rafmacalaba/datause-encoder-data huggingface dataset:
budget divided by the number of beneficiaries, and we randomly allocate this to eligible households until we reach (approximately) the (scaled) total number of beneficiaries covered by both government and donor disbursements. We assume that the per capita subsidy provided by government and donor programs is the same.

For UWEP, YLP and NUSAF we randomly allocate program-specific benefits to program-specific eligible household pools until we reach (approximately) the average number of beneficiaries and benefits delivered annually and per region according to program reporting.

Given that the YLP and UWEP programs provide loans rather than transfers, and in the absence of further information, we assume that the programs provide a benefit of foregone loan interest payments, at 25 percent of the amount of the principal disbursement.

# 3.4.3. Indirect Taxes

- <u>Excise and petrol duty: It is not possible to directly identify the excise and petrol duty tax amounts</u> paid, so instead we impute, for each purchased item, the share of the item’s value that is an excise charge using _effective_ rates of excise rather than statutory rates. The effective rates are calculated as follows: from the survey we generate a total consumption amount for each excisable good which is recorded in the survey. We also generate the share of total household consumption in the survey comprised by each good. We apply those shares of consumption to the National Accounts Final Household Consumption amount to estimate a “national accounts” total consumption amount for each excise good. We then divide the excise revenue from each good by the consumption estimate to determine an effective rate for each good.

- <u>Value-added tax (VAT): Similar to the excise duties described above, we impute the amount of VAT in</u> each purchase recorded in the UNHS by estimating the share of the good or service’s price that is a VAT charge.  This imputation assumes an effective (rather than statutory) rate of VAT applies to each purchase.  For goods and services that are exempt in the Ugandan VAT schedule, there may still be “embedded” VAT in the final purchase price when exempt producers pay non-refundable VAT on inputs that is then passed through to the final price of that producer’s item.  To estimate the potential embedded VAT in the price of VAT-exempt goods and services, we make use of an Input-Output (IO) model of the production side of the Uganda economy. For more details, please consult Harris et. al

18
Previous research that used the sample approach to provide an estimate of the extent of the Cambodian genocide by interviewing survivors and collecting information about death and survival in their families include Sliwinski (1995) and Kiernan (1996). However, their estimates were based on samples of convenience, limited by local constraints, which were not representative of the Cambodian population. The present paper uses data about siblings’ mortality collected in the 2000 Demographic and Health Survey of Cambodia (Kingdom of Cambodia 2001). With as primary goal the assessment of maternal mortality, the survey collects information about all the siblings (78,852 in total) of a nationally representative sample of 15,557 women, aged between 15 and 49 in 2000. The information includes gender, date of birth, whether the sibling is still alive and, in case the sibling died, the date of death.

Even with a nationally representative sample, estimates based on a sample of survivors are potentially subject to several shortcomings (Heuveline, 1998 (b), 2001 (a), 2001 (b)). First, the method relies on recall of past events and is therefore possibly prone to misreporting. Several tests of data quality have been performed and there does not seem to be systematic reporting biases (Kingdom of Cambodia 2001): the sex ratios at birth are in the internationally acceptable range suggesting that there is no serious under or over reporting of brothers or sisters. Similarly, the median year of birth is the same for respondents as for siblings, indicating that there is no underreporting of older siblings. Another issue is the possibility of double counting: if two sisters are interviewed, the same deaths and survivals will be reported twice. Although this possibility cannot be ruled out in the data set used, this does not appear to be a major issue in a random nationwide sample. More importantly, as long as the analysis focuses on relative rather than absolute numbers, double counting of some deaths and survivals should not affect most ratios.

A more serious problem, however, is that for a family present, say in 1975, to have a chance to be represented in 2000, at least one of the siblings need to have survived until 2000. In other words, the survey misses the families that were entirely killed during the genocide and is likely to over-sample families with many survivors. This means that the survival probabilities of brothers and sisters are not independent<sup>3</sup> and is likely to lead to a serious underestimation of the extent of the mortality. For this reason, this study will focus on relative rather than absolute numbers, and will not attempt, as most previous studies have done, to calculate “the number”, i.e. an estimate of the excess number of deaths during the Khmer Rouge period. However, in the appendix, I will, for selected

> 3 In a simple regression, controlling for year of birth and gender, a 10 percentage point increase in the proportion of siblings that died (excluding the individual under consideration) leads to a 4 percentage point increase in the probability to have died.

3
on donors’ use of them (IEG, 2011; OECD, 2011a, 2009a, 2009b; Ellmers 2011). The relationship between PFM Quality (and Civil Liberties) and use of country PFM systems remains strong and significant when controlling for donor-year fixed effects, where estimates are informed only by variation in the data over recipients within a particular year. Results are also robust to a variety of corrections for possible sample selection, omitted variables and endogeneity bias. However, PFM Quality is not the sole determinant of donors’ decisions. Use of country PFM systems increases with Civil Liberties, population, and total aid per capita in the recipient country, and with a donor’s share of the aid market in the country. There is a positive but modest time trend in use of country PFM systems, controlling for PFM Quality and other variables. The 5-year time span under study is too short, however, to assess whether the PD and AAA agenda on use of country systems will have positive medium and long-run impacts on donor behavior.

Despite domestic political and other pressures on many of the DAC donors, the PD campaign and associated “peer pressure” resulting from it has likely had some positive, impact on donors’ use of country PFM systems. Any such effects may be one-off or even temporary, however: in the medium and long term, donors’ decisions to use country systems are likely to depend more on the level of risk that their domestic constituents find tolerable, and the level of risk incurred in trusting recipient countries’ PFM systems. Slow growth in most donor countries in the last several years is creating pressure to reduce aid volumes (OECD, 2012; Dang, Knack and Rogers, forthcoming), and perhaps also to minimize fiduciary and other risks in aid management in an effort to protect aid budgets. Further sizeable increases in the use of country PFM systems may thus be contingent on a gradual, sustained improvement in the quality of systems in aid recipient countries.

29
Peru, Sweden, and the United States reported charging premiums based on risk (Demirguc-Kunt and Huizinga, 1999). As of 2016, this number has increased to 55 countries. Figure 5 shows the percentage of countries that use risk-adjusted premiums by income group. Risk-adjusted deposit insurance is especially prevalent in high-income countries. In the 2016 BRSS survey, the percent of countries charging risk-adjusted premiums among the high-income countries was 78%. For the middle and low-income countries, the same percentage has been below 40%.

Although risk-adjusted deposit insurance provides a way to internalize banks’ risk taking, it is challenging to determine the premium that should be charged to the banks. Typically, banks’ examination ratings, capital adequacy, liquidity and asset quality are used to determine premiums. The FDIC, for example, assigns banks to several risk categories based on the banks’ capitalization level and supervisory rating, and then charges a different insurance premium for each risk category. Some of the measures used can be subjective and crude, and are usually better at explaining past and current risks rather than future risks. This naturally incentivizes banks to take on new risks once the premiums are determined as a way to game the system. It may also take time for the regulators to understand and price-in the costs of new and unfamiliar risks. The premiums are also based on banks’ stand-alone risk. The costs of insuring deposits and resolving failed banks, however, are much higher when multiple failures occur at the same time. Moreover, deposit insurance based on stand-alone bank risk can encourage banks to take excessive systemic risk. Pennacchi (2006), for instance, shows that banks have incentives to take on excessive correlated risk when insurance premiums are set to an “actuarially fair” value. Laeven (2008) shows that countries have typically underpriced deposit insurance because banks in many developing countries cannot afford to pay actuarially fair premiums. Nonetheless, empirical evidence suggests that risk-adjusted premiums work better than flat-rate premiums in reducing excessive risk taking by banks (Demirguc-Kunt and Detragiache 2002, Hovakimian, Kane and Laeven 2003).

Another way to correct the incentive distortions caused by deposit insurance is to complement it with robust supervision and regulation of the banking system. More generous deposit insurance schemes are associated with lower capital buffers, making banks more fragile to shocks (Bier and Baumann 2006, Fonseca and González 2010). Theoretical models of deposit insurance suggest that banks with lower capital ratios have more incentives to take advantage of the deposit insurance subsidy by taking on more risk (Merton 1977). More stringent capital 17
As shown in Table 4, the introduction of IER to replace both GDP per capita and Gini does not reduce the explanatory power of the models.<sup>17</sup> The inequality extraction ratio is positively related to the civil war variables, and for the number of years of civil war, it is statistically highly significant. If we compare IER with Gini only, in both instances, IER outperforms Gini. While in the explanation of the casualty rate, the IER is not statistically significant, its sign is positive while Gini is marginally negative. For the duration of conflict, the difference is small: both are significant, but IER is significant at a lower _p_ -level. In interpreting the role of IER, we conclude that a ten percentage point increase in IER is associated with, on average, an additional 0.2 years (2.4 months) of civil war, and with 0.12 percent increase in the overall casualty rate. Finally, we note that, as expected, ethnolinguistic fractionalization is strongly correlated with both duration of civil wars and casualty rates, while the effect of democracy is rather ambiguous: it is negatively associated with casualty rates but positively with years of conflict.

Table 4. Regressing civil war related variables

||Number ofye|ars of civil war|Overall casu|altyrate(ln)|
|---|---|---|---|---|
|GDP per capita (ln)|-0.665**<br>(0.000)||-0.322<br>(0.073)||
|Gini (in %)|0.010*<br>(0.013)||-0.0001<br>(0.994)||
|Inequality extraction<br>ratio(in %)||0.020**<br>(0.000)||0.012<br>(0.192)|
|Democracy (Polity<br>measure)|0.148**<br>(0.000)|0.027*<br>(0.036)|-0.772<br>(0.176)|-0.133**<br>(0.002)|
|Ethno-linguistic|3.661**|3.398**|8.193**|8.262**|
|fractionalization|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|5.595**<br>(0.000)|0.180<br>(0.228)|-0.794<br>(0.633)|-3.822**<br>(0.000)|
|R<sup>2</sup>orpseudo R<sup>2</sup>|0.32|0.25|0.38|0.38|
|Number of<br>observations|143|143|143|143|

Note: For number of years of civil war we use Poisson regressions. When transforming the casualty rate into logs, 0 casualty rate is treated as 0.01. IER is expressed in percentage points (G/G* times 100); so is Gini. Democracy is measured by the democracy variable from PolityIV database. It ranges from 0 (absence of democracy) to 10 (full democracy). All explanatory variables are 1960-2002 country averages. ** (*) Indicates statistical significance at less than 1% (5%). _p_ -values between brackets.

> 17 Both Gini and the IER are expressed in percentages which facilitates the comparison between the two.

23
personnel matters added to the rigidity of the system. For example, the decree<sup>2 3</sup> that set the organizational structure and functions of the MOH, determined the exact number of positions and specializations for each public hospital in the country, beginning with the hospital director down to hospital drivers and housekeeping staff. All personnel matters in the MOH are handled by a Personnel Section that is part of the Office of the Minister _(Diwan),_ however, this section's decision rights have more to do with the processing of information and the documentation of recommendations than with policy formulation and decisionmaking.

# **4.3 Procurement**

The MOH Decree also provides for a Procurement Division, whose functions epitomized the centralization of the ministry under the pre-corporatized system. ALL decision rights over the procurement of non-labor inputs used by public hospitals in producing health services were held by this department. These inputs ranged from high-tech laboratory and surgical equipment to the provision of maintenance services, to the procurement of stationary and pencils. In addition, this department also held decision rights over some extremely important areas, such as the preparation of budget proposals made by MOH administrative units, including public hospitals, and the management of two important MOH central stocks and of their deliveries. These are (a) the capital inputs depot, including medical equipment and supplies, and; (b) the medicine depot, which supplies all drugs for all uses in public health provision in Lebanon. All inputs were centrally procured and stored in these units before they were distributed to relevant units in the public health sector.

## **4.4 Service delivery**

Similarly, decision rights over the organizational structure and functions of public hospitals were determined by the MOH Decree. This decree defined the internal organization, service mix and number of beds for each public hospital in the country. How well the actual state of affairs approximated what was laid out in the law was variable. For example, the number of beds hospitals had varies significantly from what was specified in the decree, especially in terms numbers of operational beds.<sup>24</sup> Table 4.4 summarizes the centralized decision rights allocation that prevailed in the MOH at the time when HDB launched it corporatization experiment. Under each of the four areas of hospital finance and management discussed above, the table details the principal set of relevant decision rights, and 23 Decree # 8377 (1961) defining the Organization of Ministry of Health (also referred to as the MOH Decree in this paper).

24 Information obtained from the Directorate of Medical Care, MOH, July 1999.

15
Policy Research Working Paper 9058

# **Abstract**

In this paper, the impact of salinity on maternal and child - health in Bangladesh is analyzed using data from the Bangla desh Demographic Health Surveys. A U-shaped association between drinking water salinity and infant and neonatal mortality is found, suggesting higher mortality when salinity is very low or high. With fresh drinking water, the marginal effect of salinity measured by groundwater electricity conductivity on infant death is always negative. With brackish drinking water and slightly saline water, the negative effect is small. As drinking water becomes moderately saline, the predicted probability of infant death starts to increase, and the marginal effect becomes and remains positive. The relationship between drinking water salinity and neonatal death shows a similar pattern. Finally, freshwater with very low concentration of healthy minerals and severely saline water with very high detrimental sodium can be harmful for infant and neonatal health during pregnancy. Severe salinity needs to be addressed if the recent gains in infant and neonatal mortality are to be sustained, especially in the coastal areas of Bangladesh.

This paper is a product of the Water Global Practice. 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 gjoseph@worldbank.org.

_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 the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: Dependent Variable: The Ratio of Domestic Value Added in Exports to Gross Exports <u>(DVAR)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Sample|All|All|Dom private|Foreign|Multiple Ind|Un...ltered|
|�2001|0.0301***|0.0299***|0.0764|0.0327***|0.0256***|0.0268***|
||(0.007)|(0.006)|(0.080)|(0.006)|(0.005)|(0.005)|
|�2002|0.0490***|0.0493***|0.0810|0.0492***|0.0466***|0.0493***|
||(0.004)|(0.004)|(0.106)|(0.004)|(0.006)|(0.004)|
|�2003|0.0657***|0.0663***|0.190**|0.0656***|0.0709***|0.0681***|
||(0.008)|(0.008)|(0.078)|(0.008)|(0.005)|(0.005)|
|�2004|0.0669***|0.0674***|0.140|0.0677***|0.0749***|0.0715***|
||(0.008)|(0.011)|(0.127)|(0.011)|(0.005)|(0.010)|
|�2005|0.0962***|0.0969***|0.198|0.0978***|0.117***|0.101***|
||(0.007)|(0.009)|(0.124)|(0.008)|(0.005)|(0.010)|
|�2006|0.135***|0.136***|0.257*|0.136***|0.146***|0.133***|
||(0.010)|(0.011)|(0.133)|(0.012)|(0.005)|(0.010)|
|�2007|0.147***|0.147***|0.300**|0.146***|0.161***|0.150***|
||(0.013)|(0.017)|(0.140)|(0.016)|(0.006)|(0.014)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|-0.0236***|-0.0234***|0.0190|-0.0230**|-0.0207***|-0.0108***|
||(0.007)|(0.008)|(0.060)|(0.010)|(0.006)|(0.004)|
|�wL<br>PY<br>�<br>it||-0.0010|0.0522|-0.0010|-0.0040|-0.0032|
|||(0.016)|(0.155)|(0.017)|(0.009)|(0.006)|
|N|17903|17871|858|16726|28925|31965|
|R-sq|.0729|.0733|.104|.074|.0955|.0597|

Notes: Firm and year ...xed e¤ects are always included. Data set: merged NBS-customs data. Columns (1) and (2) use the whole sample; columns (3) and (4) include only domestic private and foreign-invested ...rms, respectively. Column (5) includes ...rms that operate in multiple industries as well. Column (6) includes single-industry ...rms that do not satisfy our rules to ...lter ...rms that engage in indirect trade. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

48
"Infrastructure Coverage and the Poor: A Global Perspective" Komives, Whittington, and Wu

# **4. Who has access to services? Who has access and chooses not to connect?**

One reason that many households do not have infrastructure connections in their homes is that they live in places where they do not have the option of connecting to a utility network (i.e., no network service exists in their neighborhoods).'<sup>9</sup> Information on community access to infrastructure networks is available for most households in the urban and rural areas of five countries in our sample (Ecuador, Kazakhstan, Kyrgyz Republic, Nepal, and Panama).<sup>20</sup> Where this information is available, it is possible to begin to isolate the role that household choices play in creating the coverage patterns we observe, i.e., who has access but chooses not to connect?<sup>2</sup> ' In these five countries community access to infrastructure is high in urban areas and low in rural areas (Figures 6 and 7). Households of all income levels and in both urban and rural areas were most likely to have electricity service, and least likely to have sewer service, available in their communities. In urban areas infrastructure access was not highly dependent on household income; the percentage of households with access to services was similar across income levels. But in rural areas the wealthy were much more likely than the poor to have access to all services except sewers. Very few rural households in any 5-percent quantile had access to sewer service.

Figures 8 and 9 present infrastructure coverage among households with access to infrastructure services in their communities. These figures show that in both urban and rural areas the vast majority of households with access to electricity had connections (i.e., chose to connect). This is not true for the other infrastructure sectors. A greater proportion of rich households than poor households chose to install in-house water taps, sewer connections, and telephones. In rural areas very few of the poor households with access to water, sewer, and telephone service actually had '9 It should be noted that some such households may have consciously made this choice, i.e., located their home in a place without access because rents or land values were cheaper there.

20 Information on community access to private telephone service is not available for Nepal, and the sewer access variable is missing for Kazakhstan.

> 21 The LSMS community surveys provide an imperfect measure of access. The surveys make it possible to determine whether an infrastructure network is available within each respondent's community, but having a network in the area does not necessarily mean that it is technically or financially feasible to extend the network to all homes in the area. Despite this

10
2008, and the annualized rate translates to the frequency of new import restrictions in 2009 to be 11.9 per cent higher than the rate at which definitive new measures were imposed in 2008.

**Figure 1.  Combined Use of  Import-Restricting Trade Remedies, 1Q 2007 - 1Q 2009**

# **a. New Import-Restricting Trade Remedy Investigations at the Product Level**

<!-- Start of picture text --> 40<br>35<br>30<br>25 Initiated by<br>developing<br>20 economies<br>15 Initiated by<br>developed<br>10 economies<br>5<br>0<br>1Q- 2Q- 3Q- 4Q- 1Q- 2Q- 3Q- 4Q- 1Q-<br>2007 2007 2007 2007 2008 2008 2008 2008 2009<br><!-- End of picture text -->

**b. Newly Imposed Import-Restricting Trade Remedies at the Product Level , Including Projected Impositions through 1Q 2010**

<!-- Start of picture text --> 30<br>25<br>20<br>Projected<br>15<br>Imposed by<br>10<br>developing<br>economies<br>5 Imposed by<br>developed<br>economies<br>0<br>1Q- 2Q- 3Q- 4Q- 1Q- 2Q- 3Q- 4Q- 1Q- 2Q- 3Q- 4Q- 1Q-<br>2007 2007 2007 2007 2008 2008 2008 2008 2009 2009 2009 2009 2010<br><!-- End of picture text --> _Source_ : Compiled by the author from the _Global Antidumping Database_ . These are non-redundant AD, CVD, SG, CSG at the product level The figure 1b projections for 2Q 2009 through 1Q 2010 are based on the 2007 year rate of 79 per cent of initiations subsequently resulting in definitive measures, the 2007 average of a 4 quarter lag between initiation and imposition of final measure, and the rate of initiations between 2Q 2008 and 1Q 2009 documented in figure 1a.

6