rafmacalaba/datause-encoder-data huggingface dataset:
Where _c_ is the cluster/EA, _t_ is the year and _O_ is the refugee’s country of origin. For this instrument to be valid, it has to influence our outcome variables only through the presence of refugees. In other words, this instrument has to be a good predictor of the number of refugees within a given cluster while remaining uncorrelated with the error terms. Our assumption to satisfy this exclusion restriction is that the distance between any given cluster and the border point through which refugees come into Uganda is completely independent from the outcome variables. We further relax that assumption in Section 5.2. By linking that distance with the number of refugees from country _O_ within the cluster _c_ , and summing up over all possible _O_ , we obtain a good predictor of the total number of refugees within cluster _c_ at time _t_ . However, we will discuss further the plausibility of our identifying assumption in Section 5 (Sub-section 5.2).

According to Figure 1, there are 17 source countries registered in the UNHCR refugees data set. Only 7 countries account for 99.7% of the total number of refugees in Uganda (excluding refugees in Kampala whose source country is not known) in the study period. We focus on these 7 countries to construct the IV.<sup>12</sup>

> 12 More specifically, these 7 countries include Sudan/South Sudan, DRC, Rwanda, Burundi, Somalia, Eritrea and Ethiopia. Despite its border with Uganda and having more than 1,000 refugees over the study period, Kenya is not included in the IV analysis as it contributes only about 0.002% of the total. In addition, the refugee border crossing points are lined only along the northern, western and southern borders of Uganda. It is thus difficult to assume that Kenyan refugees had to first cross one other international border before crossing into Uganda. Yet, this assumption can be made for refugees from Somalia, Ethiopia, and Eritrea (assuming they would enter Uganda through South Sudan).

19
'PopDens' provides insights into how population density might influence or be influenced by specific flood events. The variables 'cf_cvgBuff05' and 'avg_radBuff05' described above, measure economic activity through cloud observations and nightlight radiance, respectively. Additionally, they are averaged over adjacent pixels to provide context for each location.

‘Treated' and 'Treated_after' distinguish areas affected by conflict before and after the flood in each country : March-April 2019 are months where the flood occurred in Mozambique while July 2022 was considered as the flooding month in this analysis for Nigeria. This is essential for determining the causal inference of conflict impact. 'Settlement' and 'Urban_Suburban' categorize urbanization levels to understand how different types of areas are affected by and respond to both floods and conflict events. Lastly, 'Fatalities' provides a direct measure of the human cost of conflicts.

These variables collectively enable a comprehensive analysis of the effects of floods and conflicts when they co-occur in the same location. They are used to analyze the impacts of floods and conflict on specific aspects of economic activity, as potentially inferred by nightlight changes. Table 1 describes these variables, their units of measurements, and how they will be used in the analysis.

Table 1. Description of variables

|**Variables**|**Description**|**Unit**|
|---|---|---|
|lat|latitude|Decimal coordinates|
|lon|longitude|Decimal coordinates|
|months_EE|Month since the event|Months (positive if after event, negative if before<br>event)|
|flood|Flood variable|=1 or 2 if medium reliability, =3 if high reliability|
|PopDens|HRSL population density|Person/km<sup>2</sup>|
|cf_cvgBuff05|Total<br>number<br>of<br>cloud-free<br>observations that went into each<br>pixel (averaged over the adjacent<br>pixels)||
|avg_radBuff05|Average nightlight radiance values<br>(averaged over the adjacent pixels)|nanoWatts/sr/cm<sup>2</sup>|
|Treated|Dummy<br>variable<br>representing<br>conflict-affected area|=1 if subject to a conflict within the buffer area<br>before the floods, =0 otherwise|
|Treated_after|Dummy<br>variable<br>representing<br>conflict-affected area|=1 if subject to a conflict within the buffer area<br>after the floods, =0 otherwise|
|settlement|Degree of Urbanization|=11 if uninhabited, =12 if rural, =21 if suburban<br>and =23 if urban|
|Urban_Suburban|Urbanization dummy variable|=1 if urban or suburban, =0 otherwise|
|Fatalities|Total number of fatalities associated<br>with conflicts within the buffer area||

# Overall empirical strategy

To differentiate the impact of floods on economic activity prior to the flood between conflict-affected (treatment group), and non-conflict affected (control group) areas, we first restrict the sample to flood-impacted pixels. We then apply the difference-in-differences regression method, a quasiexperimental technique commonly used for ex-post impact evaluations. The underlying concept 7
Table 2: Agricultural Production Function

||2007 cro|ss-section|First Di|ferences|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Risk index of crop portfolio|5.981<sup>_∗∗_</sup>|6.236<sup>_∗∗_</sup>|6.948<sup>_∗_</sup>|6.546<sup>_∗_</sup>|
||(2.039)|(2.013)|(3.182)|(3.015)|
|Risk index of crop portfolio (squared)|-7.526<sup>_∗∗_</sup><br>(2.363)|-7.275<sup>_∗∗_</sup><br>(2.249)|-8.339<sup>_∗_</sup><br>(4.054)|-8.619<sup>_∗_</sup><br>(3.827)|
|Variable inputs (log)|0.887<sup>_∗∗∗_</sup><br>(0.112)|0.818<sup>_∗∗∗_</sup><br>(0.106)|0.746<sup>_∗∗∗_</sup><br>(0.124)|0.753<sup>_∗∗∗_</sup><br>(0.123)|
|Area cultivated (acres, log)|0.790<sup>_∗∗∗_</sup><br>(0.232)|0.622<sup>_∗∗_</sup><br>(0.202)|0.667<sup>_∗∗∗_</sup><br>(0.194)|0.681<sup>_∗∗∗_</sup><br>(0.191)|
|Labour (hours, log)||0.230<sup>_∗∗∗_</sup><br>(0.056)|||
|Rainfall (deviation)|-0.349|-0.401|-0.010|-1.164<sup>+</sup>|
||(0.436)|(0.422)|(0.155)|(0.627)|
|Rainfall (deviation) _×_ Risk index||||3.386<sup>_∗_</sup><br>(1.580)|
|Observations|1087|1087|1087|1087|
|_R_<sup>2</sup>|0.299|0.323|0.127|0.130|

Notes: Estimation in FGLS. Dep. var.: Income from agricultural production (log). Additional controls are share of area under irrigation, fertilizer application, HYV seeds application, labor sharing, rainfall (deviation), rainfall (deviation, lag), self-reported shocks, and time trend. Cols. (1) & (2) additionally control for household characteristics: age, sex, and education of household head, and household size. Standard errors (clustered at the the sub-district) in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

37
compile information on the surveyed population versus the total population for each birth cohort in order to construct the sample weights in our regression analysis.

Our key independent variable “Life Expectancy at Birth” is compiled from the United Nation’s Population database and “Gap Minder”.<sup>10</sup> “Gap Minder” constructs a measure of life expectancy at birth for almost 200 economies back to 1900 by compiling pre-1950 data on mortality rates from the Human Mortality Database and the United Nations Population Division’s _World Population Prospects_ .<sup>11</sup> For robustness checks, we also compile life expectancy at ages 5 and 10. Because life expectancy at older ages will reflect parental investments in their children’s health and human capital in response to updated information on the cohort’s health, these measures are endogenous, but are used to compare our findings to previously published studies. Life expectancy at ages 5 and 10 are published by the Population Division of the United Nations Department of Economic and Social Affairs.<sup>12</sup> Figure 2 illustrates how life expectancy evolves globally across cohorts in our sample. Both life expectancy and years of schooling decreased through the first 35 years of the century and then both series reversed and rose for the rest of the century. The regional scatterplots in Figure 3 indicate that all regions experienced similar rising trends in life expectancy at birth across cohorts, so the apparent decline in life expectancy for the first third of the century in Figure 2 was due to the inclusion of economies with lower average life expectancy and schooling and was not a true decrease in life expectancy. In Latin America and the Caribbean, life

> 10 The UN maintains a rich database on various socio-economic indicators <u>http://data.un.org/Default.aspx.</u> 11 In the case where no estimates are available, they rely on simple model of interpolation and extrapolation to reach an approximate measure. Although “Gap Minder” admits that quality of life expectancy at birth data would vary across economies for the period before 1950, our extensive search suggests that this is the best available information covering such a wide set of economies for a long period before 1950.

> 12 - Various region, gender and age specific life expectancy data are available at http://esa.un.org/wpp/Excel <u>Data/mortality.htm. The data are reported for 5-year birth cohorts rather than specific birth cohorts, and so we used</u> the nearest age match.

14
# **Table 23f.  Piecemeal Sensitivity Analysis –Particulate Matter Emissions by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|3.0|2.6|2.5|4.0|2.9|2.9|3.1|1.5|5.9|2.8|5.0|
|**esubc = 0.5**|3.0|2.7|2.6|4.2|2.9|3.1|3.1|2.0|6.2|2.8|5.1|
|**esubc = 1.5**|3.0|2.6|2.5|3.9|2.9|2.9|3.2|1.3|5.8|2.8|4.9|
|**esub = 2**|2.5|2.3|2.8|3.7|2.5|2.5|2.3|1.0|5.1|2.4|5.0|
|**esub = 4**|3.1|2.6|2.3|4.1|3.0|3.0|3.4|1.6|6.0|2.9|4.8|
|**esubs = 0.5**|2.0|1.4|0.7|2.7|1.9|1.7|2.3|0.5|3.8|2.0|3.0|
|**esubs = 2.0**|4.6|4.7|5.8|6.2|4.4|4.9|4.5|3.1|7.7|4.1|8.6|
|**esubva = 0.5**|3.0|2.6|2.5|2.9|2.8|3.0|3.2|1.5|5.6|2.8|4.8|
|**esubva = 1.5**|3.0|2.6|2.5|4.0|2.9|2.9|3.1|1.5|6.0|2.8|5.0|
|**etad = 10**|3.0|2.7|2.3|4.0|2.9|2.9|3.1|1.6|6.4|2.7|5.0|
|**etad = 5**|3.1|2.6|3.0|4.1|2.9|3.1|3.3|1.4|5.3|3.0|5.1|
|**etadx = 3**|2.9|2.6|2.5|2.9|2.8|2.8|3.1|1.6|5.5|2.9|5.0|
|**etadx = 7**|3.1|2.6|2.6|4.1|2.9|3.1|3.3|1.4|6.4|2.8|5.0|
|**etaf = 12.5**|2.6|2.2|2.4|3.7|2.6|2.5|2.7|1.2|5.1|2.4|4.5|
|**etaf = 17.5**|3.3|3.0|2.6|4.2|3.1|3.3|3.6|1.8|6.5|3.2|5.4|
|**sigmadm = 4**|3.2|2.7|2.4|4.1|3.1|3.0|3.5|1.6|6.2|2.9|5.3|
|**sigmadm = 2**|2.5|2.2|2.7|3.8|2.5|2.5|2.4|1.1|5.0|2.5|4.6|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys centra|l values for a|llparamete|rs(see belo|w)other than|the testedp|arameter.|||||

|**Source: Authors' calculations.**<br>**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys cen|tral values for allparameters(see below)other than the testedparameter.|
|---|---|---|
|**Parameter**|**Central**|**Definitions of theparameter(see figures, especially figure 5)**|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

72
**Figure 5: Volatility of output growth in East Asia and the Pacific (by income level)**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>High Income Low Income Middle Income OECD<br>No-OECD<br>1970-80 1980-90 1990-2000 2000-06<br><!-- End of picture text --> Volatility is measured as the group’s average (weighted by GDP) of the standard deviation of output growth. OECD: Korea (Rep), New Zealand, Australia, Japan. High Income Non-OECD: French Polynesia, New Caledonia, Singapore, Hong Kong. Middle Income: Philippines, China, Thailand, Malaysia, Fiji, Indonesia, Kiribati. Low Income: Solomon Islands, Myanmar, Papua New Guinea.

World crisis volatility, which captures the frequency of major crises (Box 1), has also declined from an average of more than 4 percent of total volatility at the end of the 1970s, to less than 1 percent at the beginning of the new century (Figure 1). However, the decline has been unevenly distributed: during the period considered, OECD countries have almost lived without crisis volatility, and in China crisis volatility seems to be a matter of the past (i.e. the 1960s and 1970s); in contrast, in the Pacific islands major crises are a recurrent problem happening in almost every decade (e.g. Solomon Islands, New Caledonia, Papua New Guinea and Fiji); and for a third group of countries it is a new phenomenon, closely related to the 1997 financial crisis (e.g. Indonesia, Singapore and Malaysia).

9
- 28 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

roundness variables, while French and Dutch colonies show smaller values in both cases, indicating cities more elongated and with less smoothed perimeters than their English and Iberian counterparts. _Fullness_ distributions also show some differences, with French and Dutch colonies showing higher values than the rest. Urban texture distributions show an interesting trend: Portuguese colonies have the lower values of _Circuity_avg_ , followed by Spanish colonies, then English, then Dutch, and then the French ones. This corroborates the idea that LAC cities that were Iberian colonies have more regular urban layouts. The density of streets and intersections indicates that the English colonies have less density than the rest, with the higher densities in the Iberian colonies. _Sprawl_ distributions show that French colonies have the lower values, indicating that the population is more uniformly distributed in those cities than in the rest of the groups.

# _4.3.4 GDP (proxy: density of radiance in NTL imagery, dr_ntl)_

We used the density of radiance from the nighttime imagery as proxy for GDP ( _dr_ntl_ ). We classified the LAC cities in quartiles according their registered radiance density in 2010. _nExchange_ and _nPerimeter_ do not show legible differences across quartiles, but _Fullness_ does: the group of cities with lower density radiance values also show a trend towards lower values of _Fullness_ . This indicates the trend that the cities with more open spaces within the urban extent are less productive, in agreement with the findings of Duque et al. (2017a, 2017b). The distributions of urban texture variables corroborate the same finding: street and intersection densities both increase with radiance density, and the group of higher values of radiance density has the more regular urban layouts ( _Circuity_avg_ values closer to 1). The distributions of _Sprawl_ show that the most productive cities have the most uniform distributions of populations within the urban extent. The variable _dr_ntl_ is positively correlated with variables capturing compactness and connectivity. This is in line with the finding by Duque et al (2017a) in which compact and well-connected cities show higher levels of productivity. Good mobility reduces transportation costs, and compactness reduces the cost-perhousehold of public service infrastructure.
and unsubsidized (international) prices for natural gas. For other fuels, we used international prices or opportunity costs of fuels. Consideration of local and international prices for natural gas is important because two-thirds of existing power generation capacity and 55% of planned power generation capacities are natural gas based. Fuel prices are provided in Tables 4b and 4c. Table 4c provides local natural gas prices. International prices of natural gas are based on EU hubs prices, which range from $5.0 to $5.5/MMBTU in 2020, from $6 to $6.5/MMBTU in 2025 and from $7.0 to $7.5/MMBTU after 2025.

Since natural gas is the cheapest option in most economies, the model ends up with a corner solution implying natural gas-based generation would meet the entire electricity demand in these economies. To avoid the corner solution, we applied a constraint on the volume of natural gas to be used for power generation in each economy. The constraints are from World Bank (2020).

Renewable technologies have relatively lower capacity utilization factors because of resources (wind and solar) availability characteristics. The availability of renewable energy varies across seasons (e.g., hydro) and hourly (e.g., solar and wind). The annual average capacity factors of renewable energy technologies are provided in Table 4e. From the power system reliability perspective, we assumed that the total capacity of wind and solar PV does not exceed 40% of peak demand in a year.

**Table 4. Economic and technical characteristics of power generation technologies**

# **2a. Costs and heat rate data**

|Technology|Fuel|Heat Rate<br>(MMBtu/<br>MWh)|Capital Cost<br>(Million<br>US$/MW)|Fixed O&M<br>cost ($/MW-<br>yr)|Variable<br>O&M Cost<br>($/MWh)|
|---|---|---|---|---|---|
|Gas turbine|Oil|9.75|0.81|8,100|4.05|
||Natural gas|9.75|0.81|8,505|4.46|
|Combined cycle|Natural gas|6.0|1.22|8,370|2.24|
|Steam turbine|Coal|8.74|2.9|32,100|4.61|
||Oil|9.45|2.5|10,530|2.37|
||Natural gas|9.45|2.5|10,530|2.37|
|Diesel plant|Diesel|9.85|0.7|10,000|10|
|ISCC|Natural gas|7.45|3.77|31,180|4.42|
|Hydro|Water|-|2.00|14,130|-|
|Wind|Wind|-|1.49|31,000|-|
|Solar PV|Sun|-|0.97|10,000|-|
|Solar CSP|Sun|-|4.4|40,000|4|
|Nuclear|Uranium|10.48|5.5|96,200|2.21|

Source: KFUPM, 2011, World Bank (2009); IEA (2014) 13
Table 15. Host Community Near the Syrian Border, 2009 & 2013 Means Comparison Test

||mean(2013)-|two-sided|2013-|2009-|
|---|---|---|---|---|
||mean(2009)|means test p-<br>value|mean|mean|
|N children|-0.377|0.0000|1.28|1.66|
|N adults|-0.176|0.0000|2.24|2.42|
|N old|0.060|0.0000|0.58|0.52|
|**Characteristics of the head of household**|||||
|0-14|0.0%|.|-|-|
|15-24|-0.6%|0.0000|1.5%|2.1%|
|25-39|-5.6%|0.0000|27.6%|33.2%|
|40-54|0.9%|0.0491|35.4%|34.5%|
|55+|5.3%|0.0000|35.5%|30.2%|
|1-Male, 0-Female|-1.5%|0.0001|82.5%|84.0%|
|None|-4.7%|0.0000|23.6%|28.4%|
|Primary|-2.1%|0.0000|41.6%|43.7%|
|Secondary|3.2%|0.0000|24.5%|21.4%|
|Post-Secondary|3.7%|0.0000|10.3%|6.6%|
|**Al workers in the household**|||||
|Share of Employed Adults|3.4%|0.0000|25.5%|22.2%|
|Share of Unemployed Adults|-1.2%|0.0000|2.4%|3.6%|
|Total Hours Worked in the Whole Family|-1.62|0.0000|51.31|52.93|
|Mean Hours Worked out of all working individuals|-1.71|0.0000|50.45|52.16|
|Share Workers White Collar|5.6%|0.0000|34.4%|28.8%|
|Share Workers Blue Collar|-7.8%|0.0000|45.9%|53.7%|
|Share with Permanent Contract Type|2.9%|0.0000|36.2%|33.3%|
|1-9 employees|-8.8%|0.0000|44.3%|53.1%|
|10-49 employees|3.1%|0.0000|17.5%|14.4%|
|50+ employees|3.5%|0.0000|18.5%|15.1%|
|Share Agriculture, forestry, fishing-Mining and<br>quarrying|-5.9%|0.0000|12.2%|18.0%|
|Share Manufacturing Industry|1.1%|0.0109|13.2%|12.1%|
|Share Electricity, Gas , Steam, Water and Sewerage|0.2%|0.0470|1.0%|0.8%|
|Share Construction|1.5%|0.0000|9.7%|8.2%|
|Share Commerce and services|-1.3%|0.0126|23.6%|24.9%|
|Share Others|2.2%|0.0000|20.8%|18.5%|
|Registered with Any SS institution related to your|15.7%|0.0000|63.5%|47.8%|
|main job|||||
|Share of workers registered with any social security<br>|11.6%|0.0000|52.4%|40.8%|
|institution related to your main job|||||

_Notes_ : 2009 N = 21,324, 2013 N = 21, 467 _Source_ : LFS 2009 & 2013. World Bank author’s calculations.

30
17 43 and 45 percent respectively. Among the “anocracies”, group membership and attendance in meetings is particularly high for Tanzania.<sup>12</sup> The Afrobarometer data suggest that local-level organization of civil society into groups does not exclude the poor. For six countries—Benin, Botswana, Ghana, Senegal, South Africa, and Zimbabwe—an indicator of poverty in the Afrobarometer (when a respondent mentions that the household has gone without adequate food many times in the recent past) is significantly associated with greater attendance of group meetings (after controlling for respondent age, gender, education, media access, household size, and neighborhood availability of infrastructure). However, the extent to which these groups indeed represent the interests of the poor, or have impact on improved accountability for development outcomes are questions on which the evidence is patchy. This will be discussed in the following section. However, what can also be seen in the Afrobarometer data for seven countries-Benin, Lesotho, Mozambique, South Africa, Tanzania, Uganda, Zimbabwe—is that respondents that report going without food many times (an indicator of poverty) are significantly more likely to report fear of punishment or reprisals should they complain about poor quality of government services or misuse of government funds.

In five countries—Benin, Ghana, Senegal, Zambia, and Zimbabwe—respondents that report frequent attendance in group meetings are also more likely to be those with ethnic grievances. In these countries, ethnic grievance is also correlated with respondents reporting any instances of joining with others to raise an issue. Whether their participation in group and organized activities is geared towards broad collective action or narrower redress of ethnic grievances is an important concern for the efficacy of civil society action in addressing broad accountability failures.

These insights from research and evidence into local-level groups, and how their membership and leadership changes in response to the availability of external funding is pertinent for other donor initiatives to promote transparency and accountability through national groups and associations. We are not able to uncover any systematic evidence on the organization and incentives of such national civil society organizations. However, some types of activities undertaken by these groups have been subjected to rigorous impact evaluation in collaboration with independent researchers. The next section will discuss the state of evidence on

> 12 This is, in fact, quite visible during field visits to Tanzanian villages by one of the authors (Khemani) with posters listing village groups and their membership being plastered all over the walls of the village government office.
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 26. Affordability in Senegal is much better than in other low-income countries**

_Source:_ Banerjee and others 2008b _Note:_ LIC = low-income countries; kWh = kilowatt-hour; m<sup>3</sup> = cubic meters.

Operational inefficiencies of power and water utilities cost Senegal a further $89 million a year, close to 1 percent of GDP. The annual value of inefficiencies in the power sector ($85 million) is substantially higher than for the water sector ($4 million). The burden of utility inefficiencies in Senegal’s water sector is lower than for the benchmark countries (figure 27), though distribution losses in power are higher than in comparator countries. Senegal had distribution losses of 22.2 percent in 2008 relative to a best-practice benchmark of around 10 percent, while in the water sector, SDE had distribution losses of 21 percent in 2009 relative to a best-practice benchmark of 20 percent.

**Figure 27. Senegal’s utilities are comparatively efficient**

<!-- Start of picture text --> Uncollected bills and unaccounted losses as percentage of GDP<br>a. Power sector<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Senegal Nonfragile<br>Collection inefficiencies Unaccounted losses<br>Percentage of GDP<br><!-- End of picture text --> <!-- Start of picture text --> b. Water sector<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>Senegal Nonfragile<br>Collection inefficiencies Unaccounted losses<br>Percentage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

41