rafmacalaba/datause-encoder-data huggingface dataset:
Income is estimated directly from the data sets for Bangladesh, Colombia and Costa Rica; it is proxied using consumption in all others. When deriving the income proxy the team paid special attention to how gifts are recorded. In certain surveys, this was in the consumption module itself; in others, it was collected through a special “assistance” module. Another area of attention was the value attached to shelter (a gift for many refugees and as such deducted from income).

The value of essential needs is reflected by the global poverty lines for low-income countries (LICs), lowermiddle-income countries (LMICs) and upper-middle-income countries UMICs. These are respectively PPP$ 2.15, PPP$ 3.65 and PPP$ 6.85. To convert 2017 PPP dollars into current US dollars, an exchange rate of 1.23 current US dollars for every PPP dollar is used (January 2024). Each country’s income classification is based on World Bank data as per the end of 2022.

**<mark>Table 1: Microdata sets used to estmate refugee income poverty gap</mark>**

|**Economy**|**Survey Name**|**Income**<br>**measured as**|**Poverty**<br>**line**<br>**used**|**Survey**<br>**Year**|
|---|---|---|---|---|
|Bangladesh (*)|Cox’s Bazar Panel Survey|Income|3.65|2023|
|Chad|4<sup>th</sup>Natonal Harmonized Survey on Households’<br>Consumpton and Informal Sector|Consumpton|2.15|2018|
|Colombia|Gran Encuesta Integrada de Hogares (GEIH) Survey ;<br>Encuesta Pulso de la Migracion|Income|6.85|2021|
|Costa Rica|Encuesta Nacional de Hogares (ENAHO)|Income|6.85|2021|
|Ethiopia|Socioeconomic Survey of Refugees in Ethiopia (SESRE)|Consumpton|2.15|2023|
|Jordan (*)|Vulnerability Assessment Framework (VAF) survey for<br>refugees|Consumpton|6.85|2018|
|Kenya|Kalobeyi Socioeconomic Survey|Consumpton|3.65|2019|
||Kakuma Socioeconomic Survey|Consumpton|3.65|2021|
||Kenya Integrated Household Budget Survey|Consumpton|3.65|2016|
|Lebanon (**)|Lebanon Vulnerability Assessment Panel (LVAP)|Consumpton|6.85|2022|
|Niger|Enquête Harmonisée sur le Conditons de Vie des<br>Ménages (EHCVM)|Consumpton|2.15|2018|
|West Bank and<br>Gaza|Palestne Expenditure and Consumpton Survey|Consumpton|6.85|2023|
|Uganda<br>|Refugee and Host Communites Household Survey<br>|Consumpton|2.15|2018|

(*) these surveys do not comprise data on host communities.

(**) Lebanon became a lower-middle income country in 2022. Here the upper-middle income poverty line is preferred, mostly because the PPP conversion factor for Lebanon appears off leading to unrealistically optimistic selfreliance estimates. In addition, self-reliance estimates using the national poverty line (NPL) will be presented.

7
**Table 2:  Robustness**

||Dependent Variabl|e: Actual Expenditure /<br>Capital Defined|Allocated Budget (%)|
|---|---|---|---|
|_Explanatory variables:_|3-Year Sum|WLS|Median Regression|
|Allocated Budget|-13.2908***|-14.1117***|-15.2196***|
|(LCU, in logs)|(-15.94)|(-18.52)|(-27.78)|
|Budget from Natural Resources|0.2753***|0.3397***|0.3637***|
|(% of actual expenditure)|(9.04)|(12.75)|(17.79)|
|Population|7.4584***|7.6935***|8.1937***|
|(in logs)|(12.63)|(14.50)|(17.83)|
|Education|1.2057**|1.2295**|1.6822***|
|(average years of schooling,|(2.43)|(2.59)|(3.28)|
|population over 15 years old)||||
|Staff / Population|5.0913***|3.5782***|4.5523***|
|(ratio of white-collar staff in municipality to<br>population, %)|(3.18)|(4.07)|(5.55)|
|Poverty Rate|0.0805***|0.0939***|0.0927***|
|(% of total population)|(3.74)|(4.63)|(4.70)|
|Urban Population|-0.0567***|-0.0592***|-0.0659***|
|(% of total population)|(-2.69)|(-3.10)|(-3.28)|
|Percentage of Winning Votes|0.1853***|0.1939***|0.2106***|
|(% of votes obtained by winning Mayor)|(3.89)|(4.87)|(5.39)|
|Population Dispersion|-3.3564|-3.6300*|-3.5667|
|(1 − normalized Herfindahl index of|(-1.40)|(-1.76)|(-1.56)|
|population in villages)||||
|Incumbent Mayor|1.0641|-1.3956|-1.8064*|
|(dummy variable = 1 if incumbent,|(1.19)|(-1.56)|(-1.80)|
|and 0 otherwise)||||
|Incumbent Mayor * Year Effect 2007||8.7415***<br>(8.99)|10.7903***<br>(6.27)|
|Year Effect 2007||-8.0574***|-7.9973***|
|(dummy variable = 1 if year is 2007,||(-9.98)|(-8.83)|
|and 0 otherwise)||||
|Year Effect 2009||-4.3791***|-4.2226***|
|(dummy variable = 1 if year is 2009,<br>and 0 otherwise)||(-6.96)|(-5.25)|
|Constant|190.9864***|187.8408***|197.6846***|
||(19.47)|(23.64)|(32.36)|
|R-squared (Pseudo R-squared for col. 3)|0.339|0.280|0.137|
|rs in parentheses are the correspondi<br>No. of observation / No. of municipalities|ng robust t-statistic<br>1688 / 1688|s Standard errors a<br>4858 / 1688|re clustered by provi<br>4858 / 1688|

Numbers in parentheses are the corresponding robust t-statistics. Standard errors are clustered by province for columns 1 and 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

16
the number of years the current proprietor has been operating it), and £ is an idiosyncratic error term. We measure the probability of participation a number of different ways: (a) as an indicator variable for any participation in an individual institution, (b) as the degree of participation within a particular institution, and (c) as the degree of participation among a range of potential institutions. In each case the null hypothesis is that: (a) 3,>0, (b) 32>0, that is participation should be an increasing function of both firm size and firm age.

Table 3 shows the rate of registration with the federal treasury, the rate of registration of firms' paid workers with the social security administration (IMSS), and the rate of enumeration in the 1989 Census by firm size and by firm age. As predicted, there is a very strong positive relationship between participation and firm size/revenue. The relationship between participation and firm age is also positive, though much weaker. Table 4 reports the results from fitting probit regressions for the seven different types of institutions. In each case the coefficients on both firm size and age are positive and significant at better than the 1 percent level of confidence. (The standard errors were corrected for arbitrary forms of heteroskedasticity.) The estimated changes in the probability of participation for a unit change in each regressor are reported in Table 4.A. The relationship between size and participation is very strong: each point increase in log revenue corresponds to, for example, a 20 percent greater rate of tax compliance, a 23 percent greater rate of social security compliance, and an 1 1 percent greater rate of business guild registration. '" The relationship between age and participation is more marginally significant: a ten year difference in age increases participation in the various institutions by 2 to 4 percent.<sup>20</sup> One potential concern is that the benefits of participation undoubtedly vary by industry. Given systematic differences in average firm size and age across industry, the positive relationships between size and participation and between age and participation in Table 4 may be spurious. To test this we tried alternate specifications (not reported), both including industry

> 9 Log revenue was used to avoid giving undue weight to the small number of firms with extremely high levels of revenue.

20 As a specification check, we tried substituting the two sets of dummy variables for the revenue and age classes (from Table 2) for the linear terms. The results were qualitatively the same both in these and the subsequent regressions.

14
Specifically, of respondents ages 20-40, we included all who received the website treatment [T4], all Pilijobs.org enrollees in other treatment groups (32 respondents), 300 respondents randomly selected from each information treatment group ([T1], [T2], and [T1]+[T2]), and 300 respondents randomly selected from the control group.

At the time of the passport survey, we also interviewed and offered passport assistance to a supplemental sample of Sorsogon Province residents who enrolled in Pilijobs.org through other means that we describe in the next section (A.2), but who were not a part of our baseline sample. We do not include these respondents in our analysis.

_Endline survey (2012)_ We conducted an endline survey in mid-2012 to measure the impacts of our interventions. We visited all respondents from the baseline sample, making two attempts to reach each respondent. We interviewed another household member and administered a proxy survey when the respondent was not available, enabling us to obtain full data on respondent and household migration steps and job-search behavior when we could not directly reach the respondent. When no member of the household could be interviewed, we interviewed a neighbor using a “log” survey. The information collected in that survey was limited to the respondents’ whereabouts, and whether he or she was currently working overseas. We show in A.5 that our finding of no impacts of the treatments on migration abroad are robust to expanding the sample to include these log surveys.

Using this three-pronged approach, we obtained measures of whether the respondent migrated abroad for work from full, proxy, or log surveys for 4,089 respondents, or 98.5% of our sample. Of those, 73% were surveys with the respondents themselves, 20% 33
|**Item**|**Category**|**French equivalent or comment**|
|---|---|---|
||period)||
|22b|Purchase of intangible assets|Acquisitions incorporelles. If using TAFIRE, this is the Emplois the section.|
|22c|Purchase of property, plant, and equipment|Acquisitions corporelles. If using TAFIRE, this is the Emplois section.|
|22d|(Of which replacement of property, plant, and<br>equipment)|If available. Not applicable to TAFIRE.|
|22e|Sales of property, plant, and equipment, if any|Cessions d’immobilisations corporelles. IF using TAFIRE, this is the<br>Ressources section. Should be negative for consitencysake.|
|22f|Purchase of financial investing assets|Acquisitions financiers. IF using TAFIRE, this is the Emplois section.|
|23|Net cash flow from financing activities|Ressources nettes de financement. Entry should be positive if Emplois are<br>greater than Ressources, and vice versa.|
|23a|Dividends paid|Dividends.|
|_23b_|_(Of which, to government)_|If available|
|23c|Investment grants received|Subventions d’investissement (Ressources section, therefore should be<br>negative for consistency)|
|23d|New loans|Emprunts (2) (Ressources section, therefore should be negative for<br>|
|||consistency)|
|**Balanc**<br>|**e sheet**<br>||
|24|Current assets|Total actif circulant (NET) + Total tresorerie-actif|
|25|Noncurrent assets|Total actif immobilise (NET)|
|25a|Gross value of capitalized rehabilitation costs|<br>Charges a repartir|
|25b|Depreciation and amortization accumulated on<br>deferred rehabilitation costs|Dotations amortissements + provisions on Frais d’établissement et charges<br>à repartir|
|25c|Gross value of property, plant, and equipment|BRUT Terrains + Bâtiments + Installations, Agencements + Matériel +<br>Matériel de transport + Immobilisations concedées.|
|25d|Depreciation and amortization accumulated on<br>property, plant, and equipment|Dotations amortissements + provisions on immobilisations corporelles|
|26|Total assets||
|27|Current liabilities|Total passif circulant+total tresorerie-passif|
|27a|(Of which foreign)||
|28|Long-term liabilities|Total dettes financieres|
|28a|(Of which foreign)||
|29|<br>Equity and reserves|Total capitaux propres|
|30|Retained earnings (retained deficit) for the period|Report à nouveau|
|31|Total liabilities and equity||

_Source:_ Authors’ elaboration.

67
# **Table 4: Two-equation regression of the value of African livestock owned and the net revenue per livestock owned**

||**Value of**<br>**livestock**|**owned**|**Net revenu**<br>**livestock v**<br>**US 1000$**|**e per**<br>**alue in**|
|---|---|---|---|---|
|**Variable**|**Estimate**|**T-stat**|**Estimate**|**T-stat**|
|Intercept|-1288|-0.13|1749|5.52|
|Annual mean temperature small farms*|312|0.34|-66|-2.28|
|Annual mean temperature sq small farms*|-1.58|-0.08|1|1.70|
|Annual mean precipitation small farms*|-118|-3.38|-11|-9.75|
|Annual mean precipitation sq small farms*|0.51|2.86|0|9.17|
|Annual mean temperature large farms*|-1037|-1.29|-54|-2.09|
|Annual mean temperature sq large farms*|13.15|0.75|1|0.97|
|Annual mean precipitation large farms*|-95.7|-2.71|-14|-12.45|
|Annual mean precipitation sq large farms*|0.07|0.34|0|9.56|
|Large farm dummy|29397|2.28|79|0.19|
|Log household size|-2255|-4.58|18|1.13|
|Electricity dummy|4634|6.59|242|10.84|
|Dairy farming dummy|-1687|-2.44|138|6.29|
|Population density|134|2.91|14|9.82|
|Population density sq|-2.19|-4.33|0|-10.14|
|% Muslim|-4572|-3.06|-330|-6.95|
|% pasture|21811|9.76|-847|-11.92|
||||Ad|j rsq=0.21|
|||||N=5062|

_Note:_ Two regressions estimated using SUR. Correlation between errors was .02.

* Climate variable multiplied by farm size dummy. Interaction terms measure farm size specific climate impacts.

29
- U.S. dollar inflation is forecast to be 2 percent a year over the term of the PPA.

- The forecast average capacity of the plant is 200 MW. (This forecast might be derived from data on the maximum capacity of the plant and an estimate of the percentage of the time it will be available, given maintenance requirements and the possibility of breakdowns.)

Finally, we assume that the advice of financial experts suggests that

- The actual or estimated credit rating for this utility (in the absence of government support) by, say, Moody’s is B. (We assume, that is, the utility in this example has poorer credit than the utility in the previous example.)

- As above, the best estimate of the relationship between Moody’s credit rating and cumulative probabilities of default is that given in Moody’s (2017, Exhibit 33).

- As above, the forecast loss given default is 100 percent.

- The risk-free rate of interest in U.S. dollars is 2 percent a year. This rate can be driven from the U.S. long-term government bond yields.

We can then use these assumptions to arrive at an estimate of the expected cost of the guarantee (see Table 2).

- Given the real price of capacity ($60,000/MW every year), forecast U.S. inflation of 2 percent a year, and forecast average available capacity of 200MW, the utility’s forecast payment in year 1 is $12.2 million. In each following year, the forecast payment increases with U.S. inflation by 2 percent.

- In the first year, the assumed probability of distress is 3.573 percent, so the estimate of the government’s expected payment is approximately $0.4 million. Because the loss given default is assumed to be 100 percent, this is also the net payment. The estimated expected net payment increases each year partly because of inflation but mainly because of increases in the cumulative probability of default.

- Discounted at a rate of 2 percent of year, the estimated expected net payment in the first year has a present value of approximately $0.4 million. In the following years, discounting slowly reduces the present value of the estimated expected payments.

14
|Table 4A:|Regression <br>|results from <br>|the full samp<br>|le of153 cou<br>|ntries and all<br>|years 1988-<br>|2015<br>||
|---|---|---|---|---|---|---|---|---|
||)|Q|8)|)|6)|6|0)|8)|
|||Anyprojec|t (dy/ds)|||HD projec|t (dy/dx)||
|IBRD country|-0.141***|=-0.232"*«|=-0.187**|= -0.196***«|-0.131***<br>|= -0.226%**<br>|=-0.249%**«|= -0)_ 24poxx|
|(1=yes)|(0.044)|(0.049)|(0.076)|(0.073)|(0.032)|(0.033)|(0.047)|(0.053)|
|Logpopulation||0.079***||0.148||0.080***||0.120|
|||(0.010)||(0.173)||(0.007)||(0.133)|
|LogGNIper capita||0.019||0.061||0.036**||0.054*|
|||(0.023)||(0.049)||(0.015)||(0.032)|
|Net aid flow||0.010*||0.012**||0.002||-0.002|
|(millions of2010 dollars)||(0.006)||(0.006)||(0.003)||(0.004)|
|Number ofobservations|3,894|3,402|3,391|2.844|3,894|3,402|3,660|3,140|
|R2|0.023|0.433|0.616|0.646|0.024|0.159|0.262|0.268|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|0.731|0.766|0.696|0.723|0.731|0.766|0.742|0.779|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, “TM* Significant at 95%.
- 13 -

For instance, unanticipated power outages may damage the quality of products. Firms may have to shoulder an extra cost of inventory if transport infrastructure is unreliable.

# **IV. DATA**

Our data come from five series of the BEEPS in East Africa (i.e., 2006 BEEPS for Burundi, 2007 for Kenya, 2005 for Rwanda, 2006 for Tanzania, and 2006 for Uganda), which originally cover 2,775 firms in 17 cities (Figure 8). The analysis only uses about 2,000 observations, for which all relevant data are available. The distribution of the sample observations is shown in Table 2. The sample includes more than 600 firms in Kenya, followed by Uganda and Tanzania. The service sector has the largest share in total. But a large number of agribusinesses are included in the data for Tanzania and Uganda. In theory, the sample distribution is supposed to reflect the distribution of the entire population in each country.

The summary statistics are shown in Table 3. Firms look different in size as well as factor intensity. The operating cost ranges from US$100 to US$300 million with a mean of about US$1.2 million. The average annual wage is estimated at around US$2,000. In the sample, the labor cost accounts for 38 percent of total costs on average. The average energy cost share is about 5 percent. The transport cost accounts for 1.4 percent on average.

Regarding infrastructure quality, the number of days without sufficient electricity supply is eight days per year on average, but the service quality seems to vary significantly, depending on location. It ranges from zero to 100 days in the region. Firms do not have sufficient water services about one day a year. Firm inventory also varies significantly. Firms may keep minimum inventory (i.e., zero days) and may keep 730 days of inventory.
86

# **Alcohol**

Exhaust emissions from neat alcohol engines have fewer components compared to gasoline or diesel fuel. Generally hazardous aromatic hydrocarbons including benzene are not formed and PAH emissions are very low. The characteristic emission components are carbon monoxide, unburned alcohol, nitrogen oxides and aldehydes. Engines designed for neat methanol tend to have low emissions of CO, NOx, and unburned fuel. Evaporative emissions are also low. Methanol is also expected to significantly reduce the reactivity of exhaust and evaporative VOC's, thereby lowering tropospheric ozone levels. EPA estimates the ozone reduction potential to be on the order of 35% [USEPA 1988]. Aldehyde emissions, however, can be four to eight times higher than for gasoline vehicles. These compounds tend to be highly photochemically reactive and to contribute directly to eye irritation. There is significant evidence that formaldehyde is a human carcinogen. It is, however, important to note that most aldehydes including formaldehyde, which exist in ambient air are formed photochemically in the atmosphere and are not directly emitted. Fortunately, these compounds are effectively reduced by catalysts. Emission characteristics of alcohol fueled diesel engines are also good. They feature low emissions of NOx and PAH and virtually no particulates. Both concepts can be used together with an oxidation catalyst to effectively reduce the unburned fuel and aldehydes. Data comparing emissions from different fuel types are summarized in Table 40.

**Table 40. Emissions per kilometer from combustion engines**

||LPG<br>Gas|Leaded<br>catalysts|Gasoline|Gasohol<br>95%|Diesel|Metbmxl|
|---|---|---|---|---|---|---|
|Particulates|||||||
|rug.|-|50-100|5-10|-|750-1500|-|
|Benzene|||||||
|mg.|1|50-150|1-15|50-150|10-20|1|
|Ethylene<br>mg.|75-100|75-100|5-10|75-100|25-75|10-15|
|Formaldehyde<br>mg.|20-40|20-50|1-3|30-60|10-15|100|
|Benzo(a)pyrene|||||||
|ug.|0.1|1-10|0.1-1|1-10|1-10|0.1|
|Methyl nitrite<br>ug.|100-300|100-300|0-50|100-300|100-300|**_5-6x13_**|
|PAH<br>ug.|2-9|35-170|3|35-170|500-1000|2-9|

Source: [CAAP 1983]
Nadu). Evidence of a negative relationship between caste and female intra-household bargaining authority emerges only when we group households at the jati level. Finally, we find that there is a strong relationship between empowerment and economic status in some jatis but not in others. We also show that this matters for public policy because women from specific jatis—rather than from broad caste groups—and more likely to benefit from state-run anti-poverty programs. In conclusion, these results suggest that focussing on broad caste categories such as “Scheduled Castes” and “Scheduled Tribes” can be misleading because they mask important differences at the jati level.

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