rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.1b: Coverage bias in Ethiopia HFPS by Urban-Rural domain**

||**Ethi**|**opia (ESS -**|**Urban)**|**Eth**|**iopia (ESS -**|**Rural)**|
|---|---|---|---|---|---|---|
||**All**|**With Co**|**ntact info**|**All**|**With Co**|**ntact info**|
||||_Coverage_|||_Coverage_|
||_Mean_|_Mean_|<br>_bias†_<br>_(2) -(1)_|_Mean_|_Mean_|<br>_bias†_<br>_(5) -(4)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|**Sample size(number of households)**|_3,655_|_3,410_||_3,115_|_1,962_||
|Average household size|3.647|3.651|0.004|4.885|5.067|0.182|
|Dependencyratio|0.651|0.635|-0.016|1.103|1.113|0.010|
|Female head(%)|0.346|0.339|-0.008|0.223|0.208|-0.015|
|Age of head|38.728|38.584|-0.144|44.924|44.332|-0.592|
|**_Religion of head_**|||||||
|Orthodox|0.574|0.579|0.006|0.431|0.418|-0.013|
|Muslim|0.207|0.201|-0.006|0.335|0.341|0.007|
|Christian|0.213|0.214|0.001|0.210|0.220|0.010|
|**_Marital status of head_**|||||||
|Married|0.618|0.621|0.003|0.798|0.824|0.026|
|Formerlymarried|0.220|0.214|-0.006|0.174|0.148|-0.026|
|Never married|0.162|0.164|0.003|0.029|0.028|0.000|
|**_Education level of head_**|||||||
|Head Literate|0.753|0.775|0.023|0.408|0.454|0.046*|
|None|0.287|0.263|-0.024|0.637|0.592|-0.045|
|Primary|0.296|0.301|0.005|0.296|0.317|0.022|
|Secondary|0.215|0.226|0.010|0.047|0.060|0.013|
|Tertiary|0.202|0.210|0.008|0.020|0.031|0.010|
|**_Working status of head_**|||||||
|Any|0.592|0.594|0.002|0.804|0.816|0.011|
|Wage|0.302|0.310|0.008|0.051|0.063|0.012|
|Agriculture|0.124|0.115|-0.009|0.756|0.756|-0.001|
|Nonfarm enterprise|0.219|0.220|0.001|0.066|0.077|0.011|
|**_Consumption_**|||||||
|Mean expenditure(LCUper capita)|22,876|23,088|212|11,130|11,449|319|
|**_Quintiles_**|||||||
|Quintile 1 -poorest|0.059|0.053|-0.006|0.268|0.245|-0.023|
|Quintile 2 -poorer|0.107|0.100|-0.007|0.245|0.245|0.001|
|Quintile 3 - middle|0.158|0.159|0.001|0.221|0.240|0.019|
|Quintile 4 - richer|0.274|0.277|0.002|0.164|0.170|0.006|
|Quintile 5 - richest|0.402|0.411|0.009|0.103|0.100|-0.003|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

16
generate productivity peaks during the workweek. This motivates the first criterion, which is that the model’s parameters must be consistent with an interior maximum of productivity over the workweek.

This first restriction is consistent with Brogmus [2007], who documents that the occupational injury rate in the US is minimized on Wednesdays. If the weekly variation in the occupational injury rate reflects weekly variation in fatigue and in absent-mindedness that is monotonically related to variation in productivity, this finding suggests that productivity peaks mid-week.

The second criterion relates to the range of variation, measured as the maximum level of _Yd_ . The estimates for different RTOs suggest that output per worker varies by between 8.6% and 22% over the workweek. This range is inconsistent with a “naive” model in which output per worker is constant over the workweek; nor is it consistent with extreme parameters which imply wild variation in output per-worker.

The last criterion is based on empirical estimates of human capital depreciation due to absence from work. Mincer and Ofek [1982] and Keane and Wolpin [1997] (among others) document that recent unemployment or non-participation in the labor market is associated with lower wages. With the usual caveats, unemployment or non-participation can be thought of as a long vacation. Their findings can therefore be interpreted as suggesting that, for relatively large increases in vacation, the “learning by doing” effect dominates the fatigue effect, resulting in an overall loss in worker productivity. The empirical estimates suggest that a year of absence is associated with a reentry wage that is 3.5-30.5% lower than the continual employment wage. These estimates are inconsistent with parameters that imply long-run gains from rest that outweigh the losses from the depreciation of the stock of memory.

To isolate the set of admissible parameters, I evaluate the criteria on 100<sup>4</sup> points in the parameter space ( _αr, αm, δr, δm_ ) _∈_ (0 _,_ 1)<sup>4</sup> . I compute the steady state productivity cycle implied by each vector of parameters, 19
|PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPSIIO Impact of Lower Oil Prices on<br>Renewable Energy Technologies|E. Terraoo<br>M. Mendis<br>K. Fitzgerald|||
|WPSIII Recent World Bank Activities in Energy|Industry<br>and Energy<br>Department|||
|WPS112 A Visual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Reform|Barbara Nunberg|October 1988|J. Cheeseman<br>61703|
|WPS114 Africa Region Population<br>Projections<br>1988-89<br>edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population<br>Projections<br>1988-89 edition|My T.Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS117 Europe, Middle East, and North Africa<br>(EMN) Region Population<br>Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS118 Contract-Plans<br>and Public<br>Enterprise<br>Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119 Recent Developments in Commodity<br>Modeling: A World Bank Focus|Walter C. Labys|October 1988|A. Daruwala<br>33716|
|WPS120 Public Policy and Private Investment<br>in Turkey|Ajay Chhibber<br>Sweder van Wijnbergen|||
- **PRODUCE.** 2020 _b_ . “Suspensiones preventivas de zonas DGSFS - PA – SP.” Ministerio de la Producci ́on (PRODUCE). `https://www.produce.gob.pe/index.php/dgsfs-pa/ suspensiones-preventivas-de-pesca` .

- **Quaas, Martin F, Till Requate, Kirsten Ruckes, Anders Skonhoft, Niels Vestergaard, and Rudi Voss.** 2013. “Incentives for optimal management of age-structured fish populations.” _Resource and Energy Economics_ , 35(2): 113–134.

- **R Core Team.** 2019. “R: A Language and Environment for Statistical Computing.” Vienna, Austria, R Foundation for Statistical Computing.

- **Sala, Enric, Juan Mayorga, Christopher Costello, David Kroodsma, Maria LD Palomares, Daniel Pauly, U Rashid Sumaila, and Dirk Zeller.** 2018. “The economics of fishing the high seas.” _Science Advances_ , 4(6): eaat2504.

- **Salvatteci, Renato, and Jaime Mendo.** 2005. “Estimaci ́on de las p ́erdidas bioecon ́omicas causadas por la captura de juveniles de anchoveta ( _Engraulis ringens_ , J.) en la Costa Peruana (Estimation of bio-economic losses caused by the capture of juvenile anchovy ( _Engraulis ringens_ , J.) in the Peruvian Coast).” _Ecolog ́ıa Aplicada_ , 4(1-2): 113– 120.

- **Smith, Martin D.** 2012. “The New Fisheries Economics: Incentives Across Many Margins.” _Annual Review of Resource Economics_ , 4(1): 379–402.

- **Smith, Vernon L.** 1969. “On Models of Commercial Fishing.” _Journal of Political Economy_ , 77(2): 181–198.

- **SUPNEP.** 2017. “Convenio Colectivo por Rama de Actividad, SUPNEP - AANEP, 12 de marzo 2017 al 11 marzo del 2022.” Sindicato Univo de Pescadores de Nuevas Embarcaciones del Peru (SUPNEP). `http://www.supnep.pe/assets/convenio_colectivo_ supnep_aanep_2017_2022.pdf` .

- **Tveteras, Sigbjorn, Carlos E Paredes, and Julio Pe ̃na-Torres.** 2011. “Vessel Quotas in Peru: Stopping the Race for Anchovies.” _Marine Resource Economics_ , 26(3): 225–232.

- **Wallace, Richard K, and Kristen M Fletcher.** 1997. “Understanding fisheries management: A Manual for understanding the Federal Fisheries Management Process, Including Analysis of the 1996 Sustainable Fisheries Act.”

52
roughly 3%, which was equally distributed across public and private schools. Net enrollment increases in private primary schools were significantly larger than in private secondary schools, – – while at least for the unconditional effect of FPE the opposite is true in public schools.

Furthermore, changes in socio-economic sorting in primary schools were not reflected in secondary schools. The increase in access to public primary schooling among lower socio-economic groups after FPE was not mirrored at the secondary level, and the increased sorting of wealthier households into private primary school was also not reflected in the secondary sector. This can be seen by comparing column (2) to (5) and (3) to (6), respectively.

– This failure to find the same systematic pattern of enrollment shifts an increase in private relative to public enrollment and a move of poorer households into government schools and richer – households into private schools provides additional confidence that the effects we observe in the primary sector are indeed due to the abolition of school fees in public primary schools rather than an artefact of secular trends independent of the FPE policy. While secondary schools do – – not make for a perfect control group given selective admission and capacity constraints we would nevertheless argue that this comparison contributes some additional evidence to support the conclusions drawn with regards to primary schools.

In sum, the results from estimating the multinomial choice model confirm the descriptive statistics: The main effect of FPE was to increase enrollment in private primary schools. Despite the overall stagnation in public enrollment, FPE had a positive effect on equalizing access to government schools as evinced by a negative association between socio-economic background and public enrollment. However, the education system became more stratified following the abolition of fees, as better off households exited to the private sector, which increasingly became a preserve of the

# **Price changes**

Next, we examine the price effect of the introduction of FPE. In other words, did FPE successfully reduce the cost of schooling for children enrolled in public primary school, and how did fees in private schools respond? To examine this question, we simply look at household expendi17
**Table 5: Share of population with data on the dimensions of vulnerability in 2019 (%)**

||Income|Education|Social<br>protection|Financial<br>inclusion|Water|Electricity|
|---|---|---|---|---|---|---|
|_Region_|||||||
|East Asia and the Pacific|98|97|97|97|30|30|
|Europe and Central Asia|100|89|81|89|88|87|
|Latin America and the Caribbean|97|90|92|90|90|87|
|Middle East and North Africa|97|58|58|81|58|58|
|Other High-Income economies|93|30|0|30|30|30|
|South Asia|97|96|96|97|22|96|
|Sub-Saharan Africa|97|82|84|85|82|82|
|_Income group_|||||||
|High|93|36|5|36|36|34|
|Upper-middle|100|95|97|98|39|39|
|Lower-middle|98|93|92|94|51|93|
|Low|87|62|60|71|62|62|
|_FCV_|92|63|70|75|63|63|
|_World_|97|46|60|84|46|45|
|_World (incl. data older than 5 years and_<br>_adding in datafrom WDI)_|97|82|78|84|66|83|

Source: Authors’ compilation from the GMD, ASPIRE and Findex databases.

Notes: Income refers to having less than 1.5*$2.15 per household member, education refers to no adult having primary schooling, social protection refers to not receiving any social protection, and financial inclusion refers to not having a bank or mobile money account. Water refers to access to limited standard water, and access to electricity is as written. The coverage year for education, water and electricity is based on the filters of +- 5 years from 2019, and for social protection is based on the filter of +- 5years from 2017.

# 5 RESULTS

## 5.1 EXPOSURE TO EXTREME WEATHER EVENTS AND POVERTY

Results are first presented for the number of people exposed to flood, cyclone, drought, and heatwave (Figure 6). Exposure numbers are presented for a range of return periods (from 5 to 100) and using different intensity thresholds to define extreme events: flood inundation of 15cm, 50cm and 150cm; category 1, 2 and 3 winds for cyclones, drought affecting 30 percent and 50 percent of the local area, and heatwaves of 32-36 degrees Celsius. Exposure falls with the event severity and increases with the return period. The number of people exposed to heatwaves is the highest: 3.7 billion people are exposed to the lowest intensity event we consider with a 100-year return period. For floods 1.8 billion are exposed to the lowest intensity event we consider with a 100-year return period, and 1.5 billion for cyclones. 1.4 billion people are exposed to drought with about a 40-year return period. Exposure drops considerably for cyclones when going to the next level of intensity—0.6 billion are exposed to category 2 winds for a return period of 100 years. The drop is less pronounced for other types of events. These graphs show how the choice of severity and probability determine the total exposure (and other subsequent numbers).

23
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66
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 2. Benchmarking power infrastructure and capacity, access, and utility performance**

||Unit|Republic of Congo|Resource-rich countries|
|---|---|---|---|
|Installed power generation capacity|MW/mil. people|–85.6|42.2|
|Power generation|kWh/capita|250|200.2|
|Power outages|# in a typical month|27.4|15.8|
|Firms’ value lost due to power outages|% sales|15.7|7.2|
|Access to electricity|% population|34.9|46.1|
|Urban access to electricity|% population|51.3|78.7|
|Rural access to electricity|% population|16.4|27.6|
|Revenue collection|% billings|91.0|77.5|
|Distribution losses|% production|47.4|26.5|
|Operational cost recovery<br>Cost recovery|% cost|112<br>53|77.7<br>55.9|
|Total hidden costs|% revenue|86.1|83.6|
|Tariff (US cents per kWh)|Congo|Predominantly hydro<br>generation|Other developing countries|
|Power tariff (residential at 100 kWh)|15.0|10.27||
|Power tariff (commercial at 900 kWh)|10.0|11.73|5.0–10.0|
|Power tariff (industrial at 50,000 kWh)|9.9|11.39||

_Source_ : Eberhard and others 2008.

Only the south of the country has access to grid electricity from SNE, while the north has to rely on costly off-grid generation. As a result, the effective price of power is three times higher in the North. For example, all forest concessionaires in the north (and some in the south) are too remote to benefit from grid electricity and must rely on diesel powered own generation facilities for onsite timber processing. The cost of diesel generation is estimated to be $0.23 per kilowatt hour for the concessionaires in the south, where diesel is less expensive due to proximity of Pointe-Noire; but it is $0.62 per kilowatt-hour— nearly three times higher—for the northern concessionaires. By comparison, the long-term marginal cost of grid electricity is $0.08 per kilowatt hour, and this would fall to $0.06 per kilowatt hour if the country had the necessary infrastructure to import power from Cameroon.

11
# Table 2: Altemative Samples and Estimated Wage Differendals

||Standard Deviation|Sample Size|
|---|---|---|
|_Age_|||
|18-25|0.179|2,133|
|35-65|0.187|2,645|
|_Education_|||
|Low|0.342|4,742|
|High,|0.149|1,896|
|_(One industy outlier omitted)_|||
|LowB|0.202|4,742|
|High|0.149|1,896|
|_Sex_|||
|Males|0.157|4,908|
|Females|0.350|1,730|
|_Finr Size_|||
|> 10 employees|0.162|4,224|
|<10 employees|0.300|2,414|

Source: ENS 1989.

a. 21 Industry dummies were derived by combining the one-digit industry variable and the occupation variable. While agrculture and mining are taken as homogeneous groups, employees in other industries are divided in more homogeneous groups, i.e., manual versus non-manual workers and when sample sizes permit professionals, administrators, office workers etc., within each industry.

# Dispersion of wage differentials based on gender and firTm size are substantial. Here, one

would expect that dispersion would be greater for male employees and employees of larger firms. However, the data indicate otherwise, possibly suggesting that there is more arbitrariness in wage determination for females and employees of small firms.

5
65

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# **Table 1:** **<u>Sample sizes by country and year</u>**

|**Country**|**1999**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2008**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|Afghanistan||||||325||640|965|
|Bangladesh|||1,085||||1,504||2,589|
|Bhutan||||||||250|250|
|India|886|1,825|260||2,286||||5,257|
|Maldives|||||145||||145|
|Nepal|222|||||||368|590|
|Pakistan||964||||931|||1,895|
|Sri Lanka||||425|||||425|
|Total|1,108|2,789|1,345|425|2,431|1,256|1,504|1,258|12,116|

# **Table 2:  Median employment** **<u>by country and year</u>**

|**Country**|**1999**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2008**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|Afghanistan||||||15||10|11|
|Bangladesh|||153||||45||80|
|Bhutan||||||||15|15|
|India|40|18|28||17||||20|
|Maldives|||||28||||28|
|Nepal|82|||||||15|25|
|Pakistan||26||||12|||20|
|Sri Lanka||||113|||||113|
|Total|48|20|108|113|17|13|45|11|24|

# **Table** **<u>3:  Means of measures of firm productivity and growth</u>**

|**Country**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|0.23|9.37|3.07|0.79|na|na|10.81|7.4|0.13|
|Bangladesh|0.41|8.27|2.84|0.32|0.45|5.83|11.97|7.06|0.2|
|Bhutan|0.64|9.61|na|na|na|na|na|na|0.26|
|India|0.38|9.45|3.11|0.28|0.53|32.7|13.38|10.72|0.18|
|Maldives|0.61|9.63|3.56|na|0.44|43.92|11.55|9.33|0.69|
|Nepal|0.43|8.61|2.76|na|0.21|na|12.47|7.63|0.1|
|Pakistan|0.37|9.55|3.27|0.47|0.12|35.46|12.65|6.87|0.11|
|Sri Lanka|0.51|8.78|3.07|0.1|0.21|28.95|11.44|9.05|0.4|
|Total|0.39|9.16|3.07|0.31|0.44|31.7|12.63|8.92|0.18|

Notes: (1) Expanding employment; (2) log VA/L; (3) TFP; (4) R&D; (5) Sales to MNCs; (6) Education of the top manager; (7) education of the workforce; (8) training programme in place; (9) trained workers.

40