rafmacalaba/datause-encoder-data huggingface dataset:
By end-2013, overseas bank assets of EMDE groups amounted to $1.2 trillion, of which $1 trillion was through subsidiaries located in EMDE host countries, mostly in East Asia (Table 2, Panel A).<sup>1</sup> Between 2009 and 2013, EMDE group assets in foreign banking subsidiaries grew by 10.9 percent on average per year; at 11.3 percent per annum, foreign assets in host EMDEs grew even faster.<sup>2</sup> The recent expansion of cross-border banking in EMDEs is driven by EMDE groups that hail from South Africa, Russia, Colombia, China, Turkey, Nigeria, Kenya, Lebanon, and Mali.

The expansion of cross-border banking in EMDEs comes with benefits and costs, and the tradeoff depends on the interaction of bilateral and regional supervisory and regulatory frameworks, economic and political factors, and bank group characteristics, including funding models, structure, and strategies. On the positive side, growing geographical reach and relative size of EMDE groups further regional financial integration, risk diversification, and the mobilization of savings. It can also promote financial market deepening and credit provision to the real economy in host countries and can also help smooth local economic cycles, particularly since many have shallow, bank-centric systems. On the downside, cross-border banking of increasingly complex groups with activities across multiple sectors can give rise to financial instability and regional contagion risks, and may interfere with local credit cycles and monetary policy.

**Table 2: Cross-border banking ties between EMDEs (2013)** Panel A: Assets, loans, and deposits of EMDE groups in overseas banking subsidiaries

|Home region|Total assets i|n host EMDEs|Net loans pr<br>EMDEs|ovided in host|Deposits hel|d in host EMDEs|
|---|---|---|---|---|---|---|
||US$billion|% of total|US$billion|% of total|US$billion|% of total|
|EAP|701.2|69.6|378.3|68.5|584.0|70.3|
|ECA|52.4|5.2|33.7|6.1|41.9|5.0|
|LAC|61.1|6.1|38.9|7.1|49.2|5.9|
|MENA|91.8|9.1|47.9|8.7|73.2|8.8|
|SAR|6.1|0.6|2.2|0.4|3.5|0.4|
|SSA|94.3|9.4|51.1|9.3|78.8|9.5|
|Total|1006.8|100.0|552.1|100.0|830.7|100.0|

> 1 Foreign and overseas are used interchangeably.

> 2 This figure is not driven by a coverage bias in the data. The average annual growth rate of assets of the 216 foreign subsidiaries which were active between 2009 and 2013 is 14.3 percent.

8
- information (OECD, Eurostat, and WHO 2011). In line with convention, the analysis discussed in this work only considers current spending estimates.

- 2) **Education** : The database of education expenditures used for this analysis was curated by members of the World Bank’s Education Global Practice (Al-Samarrai, Cerdan-Infantes, and Lehe 2018). The data set combines estimates from the Unesco Institute of Statistics (UIS) (Unesco 2019) and the International Monetary Fund (IMF), with the latter being used when UIS information was missing.<sup>3</sup> The data include total government spending on all aspects of education, not distinguishing by level of educational attainment or per-student costs. Unlike for health, information for education spending is not disaggregated into capital and current spending. Even when available, the disaggregation is considered not reliable. For this reason, in the rest of the paper we will consider the sum of capital and current spending estimates.

- 3) **Social protection** : Social protection spending includes conditional/unconditional cash transfers, fee waivers, in-kind, public works, school feeding, social pension (non-contributory), and other social assistance. Notably, it excludes pensions and other contributory programs.<sup>4</sup> This data source is the most limited; it only has 124 observations (1 observation for each of the 124 countries), making it impossible to examine changes over time. The information has been curated by the Social Protection Global Practice at the World Bank and made available through the Atlas of Social Protection – Indicators of Resilience and Equity (ASPIRE) data set (World Bank Group 2019). ASPIRE only provides data on spending as a percent of GDP, not as a percent of total government expenditure, so this is also what we present here. Both current and capital spending are included in these values. While ASPIRE data have limitations, they are the most comprehensive set of social protection spending data for low-income countries. In line with IMF’s recent work on social sector spending (IMF 2019), we choose ASPIRE over EURSTAT, which has data only for a limited number of countries.

> 3 See “Mobilizing Resources for Education and Improving Spending Effectiveness: Establishing Realistic Targets Based on Past Trends” (Al-Samarrai, Cerdan-Infantes, and Lehe 2018) for further details.

> 4 Our main interest is to examine the relationship between social protection spending and the HCI. We exclude pensions because spending directed toward those of pension age is unlikely to have a strong impact on the human capital of a child born today. While some evidence documents that pensions received by older women may impact the human development of the recipients’ granddaughters (Duflo 2000), the context of this study may not be globally applicable. We exclude contributory programs because our interest is in examining _government_ expenditure on human capital, rather than the contributions of its citizens, which are likely highly correlated with income.

4
### **SUPPLEMENTARY MATERIAL: “Do Public Health Interventions Crowd Out Private Health Investments? Malaria Control Policies in Eritrea”**

# **A Randomization procedures**

This section presents the treatment allocation used for the IRS campaing in Eritrea.

## **A.1 Village lists and treatment allocation**

In relation to the implementation of the study and the intervention, we can identify four village lists that were used during the RCT conducted in Eritrea:

1. An initial village list, provided by the NMCP of Eritrea to J. Keating to conduct the initial random allocation to treatment (2008);

2. A village list provided by the NMCP to the spraying teams that conducted the IRS campaign in Gash Barka (Gash Barka) in June-July 2009. This list includes only the names of treatment villages, because spraying teams needed not visit the other villages<sup>2</sup> ; 3. A village list provided by the NMCP to data collectors (October 2009), including both treatment and control villages; 4. A final village list, provided by the NMCP to The World Bank at the end of all field operations (November 2009).

Comparison between List 1 and List 4 reveals some differences. Out of 116 villages, 82 (71%) have the same name in both lists and another 10 (9%) villages have names that can be matched using additional information. Two villages were replaced with two additional ones in one sub-zone. We are left with 22 (19%) cases of mismatch that we can’t explain.

2Names of control villages were added by hand; this was probably done by NMCP staff in Gash Barka.

1
Table 3. Effect of Extension of School Day in Maternal Labor Supply, Single and Married Mothers of 8–13 yearolds, without and with Younger Children (FSD Proportion of School)

|||No youngerchi|ldren||Withyoungerch|ildren|
|---|---|---|---|---|---|---|
||||Coefficient on<br>share<br>(FSD)*Mother<br>8–13 (model 2)<br>comparison<br>group|||Coefficient on<br>share<br>(FSD)*Mother<br>8–13 (model 2)<br>comparison<br>group|
||Pre-<br>initiative<br>mean<br>(1996)|Coefficient<br>on share<br>model 1|(Has) 6–7 year<br>old|Pre-<br>initiative<br>mean<br>(1996)|Coefficient<br>on share<br>model 1|(Has) 6–7 year<br>old|
||1|2|3|5|6|7|
|A.<br>Single women|||||||
|a.<br>Labor participation (=1)|0.74|0.04|0.04|0.68|0.03|-0.01|
||(0.44)|(0.03)*|(0.01)***|(0.47)|(0.04)|(0.02)|
|b.<br>Worked last week(=1)|0.70|0.06|0.04|0.62|0.01|-0.02|
||(0.46)|(0.03)**|(0.02)**|(0.49)|(0.03)|(0.02)|
|c.<br>Hours last week|44.45|-0.80|-1.61|44.17|-0.89|-0.66|
||(15.96)|(1.43)|(0.79)**|(16.90)|(1.85)|(0.86)|
|B.<br>Married women|||||||
|a.<br>Labor participation (=1)|0.38|0.00|-0.01|0.30|-0.02|-0.02|
||(0.49)|(0.02)|(0.01)|(0.46)|(0.01)|(0.01)**|
|b.<br>Worked last week (=1)|0.36|0.00|0.00|0.28|-0.01|-0.01|
||(0.48)|(0.01)|(0.01)|(0.45)|(0.01)|(0.01|
|c.<br>Hours last week|43.39|1.14|-0.06|43.05|-2.35|-0.1|
||(18.15)|(1.09)|(0.69)|(18.53)|(1.37)*|(0.76)|
|Controls|||||||
|Municipality fixed effects||Yes|Yes||Yes|Yes|
|Timefixed effect||Yes|Yes||Yes|Yes|

_Notes:_ Each entry in panels A–B, rows a–c, columns 2–5 represents a coefficient from a different regression. All the regression include municipality fixed effects, year fixed effects, a vector of maternal background, and a vector of municipality characteristics. All the regressions control for cluster by municipality, and standard error are consistent for heteroskedasticity and error correlation within municipalities over time.

*** _p_ < .01   ** _p_ < .05  * _p_ < .1

_Source:_ Underlying data are from the Chilean socioeconomic household survey (CASEN) and administrative data from the Ministry of Education for 1990–2011.

33
**Annex Table 4**

**Odds ratios of Logistic Regression Models Predicting the Probability of Labor Force Participation**

||**R**|**URAL MEN**||**U**|**RBAN MEN**||
|---|---|---|---|---|---|---|
|age|_1.242***_|_1.244***_|_1.243***_|_1.338***_|_1.339***_|_1.338***_|
||_(0.019)_|_(0.019)_|_(0.019)_|_(0.029)_|_(0.029)_|_(0.029)_|
|Age Squared|_0.996***_|_0.996***_|_0.996***_|_0.995***_|_0.995***_|_0.995***_|
||_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|married|_2.753***_|_2.735***_|_2.725***_|_2.888***_|_2.886***_|_2.874***_|
||_(0.088)_|_(0.089)_|_(0.089)_|_(0.132)_|_(0.132)_|_(0.133)_|
|hhsize|_1.012_|_1.014_|_1.015_|_1.015_|_1.014_|_1.015_|
||_(0.012)_|_(0.012)_|_(0.012)_|_(0.015)_|_(0.015)_|_(0.015)_|
|below_prim|_1.512***_|_1.549***_|_1.559***_|_1.663***_|_1.671***_|_1.702***_|
||_(0.093)_|_(0.094)_|_(0.094)_|_(0.167)_|_(0.168)_|_(0.171)_|
|prim_comp|_1.634***_|_1.682***_|_1.873***_|_2.492***_|_2.509***_|_2.517***_|
||_(0.088)_|_(0.089)_|_(0.117)_|_(0.143)_|_(0.143)_|_(0.193)_|
|postpri|_1.952***_|_2.054***_|_2.241***_|_2.743***_|_2.782***_|_3.301***_|
||_(0.076)_|_(0.079)_|_(0.095)_|_(0.107)_|_(0.112)_|_(0.142)_|
|HH head|_3.233***_|_3.217***_|_3.226***_|_2.963***_|_2.942***_|_2.970***_|
||_(0.105)_|_(0.106)_|_(0.106)_|_(0.141)_|_(0.141)_|_(0.142)_|
|spouse|_0.065***_|_0.067***_|_0.066***_|_0.034***_|_0.033***_|_0.033***_|
||_(0.267)_|_(0.263)_|_(0.262)_|_(0.420)_|_(0.420)_|_(0.422)_|
|land_poss|_1.274***_|_1.275***_|_1.277***_|_1.109**_|_1.108**_|_1.112**_|
||_(0.040)_|_(0.041)_|_(0.041)_|_(0.049)_|_(0.049)_|_(0.051)_|
|north|_0.955_|_1.010_|_1.004_|_0.734**_|_0.749**_|_0.750**_|
||_(0.089)_|_(0.095)_|_(0.096)_|_(0.139)_|_(0.138)_|_(0.138)_|
|south|_0.960_|_0.985_|_0.983_|_0.854_|_0.858_|_0.874_|
||_(0.081)_|_(0.082)_|_(0.082)_|_(0.127)_|_(0.127)_|_(0.127)_|
|east|_0.765***_|_0.756***_|_0.752***_|_1.027_|_1.023_|_1.037_|
||_(0.085)_|_(0.085)_|_(0.085)_|_(0.167)_|_(0.166)_|_(0.166)_|
|west|_0.952_|_0.951_|_0.955_|_0.770**_|_0.776*_|_0.783*_|
||_(0.105)_|_(0.105)_|_(0.105)_|_(0.132)_|_(0.131)_|_(0.130)_|
|NE|_1.097_|_1.133_|_1.170_|_0.763_|_0.765_|_0.799_|
||_(0.118)_|_(0.122)_|_(0.123)_|_(0.221)_|_(0.226)_|_(0.229)_|
|muslim||_0.921_|_0.968_||_1.016_|_1.112_|
|||_(0.090)_|_(0.116)_||_(0.120)_|_(0.199)_|
|otherel||_0.712***_|_0.715***_||_0.817_|_0.825_|
|||_(0.118)_|_(0.118)_||_(0.141)_|_(0.141)_|
|sc||_1.238***_|_1.321***_||_0.996_|_1.346*_|
|||_(0.078)_|_(0.102)_||_(0.115)_|_(0.180)_|
|st||_1.359***_|_1.687***_||_1.118_|_1.732*_|
|||_(0.110)_|_(0.134)_||_(0.224)_|_(0.284)_|
|SCprim_comp|||_0.800_|||_0.879_|
||||_(0.204)_|||_(0.312)_|
|SCpostpri|||_0.926_|||_0.547**_|
||||_(0.176)_|||_(0.239)_|
|STprim_comp|||_0.794_|||_0.949_|
||||_(0.319)_|||_(0.559)_|
|STpostpri|||_0.413***_|||_0.434*_|
||||_(0.242)_|||_(0.443)_|

22
Pouliquen, 2022), there is some emerging evidence from Africa as well. Santoro, Amine and Magongo (2022) evaluate the effect of the Eswatini government mandate to adopt e-filing and e-payment services for all income taxpayers as a response to the pandemic, introduced in September 2020. Using extensive administrative data from the tax agency, the authors examine the impact of IT adoption by exploiting the partial take-up of the mandate. A first key finding is that, indeed, more than 8 months after the mandate came into force, only a minority (41 percent) of taxpayers moved to filing through the e-Tax system. In line with other research from Africa described in Box 4, qualitative evidence from interviews with the tax agency reveals that most small taxpayers lack the equipment, an adequate connection, and the knowledge to navigate e-Tax. These taxpayers, mostly from rural areas where internet coverage is poor, then resort to imperfect solutions such as bringing their manual returns to the revenue office or emailing them and asking staff to help enter them in e-Tax.

In terms of impacts, the evidence from Eswatini is rather positive, as e-Tax uptake significantly improved filing behavior, as e-Tax registered taxpayers are almost 60 percent less likely to file nil, and they declare 32 percent more turnover and 50 percent more taxable income. However, surprisingly, final tax liability remains unchanged due to corresponding increases in expenses and deductions. For payment behavior, the authors find a strong impact of e-Tax registration on the probability to pay conditional on filing, as well as on the final tax amount paid (70 percent and 64 percent increases, respectively).<sup>8</sup> In terms of mechanisms, the study suggests that technology is facilitating compliance through a salience or reminder effect and helping taxpayers to track their records and fully report their expenses and deductions.

As a cautionary note to these positive impacts of technology on income taxes, in Rwanda, Santoro et al. (2022) do not find a positive relationship between adoption of E-tax or M- declaration solution and self-reported taxpayer outcomes such as tax compliance, perceived

> 8The divergent impact on tax declared and tax paid is not surprising in a context where filing and payment behavior are often disconnected, and where revenue authorities often prioritize payment over filing (Santoro and Waiswa, 2022).

23
Our key identifying assumption is that, conditional on demographic characteristics, household income, accessibility of schools and the county-year fixed effects, the topographic characteristics of the villages should affect people’s education only through the water treatment program. Topography, or land gradient, has been shown in the literature as affecting agricultural productivities (Udry, 1996), crop types (Qian, 2008) and infrastructure construction (Duflo and Pande, 2007; Donaldson, 2010; Dinkelman, 2011). It is plausible that these factors may affect individuals’ educational status—other than through the water treatment program—mainly through the household income. Therefore, controlling for household income in the regressions can help satisfy the exclusion restriction when using the villages’ topography as the instrument. Based on the description of the topography of a village in the CHNS survey, we construct a dummy variable of a village being non-flat as the instrument for Water Plant.<sup>31</sup> The F-statistics for the excluded instrument in the first stage is 21.23, which suggests that our topographic instrumental variable is not weak.<sup>32</sup> Note that the village’s topography was only recorded in the survey in 1991. As a result, the sample size for IV regressions is smaller since the topography of the newly-added villages after 1991 is not available.

Table 4 presents the IV estimates along with our baseline estimates. The qualitative conclusions from the IV estimates are similar to the baseline estimates, although the magnitudes of IV estimates double those of the OLS ones. For instance, Water Plant has a coefficient of 2.41 (statistically significant at the one percent level) while the baseline estimate is 1.02 (and statistically significant at the one percent level). The IV coefficient for graduation from a middle school remains positive and significant, with a larger magnitude (0.359 in IV vs 0.128 in FE). The coefficient for graduation from a high school remains positive and also statistically significant.

We cannot reject the null hypothesis that the IV and the FE coefficients are equivalent jointly. In particular, we conduct a bootstrapped Hausman test for each outcome variable with the

> 31 The survey allows us to construct slightly finer instrument (i.e., distinguishing hilly or mountainous areas).  But it is often not obvious to distinguish between being hilly or mountainous, and the results using two geographical instruments actually lead to weaker instruments as shown in the F-statistic.  The IV estimates of the treatment effects using the two IVs are still positive and large, but less precisely estimated.

> 32 As recommended by Baum, Schaffer and Stillman (2007), we compare the statistic to the rule of thumb (10) as well as the Stock-Yogo critical values because critical values for the heteroskedastic-robust Kleibergen-Paap Wald rk F Statistic of the test have not yet been calculated. The Stock-Yogo weak ID test critical value is 16.38 for 10% maximal IV size.

19
the fall is due to the decrease in the number of urban poor in India, due to the inclusion of imputed housing rent for homeowners in the standardized welfare aggregate.

**Figure 1. Sensitivity of the international extreme poverty rate when national and standardized consumption aggregates are used to measure poverty**

<!-- Start of picture text --> National consumption aggregate Standardized consumption aggregate<br>.25 .5 1 1.9 5 10 .25 .5 1 1.9 5 10<br>Daily Per Capita Consumption (2011 PPP USD) Daily Per Capita Consumption (2011 PPP USD)<br>BGD BTN IND LKA BGD BTN IND LKA<br>MDV NPL PAK MDV NPL PAK<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). The vertical line shows where the international poverty line lies.

**Table 12. Number of extreme poor using standardized and national consumption aggregates**

|||**Number of ext**<br>**(in milli**|**reme poor**<br>**ons)**|**Difference**|
|---|---|---|---|---|
|**Country**|**Year**|**Standardized**<br>**consumption**<br>**aggregates**|**National**<br>**consumption**<br>**aggregates**|<br>**(1)-(2)**|
|||**(1)**|**(2)**|**(3)**|
|Afghanistan|2012|n.a.|n.a.|n.a.|
|Bangladesh|2010|28.87|27.49|1.37|
|_- Rural_|_2010_|_26.12_|_24.89_|_1.23_|
|_- Urban_|_2010_|_2.74_|_2.6_|_0.13_|
|Bhutan|2012|0.02|0.01|0.01|
|India<sup>1</sup>|2011/12|217.52|239.1|-21.58|
|_- Rural_|_2011/12_|_191.75_|_196.7_|_-4.95_|
|_- Urban_|_2011/12_|_25.76_|_42.4_|_-16.64_|
|Maldives|2009|0.02|0.02|<0.00|
|Nepal|2010|4.09|4.2|-0.11|
|Pakistan|2010/11|11.96|10.26|1.69|
|Sri Lanka|2012/13|0.43|0.38|0.05|
|Total||262.91|281.47|-18.55|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). Population numbers were obtained from PovcalNet accessed on September 25, 2017.

18
**Table 2. Countries with** FOIA

|Country|Yearof<br>Introduction|NameoftheLaw|
|---|---|---|
|Albania|1999|Lawontherightofinformationoverofficialdocuments|
|Argentina|1998|FOIlaw|
|Australia|1982|FOIA|
|Austria|1986|Obligation to Infonnationlaw-itisnotFOIA|
|Belgium|1991-2001|seriesoflaws and decrees|
|Belize|1994.|FOIA|
|Bosnia-Herzegovina|2001|FOIA|
|Bulgaria|2000|Access to Information Act|
|Canada|1983|AccesstoInformationAct|
|Chile|1999|LawonAdministrative Documents|
|Colombia|1888,1985||
|Czech Republic|1999|Law onfreeaccesstoinformation|
|Denmark|1985|The PublicInformationLaw|
|EU|2001|Regulation regardingpublicaccesstoEuropean Parliament, CouncilandConmissiondocuments|
|Estonia|2000|PublicInformationAct|
|Finland|1951,1999|Acton theOpennessofGovernmentActivities|
|France|1978|Freedomofaccess totheadministrativedocuments|
|Georgia||FreedomofInformation (chapter3)|
|Ghana|1999|RighttoInformationBill|
|Greece|1986,1999|AdministrativeProceedingsCode|
|Hong Kong|1995|Code onAccesstoInformation|
|Hungary|1992|Data Protection Law|
|KyrgyzR|2001|LawontheGuarnteesandFreedomofAccesstoInfoTmation|
|Iceland|1996|InformationAct|
|Ireland|1997|FreedomofInfo_mtionAct|
|Israel|1998|FreedomofInfomatonLaw(5758-1998)|
|Japan|1999|Law concerningtheDisclosurofInformationheldbyalministiveorgans|
|Latvia|1998|-_<br>FreedomofInformationLaw|
|Lithuania|1996|LawonProvisionofIonfoationtothe_Public|
|Mexico|2002|FredomofInfom-tionAct|
|Moldova|2000|Law onAccesstoInformation|
|Netherlands|1991|Law onOfficialInformation|
|NewZealand|1982|OfficialInformatonAct|
|Nigeria|1999|FreedomofInformationBill|
|Norway|1971|FreedomofInformationAct|
|Panama|2002|LawonFree Access toPublicRecords|
|Poland|2001|FreedomofInformationAct|
|Portuga|1993|LawonAccessto AdministrativeDocunments|
|Romania|2001|Lawregardingthefree accesstothe informationofthepublcinterest|
|Russian Federation|1995|<br>LawonInfinrmation,Infromatization andProtectionofInformation;LawonState Secrets|
|Slovalda|2000|Actonfreeaocesstoinformation|
|SouthAfrica|2000|PromotionofAccess to informationact|
|South Korea|1996|ActonDisclosureofInformationbyPublicAgencies|
|Spain|1992|Legalregimeofthepublicadministrationsandthecomnaonadministrative procedure|
|Sweden|1766.|FreedomofthePress Act|
|Thailand|1997|OfficialInfonrmationAMt|

# **Descriptive Statistics**

Table 3 shows the transparency index for selected countries. Aggregating and averaging the transparency indicator among countries of different income levels shows that rich countries are more than twice as "transparent" (Table 4). What is interesting though is that some high 16
19 Table 2

# Correlation Table

# (Correlation Coefficients/ Prob > R under HO: Rho-O)

||RR|RFSTS|DYY|IOCR|IGDP|
|---|---|---|---|---|---|
|RR|1.0000<br>0.0000|||||
|RFSTS|0.5196<br>0.0001|1.0000<br>0.0000||||
|DYY|0.6193<br>0.0001|0.5668<br>0.0001|1.0000<br>0.0000|||
|IOCR|0.6175<br>0.0001|0.3714<br>0.0020|0.8249<br>0.0001|1.0000<br>0.0000||
|IGDP|0.2130<br>0.0812|0.1916<br>0.1203|0.2997<br>0.0130|-0.1223<br>0.3203|1.0000<br>0.0000|

# 4.2 Cross-Countrv Regressions.

# 4.2.1 Interest Rates. Growth and Efficiency

The first question is the relationship between interest rates and growth, and whether this is effected more through investment effects (that is, changes in the relationship of investment to GDP) or efficiency effects (as indicated by the incremental output capital ratio or IOCR). A range of regressions has been run on the overall averages and on the pooled set of two-period averages, for a set of "models" which progress from the simplest relationships to more complex ones. The latter include, as additional explanatory variables, CDP per head (GDPPE), inflation (INF), and population growth (POPGP). All pooled-sample regressions include the post 1973 shift dummy, GROUP. In some, the index of volatility was included.22 22 Following IMF, 1983, some regressions were also run using interest rate ranges, with countries divided into three groups: interest rates positive, negative but more than -5% and less than -5%, but these did not add more to the analysis and will not be discussed further. Volatility usually entered with the "expected" sign, but was not significant.