rafmacalaba/datause-encoder-data huggingface dataset:
World Bank. 2017. “Climate Vulnerability Assessment: Making Fiji Climate Resilient.” 120756. The World Bank. http://documents.worldbank.org/curated/en/163081509454340771/Climate-vulnerability-assessment-makingFiji-climate-resilient.

World Bank (forthcoming). Ghana Multi-Sectoral Investment Framework for Climate and Disaster Risk Management.

WSMP (2008) Use of Toilet Facilities in Ghana. WSMP brief Yoshida, N., R. Munoz, A. Skinner, C. Kyung-eun Lee, M. Brataj, W. Durbin, D. Sharma, and C. Wieser. (2015). SWIFT Data Collection Guidelines version 2. The World Bank.

# **Appendix 1 – Sample selection methodology**

We have four different categories for the sampling – (i) low elevation and high poverty incidence; (ii) high elevation and high poverty incidence; (iii) low elevation and low poverty incidence; (iv) high elevation and low poverty incidence. Table 12 shows the number of EAs that belong to each category.

_Table 12: Strata and Number of EAs_

|Strata||Total number of EAs|
|---|---|---|
|(i)|High Poverty Incidence & High flood risk|62|
|(ii)|High Poverty Incidence & Low flood risk|21|
|(iii)|Low Poverty Incidence & High flood risk|31|
|(iv)|Low PovertyIncidence & Low flood risk|31|
|Total||145|

Source: Authors' calculation using Engstrom et al. (2017).

The sample size is determined using the power calculation, which is to find a minimum sample size under which differences in key statistics between the groups of interest – in our case the four strata – are significant at a certain level of precision. For this study, since we aim to find systematic differences in household characteristics and behavior by flood risk or poverty incidence, we adopt the power calculation approach for determining the sample size. Table 13 summarizes the results of the power calculations which concluded that 240 households were needed from each stratum – adding up to a total of 960. Additional households were added to the final sample to mitigate risks of having to drop observations due to data quality issues, resulting in a total sample of 1,006 households.

_Table 13: Final sample by strata_

|Strata||Neighborhoods|Number of EAs|Sample Design<br>Number of house-<br>holds|Interviewed num-<br>ber of households|
|---|---|---|---|---|---|
|(i)|High Poverty Inci-<br>dence & Low Ele-|Jamestown|11|240|251|
||vation|Korle Dudor|1|||
|(ii)|High Poverty Inci-|||||
||dence & High Ele-<br>vation|Nima|12|240|250|

31
Policy Research Working Paper 9625

# **Abstract**

This paper examines to what extent household surveys and censuses in low- and middle-income countries include disability questions and the types of questions under use. A pool of 734 data sets and 1,297 data set-years from 133 low- and middle-income countries from 2009 to 2018 were screened for disability questions, which were classified by type of questions: Washington Group short set of questions, other functional difficulty questions, activities of daily living, broad activity limitation, general disability, and other disability questions. Only 31 percent of the data sets under review had at least one disability-related question and 15 percent of the datasets of low- and middle-income countries have functional difficulty questions that meet international standards, whether the Washington Group short set or other functional difficulty questions. The most commonly found disability question is the general question “Do you have a disability?” which does not produce meaningful and internationally comparable data. The adoption of functional difficulty questions such as the Washington Group short set in national censuses and surveys and in international surveys is needed to monitor the implementation of the 2030 Agenda for Sustainable Development as well as the Convention on the Rights of Persons with Disabilities.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mitra@fordham.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
tion”. The main threat to this assumption relates to the possibility of lightning strikes influencing labor market via channels such as information technology (IT) adoption by firms (households) and the consequent effect on demand (supply) for labor as lightning has been shown to affect the diffusion of technologies such as mobile phones and computers (Andersen et al., 2012; Manacorda and Tesei, 2020; Guriev et al., 2020). To address this concern, I control for the diffusion of mobile phone networks (2G, 3G, & 4G) as a proxy for (general) technology adoption. Thus, by partialling out the effect of lightning strikes on the diffusion of technology, the IV strategy exploits variations in outages induced by lightning strikes. In other words, while the exclusion restriction assumption may not hold unconditionally, the assumption is highly plausible conditional on controls such as mobile network penetration, and spatial and time fixed effects.

In addition to the cross-country analysis, I conduct two country case studies. First, I exploit a unique quasi-natural experiment in Ghana induced by a four year nationwide power (“Dumsor”) crisis between 2013 and 2016.<sup>4</sup> The crisis led to severe power rationing in the country. I estimate the impact of the power crisis on employment by exploiting plausibly exogenous variation in exposure to the crisis using a difference-in-difference design. In addition, I present evidence from Nigeria where I rely on household panel data on employment outcomes and quality of electricity supply to estimate the effect of unreliable electricity supply on employment rates using a panel fixed effect design.<sup>5</sup> Finally, in terms of causal mechanisms, I provide evidence on the intensive and extensive margins. On the intensive margin, I use firm-level data from 10 African countries to evaluate the effect of outages on firm performance and labor demand using the same IV design. On the extensive margin, I evaluate the role of quality of electricity provision on firm entry (and exit) using two approaches. First, I use firm census data from Ethiopia to show how reliability of electricity influences (net) entry of firms using a fixed effect

> 4See and https://en.wikipedia.org/wiki/Dumsor

> 5Identification here relies on within household variations in exposure to outages.

3
16 We found that the higher the number of connections per kilometer of network, the higher the water losses as a denser network tends to be linked with more pressure required in the system. As a result of the higher pressure, a very dense network of connections is likely to require more maintenance to preserve the network. The effect of this variable is very significant, with a t-value of more than 38. The coefficient is also large showing that a 10 percent increase in connection density increases the water losses by more than 12 percent.

Population per connection is also a highly significant variable. The higher the number of people served per connection, the higher water losses will be. More people using the same connection will put more pressure on the system. The coefficient of this variable is large - a 10 percent increase in people per connection increases the water losses by 12 percent.

LN_LENGTHWAT is also positively related with water losses. A larger water supply network results in higher water losses. An increase in the network with 10 percent results in a 1.1 percent increase in water losses. An increase in LN_ENERGY_SHARE is resulting in higher water losses. The effect is very significant; a 10 percent increase in the energy share, results in a 11 percent increase in warer losses. NRW is lower in a mature water supply network with universal access to its customers (measured by WATCOVDUMMY) but the effect is small. SEWCOV_DUMMY’s effect is positive and significant.

We now turn to the role of managerial factors in explaining water losses. LN_STAFFPROD turns out to be a significant variable. The higher the number of staff per 1,000 people served (or the lower the staff productivity) is, the higher water losses will be: an increase of 10 percent in staff productivity will result in an almost 4 percent decline in water losses. Total revenues per capita per year (in US dollars) (LN_TOTREVPCAPUSD) has a positive relationship with water losses: the higher the total annual revenues per capita are the higher the water losses; an increase in the utilities’ revenue base of 10 percent, increases water losses by almost 7 percent. The higher total revenues per capita per year will provide the utility with sufficient financial ability to deal with the water losses and hence does not provide utilities with incentives to reduce them. The OCCRDUMMY which measures either a positive or negative value of the difference between total revenues and total operation and maintenance costs per capita per year shows a negative
# **(1) Introduction**

Economic liberalization in the 1980s and 1990s resulted in increasing inequality in many developing countries (World Development Report 2006). Intra-country spatial inequality also increased in most cases, with an increasing gap in living standards between rural and urban households (Kanbur and Venables (2005)).<sup>2</sup> Using Demographic and Health Survey (DHS) data on 65 countries, Young (2013) finds that rural-urban inequality accounts for 40 percent of the mean country inequality and much of the cross-country variation. To what extent the observed inequality is due to the roots of intergenerational persistence in economic status is an important question for the policymakers. It is thus surprising that there is little research on the rural-urban differences in intergenerational mobility in developing countries. Most of the existing studies focus exclusively on urban areas, and a comparative analysis of rural vs. urban areas remains relatively unexplored.<sup>3</sup> We provide a theoretically grounded empirical analysis of the rural-urban divide in intergenerational educational mobility in Indonesia in the 1990s and 2000s.<sup>4</sup> Inequality in Indonesia increased dramatically over this period: the consumption Gini rose from 29.2 in the 1990s to 38.9 in the 2000s. There are important rural-urban differences: inequality is lower in the rural areas, and the rise in inequality from the 1990s to 2000s has also been less pronounced compared to the urban areas (Kanbur and Jhuang (2014)). A link between higher inequality and lower intergenerational mobility has been the focus of a growing literature on the Great Gatsby Curve (Corak (2013), Fan et al. (2020), Neidhofer et al. (2018)). Does the _higher_ level of inequality in the urban areas imply that there is a “rural bias” where urban children face _lower_ educational (and economic) mobility compared to

> 2In Indonesia, the rural-urban gap in per capita monthly household expenditure increased from 12,991 rupiahs in 1984 to 18,005 rupiahs in 1996 (deflated by food price deflator, see Friedman (2005)). The rural-urban divide has been the focus of a long literature in development economics, from Lewis model to Kuznet’s inverted-U process of inequality, to political economy analysis highlighting “urban bias” (Lipton (1975))) in public goods allocation and “price scissors” (Sah and Stiglitz (1987)) designed to extract surplus from the rural areas in the 1960s and 1970s.

> 3Among the few available studies, see Iversen et al. (2017) on rural-urban differences in occupational mobility, and Emran and Shilpi (2015), and Asher et al. (2018) on rural-urban educational mobility in India. However, the estimating equations in these studies are not derived from a theoretical model.

> 4The focus on education is motivated primarily by the role played by skill premium in rising inequality, and an emphasis on educational mobility as the key to tackling economic inequality (Goldin and Katz (2008), Stiglitz (2012), Autor (2014)).

1
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.6 Infrastructure endowments by historical variables**

||Oil-exporter|Not oil<br>exporter|Lusophone|Anglophone|Francophone|
|---|---|---|---|---|---|
|_Transport_||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|14|57|95|84|31|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|246|1,273|1,315|2,705|188|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|70|173|152|240|136|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,909|2,720|2,295|4,861|1,425|
|_Information and communication technology_||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|16|38|148|48|4|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|118|97|86|139|46|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|1.7|3.1|3.7|4.8|1.5|
|_Energy_||||||
|Electrical generating capacity (MW per 1 million<br>people, 2003)|66|71|49|145|22|
|Access to electricity<br>(% of households with access, 2004)|26|16|15|23|15|
|_Water and sanitation_||||||
|Water<br>(% of households with access, 2002)|59|64|59|72|60|
|Sanitation<br>(% of households with access, 2002)|34|35|35|46|29|

_Sources_ : As for table 2.1.

The OLS regression analysis reveals that levels of infrastructure stocks are primarily related to per capita income and demographic variables, and less so to other variables (table 2.7). Income is statistically significant in almost all cases, except for household access to water and electricity. Demographic variables also seem to be important. Higher levels of urbanization are associated with significantly higher rates of household access to water, sanitation, electricity. By contrast, higher rates of urban growth significantly hold back rates of access to services, suggesting difficulties in keeping up with the rate of urban expansion. Moreover, countries with higher population density seem to have significantly less road area, although this difference disappears when arable land is used to measure road density.

None of the other geographical, cultural, or structural variables prove to be statistically significant in explaining infrastructure stocks. This suggests that in the earlier analysis, the seeming effect of geographical and cultural variables was likely being confounded with the effect of income and demographic variables with which they are associated. Finally, it is noteworthy that the coefficients for infrastructure aid per capita and governance are rarely significant and do not always have the expected signs.

An alternative log-log specification of the model, designed to test the sensitivity of the estimates, yields similar results, while revealing relatively low income elasticities and somewhat 13
logistics and production. This was solved by requiring firms to provide evidence of what they had implemented in the form of bringing photos to the group meetings. In addition, firms in the group treatment still had a monthly one-on-one visit, which took place at the plant, when a consultant would meet with senior management, and one hour at the end of each meeting was used to visit the plant and review improvements.

This process enabled the group intervention to be significantly cheaper than the individual intervention, with an average cost of US$10,500 per firm receiving treatment. Firms received 408 hours of consultant time each, consisting of 40 hours group training, and 92 4-hour group sessions.

# **4. Take-up, Data sources, and Attrition**

# **4.1 Take-up**

The take-up rate for the individual intervention was 86.8%, with all 46 of the 53 firms which started this intervention completing it. The longer delay until beginning the group intervention reduced the take-up rate for this intervention, with 40 of the 53 firms in this group (75.4%) starting the intervention, and 36 firms (67.9%) completing it. Table A4.1 shows the baseline characteristics of those who completed the intervention are not statistically different from those who dropped out, with the one exception being that dropout from the individual treatment was more common in the Antioquia region than elsewhere. The main reasons given for drop-out from both groups were lack of owner time to participate, and lack of continuity in the program (especially for the group treatment).

# **4.2 Data Sources, Measurement of Key Outcomes, and Attrition**

Baseline data were collected from the application form and diagnostic phase and cover firm characteristics in 2013. We then use three types of follow-up data, discussed in detail in Appendix 3. The first is data on the management practices in the firm. Our main measure is the Anexo K management score, which is a score measuring the average adoption rate of the 141 different practices detailed in Appendix 3. This was collected by CNP during in-person visits to the firms. It was measured during the diagnostic for 156 of the 159 firms (3 of the firms had components missing), monthly from the treatment groups during the time of their interventions, as well as annually in 2014 and 2015 for the individual and control groups, and in 2015 and 2016 for the group treatment. The second type of data consists of key performance indicators (KPIs) from the firms, which were collected during in-person visits. We use this to measure impacts on firm sales 12
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# Tables

_Table 1: Enterprise Surveys – Countries and Years Surveyed_ _<u>(bold denotes presence of question “Which Bank Provided the Most Recent Line of Credit or Loan?”)</u>_

|**Region**|**Country**|**Survey Years**|**Region**|**Country**|**Survey Years**|
|---|---|---|---|---|---|
||Albania, Bosnia and Herzegovina, Bulgaria,<br>Croatia, Czech Republic, Estonia, Hungary,<br>Kosovo, Latvia, Lithuania, Moldova,<br>Montenegro, North Macedonia, Poland,<br>Romania,Serbia,Slovak Republic,Slovenia|2009,**2013, 2019**|**Asia**|Azerbaijan, Kazakhstan, Kyrgyz<br>Republic, Mongolia|2009,**2013, 2019**|
|||||Georgia,Tajikistan,Uzbekistan|2008, **2013, 2019**|
||Belarus|2008,**2013, 2018**||||
|**Europe**|Türkiye,Ukraine<br>Russian Federation|2008, **2013, 2019**<br>2009, **2012, 2019**||Armenia|2009, **2013, 2020**|
|||||Egypt, Arab Rep.|**2013**, 2016,**2020**|
||Cyprus,Italy,Malta,Portugal|**2019**|**Middle East**|||
||Greece|**2018**|**and North**<br>**Africa**|Tunisia|**2013, 2020**|
|||||Jordan, Lebanon, Morocco, West<br>Bank and Gaza|**2013, 2019**|

19
Samuel Hall / NRC / IDMC (2018) Escaping war: where to next? A research study on the challenges of IDP protection - in Afghanistan. Available at <u>https://www.nrc.no/globalassets/pdf/reports/escaping war---where-to-next/nrc_idp_escaping-war_where-to-next.pdf (accessed 10 December 2021).</u>

- United Nations Institute for Training and Research (UNITAR) (2011) UNOSAT Brief 2011—satellite applications for human security. Geneva, Switzerland: United Nations Institute for Training and Research. Available at <u>https://reliefweb.int/sites/reliefweb.int/files/resources/UNOSAT_Brief_Sat_App_for_Human_Sec_2011_0. pdf</u> (accessed 10 December 2021).

United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) (2020) Afghanistan: Snapshot of Population Movements - January to December 2019. Available at <u>https://reliefweb.int/sites/reliefweb.int/files/resources/afg_population_movement_snapshot_20200115.p df</u> (accessed 10 December 2021). United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) (2021) Afghanistan: Snapshot of Population Movements - January to December 2020. Available at <u>https://reliefweb.int/report/afghanistan/afghanistan-snapshot-population-movements-january-december2020-23-jan-2021 (accessed 10 December 2021).</u>

- Wang S, So E and Smith P (2015) Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery. _International Journal of Applied Earth Observation and Geoinformation_ , _36_ , 87-93.  https://doi.org/10.1016/j.jag.2014.11.013.

- Witmer FD (2015) Remote sensing of violent conflict: eyes from above. _International Journal of Remote Sensing_ , _36_ (9), 2326-2352. https://doi.org/10.1080/01431161.2015.1035412.

- Witmer FD and O'Loughlin J (2011) Detecting the effects of wars in the Caucasus regions of Russia and Georgia using radiometrically normalized DMSP-OLS nighttime lights imagery. _GIScience & Remote Sensing_ , _48_ (4), 478-500. <u>https://doi.org/10.2747/1548-1603.48.4.478.</u>

- World Bank (WB) (2020) Remotely-sensed data of Nighttime lights. In _<mark>Open Night Lights</mark>_ <mark>tutorial. A</mark> vailable at <u>https://worldbank.github.io/OpenNightLights/tutorials/mod1_2_introduction_to_nighttime_light_data.ht ml (accessed 10 December 2021).</u>

15
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