rafmacalaba/datause-encoder-data huggingface dataset:
the probability is roughly three times as large at 3.2 percent. When comparing columns 2 and 3, financial literacy leads, as previously discussed, to higher elasticities in magnitude. When individuals use brochures to compare credit products, the probability that a loan will be chosen first when its costs increases by 1 percent declines by 0.8 percent for a financially illiterate individual to 1.2 percent for a financially literate one. Similarly, when individuals use a simplified KFS the probability declines by 3 and by 3.2 percent, respectively for a financially illiterate and literate individual. Also notably, the simplified KFS is able to correct differences in financial literacy by not only increasing the magnitude of everyone’s price elasticity but also by making the difference in price elasticity between financially literate and illiterate individuals smaller, in percentage terms. Related, Online Appendix Table OA2 reports individual perceptions about the different formats and their knowledge of the interest rate for the chosen product. As shown in column 3, financially illiterate individuals are 17 percent more likely to perceive the simplified KFS as being clear compared to the marketing brochures. Similarly, in column 6 these individuals are 13.2 percent more likely to correctly state the interest rate of the loan chosen.

For savings products, the results are far more muted in magnitude and statistical significance. In column 1 of Table 5, an increase in the yield of a savings account by 1 percent increases the probability of choosing that product first by 0.02 percent when individuals use brochures to compare across savings products. When individuals use simplified KFS, the probability of choosing the account first increases by 0.03 and the difference in elasticities is not statistically significant. Columns 9 and 12 of Online Appendix Table OA2 show neither differences in clarity nor correct recall of interest rates between formats. Why are the results for savings so muted? There are a couple of reasons. First, unlike Peru, the simplified KFS in Mexico did not contain the net yield from the account to be earned in a year. Thus, while the simplified KFS in Peru is the only disclosure format with a positive and significant elasticity, in Mexico the simplified KFS is not at all effective. In fact, the price elasticity is lower than that of the brochures and is not significant for financially illiterate individuals. Second, when choosing a savings product, the net yield from the account may not be as relevant as other factors 19
Poor governance spurs rent-seeking behaviors and diverts public funds to unintended activities, lowering the effectiveness of public investment.

**Table 4:** The response of private investment to government investment, corruption of control

||(1)<br>C<br>|(2)<br>umulative M<br>|(3)<br>ultipliers (ho<br>|(4)<br>rizon h in year<br>|(5)<br>s)<br>|
|---|---|---|---|---|---|
||h=0|h=1|h=2|h=3|h=4|
|||Higher<br>|control of co<br>|rruption<br>||
|Change in government investment|0.870*|2.519**|2.514**|2.272**|1.450|
||(0.484)|(1.094)|(1.074)|(1.122)|(1.307)|
|Observations|1,955|1,902|1,849|1,795|1,742|
|F-statistic|11.67|10.80|11.06|13.53|13.05|
|KP Underid stat|0.00171|0.00263|0.00232|0.000934|0.00112|
|No. of countries|93|93|93|93|93|
|||Lower<br>|control of co<br>|rruption<br>||
|Change in government investment|4.764|1.661|1.686|1.965|2.005|
||(4.312)|(3.069)|(3.010)|(2.449)|(3.164)|
|Observations|1,394|1,340|1,287|1,234|1,180|
|F-statistic|1.580|1.378|1.658|2.049|1.617|
|KP Underid stat|0.196|0.225|0.183|0.140|0.186|
|No. of countries|70|70|70|70|70|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country-fixed effects and year effects and one year lagged dependent variable. “KP” is Kleibergen and Paap underidentification test.

To examine for the impact of corruption on the ability to crowd-in private investment, we divide the sample (that is, country-year pairs) into those above (high corruption control) and below (low corruption control) the median outcome for corruption control from the Worldwide Governance Indicators (Kaufmann et al. 2011). The control of corruption indicator measures perceptions of the extent to which public power is exercised for private gain.

We find that the crowding-in effect of public investment on private investment is only statistically significant in the high control of corruption group, with the cumulative impact increasing up to four years ahead (table 4). When control of corruption is weak, the estimated coefficient across the different horizons, while generally smaller in magnitude, is 15
**_Energy demand models for policy_** **_<u>formulation</u>_**

structural analysis of households using statistical techniques and then modeling demand equations. Although this method was applied to a case study, there is no evidence that this has been widely used. Although the survey-based studies tend to capture the diversity of demand by income and location, they provide insight at a given point in time. While these studies surely add value in understanding the demand, the frequency of such surveys and the cost of gather such data could be a hurdle for many developing countries.

Although a large number of econometric studies exist for the developed countries, limited focus has been given on residential energy demand in developing countries and especially for rural areas. The main difficulty often faced by the residential and commercial sectors in analyzing energy demand is the availability of data, especially of end-use breakdowns of energy consumption. Moreover, traditional fuels play a vital role in many countries to meet the energy demand of residential and commercial sectors but data is often not available in a systematic and regular manner. In addition, as the end-use efficiency of traditional fuel use is comparatively low, the final energy consumption including traditional energies may hide certain changes taking place within the sectoral energy consumption pattern. The conventional econometric analysis tends to ignore the nonpriced transaction of traditional energy use in rural areas, especially due to the lack of a reliable time series. However, such omission can be difficult to justify given that a large section of the rural population is expected to continue to rely on traditional energies even in 2030 (IEA, 2002).

Further, the single equation models or aggregated analysis do not capture the technological diversity and the spatial difference in energy demand. The problem can be worse where energy prices are controlled by the government because the econometric relations may not prove statistically significant or meaningful.

# **End-use models**

Changes in energy demand in the residential sector are often related to the population change, and changes in demand per capita. Measuring activity is difficult since there are 72
35 the 1963-67 period at .58 -- and this is the maximum range correlation that the data allow. A similar pattern, though less pronounced, exists on the entry rate side. The persistence of industry effects over long periods seems to indicate both the importance of continuing turbulence as a factor in the general patterns and also the industry-specific character of major determinants of turbulence -- but a fifteen or twenty year period is not quite long enough by industry life cycle standards to make this judgment secure. After correction for fixed industry effects, one gets the expected negative correlation between entry and exit rates over any given inter-census interval: there are good times and bad times, and they do not occur simultaneously in all industries. Good times favor entry and depress exit; bad times do the reverse.

One apparent contrast between the Wisconsin data and the Census Bureau data involves to the size of survivors in a particular cohort relative to overall average firm size. Five years after entry, the Census Bureau data indicate a value in the 30% - 40% range, while the corresponding value in the Wisconsin data is around 15%. However, the Dunne, Roberts and Samuelson definition of entry includes entry by established firms from other industries, which tend to be larger (even in the newly-entered industry alone) than new firms. Also, the size measures are different (employment in Wisconsin, value of output in the Census Bureau data).
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Expansion of standposts and improved latrines in Niger**

Population gaining access per year between 1998–2006 <!-- Start of picture text --> a. Water b. Sanitation<br>5 5<br>4 4<br>3.4 3.7 3.4 3.4 3.7 3.4<br>3 3<br>2 2<br>1 1<br>0 0<br>National Urban Rural National Urban Rural<br>-1 -1<br>-2 -2<br>Piped Water Standposts Flush Toilets Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> _Source:_ WHO Joint Monitoring Program 2010, from the Demographic and Health Surveys for 1998 and 2006.

# Power

## **Achievements**

The performance of NIGELEC,<sup>10</sup> Niger’s power utility, improved significantly after the introduction of an internal restructuring program in 2006. System losses accounted for 27 percent of the power produced in 2005 and dropped to 17 percent in 2009, below what is observed in power utilities on average in SubSaharan African low- and middle-income countries. But system losses are still above the international best practice of 10 percent. At 96 percent of billings, NIGELEC’s collection ratio exceeds that of other African MICs and LICs, and collections have increased as the government is paying its bills.

Recovery of costs accompanies NIGELEC’s performance improvements. At $0.17 per kilowatt-hour (kWh), Niger’s consumers pay approximately $0.07 more for power than the average African country that derives its power from thermal-based resources (figure 14). With this level of tariff, NIGELEC’s revenues were enough to recover costs of production in 2009. Thus, at 100 percent cost recovery, NIGELEC performs better than utilities in other low- and middle-income countries (table 12).

> 10 Société Nigérienne d’Electricité (Niger Electricity Company).

26
those who needed a license, (52+21=) 73 percent, the percentage that could apply for a license is (21/73 =) 29 percent.

**Figure 2: Percentage of Respondents Who Needed and Applied for a Government Provided Good or Service** Percentage of All Adults

<!-- Start of picture text --> Driving License<br>Register Land/Property<br>Utilities<br>Public School<br>Healthcare<br>Aadhaar Card<br>Voter ID Card<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Percentage of Respondents<br>Did Not Need Needed But Could Not Apply Needed and Applied<br><!-- End of picture text --> That 70 percent of respondents needed a driver’s license appears high, but we have a strong criterion for coding “ _Did not need and Did Not Apply_ .” Furthermore, India is a young country where more than half the population is younger than 25 and two-thirds are less than 35. This creates more demand for such services as compared to the demand observed in more mature and wealthy economies.

In the case of driver’s licenses, employment opportunities are also a significant factor. The total commercial goods transported in India has grown by 10 percent year-on-year over the last decade. More than half of the commercial goods transport is done by road. Additionally, motor vehicle ownership has increased by more than 10 percent year-on-year, and in India many car owners employ chauffeurs. A job seeker needs a driver’s license to be employed as a truck driver or a car 11
# **Partial Peace Rebel Groups Inside and Outside Civil War Settlements***

## Desirée Nilsson

_Department of Peace and Conflict Research, Uppsala University_

> * I would like to thank Håvard Hegre, Gary Milhous, Ibrahim Elbadawi, and colleagues at the Department of Peace and Conflict Research, Uppsala University, in particular, Mats Hammarström, Margareta Sollenberg, Hanne Fjelde, Lisa Hultman, Isak Svensson, Anna Jarstad, and Erik Melander, for excellent comments and suggestions. The research was supported by the World Bank research project on ‘Political Institutions, Development, and Domestic Civil Peace’, funded by the Government of Norway. The replication data and a web appendix are available at http://www.prio.no/jpr/datasets. For recent versions of the UCDP data used here, see http://www.ucdp.uu.se. For correspondence: desiree.nilsson@pcr.uu.se.
produce frozen fillets, competed more intensely within a diminished market (Figure 20). This reflected not only the closure of EU markets as a direct result of the restrictions, but also the diminished reputation of fresh Nile perch amongst many buyers world-wide because of the EU’s actions.

**-** **<u>Table 9. Exports of fresh, chilled and frozen fish fillets by destination, 1997 2002 (Tonnes):</u>**

|**Country**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|
|Israel|4244|5252|4550|7160|3984|2887|
|Netherlands|3033|856|3208|26|4126|4458|
|Singapore|1669|745|532|910|346|323|
|Germany|1550|175|298|0|1449|1383|
|Greece|789|1002|183|0|103|118|
|Japan|387|1221|1951|2183|1234|1669|
|Malaysia|318|36|87|360|399|299|
|France|309|19|0|0|0|0|
|USA|288|134|167|415|1491|1244|
|Portugal|244|25|124|0|166|202|
|UnitedArabEmirates|222|352|233|449|440|208|
|Belgium|208|27|7|1|822|788|
|HongKong|175|173|235|1226|211|374|
|Australia|174|772|1829|1439|2083|1323|
|Italy|1|0|0|0|402|721|
|Other|452|200|547|1359|649|489|
|**Total**|**14064**|**10989**|**13949**|**15528**|**17906**|**16482**|

_Source: CBS (2002)._ The unit price of frozen fish fillets in non-EU markets remained stable through the period 199698, reflecting the greater number and variety of markets available to exporters and their ability to divert supplies between these according to market conditions (Figure 21). However, the price of frozen fillets did decline in the EU such that the differential between EU and non-EU market prices was almost eradicated by the end of 1998. Through 1999 and 2000, however, EU prices recovered and in 2001 there was a premium of over 15 percent over frozen fillet exports to nonEU markets.

# **7.2. Landed volume and price of Nile perch**

The restrictions on exports to the EU were imposed at a time when the landed catch of Nile perch was already in decline. For example, over the period 1994-96 the landed volume dropped from 103,995 tonnes to 96,471 tonnes (Figure 22). However, the loss of access to EU markets immediately resulted in a reduction in demand for raw fish as industrial processing facilities ceased operating or reduced output. The local market was unable immediately to absorb the surplus, for example because the artisanal processing sector is now insignificant on many landing beaches, and the catch declined significantly in 1997 as fishers reduced their fishing effort and/or diverted attention to other fish, in particular tilapia.

47
system in 2005 to a more balanced hydro-thermal system in 2009 (EIA 2009), Bujagali will skew the generation portfolio once again toward hydropower.

# **Challenges**

_Access to power in Uganda is very limited._ As of 2009, only 9 percent of the population had access to power, less than a third of the rate in other low-income African countries and a fraction of the rate in resource-rich countries. Uganda’s access rates are more or less equal to access rates in Malawi, amongst the lowest in Africa. Access to electricity in urban areas is also limited. Even in urban areas, only 50 percent of the population has access to power compared to 86 percent in other LICs. In rural areas, only 5 percent of the population has access to power compared to 12 percent in LICs, falling short of Uganda’s national rural access target of 10 percent (table 10).

**Table 10. Benchmarking Uganda’s power infrastructure, 2006**

|||Uganda|Low-income,<br>nonfragile<br>countries|Middle-<br>income<br>countries|Resource-<br>rich<br>countries|
|---|---|---|---|---|---|
|Access—national 2009|% population|9|33|50|46|
|Access—urban 2009|% population|50|86|100|100|
|Access—rural 2009|% population|6|12|33|28|
|Access—capitalcity2009|% population|60|81|81|89|
|Installed generationcapacity|MW|568|651|36,971|4,105|
|Installed generationcapacity|MW per<br>million<br>people|18|20|799|43|
|Emergency generationcapacity|MW|100|46|0|0|
|Self-generationcapacity|MW|37|17|30|13|
|Firms thatfind powera constraintforbusiness|% of firms|45|52|31|56|
|Firmswithowngenerator|% of firms|29|41|18|63|
|Outages,number, annually|number per<br>year|132|124|71|174|
|Outages,valuelost|% ofsales|10|6.5|1.6|7|
|Collection rate,reported by utility, electricity|% ofbilling|94|92|91|70|
|Cost-recoveryratio,historical|%|67|89|85|97|
|Revenue perunit|US cents per<br>kWh|15|14|13|13|
|System losses|% generation|39|24|20|52|
|Total hiddencosts|%revenue|103|69|0|168|

_Source:_ Access to power (national) is based on information in 2009 and taken from World Energy Outlook, 2010; Access to power in rural areas taken from EIU (2011).

_Note:_ All data for 2006 unless indicated otherwise.

33
**Figure 1 Ranking of 36 SIDS in the ND-GAIN sub-index of Exposure to Climate Change**

<!-- Start of picture text --> 250<br>200 180 182 185 186 187 188 189 190 192<br>169 173<br>158<br>152<br>150 120 125 126 129 131 136 139 143<br>105 107<br>100 90 94<br>85<br>79 79<br>73<br>66 66<br>60<br>50 53 54<br>50 46<br>0<br>Dominica Grenada Cape Verde Saint Lucia Barbados Guyana Trinidad & Tobago St Vincent & Grenadines Bahrain Suriname Bahamas Saint Kitts and Nevis Haiti Dominican Republic Jamaica Fiji Comoros Antigua and Barbuda Sao Tome & Principe Samoa Mauritius Vanuatu Papua New Guinea Cuba Guinea-Bissau Timor-Leste Palau Tonga Solomon Islands Nauru Marshall Islands Seychelles Micronesia Kiribati Tuvalu Maldives<br><!-- End of picture text --> _<u>Note: There are a total of 192 countries ranked in this sub-index. Exposure captures the physical factors</u> external to the system that contribute to vulnerability. It is a component of vulnerability independent of the socio-economic context. A higher ranking denotes greater exposure._ _<u>Source: ND-GAIN</u>_ Although SIDS are a heterogeneous group of countries, many of them share common factors which expose them to the hazards of climate change. Most are located geographically in tropical cyclone regions. Many also have extensive low-lying coastlines. Most also have small undiversified economies heavily dependent on a single export sector, such as tourism. Small islands are unique in that almost all of their territory is exposed to a natural hazard and their small land area means that a single disaster can have a systemic impact.

The main type of climate-related natural disasters<sup>3</sup> which affect most SIDS—both in terms of incidence and especially in terms of the economic damage caused—are tropical cyclones, which accounted for 95 percent of the total damages from climate-related natural disasters that SIDS in the Caribbean and Oceania suffered from 1995 to 2022 as recorded in the EM-DAT. SIDS are also affected by riverine floods and droughts, although the economic damages they cause are generally much less than those caused by tropical cyclones.<sup>4</sup>

> 3 These are classified as meteorological (e.g. tropical cyclone, invective storm), climatological (e.g. drought, wildfire) and hydrological (e.g. landslide, riverine flood, coastal flood, flash flood) in the EM-DAT.

> 4 Some SIDS are also highly exposed to geological hazards such as earthquakes and volcanic eruptions. These risks also need to be incorporated into the disaster risk planning and management of governments and the private sector.

4
**Table 8: Identifying the $1.90 poor based on covariates of poverty**

_<u>(Pooled data across countries with equal weights for each country)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|-0.007***|-0.133***|0|-0.158***|-0.284***|0|683,533|
|Asset index|-0.041***|-0.139***|0|-0.380***|-0.478***|0|431,436|
|Asset ownership|-0.007***|-0.033***|0|-0.095***|-0.121***|0|264,680|
|Literacy|-0.010***|-0.065***|0|-0.136***|-0.191***|0|694,463|
|Not employed in the<br>agricultural sector|-0.009***|-0.031***|0|-0.052***|-0.074***|0|450,521|
|Access to electricity|-0.017***|-0.093***|0|-0.263***|-0.339***|0|697,574|
|Piped drinking<br>water|-0.006***|-0.020***|0.0174|-0.095***|-0.108***|0.0174|595,934|
|Improved sanitation|-0.011***|-0.049***|0|-0.134***|-0.172***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to electricity, etc. Ranked residuals are used for only the first two covariates, years of schooling and asset index, which are continuous variables by construction. The remaining covariates are binary variables (e.g., literacy, ownership of a computer, etc.). With limited asset categories (e.g., only three assets for Colombia, Mali, and Pakistan), it makes more sense to create a binary variable than an asset index. The regressions are based on all countries with data for each covariate of poverty, with each country having a weight of 1. Table 2 above has summary statistics on the various covariates of poverty, thus providing information on the availability of data from the different countries pooled together in these regressions. The per capita poor are defined using the $1.90 line for all countries, and the root N poor are defined using the root N-equivalent poverty line, which is country-specific (see Table 4). The per capita poor only are those individuals who are only poor by the per capita allocation rule but not the root N allocation rule. The regressions run on a subsample of household heads only. Column 4 indicates the results of a test of equality between the coefficients reported in columns 2 and 3 under the assumptions of seemingly unrelated regressions. Column 7 indicates the results of a test of equality between the coefficients reported in columns 5 and 6 under the assumptions of seemingly unrelated regressions.

37
|fou<br><br>Chosen LoB's<br><br>|een<br>|[|ow<br>LR Std Dev?<br><br>||<br>|
|---|---|---|---|
|[Motorvehiclefiabiity<br>|<br>|80%|<br>no]<br>-|<br>+[100%|

<!-- Start of picture text --> Chosen LoB's LR Std Dev?<br>fou een [| ow |<br>[Motorvehiclefiabiity | 80% | no] -+[ 100%<br><!-- End of picture text -->

|Motor vehicle liability|10.00%|
|---|---|
|Motor, other classes|7.00%|
|Marine, aviation, transport (MAT)|17.00%|
|Fire and other property damage|10.00%|
|Third-party liability|15.00%|
|Credit and suretyship|21.50%|
|Legal expenses|6.50%|
|Assistance|5.00%|
|Miscellaneous|13.00%|
|Medical expense|4.00%|
|Income protection|8.50%|
|Workers‘compensation|5.50%|
<!-- Start of picture text --> a<br>N60<br>TM<br>Le<br>Q 40<br>ca<br>a.<br>wn<br>—<br>Z : 7<br>= 0<br>@<br>D<br>c<br>G -20<br>non-Tariffed Tariffed<br>M8 World MM China MM RoW<br><!-- End of picture text -->