rafmacalaba/data-use-mentions-extended huggingface dataset:
2%|6%|
|GJ12|281,750|2%|27%|62%|49%|68%|2%|3%|2%|4%|2%|
|GJ13|98,831|0%|4%|5%|3%|34%|0%|0%|0%|19%|2%|
|GJ14|448,126|0%|0%|10%|4%|14%|4%|1%|0%|0%|0%|

# <u>Actuarial justification</u>

Historical loss costs are the fundamental building block for experience-based approaches to ratemaking. This differs from the Normal Theory Method used for NAIS, under which the average historical yield and coefficient of variation are the fundamental building blocks. Since premium rates will be calculated at the crop/district level, it is appropriate for the weighted average crop/district level loss costs to be the fundamental building block for rates.

Historical loss costs are statistically informative if historical yields are, at least in a probabilistic sense, a good guide to the future. If the actuary has judged that the yield data should be de-trended it is appropriate and consistent to use de-trended data for calculation of both probable yields and premium rates. This increases the predictive quality of historical loss costs. If, for example, there is judged to have been a large positive trend in yields over the last ten years but this trend is not removed for ratemaking calculations, the resulting premium rate will be unreasonably high.

# _Capping of Loss Costs_

# <u>Description of step</u>

Judgment should be applied to choose a loss cost cap percentile (LCCP) at the crop/state level. This LCCP is applied to the data as follows.

For each district, calculate the LCCPth percentile historical loss cost. This is called the loss cost cap (LCC). For example, if
and sharia-compliant loans are less authentic than banks that specialize in sharia-compliant products. Tathmeer was not a lender known in the market, and was the intended name of the new Sharia-compliant subsidiary to be opened by Tamweelcom. The word “Tathmeer” means “to be fruitful” and does not have any religious connotation. Jordan Microfinance Company, on the other hand, is a known brand in the community.

In a separate, auxiliary marketing experiment, we tested whether the name brand (Jordan Microfinance Company) generated higher demand than the unknown brand (Tathmeer), but strictly for conventional loans. The name brand generates higher demand than the unknown brand. Section 4 discusses the ways in which this can affect the interpretation of the results.

# _Baseline Balance_

Table 1 displays the basic demographics of those recruited in the sample. Column 1 shows that individuals average 36 years old, and 57 percent are male. A third of the sample lives in Amman, the capital of Jordan, and a bit more than half the sample have a high school education or less. Sixty-five percent are married, 98 percent are Jordan nationals, and 11 percent plan to use the loan for home repair. Because it was important that respondents were not aware that they were part of a study we were unable to implement a large baseline survey which could have allowed us to collect detailed data on religiosity and financial literacy. Instead, we asked individuals whether or not they watch religious television programming<sup>15</sup> , in an effort to find a simple proxy for religiosity: 80 percent of the sample claim to watch any religious TV<sup>16</sup> . Half way through the study we updated our religiosity question to allow for a response on a 5 point scale. This allows us to identify individuals who claimed to “mostly” or “always” watch religious TV as “very religious”, which about 20% of our sample claim to do. Our results are qualitatively similar using either

> 15 We
income on the log-log scale. And the estimates from the piecewise regression (Appendix Table 7, Eq. 1) suggest that the income elasticity for low-income cities is not statistically different from that for high-income cities. Finally, similar to the national equations, the income elasticity estimates from the firstdifference urban equations turns out to be about 1.0.

It should be noted that the per capita incomes used in the urban area data set are actually country income data. It is known that the urban and national difference in per capita income is much greater in developing countries than in high income countries. This implies that the true range of urban income levels for the urban sample is narrower than that suggested by the income data used. Therefore, the true income elasticities for urban areas may be greater than the current estimates. The use of country income data in urban equations may affect the cross-section estimates more than the first-difference estimates, because changes in urban incomes would be quite similar to changes in country incomes over a short period of time.

# Roads

Because roadways complement motorization, the growth of the road network is likely to be associated systematically with changes in the same economic variables that affect the growth of the motor vehicle fleet. Empirical analyses by Ingram and Liu (1997) at the national level found that income per capita is a major determinant of road length, and that road provision is quite responsive to demand. While total road networks expand more
Wen, Gong<br>and Cai<br>(2016)|WTI|Heterogeneous<br>autoregressive models|Different models<br>exhibit different<br>predictive power in<br>forecasting the 1-<br>day, 1-week and 1-<br>month volatility of<br>crude oil futures.|Nominal<br>prices|Out-of-sample<br>forecasts|

_Note_ : APARCH (Asymmetric Power ARCH); EGARCH (Exponential GARCH); FIGARCH (Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedasticity); GARCH (Generalized Autoregressive Conditional Heteroskedasticity); Gene expression programming (GEP); HAR (Heterogeneous Autoregressivetype volatility models); TGARCH (Threshold Generalized Autoregressive Conditional Heteroskedasticity).

**Table 2. Sign restrictions on impulse responses**

||Supply shocks|Demand shocks|
|---|---|---|
|Oil or metal production|−|+|
|Global economic activity|−|+|
|Realprice of oil or metals|+|+|

32
WPS3825

# International Benchmarking of Lesotho’s Infrastructure Performance

## Željko Bogetić

Abstract: This note provides a preliminary benchmarking of infrastructure performance in Lesotho in four major sectors––electricity, water and sanitation, information and communication technology, and road transportation––against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. The results of the benchmarking are revealing of several major, comparative deficiencies in infrastructure performance in Lesotho: (i) extremely low access to electricity and its affordability; (ii) poor coverage, quality, and the cost of local (non-cellular) telephony; and (iii) poor quality of roads. Infrastructure service delivery in electricity, telephony, and roads is well below what would be expected, on average, for a country in Lesotho’s income group. In these sectors, Lesotho also compares unfavorably with many other geographical country groups. Unless addressed, such infrastructure shortfalls are likely to adversely affect the welfare of Lesotho’s poor, and the cost competitiveness and growth prospects of a range of economic sectors (e.g., tourism, trade) that depend critically on a stable and competitive supply of basic infrastructure service. They could also affect the speed and quality of Lesotho’s regional economic integration within the SACU sub-region with attendant consequences for the longterm growth of regional trade and real output. By contrast, Lesotho’s performance is solid in the access to improved water and sanitation, in the aggregate and in both rural and urban areas. Finally, this benchmarking, combined with more in-depth, sector analyses, could provide policymakers in Lesotho a useful guide to the areas of infrastructure performance requiring attention.

## JEL classification: H54, H, R, O, O57

World Bank Policy Research Working Paper 3825, January 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get
Hence, it is possible for nominal price increases to induce real wage declines, and there is significant evidence pointing to the adverse impact of inflation on poverty reduction (see Ferreira, Prennushi, and Ravallion 2000).<sup>17</sup> 17 Ferreira et al. (2000) write, “While changes in the relative short-term returns to holding bonds versus stocks may redistribute income only among the non-poor, there is one major asset-type impact which affects the poor: inflation. The rate of inflation is a tax on money holdings. Because there are barriers to entry in most markets for non-money financial assets, the poor are constrained in their ability to adjust their portfolio to rises in inflation. Typically, they will hold a greater proportion of their wealth in cash during inflationary episodes than do the non-poor. The non-poor are generally better able to protect their living standards from inflationary shocks than the poor.” They go on to cite evidence from India, Brazil, the Philippines, and a larger cross-country review.

24
. The final dataset that was fully matched with caseload data included vignette data from 14,367 health care providers practicing at 7,915 health facilities. A complete description of the vignette sample selection and administration process is presented in Daniels _et al_ . (2024).<sup>9</sup>

## **2.2 Primary Outcomes**

Using the SDI data, we constructed two primary outcome measures. The first was the perprovider “outpatients per working day,” a measure of each provider’s caseload calculated from the facility survey and staff survey. The second was the “vignettes competence,” a measure of the quality of each health care provider in terms of their medical knowledge. We considered each provider’s ability to correctly diagnose and treat the conditions presented in the vignettes based on the presentations and definitions in **Supplemental Appendix Table 1** , and we present the vignette competence here as the predicted share of vignette cases each provider could have correctly managed.

Using the reported number of outpatients seen at the facility in the last three months as recorded by the SDI, we computed the mean daily outpatient caseload for each provider in the sample following Kovacs and Lagarde (2022) as _PLf_ = _Sta f f Consultations f ×_ 20 _×_ 3 _<u>f</u>_ . _PLf_ , the estimated average daily patient caseload per provider at the facility level, is calculated as the total number of monthly consultations at the facility level divided by the number of staff serving

**5/30**
# **Financial Inclusion and Economic Development: A Review of the Data and Evidence**

## **Saniya Ansar, Leora Klapper, and Dorothe Singer***

JEL: G51, G53, G21, G23, G28 Keywords: Financial inclusion; Mobile money; Household finance; Digital payments

> * All authors are at the World Bank. Corresponding author: Klapper, lklapper@worldbank.org. We are grateful to Alexandra Norris and Asli Uyanik for outstanding research support and Laura Starita for her excellent editorial assistance. We gratefully acknowledge the Knowledge for Change Program (KCP) for its generous support in enabling the collection of the Global Findex data. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views or policies of the World Bank, its Executive Directors, or the economies they represent.
ELA assignment and their interaction, we require village level disruptions to be as good as random. Official WHO estimates show considerable variation in Ebola intensity across districts in Sierra Leone, but official data is unavailable at any finer level. The central map in Figure 5 shows WHO measured caseloads across the 14 districts, with the shading reflecting the quintile of caseloads for each. The highest intensity districts are Port Loko (close to, but not including the capital, Freetown), and Kailahun (where the initial outbreak occurred). Our study covers four districts: Port Loko, Kambia, Moyamba and Pujehun, where intensity levels are high, intermediate and low.

Given that WHO caseload data is unavailable at a lower level than by district, we use another approach to construct our village specific measure _Evd_ . We proceed in three steps.

First, we begin by noting that a body of earlier work has measured correlates of the local intensity of the Ebola crisis using spatiotemporal models to simulate the spread of the virus [Backer and Wallinga 2016, Maffioli 2017, Fluckinger _et al._ 2018]. These find that beyond covariates such as population, few observable characteristics of locations predict the severity of Ebola infection in those locations. Taken together this existing body of evidence suggests localized Ebola-related disruption might be as good as random.<sup>30</sup> Second, we use information from the village leader survey to construct a proxy for _Evd_ . We focus on dimensions that: (i) are credibly unaffected by ELA clubs operating in treated villages; (ii) have geographic variation _within_ district. Our disruption index combines data on the functioning of the nearest primary health unit (PHU) facility, and the nearest secondary school. Both serve multiple villages over a wide radius. For example, the lowest tier PHU facility typically serves a population of up to 5 _,_ 000 within a 3-mile radius covering 10 to 20 villages. In our evaluation sample, the average distance to the nearest PHU is 1 _._
# 2.2. Bootstrap point estimates

We propose a Bayesian-inspired innovation to the synthetic panel method that allows us to present point estimates of transition quantities.<sup>6</sup> Our point of departure is as follows: we do not know the actual degree of household income correlation in Malaysia, but several panel studies have documented such correlations over time for a range of countries. We assume that the true values for Malaysia will be somewhere in this range of empirical correlations. Our proposition, then, is that a bootstrap sample of these empirical income correlations, coupled with the assumption that residuals follow a bivariate normal distribution, yields a meaningful point estimate for transition quantities, and plausible standard deviations. Obviously, we do not know what the actual Malaysian correlations are in each interval, so that we will focus on the bandwidth around the point estimate, rather than the actual estimate itself.

Our point estimates are based on the method put forward by Dang et al. (2014), the same paper that introduced bound estimates. Precision comes at the cost of introducing additional assumptions, however. Notably, we assume that the errors of our income model, and , follow a bivariate normal distribution characterized by the correlation coefficient . We derive hypothesized ii1 ii2 εε εε values for the correlation of the error terms ( ) by drawing on income correlation coefficients ( ) ρρ from actual panel data in other countries. Dang and Lanjouw (2023) provide the following proposition ρρ ρρyy1yy2 for doing so: The equation combines measures of the variation in our data with a hypothetical income correlation to arrive at an estimate of . Our sample of the income correlations has been obtained from Colgan (2023, 2024) and Dang and Lanjouw (2023), who provide actual panel correlations for fourteen ρρ countries in the EU-SILC database and the United States.

We standardize these empirical correlations to a two-year reference interval, since the correlations reported covered intervals of different duration. Subsequently, we draw a bootstrap

> 6 Dang & Lanjouw (2023)
any MDP measure is that it directly captures deprivations households face and allows policies to prioritize specific deprivations. However, it is more difficult to interpret and compare across space as multiple deprivations are aggregated into one number, and the choice of what measures to include and their weight is a normative decision subject to significant debate. As such, monetary poverty is often used in practice, as it precludes one from having to make normative decisions on the type and weight of different indicators.

Adopting a monetary poverty index versus an MDP index may highlight a different number and spatial distribution of poor households. Examining this at an aggregate level, Evans _et al._ found that they are comparable at the country level – a country in monetary poverty is likely to face significant MDP and vice versa. At the individual level however, these authors instead find a significant discrepancy between monetary poverty and MDP, with limited overlap between the set of households in monetary poverty and in MDP. The 2019 Global MDP covering 100 countries showed that two-thirds of the multidimensionally poor (886 million people) live in middle-income countries (UNDP, 2019), showing that there is a misalignment between monetary poverty and MDP.

In practice, both monetary and MDP measures of poverty can be used to construct an aggregate measure of welfare, _i.e.,_ individuals’ well-being. Monetary poverty can be used to construct a welfare measure by aggregating a convex function of individual income or consumption; it is a measure of economic _advantage_ and continuously increases with economic growth _._ A welfare measure based on MDP is instead a measure of economic _disadvantage_ – once no households face a deprivation, this measure goes to zero and becomes obsolete. It could thus be argued that 8
Policy Research Working Paper 11043

# **Abstract**

This paper presents a fundamental reassessment of the global - human resources crisis in primary health care, using nation ally representative survey data from 7,915 health facilities across 10 Sub-Saharan African countries. The reassessment consists of three main parts. First, in contrast to a literature that posits pervasive health workforce shortages, the paper estimates that the median primary health care provider sees 10.9 patients each day and spends under two hours doing so. However, variation in patient loads across facilities implies that most patients visit busier facilities, and therefore the median patient experiences long wait times. Second, by combining caseload data with measures of medical competence for 14,367 individual providers, the paper demonstrates that provider caseload is very weakly correlated with medical competence. As a result, the most competent doctors in each system are nearly as likely to be underutilized as the least competent. Third, the paper assesses how much productivity is lost due to the low observed correlation between caseload and competence, by calculating potential quality improvements from matching the most competent providers to the busiest postings. Such transfers could increase the likelihood that a patient sees a provider who can correctly manage simple cases by 4.5 percentage points, or 12 percent, but with substantial variation across countries. The paper concludes that in half of the countries in the sample, there are substantial numbers of competent but underutilized providers; but in the other half, quality improvements will require a full overhaul of the training infrastructure and spatial distribution of facilities.

This paper is a product the Office of the Chief Economist, the People Vertical and the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ayichang
1988 revision (prolim.)|Bank est.|Bank est.|
|Morocco|Sep 82|20420 F|U.N. 1984 assessment|1987 DNS|Bank est.|
|Mozambique|<br>Aug 80|11674 F|Bank projection<br>from census,<br>adjusted for 4X undercount|Bank est.|Based on official<br>CMR|
|Namibia|May70|762 F|Bank projection from<br>urpublished 5/81 census|U.N. 1988 revision (preLim.)|U.N. 1988 revision (prelim.)|
|Nauru|Jan 7?|7 F|UNPVSR<br>4/87 (official<br>est.)|||
|Nepal|Jun 81|15023 J|U.N. 1984 assessment|Based on 1986-87|Based on 1986-87|
|||||Demographic Sample Survey|Demographic Sample Survey|
it was estimated that 120 pregnant and recently delivered women would be needed in each cluster to detect a change of 2 percent in the proportion of facility-based births between the baseline and a one-year follow-up among the treatment groups for a two-sided T-test at 5 percent significance and 90 percent power (120 deliveries * 22 clusters = 2640 female respondents).

Based on the target of 120 deliveries in each cluster and using the UBOS census figures to estimate village populations, the necessary number of villages was selected from a list of villages in each cluster, limiting the selection to villages with 20 or more estimated deliveries a year. The additional 467 women included in the sample size were intended to account for nonresponses.

However, a deviation from the original plan occurred immediately after the baseline survey was carried out because of insufficient communication between the researchers and the program operations team. The program operations team had recruited voucher distributors in villages 12
of broad money to reserve money) has significantly increased over time. It has doubled over the last 7 fiscal years. In September 2018, the money multiplier was 8.6 percent higher than a year earlier. This makes the forecasting of the demand for money in any NBE financial programming exercise quite uncertain, and so the new injection of reserve money consistent with the inflation objective. Second, the forecasting of real GDP growth introduces another major uncertainty in the exercise; especially when the forecasting error tends to be systematically positive. Using data on intensity of light emitted at night as measured by satellites from outer space, it has been estimated that GDP in Ethiopia may have been over‐estimated by around 17 percent since 2008.<sup>68</sup> The combination of these uncertainties and biases has loosened the control of the NBE on broad money growth and ultimately inflation.

Annual inflation increased to 16.8 percent in 2017/18 against a target of 8 percent. Annual inflation accelerated in the aftermath of the October 2017 devaluation and peaked at 16.8 percent in June 2018. It has since decelerated to 10.9 percent in January 2019 as monetary policy was tightened. This was facilitated by some inflation repression. Several goods and services are subject to direct or indirect price controls in the forms of subsidies (e.g., wheat, edible oil, petroleum products), export bans on selected food items (e.g., teff), and public utilities. The Trade Competition and Consumers’ Protection Authority is reportedly exercising at times moral suasion when private sector operators decide to increase their sale prices.<sup>69</sup>

> 65 Proclamation No. 591/2008.

> 66 NBE (2009). NBE’s Monetary Policy Framework, Economic Research and Monetary Policy Process.

> 67 Reserve money or base money is defined as the sum of currency in circulation and deposits of commercial banks at the NBE.

> 68 McGregor, Thomas (2018).