rafmacalaba/datause-encoder-data huggingface dataset:
the mortality data are widely available, it<sup>maybepossibletomakeselecta</sup> representative sum in every aimag to begin the<sup>pilot.Thiswould giveasmuch</sup> geographic spread as possible and provide the needed<sup>publicityacrossMongolia</sup> for the concept. Great care should be taken<sup>in makingcertainthat the pricethat is</sup> charged reflects the relative risk. The premium<sup>rateschargedherdersandthe</sup> design of the contracts should be consistent with<sup>market principles. Initially,the</sup> govemnment could provide some level of reinsurance<sup>toprivateprovidestoget</sup> their involvement. Simultaneously, the concept and pilot design<sup>shouldbe</sup> presented to the intemational capital markets obtaining<sup>their inputandattempting</sup> to get their involvement in offering reinsurance.

4. **Develop an extended education and marketing**<sup>**program.**Anysuccessfulpilot</sup> must educate herders about the potential value<sup>anduseofthis insurance. Some</sup> considerable attention should be paid to an educational<sup>effort.</sup>

**5. Establish appropriate feedback and monitoring**<sup>**of thepilot.**Apilot program</sup> should be designed to allow for learning about<sup>theconcept.This learningmust</sup> involve a number of dimensions: a) how have<sup>theprivateinsurersrespondtothe</sup> opportunity? b) how have the herders responded?<sup>c)areherders thinkingof and</sup> using infonnal and formal mechanisms to share<sup>theindex paymentswithinthe</sup> community? d) has the introduction of the index<sup>insurancechangedthe data</sup> development process in any significant fashion?

Much of the effort for a pilot test of mortality insurance<sup>couldbesupportedin the</sup> pastoral risk management project. However, it would also be an opportunity to gain support from some traditional NGOs. They should<sup>be keen toseeprogressmadeinthis</sup> direction. If the pilot were offered in areas that<sup>comprisedabout5%of thelivestockin</sup> Mongolia, the total possible market would be<sup>5%of Tg1 TrillionoraboutTg 50 billion.</sup> Initial sales would likely not exceed 10% bringing<sup>thenumbertoTg5 billion.With</sup> premiums set at a 3% rate, it would require only<sup>about $US60,000tosetpremiumsat a</sup> break even rate for herders. This level of support may be wise in a pilot program. Additional financing would be needed for resources<sup>to examinethe issues outlinedabove</sup> and for education and marketing.
# References

Aldasoro, I., B. Hardy, and N. Tarashev. 2021. “Corporate Debt: Post-GFC through the Pandemic.” BIS Quarterly Review, June, 1-14, Bank for International Settlements, Basel.

Arellano, C., and A. Ramanarayanan. 2012. “Default and the Maturity Structure in Sovereign Bonds.” Journal of Political Economy 120 (2): 187-232.

Arteta, C., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Coming U.S. Interest Rate Tightening Cycle: Smooth Sailing or Stormy Waters?” Policy Research Note. World Bank, Washington, DC.

Avdjiev, S., P. McGuire, and von Peter, G. 2020. “International Dimensions of EME Corporate Debt.” BIS Quarterly Review, June, 1-13, Bank for International Settlements, Basel.

Azzimonti, M., and P. Yared. 2019. “The Optimal Public and Private Provision of Safe Assets.” Journal of Monetary Economics 102: 126-144.

Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.” World Bank Policy Research Note 15/01, World Bank, Washington, DC.

Battistini, N., G. Callegari, and L. Zavalloni. 2019. “Dynamic Fiscal Limits and MonetaryFiscal Policy Interactions.” ECB Working Paper 2268, European Central Bank, Frankfurt.

Blanchard, O. 2019. “Public Debt and Low Interest Rates.” American Economic Review 109 (4): 1197-1229.

Blanchard, O., and L. H. Summers, eds. 2019. Evolution or Revolution? Rethinking Macroeconomic Policy after the Great Recession. Cambridge: MIT Press.

Borensztein, E., and L. S. Ye. 2018. “Corporate Debt Overhang and Investment: Firm-Level Evidence.” World Bank Policy Research Working Paper 8553, World Bank, Washington, DC.

Broner, F. A., G. Lorenzoni, and S. L. Schmukler. 2013. “Why do Emerging Economies Borrow Short Term?” Journal of the European Economic Association 11 (S1): 67-100.

Buckle, R. A., and A. A. Cruickshank. 2013. “The Requirements for Long-Run Fiscal Sustainability.” New Zealand Treasury Working Paper 13/20, New Zealand Treasury, Wellington.

Calderón, C., and L. Servén. 2010. “Infrastructure and Economic Development in SubSaharan Africa.” Journal of African Economies 19 (suppl_1): i13-i87.

Chang, R., and A. Velasco. 2000. “Financial Fragility and the Exchange Rate Regime.” Journal of Economic Theory 92 (1): 1-34.

Claessens, S., and M. A. Kose. 2014. “Financial Crises Explanations, Types, and Implications.” In Financial Crises: Causes, Consequences, and Policy Responses, edited by S. Claessens, M. A. Kose, L. Laeven, and F. Valencia, 3-59. Washington, DC: International Monetary Fund.

18
**Table 6. Regressions with Regulation and Property Rights**

|||**Regu**|**lation**|||**Prope**|**rty Right**|**s**|
|---|---|---|---|---|---|---|---|---|
||**Model 1**<br>**(OLS)**|**Model 2**<br>**(IV Aid)**|**Model 3**<br>**(IV Aid)**|**Model 4**<br>**Changes **|**Model 1**<br> **(OLS)**|**Model 2**<br>**(IV Aid)**|**Model 3**<br>**(IV Aid)**|**Model 4**<br>**Changes**|
|||||(IV)||||(IV)|
|Aid|-.14***|-.22***|-.22***|-.33***|.03*|-.09**|-.06*|0|
|(log)|-0.04|-0.06|-0.06|-0.09|-0.08|-0.11|-0.11|-0.13|
|Initial Institution|-.47***|-.47***|-.49***||-.60***|-.63***|-.61***||
|(in year 1985)|-0.08|-0.08|-0.09||-0.1|-0.1|-0.1||
|lpopulation|-1.07|-0.76|-0.8|-0.95|-0.99|0.998|-0.85|-0.9|
|(‘85~’00)|-0.72|-0.76|-0.77|-1.11|-1.15|-1.3|-1.3|-1.65|
|lgnp|-.25**|-0.39|-.45*|-1.11***|.93**|0.7|0.61|0.22|
|(‘85~’00)|-0.25|-0.26|-0.26|-0.38|-0.41|-0.45|-0.47|-0.54|
|English Legal Origin|||0.3||||0.53||
||||-0.3||||-0.43||
|French Legal Origin|||.02*||||0.11||
||||-0.28||||-0.44||
|Latitude|||0.001||||0.01||
||||-0.004||||-0.01||
|Constant|3.85***|4.09***|4.06***|2.19***|3.49***|4.12***|3.52**|1.04***|
||-0.49|-0.59|-0.58|-0.49|-0.66|-0.72|-0.82|-0.56|
|# obs|75|74|74|74|78|76||76|
|R<sup>2</sup>|0.61|0.59|0.61|0.27|0.5|0.49||0.25|
|(uncentered)|||||||||

# Notes:

- 1) Dependent variable is change in the institutional indicator, 1985~2000. For column 4, the variables are all specified in changes.

- 2) instrument 1: log population in 1975, log gnp in 1975

- 3) robust standard error is reported in the brackets below the coefficient

- 4) ***designates significance at .01 level, ** at .05 level, * at .10 level **.**

19
**Table 5: Impact of PBF on content of care**

|**Variable**|**Overall**<br>**mean**|**PBF**<br>**impact**|**p-**<br>**value**|**N**|
|---|---|---|---|---|
|**Panel A: Adult consultations**<sup>**a**</sup>|||||
|Proportion of core clinical history items<br>asked|0.397|0.102|0.149|2,614|
|CVD risk score calculated|0.384|-0.058|0.659|2,614|
|CVD risk score properly calculated|0.038|0.021|0.545|2,614|
|Clinician washed their hands before<br>starting the exam|0.609|-0.148|0.396|2,614|
|Blood pressure measured|0.898|0.075|0.107|2,614|
|Average consultation time (minutes)|12.75|-2.275**|0.020|2,612|
|**Panel B: Child consultations**<sup>**b**</sup>|||||
|Vaccination history checked|0.501|0.097|0.280|2,582|
|Clinician washed their hands before<br>starting the exam|0.644|-0.091|0.551|2,582|
|Height and weight measured|0.847|0.120**|0.037|2,582|
|Proportion of core physical exam<br>activities completed|0.341|0.107**|0.040|2,582|
|Average consultation time (minutes)|12.59|0.474|0.744|2,526|

Note: data from direct clinical observations conducted at the rural health centers. The PBF impact is estimated using a propensity score weighting specification as only follow-up observations are being included in the analysis. Standard errors are clustered at the district level.

a Data from consultations of adults aged 40 and above. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, age and gender of patient, gender age and position of provider, and a binary indicator for experience of over 20 years. b Data from consultations of children under-5. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, gender age and position of provider, and a binary indicator for experience of over 20 years. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 27
Table A26: Robustness of impact on Gender attitudes index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimming|treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.21**<br>(0.09)|0.26***<br>(0.09)|0.15*<br>(0.09)|0.20**<br>(0.09)|0.20**<br>(0.09)|0.21**<br>(0.09)|0.21**<br>(0.09)|
|T1|+Leaders|0.08<br>(0.11)|0.28***<br>(0.08)|-0.08<br>(0.10)|0.04<br>(0.10)|0.06<br>(0.10)|0.08<br>(0.10)|0.08<br>(0.11)|
|T2|(Safe Spaces + IGA)|0.16**<br>(0.08)|0.29***<br>(0.07)|0.05<br>(0.08)|0.12*<br>(0.08)|0.14*<br>(0.08)|0.16**<br>(0.07)|0.16**<br>(0.08)|
|T2|+Leaders|0.07<br>(0.10)|0.35***<br>(0.08)|-0.12<br>(0.09)|0.02<br>(0.10)|0.04<br>(0.10)|0.06<br>(0.10)|0.08<br>(0.10)|
|T3|(Safe Spaces + Husbands Clubs)|0.22***<br>(0.08)|0.42***<br>(0.08)|0.04<br>(0.07)|0.15*<br>(0.08)|0.18**<br>(0.08)|0.20***<br>(0.07)|0.23***<br>(0.08)|
|T3|+Leaders|0.20***<br>(0.07)|0.29***<br>(0.07)|0.15**<br>(0.07)|0.19***<br>(0.07)|0.20***<br>(0.07)|0.20***<br>(0.07)|0.21***<br>(0.07)|
|T4|(control spillover)|0.04<br>(0.07)|0.05<br>(0.07)|0.04<br>(0.07)|0.04<br>(0.07)|0.04<br>(0.07)|0.04<br>(0.07)|0.05<br>(0.07)|
|N||3003|3002|3002|3004|3004|3004|3003|
|Con|trol Mean|0|0|0|.002|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

58
26 Of the other factors controlled in the empirical funding analysis, very few had potent effects. Somewhat surprising was the null effect of state requirements that pension plans had to be funded, requir-nents that benefit levels had to be guaranteed by law, and requirements that states must balance their budgets from one year to the next. Nor were funding levels different when dedicated or special taxes were earmarked for pension revenue.<sup>8</sup> Employee type (i.e. teachers) and unionization status also had no effect. In fact, only one other variable was found to systematically and powerfully reduce stock funding: namely, fiscal stress, measured here by the deviation of recent unemployment from the levels experienced over the last decade. The effect was substantial, suggesting that a 1 percentage point increase in a state's unemployment rate over the long run average would cut stock funding by 6 percentage points. It appears that economic recessions have long as well as short term effects on public budgets through pension plan funding. This effect is consistent with previous studies on public plans (Mitchell and Smith 1992a).

In summary, there is reason to believe that the stock funding figures are of superior quality than the flow funding data, so it is appropriate to emphasize those models and findings. In this dataset, pension underfunding patterns are explained by several factors pertinent to the environment in which the pension Board operated. The most important findings were:

- (1) Fiscal stress reduced stock funding ratios.

- (2) Stock funding rates were lower, the higher the fraction of elected retirees and elected active workers represented on the pension system Board.

- (3) Stock funding ratios were higher when the system had in-house actuaries, when the Board was required to authorize benefit levels, and when Board members had

Olivia S. Mitchell - Public Pension Governance and Performnance
# **3 Population density, sanitation and child health in developing countries: Evidence from 172 DHS**

In these analyses, we use a dataset of 172 DHS surveys collected between 1990 and 2012 in 69 developing countries to assess whether the importance of dense settlement for infant mortality and child height in developing countries is moderated by exposure to local sanitation behavior. As motivation, we begin with a description of how urban place, sanitation, and population density predict infant mortality. We find that the urban infant survival advantage is importantly diminished after controlling for local sanitation, population density, and their interaction.

We then focus directly on the interaction of population density with local open defecation in predicting height and infant mortality. Although this multi-country analysis is not intended to precisely identify a causal effect, we demonstrate that the effect of population density on the sanitation-health gradient is quantitatively robust to model respecifications, including the introduction of a range of fixed effects and controls, suggesting that the interaction we document is unlikely to reflect omitted variables. To provide additional evidence that this relationship is not due to omitted variables, we conduct falsification tests that demonstrate that other measures of socioeconomic status do not similarly interact with population density to predict infant mortality. Finally, we model the shape of the dependence of the sanitation-mortality gradient, and the sanitation-height gradient on population density.

## **3.1 Data & summary statistics**

These analyses combine data from two sources: population density from census or other aggregate demographic data, and sanitation, health, and other covariate data from DHS surveys collected between 1990 and 2012. DHS surveys are internationally comparable, nationally representative surveys collected in poor and middle income countries. We append 8
**Table 3: Household Characteristics by Enterprise Ownership**

||**Enterp**<br>**Own**<br>|**rise**<br>**ers**<br>|**Non-Ent**<br>**Own**<br>|**erprise**<br>**ers**<br>|
|---|---|---|---|---|
||**Mean**|**Sd**|**Mean**|**SD**|
|Household Size|4.97|2.26|4.94|2.27|
|**_Characteristics of the House_**|**_hold Head_**||||
|Age of the Head|35.76|12.73|43.80|16.30|
|Male|0.53|0.49|0.80|0.38|
|Years of schooling|2.69|2.81|2.11|2.42|
|**_Distances (log kilometers)_**|||||
|- to all weather road|1.65|1.16|1.96|1.06|
|- to the market|1.70|0.81|1.99|0.69|

**Table 4: Participation in the Nonfarm Enterprise Sector: Probit Results**

||Marginal effects<br>(standard error)|
|---|---|
|Household size|0.013***<br>(0.002)|
|**_Household head_**||
|Age|-0.002*|
||(0.001)|
|Age2|-0.024|
||(0.015)|
|Female|0.216***|
||(0.008)|
|Years of schooling|0.047***|
||(0.005)|
|Years of schooling squared|-4.317***|
||(0.454)|
|**_Rural Town_**||
|Rural town|0.252***|
||(0.030)|
|**_Distances_**||
|Distance to all weather road|-0.012***<br>(0.004)|
|Distance to the market (road)|-0.035***|
||(0.005)|
|_Region_||
|Tigray|0.011|
||(0.013)|
|Amhara|-0.040***|
||(0.010)|
|SNNP|0.137***|
||(0.015)|
|Number of observations|12,527|
|Pseudo R2|0.148|

_Source:_ Authors’ calculations based on RICS-AgSS. _Note:_ An intercept is included in the regression. Standard errors are clustered by enumeration area. Significance at the 10%, 5%, and 1% level is indicated by *, ** and ***, respectively. The omitted region is Oromia

-25-
**Annex: Secured Transaction Regimes for Movables in the EU 8**

|**Slovak**<br>**Republic**|**Yes**|**OVERVIEW**<br>• Has a modern and efficient regime for secured transactions (EBRD<br>2003 Transition Report). System considered one of the best in Europe.<br>• Registration is made in the new Charges Register operated by the<br>Slovak Chamber of Notaries.<br>• The register is maintained in a single electronic database. Registration<br>is made instantly via a terminal in a notary’s office.<br>**TYPES OF PLEDGES**<br>• Under the new law, any legal or physical person can give a charge<br>over its assets (objects, residential or non-residential property, rights<br>or other assets) in favor of any creditor, to secure his own or another<br>person’s debt. The charge can be taken over any single or collective<br>object, right or asset that is capable of being transferred. This may<br>include:<br>• movable assets,<br>• land and buildings,<br>• rights and receivables,<br>• or even a group of assets, which is constantly changing, such as<br>inventory, trade receivables, stocks of raw materials or an entire<br>enterprise.<br>• It may also include assets to be acquired in the future. The fruits<br>of the collateral are included until they become separated unless<br>otherwise agreed.|
|---|---|---|
|**Slovenia**|**No**|**OVERVIEW**<br>• This area is currently under reform. There is no public register to allow<br>tracking of priorities and to alert third parties of an existing charge,<br>although the Law on Enforcement provides for a register, which is to<br>be developed by the Chamber of Notaries.<br>**TYPES OF PLEDGES**<br>• Only charges on motor vehicles are currently registered with the Min-<br>istry of Interior on the basis of an agreement with the Chamber of<br>Notaries.|

Source: EBRD Regional Survey of Secured Transaction Laws, national country websites, and national civil codes and laws on property rights, pledges and secured transactions.

**38**

**THE WORLD BANK**
# 1. Introduction

Population exposure to inorganic arsenic through drinking water supply continues to be a public health issue across the world, from numerous regions of the Americas to Asia. Naturally occurring inorganic arsenic in groundwater has particularly overwhelmed Bangladesh, in what has been deemed the “largest mass poisoning of a population in history” (Smith et al., 2000). An estimated 35 million to 77 million Bangladeshis were chronically exposed to some level of arsenic through drinking water from 2000 to 2010 alone (Flanagan, et al., 2012). The exact mechanisms of the natural occurrence of arsenic in groundwater supply have been hypothesized and debated and can vary depending on hydrogeological and physiogeological contexts of regions in Bangladesh (Burgess, et al., 2010; Ravenscroft., et al., 2005). However, the presence of arsenic in drinking water supply and its effects on human health have been well documented since its discovery in well water in the early 1990s in Bangladesh. Though arsenic presents itself as colorless, odorless, and tasteless in drinking water, the heavy metal causes severe health impacts ranging from skin lesions to various deadly cancers and diseases (Ravenscroft, et al., 2009).

Similar to other heavy metals, such as lead, mercury, and manganese, arsenic can also penetrate the blood-brain barrier and have neurotoxic effects that may lead to irreversible neurological and cognitive damage. Numerous epidemiological studies have demonstrated adverse effects of arsenic on cognitive performance in both children and adults. For example, Wasserman et al. demonstrated in two separate studies that children exposed to low to moderate levels of arsenic had overall lower intellectual functioning among 10-year old and 6-year old Bangladeshi children (Wasserman, et al., 2004) (Wasserman, et al., 2007). A few studies additionally report the specific domains of cognitive function that are most impacted by arsenic in school-aged children including negative effects on memory, ability to focus and solve problems, sensory and motor function, and verbal and linguistic capability (Rosado, et al., 2007) (Asadullah & Chaudhury, 2008; Parvez, et al., 2011; Tsai, et al., 2003). Apart from the literal neurotoxic effect of arsenic poisoning, there are reasonable speculations on the indirect effect of arsenic poisoning on economic opportunities primarily acting through health status and cognitive abilities of victims. As such, those suffering from symptoms of arsenicosis have poorer health status and may subsequently have relatively poorer school or work attendance and performance. Victims may even be ostracized from public settings (Asadullah & Chaudhury, 2008). Murray and Sharmin, for example, observed that Bangladeshi children (aged 6-10 years) who drank arsenic2
# Table 9 System Estimation of Two Equations: Three Stage Least Squares

||Depende|nt Variable|
|---|---|---|
|Independent Variables|Infant Mortality|Life Expectancy|
|Constant|4.321**|-2.660**|
||(1.966)|(1.278)|
|Dummy for 1974-83|-0.116<br>(0.179)|0.089<br>(0.116)|
|Dummy for 1984-93|<br>-0.349**<br>(0.186)|0.311 ***<br>(0.121)|
|Initial Per Capita GDP|<br>-0.584***|0.303***|
||(0.157)|(0.102)|
|Per Capita GDP Growth|-7.534<br>(6.113)|-1.378<br>(3.974)|
|Social Spending/GDP|-0.516** *<br>(0.124)|0.233***<br>(0.081)|
|Quality of Bureaucracy|-1.074**|0.077|
|(0 worst, I best)|(0.557)|(0.362)|
|Political Freedom|0.129|-0.121*|
|(I high, 7 low)|(0.101)|(0.066)|
|Income Inequality|0.070***|-0.034***|
||(0.017)|(0.011)|
|Latin America Dummy|-0.752**|_0.354*_|
||(0.320)|(0.208)|
|East Asia Dummy|<br>_-0.560**_|0.267|
||(0.311)|(0.202)|
|Sub-Saharan Africa Dummy|<br>-0.892***|0.004|
||_(0.351)_|(0.228)|
|Socialist Dummy|<br>-0.220|-0.159|
||(0.527)|(0.343)|
|Number of Observations|91|91|
|System R-Squaredb|0.8230||

*' significant at 1% level ** significant at 5% level significant at 10% level

- Correlation between the error terms of the two equations is -0.79***. Standard errors are in parentheses. The instruments are: initital per capita GDP; initial per capita GDP squared; population growth; initial schooling; quality of bureaucracy; terms of trade changes; number of assassinations; number of coups, age dependency ratio; urbanization ratio; population density; years since independence; federal dummy; socialist dummy; Latin America dummy; East Asia dummy; Sub-Saharan Africa dummy and decade dummy variables.

b System R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

44