rafmacalaba/data-use-mentions-extended huggingface dataset:
.5|
|Mean (geometric,<br>annualized)|2.7|1.8|1.6|1.5|6.8|6.5|6.3|6.2|6.3|6.3|6.4|6.5|6.5|<br>6.5|

_Note:_ This table shows holding periods ranging from Q1 to year 10. The average geometric mean is reported for quarterly holding periods on a quarterly basis, that is, it gives the average, real compounded return for one quarter. For the annual holding periods, the geometric mean states the average, annualized return for the specific holding period. All return statistics are based on a global portfolio of outstanding foreign-currency sovereign bonds.

Figure 6 zooms in on the subsample of the worst-performing bonds, in which the repudiation cases figure prominently. We calculate the share of bonds with negative real compounded returns after different holding periods. In the full sample, in year one, more than 26% of the observations see negative returns, and this drops to below 20% in year 10 (Panel A). Moreover, there are about 10% of observations with substantial losses, showing a negative return of -30% or worse, as well as a small but growing share of bonds with returns between -30% and -60%. Observations in this bottom bin are dominated by spells after the Great Depression and around WW2, for example, in many Communist countries (China, Russia, and Eastern Europe), which saw a large initial collapse in bond prices and defaults that persisted for decades.

In the modern (post-1995) period, the share of bonds with negative returns is much lower than in the historical sample (Panel B), partially reflecting the relative absence of full repudiation cases. After three years, only about 7% of the country observations remain in negative territory. The share with very low 26
# **4. Data source and main variables**

Our data source is a firm-level survey of private firms in Malaysia conducted by the World Bank’s Enterprise Surveys (ES) in 2019. The ES are nationally representative surveys of private registered manufacturing and services firms that have five or more employees. The surveys are stratified by size, sector (within manufacturing and service), and region within the country. Informal or unregistered firms and those with fewer than five employees are excluded from the sample. We focus on the sample of hotels (ISIC Rev. 3.1 industry code 5510) in the ES. Our estimations below use all the hotels surveyed by the ES for which information is available on the main variables of interest. There are 90 hotels in the baseline sample. Sampling weights provided by the ES to correct for oversampling are used throughout. In the Appendix, a formal definition of all the variables used in the regressions is provided in Table A1. Summary statistics are provided in Table A2.

Our baseline results are based on estimating the following equation: E 1 ∗H 2 3H iirr iirr iirr iirr iirr EEMMEEMMEEMMEEyy = αα+ ββ XX HHEEHH ssMMssEEE + B+ ββ XX + ββ HHEEHH ssMMssEE ... . . (1) rr iirr iirr + RREERRMMHHMM EEMMxxEEff E EEHHss ssEEHHM EE CCHHMMHHrrH ss + uu where subscript _i_ denotes the hotel, _r_ denotes the region (city), _X_ is volatility in occupancy rate (defined below). The key parameter of interest is 1, which captures how the impact of volatility in occupancy rate on a hotel’s efficiency varies with the size of the hotel. In the robustness checks, ββ several interaction term controls are added to equation (1). The estimation method used is Ordinary Least Squares (OLS) with robust standard errors clustered on the region times star rating of the 11
For their part, the National Bureau of Statistics of China (NBSC) also conducted a survey on employment at the end of 2008 (NBSC, 2009). Released in March 2009, the report suggested that 23 million migrant workers were out of a job. While important, this report suffered from several shortcomings. First, it was conducted early in the financial crisis and, as such, did not pick up the adjustments made by laid off workers. Second, the migrant segment of the labor force also only represents half of the rural off-farm labor force. The report did not report on disaggregated findings from the data or answer the question about who was hurt and who was not hurt.

The overall purpose of our paper is simple. This is a mostly descriptive paper that relies primarily on a nationwide dataset that was collected in May 2009 on rural households (and households from the same sample villages that moved to the city since 2000). The broad goal of the paper is to document the effect of the financial crisis on the rural labor force in China. To meet this general goal, we have four specific objectives. First, we seek to compare the difference between the actual off-farm employment rate and the off-farm employment rate under the assumption of business as usual (BAU—a counterfactual of what off-farm employment would have been in the absence of the global financial crisis). Second, we estimate the impact on the monthly earnings of those that did not lose their job. Third, we sketch profiles of both those that tended to be hurt and those more likely to have kept their jobs. Finally, we track the progress of those who were laid off and document their progress in finding new employment. To achieve this final objective, we rely not only on the May 2009 data, but also a set of follow-up interviews conducted in September 2009. Ultimately, we want to provide policymakers inside and outside of China with an accurate picture of China’s response to the global 4
rates. Furthermore, a comparison between camp and non-camp population would ignore differences in housing, health and education between these populations, substantially limiting the value of a comparison. These questions are still largely unresolved and the search for poverty lines adapted to FDP contexts is still in its infancy.

# Comparisons across time and populations

Understanding how poverty among FDPs evolves over time and how this compares to other populations is another essential pillar of poverty measurement.

Tracking FDPs over time can be particularly helpful to monitor changes in livelihoods and changes in displacement status, location as well as the end of displacement.<sup>11</sup> Panel surveys in this respect are useful

> 11 While IOM uses the Displacement Tracking Matrix (DTM), it only provides an aggregated view of the location of FDPs.

18
seek timely care. This is because RMNCH-N care-seeking also depends on certain demand-side factors, and utilization patterns are largely context- and time-specific. This situation necessitates that the determinants of RMNCH-N utilization be identified and addressed to ensure prioritized investment decision-making.

In Bangladesh, the evidence-base is inconclusive on the demand- and supply-side determinants that should be addressed urgently for improving RMNCH-N utilization. While the prevailing evidence largely points to socio-economic, cultural and regional factors as the key drivers of RMNCH-N utilization,<sup>8</sup> little is known about the role of service delivery status and quality of care in RMNCH-N uptake. Similarly, the relative significance of supply-side determinants over the demand-side determinants is not clear. For instance, if quality of care is the key determinant of service uptake, improving quality versus sizably investing in addressing certain demand-side determinants (e.g., cultural barriers) would be an immediate priority. While most existing studies have used DHS data and demand-side determinants alone to examine the patterns of RMNCH-N utilization, assessments which combine both demand- and supply-side determinants of RMNCH-N utilization, are limited.

It is against this backdrop that the World Bank, in collaboration with the Ministry of Health and Family Welfare (MoHFW), carried out this technical assistance study to ascertain the most important determinants that need to be addressed, on a priority basis, to improve RMNCH-N outcomes further. A comparative assessment of the drivers of RMNCH-N performance across time periods (2014 and 2017 surveys) reflects the success, as well as gaps, in the government strategies and interventions for further policy navigation. Study findings would also be influential to RMNCH-N policy making in the post-COVID-19 context.

## **1.4 Aim**

The overall aim of this study was to inform strategic RMNCH-N investment decision-making, through a three-pronged approach that involved:

1. Identifying the top ten determinants that need to be addressed on a priority basis to improve RMNCH-N utilization, quality of care and outcomes nationally and for divisions (or states)

2. Comparing the relative
the Government of Ethiopia, working closely with Wageningen UR, CGIAR, and other partners, has experimented with seed market liberalization to allow firms to market seed directly to farmers rather than through the state’s rural administrative apparatus. This Direct Seed Marketing (DSM) approach aimed to increase and sustain the supply of improved varieties and quality seed to farmers while rationalizing the use of public resources allocated to the distribution of seed to fragmented, spatially dispersed, resource-poor smallholders that make up the vast majority of farmers (MoA/ATA 2016).

Since DSM’s launch in 2011, several governmental entities—the Agricultural Transformation Agency (ATA), the Ministry of Agriculture (MoA), the regional bureaus of agriculture, development projects such as the Integrated Seed Sector Development (ISSD) initiative—have promoted and closely monitored its implementation. Benson, Spielman, and Kasa (2014) conducted an operational evaluation of the pilot DSM program and found encouraging results, ultimately recommending that the government stay the course and advance the seed sector reform process. Mekonen et al. (2019) conducted a descriptive analysis of DSM’s key performance indicators, including seed availability, quality, sufficiency, price competitiveness, supply timeliness, and accountability. Their analysis documented mixed results, including differences in performance across regions and crops.

However, to date, there has been no rigorous assessment of DSM’s impact on the amount of fresh seed purchased by farmers, a first-order performance indicator. Nor has there been any assessment of DSM’s impact on key outcomes, such as changes in farm-level productivity. The availability of three rounds of household-level panel data from DSM and non-DSM _woredas_ (districts) in 2012 (the beginning of DSM’s pilot phase), 2016, and 2019 (after the program had expanded to more than two hundred districts) provides an opportunity to fill this gap in the evaluative literature on DSM.

This study exploits the staggered scale-up of the DSM approach over time, across districts, and in crop coverage. Using
<mark>Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture.</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(3), 106-40.</mark> **[3]** <mark>Cai, R., Feng, S., Oppenheimer, M., & Pytlikova, M. (2016). Climate variability and international migration: The importance of the agricultural linkage.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>79</mark>_ <mark>, 135-151.</mark> **<mark>[</mark> 1]** <mark>Carletto, C., & Gourlay, S. (2019). A thing of the past? Household surveys in a rapidly evolving (agricultural) data landscape: Insights from the LSMS‐ISA.</mark> _<mark>Agricultural Economics</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>, 51-62.</mark> **<mark>[</mark> 2]** <mark>Carletto, C., Larrison, J., & Özden, Ç. (2014). Informing migration policies: a data primer. In</mark> _<mark>International Handbook on Migration and Economic Development</mark>_ <mark>. Edward Elgar Publishing.</mark> **<mark>[</mark> 2]** <mark>Carter, M. R., & Barrett, C. B. (2006). The economics
largest equalizing effect is achieved through in-kind transfers, which in Bhutan are mainly the benefits stemming from education and health services—the Gini index based on final income is notably lower at 31.3. The overall reduction in inequality is equivalent to around 7.4 Gini points from market income to final income.

The magnitude of the overall reduction in inequality compares relatively favorably in international comparison. Bhutan’s tax-benefit system ranks above the middle when compared with other middle-income countries by the magnitude of its redistributive effect, measured using the decrease in the Gini index (figure 3). The reduction of 7.4 points in Bhutan’s Gini index is similar to the performance of the net fiscal systems of Ecuador and Turkey, and lower than that of Mexico and Brazil, though these countries have a significantly higher GDP per capita than Bhutan. In fact, the redistributive impact of fiscal policies is significantly larger in high-income countries compared with low- or middle-income countries. The initial

> 24 We do not use the actual consumption approach, which allocates the transfers based on actual health care usage. That approach has a few shortcomings: (i) the demand for health care services is irregular and we would only observe care utilization during a short reference period; (ii) individuals with worse health conditions are assigned a higher transfer, which would have a significant impact on the income distribution that may not reflect the purpose of public health services, which is to provide equitable access to health care for everyone.

16
CEN-PSU”).<sup>29</sup> The final three models are all traditional methods that we use to benchmark the performance of gradient boosting. We present unit-level (CensusEB), unit-context, and area level models. We consider three alternative implementations of the area-level model that, much like for gradient boosting, vary the level of analysis and the covariates used. In particular, the empirical best (CensusEB) model is often considered the “gold standard” of small-area estimation and corresponds to an ideal scenario where up-to-date, micro-level census data are available. For these traditional models, all available covariates enter the models linearly (i.e., we do not include interaction or quadratic terms).

One advantage of the MSE is that it can be calculated for each geographical entity. As such, each box plot in panel (a) of Figure 2 summarizes the distribution of municipality-level MSE estimates rather than the aggregate MSE for each sample (i.e., the results are based on Eq. [26]). It is useful to first consider how gradient boosting performs relative to the traditional models when using the same census-based covariates. As is evident in the results from panel (a) and (b) of Figure 2 the gradient boosting methods using census aggregate data – Gradient boosting (CEN-PSU) and Gradient boosting (CEN-MUN) – perform better than unit-context and Fay-Herriot models, despite using similar data, and approximate the performance of unit-level (CensusEB) estimates. The median MSE for the CensusEB model is 0.003 while that of gradient boosting methods is 0.002 and both methods have similar interquartile ranges. The results suggest that gradient boosting methods can approximate CensusEB unit-level methods and will likely outperform unit-context and Fay-Herriot methods by a considerable margin, which is quite encouraging for cases where estimates are needed when the survey and census data are not contemporaneous.<sup>30</sup>

> 29Recall that the geo-referenced covariates are only available at the municipality level.

> 30The unit-context models
<sup>58</sup> The advantage of such switch would be to allow accounting for the unequal individual outcomes observed in the data.

One disadvantage with such switch in unit of analysis is the risk of misinterpretation. Individual-level well-being indices obtained in this way may lead to improper interpersonal comparisons. To give a concrete example, DHS surveys provide information on underweight women but often do not provide information on underweight men. Taking advantage of this information on underweight women makes sense, but the resulting individual well-being indices cannot be compared across men and women.<sup>59</sup> However, they can certainly improve the inter-personal well-being comparisons across women. Impor-

> 56This literature relies on methods that are complex, even though some simplified methods have recently been proposed by Lechene et al. (2022) with limited additional data requirements. These methods are certainly worth further exploring.

> 57This need not be the case of children multidimensional poverty measures, where the unit of analysis is sometimes the individual child (Dirksen and Alkire, 2021; Alkire and Haq, 2023). 58Observe that such assumptions are already implicitly made when the unit of analysis is at household level. Indeed, a person living in a household identified as multidimensionally poor is herself considered to be multidimensionally poor.

> 59Observe that, similarly, interpersonal comparisons across men and women cannot be made using the World Bank’s MPM or the global MPI, at least not without the qualifier “living in multidimensionally poor household”.

31
**68**<sup>*****</sup>|100|
|Distribution losses|% production|43.6|**44.9**|27.4|
|Cost recovery|% total costs|55.6|**65.4**|80.6|
|Total hidden costs as % of revenue|%|270.4|**236.4**|<br>855.2|
|US cents per m<sup>3</sup>|**Zambia**|Scarce water re|sources|Other developing regions|
|Residential tariff|**48**|60|||
|Nonresidential tariff|**59**|120||3.0–60.0|

_Source:_ Banerjee and others 2008. Derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data _Note:_<sup>*****</sup> Average of three largest utilities.

# **Challenges**

The share of the population without access to safe solutions is increasing over time (figures 5 and 6). Despite doing well at the high end of the coverage spectrum, Zambia does not fare much better than its peers when it comes to the percentage of the population relying on surface water or practicing open defecation. A full 19 percent of Zambia’s population continues to rely on surface water and as much as 27 percent of the population continues to practice open defecation. Moreover, trends in household access to WSS services from successive household surveys show that the share of the population living in these insanitary conditions continues to increase. An additional 0.8 percent of the population each year relies on surface water and an additional 0.4 percent of the population practices open defecation. The high health risk associated with these practices makes this a very troubling finding
438|2.061|3.519|3.373|
|Prob>F|0.000|0.854|0.054|0.002|0.003|
|N|27522.000|1680.000|8118.000|7712.000|9032.000|

Notes: Fixed effects regressions. Robust standard errors, clustered by household in parentheses. Significance: * 0.10 ** 0.05 *** 0.01

# **5. Conclusion**

Our results do not reveal substantial biases in accounts of life satisfaction due to scaling. In particular, the relationship between important determinants of life satisfaction and reported life satisfaction at the individual level is robust to alternative formulations and scales of the life satisfaction question. Furthermore, the country-level relationships between subjective well-being and objective measures of welfare are broadly consistent across the two subjective well-being scales (with the exception of Central Asia), although the ranking of countries is not invariant to the scale of the life satisfaction measure.

18
et al. originally applied their algorithm to France using detailed data on every building footprint in the country as input, we instead apply the algorithm on a much larger spatial scale – i.e., to the entire SSA region – using constrained WorldPop data for 250 m*250 m grid cells as input. We also modify the algorithm to exclude deserts from the definition of a country’s potentially inhabitable area. While excluding deserts is not important for France, it is for SSA countries such as Mauritania, Niger, Mali, and Chad. For all four of these countries, more than 50 percent of their land areas are desert.<sup>10</sup> Not excluding deserts would mechanically decrease the counterfactual density and make these countries appear more urbanized overall than they really are. Our exclusion of deserts, and, more generally, of uninhabitable areas, also distinguishes our application of the “dartboard” algorithm from the version applied by Henderson et al. (2022) in their analysis of SSA urbanization dynamics.<sup>11</sup> Another notable advantage of the “dartboard” algorithm over other approaches for delineating urban areas is that the counterfactual density threshold to be considered significantly urban is specific to each cell and, most importantly, relative to a country’s own overall density instead of being common to all countries. Therefore, the approach much better discriminates urban areas. This is especially so for either very densely populated countries, which appear as very highly urbanized, or very sparely populated countries, which appear little urbanized partly by construction in the degree of urbanization and similar approaches.

The third, and final, literature to which our paper contributes is the descriptive literature on the structures of both urban systems and the internal structures of urban areas. This includes the extensive

> 8 The degree of urbanization uses a variety of such thresholds to distinguish different types of urban area. In “level 1” of the approach, a distinction is drawn between urban areas (“towns and
sampled households in the overall population is unknown. We therefore combine the WWS Household Survey data with quintile cut-offs from the 2009 Household Budget Survey (HBS). Because the HBS is representative of the entire Latvian population, the quintile cut-offs generated from that welfare distribution can be used to determine the quintile to which the WWS beneficiary household belongs.

# **3.2 Program Impact: The Impact of the WWS Program on Welfare**

This section describes the methodology used to measure the short-term impact of the WWS program on household welfare. This requires that we construct a counterfactual income for participating households in the absence of the program. Since a counterfactual income in the absence of the program is not available, assumptions are made to construct the counterfactual. Assumptions made in program evaluations are often dictated by data availability (Jalan and Ravallion, 2003).

This study exploits the fact that excess demand for the WWS program existed throughout its implementation. The long waiting list for WWS demonstrated that there were people interested in the program, who were similar to WWS participants in their preference for the WWS program, but who were not benefiting from the program. Applicants were wait-listed for the WWS program in the order in which they signed up. These applicants had already indicated a preference for program participation and to some extent had already revealed unobserved factors influencing their choice to participate (Galasso and Ravallion, 2004). However, latent heterogeneity between WWS participants and those on the WWS waiting list may bias impact estimates. As WWS enrolment is on a first-come, first-served basis, the possibility remains that those individuals who were more likely to be impacted by _any_ crisis are first to register for the program and the first to participate in the program. To control for observable heterogeneity, propensity score matching is used to construct a counterfactual 10
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