depends on the time allocated to human capital investment. They incorporate this aspect in an alternative exercise, disciplined by a Ben-Porath model of human capital accumulation. They find that the resulting contribution of experience to development accounting is marginally lower.

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cushion the impacts on the most vulnerable. To that end, using the World Bank’s FY2021 FCV list of countries, we disaggregate the total poverty increases in Africa into countries that are in the FCV group and those that are not. Out of the 54 countries in Africa, the World Bank’s FY2021 FCV list designates 21 to be affected by some form of conflict or fragility.<sup>7</sup> Of the 21 fragile and conflict affected countries, two (Burundi and Eritrea) had minimal to no reported social protection responses, and only eight (38%) had major social protection responses, where we define “major responses” as those including an increase in cash transfers or unemployment insurance that persisted for more than three months. The remainder had moderate social protection responses such as one-off cash transfers increases, or increased food aid. By contrast, of the 32 non-conflict affected states, all had at least moderate social protection responses and 15 (47%) had major social protection responses (Gentillini et al., 2021).

Figure 5 reports the trends in poverty across these groups of countries. First, the levels of poverty in the two groups are drastically different. While 26% of the population (amounting to 196 million people) were living in extreme poverty in the non-FCV group of countries in 2019, there were 45% living in poverty in the FCV countries (amounting to 248 million people) in that same year. For the non-FCV group, an additional 11 million people were pushed into poverty in 2020 (equivalent to 1.5 percentage points increase) and 16 million in 2021 (equivalent to 2 percentage points increase) due to the COVID-19 pandemic. For the FCV group, the expected additional poor is about 12 million people in 2020 (equivalent to 2.1 percentage points increase) and over 14 million in 2021 (equivalent to 2.5 percentage points increase). Not only is the stock of poor higher in conflict ridden countries, the increases due
The analysis demonstrates that if the predictors in the target survey are elicited through questions that are identical to their counterparts in the base survey, imputation accuracy is not impacted by the remaining differences between the base and target surveys in terms of scope and complexity. Basic imputation models, including a core set of predictors on demographics, employment, household assets and housing, and/ or utility expenditures, yield highly accurate predictions vis-à-vis the true poverty rate. Furthermore, regarding TA 2 or TA 3 with modified (either shortened or aggregated) food and non-food consumption modules, imputation models including food consumption or non-food consumption expenditures as predictors do well _only if_ the distributions of the predictors are _standardized_ vis-à-vis the base survey (which can be either the TZNPS or TA 1). Finally, for the best-performing models to reach acceptable levels of accuracy, the analysis shows that the minimum-required sample size should be 1,000 observations for both the base survey and the target survey. The results are robust to the choice of base surveys used for imputation model estimation; different poverty lines; and alternative (quarterly or monthly) CPI deflators. Our proposed approach to imputation is also shown to perform better than multiple imputation and a range of machine learning techniques.

This paper consists of six sections. Section 2 presents the experimental design (Section 2.1) and descriptive statistics (Section 2.2). Section 3 discusses the analytical framework. Section 4 presents the main estimation results (Section 4.1) and robustness checks (Section 4.2), followed by section 5 on various extensions. Section 6 concludes. We provide additional estimation results in Appendix A, further description of the consumption aggregates in Appendix B, and more detailed discussion of the formulas and intuition behind the method in Appendix C.

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