rafmacalaba/data-use-mentions-extended huggingface dataset:
has carried out the Pakistan Social and Living Standards Measurement (PSLM) survey and the Household Integrated Economic Survey every alternate year since 2004-05 and, except for a few years, the labor force survey (LFS) has been conducted on an annual basis between 2001 and 2017. Similarly, Sri Lanka has a triennial Household Income and Expenditure Survey (HIES) and an annual Labor Force Survey (LFS) which is nationally and quarterly representative.

In India, labor market information is collected using the National Sample Survey (NSS), which every five years includes a separate employment-unemployment module (Schedule 10). In the intervening years, basic employment information is gathered together with the household consumer expenditure module. The quinquennial surveys have large samples and are referred to as the ‘thick rounds’, whereas the intervening surveys are known as the ‘thin rounds’ because of their relatively smaller sample size. An annual Periodic Labor Force Survey (PLFS) was started in 2017 to provide more frequent and timely labor market data that is nationally and regionally representative.

Primary data from these various sources is not always publicly available. Table 1 lists all the relevant censuses and surveys conducted in South Asian countries between 2001 and 2017 and highlights in bold the 62 we got access to. Of these, 19 are from Pakistan, 17 from Sri Lanka, 9 from India, 8 from Bangladesh and 3 each from Afghanistan, Bhutan, and Nepal.

3
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 17. Freetown’s water utility suffers from hidden costs of inefficiencies**

Hidden costs as percentage of revenues <!-- Start of picture text --> Sierra Leone<br>Nigeria<br>Mali<br>Liberia<br>Ghana<br>Cote d'Ivoire<br>Senegal<br>Niger<br>Benin<br>Burkina Faso<br>Cape Verde<br>0 50 100 150 200 250 300 350<br>Percentage of revenues<br>Losses Underpricing Collection Inefficiencies<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and others (2009).

Note: Hidden costs for Sierra Leone are calculated only for GVWC, a parastatal established in 1961. GVWC is responsible for the water supply of the city of Freetown and its environs.

# Information and communication technologies

## **Achievements**

Although Sierra Leone‘s mobile penetration is below the average for Sub-Saharan Africa (table 10), it is still a significant accomplishment given Sierra Leone‘s postconflict situation. Sierra Leone is an example of a postconflict country using wireless communications to leapfrog its ICT development. Despite a civil war and absence of a regulatory authority, several mobile operators established operations in the country in the early 2000s. The number of mobile subscriptions went from just 6,000 in 2000 to 1.4 million by 2009 for a penetration rate of one quarter of the population. The level of access to mobile communications is higher than the penetration rate given that multiple members of a household can use a single subscription. According to a 2008 household survey, 28 percent of Sierra Leonean households had a mobile phone (SSL and ICF Macro 2009). Considering that only 12 percent of households reported having electricity, recharging a mobile handset was more of a challenge than adding prepaid credits 35
27 Tomczak, M. 2003. "Spatial Interpolation and its Uncertainty using Automated Anisotropic Inverse Distance Weighing (IDW) ‐ Cross‐validation/Jackknife Approach", in _Mapping Radioactivity in the Environment: Spatial Interpolation Comparison 97_ , edited by G. Dubois, J. Malczewki, and M. De Cort, pp 51‐62. Available at http://www.ai‐geostats.org/index.php?id=45.

Trung, Le Dang, Tran Ngo Minh Tam, Bob Baulch, and Henrik, Hansen, 2007, Testing for Food market integration: A study of the Vietnamese paddy market, _DEPOCEN Working Paper Series No. 2007/11_ UNISDR, 2009, _Global Assessment Report on Disaster Risk Reduction_ , United Nations International Strategy for Disaster Reduction Secretariat: Geneva.

UNEP/GRID‐Europe, 2007a, GNV199 Tropical Cyclone Tracks by Year, Downloaded from http://www.grid.unep.ch/activities/earlywarning/preview/data/trop_cycl/trop_cycl.php on August 21, 2008.

UNEP/GRID‐Europe, 2007b, GNV200 Tropical Cyclone Asymmetric Windspeed Profile, Downloaded from http://www.grid.unep.ch/activities/earlywarning/preview/data/trop_cycl/trop_cycl.php on September 4, 2008.

Yamano, Takashi, Harold, Alderman, and Luc Christiaensen, 2005, Child Growth, Shocks, and Food Aid in Rural Ethiopia, _American Journal of Agricultural Economics_ , 87‐2: 273‐288.

Zubair et al., 2006, Natural Disaster Risks in Sri Lanka: Mapping Hazards and Risk Hotspots, in Arnold et al., eds., Natural Disaster Hotspots: Case Studies, _Disaster Management Series No. 6_ , World Bank: Washington D.C.
10 countries as in developed countries. If anything, the relationship in developing countries may even be negative (Frick and Rodriguez‐Pose, 2016) (see further below).

This differential effect on poverty of migration to small towns versus big cities is an important phenomenon which needs to be explored and interrogated further, with cross‐country and country specific data. It is starting to attract attention (Dorosh and Thurlow, 2013; 2014; Datt, Gibson, Murgai and Ravallion, 2016; Sekkat, 2016). One such country specific exercise, for Tanzania, a country in which the composition of urbanization has some expected and perhaps some striking features, is particularly illustrative. The capital city, Dar es Salaam, had a population of 4.5 million according to the 2012 census. It constitutes 10% of the national population, it is the largest urban agglomeration by a huge margin, and it has grown dramatically over the past 50 years, driven largely by in‐migration. Yet as Figure 5 (taken from Wenban‐Smith and Ambroz, 2014) shows, the composition of urbanization throws up some interesting patterns. In 2012 Dar accounted for about one‐third of the urban population. But it also accounted for about one‐third of the urban population half a century ago. Further, towns other than district capitals (average population around 20,000) account for a growing proportion of the urban total. There is surely a lot of migration into Dar, but movement out of rural areas is as much to small towns as to the capital city.

A unique lens on the poverty impacts of migration from rural areas to the city versus small towns is provided by the Kagera Health and Development Survey (KHDS) which tracks migrants from this remote rural region in North‐West Tanzania. While it is only focused on out‐migrations from one region, we have information on 4,339 individuals, first interviewed in their baseline communities in the early 1990s and then re‐interviewed nearly two
521)|(0.884)|(3.062)|(3.632)|(5.149)|(0.930)|(1.112)|(1.227)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.061|0.072|0.043|0.068|0.077|0.057|0.024|0.024|0.062|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|12937|9337|3600|12937|9337|3600|12937|9337|3600|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm (first three columns) or “started log” (last six columns) of the components of total household consumption expenditure per adult equivalent. Household sample weights from ESS2 applied. The first, middle, and last three columns report results for food, education, and other non-food per adult equivalent consumption expenditures, respectively. Columns labeled (1), (2), and (3) report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

54
ing is confirmed by the event study we run and show in panel (b) of the same figure. A similar conclusion can be reached if we look (again in Figure C1) at the time period from 2000 to 2016 (which is the year of the first round of data collection) or if we focus on the treatment period of 2016 to 2018.

To produce Figure C1 we focus on the 3 wards that were sampled in each LGA for this study.<sup>20</sup> In Appendix C we include graphs on different samples which summarize different ways of computing the average nighttime lights for each group (see Figure C2, Figure C3, and Figure C4). Overall, these further explorations suggest that trends are not drifting apart between the evaluation groups. At the same time, there is some indication that comparison areas might be catching up with treatment areas in terms of local economic activity over time.

# **4 Empirical Strategy**

As discussed above, our goal is to assess whether LGAs that received the project improved more between the first and second survey rounds when compared to non-project LGAs. For both the government officials and household surveys, we use responses at the individual level and, in order to formally test for different improvement rates, estimate a difference-in-differences model of the form: Yilt = α + δ1{ULGSP}ilt + γ1{SecondRound}ilt + β1{ULGSP}ilt _∗_ 1{SecondRound}il + ΠXilt + εilt (1) where Yiltis some outcome of interest of individual _i_ in LGA _l_ and period _t_ ; 1{SecondRound}ilt is an indicator that takes the value 1 if the respondent belongs to the second survey round in 2018, and 0 otherwise; 1{ULGSP}ilt is an indicator that takes value 1 if the respondent resides in a project LGA, and 0 otherwise; Xilt are individual/household characteristics used as comparisons (welfare index, household size, age, gender, marital status, and education levels
|Formalization rate in Control hetero=1|0.014|0.013|0.014|0.015|0.013|0.014|0.016|0.015|0.016|0.011|0.01|0.012|0.014|0.014|0.014|
|Mean Outcome control year1 hetero=0|60,623|‐0.067|1.606|45,481|‐0.124|1.316|81,683|0.241|1.744|62,789|0.023|1.074|68,219|0.159|1.669|
|Mean Outcome control year2 hetero=0|62,985|‐0.037|1.646|50,227|‐0.085|1.347|83,543|0.232|1.792|62,399|0.011|1.248|68,881|0.16|1.703|
|Mean Outcome control year1 hetero=1|48,918|0.009|0.856|83,291|0.393|0.433|46,930|‐0.078|0.996|46,554|‐0.05|1.179|39,120|‐0.188|0.617|
|Mean Outcome controlyear2 hetero=1<br>Note : Panel data from midline and endlin<br>for each triplet). α: truncated at the 99th p|49,424<br>e surveys i<br>ercentile.|0.018<br>n 2015 an<br>μ: Sharpe|0.986<br>d 2016. Al<br>ned two‐st|72,117<br>l regressio<br>age q‐valu|0.
Mountain in provinces 6 and 7 is an unpaved walking path, _unless_ the data set explicitly says otherwise.

- Every SRN marked Hill or Mountain in provinces 6 and 7 is an unpaved road, _unless_ the data set explicitly says otherwise.

## Limitations

We must note several limitations to this model and the resulting analysis. Localized inaccuracies in our model are inevitable given the contrast between the high resolution of the data set and local shortcomings in input data quality and completeness. Errors in coverage or geolocation from the service data sets, sourced from relevant ministries, would naturally lead to inaccuracies in the model outputs. For instance, we were unable to source a complete, high‐ quality bridges data set that aligned with spatial data on rivers. Therefore, roadways crossing rivers were the only representation of bridge crossings, which are important chokepoints for transportation in Nepal’s hills. We assumed walkers used these vehicle roads for river crossings, although in reality walkers in rural Nepal commonly use trail bridges and cable‐pull tuins. Thus, the model may exaggerate travel times in some areas where walking is the principal modality.
# **The Double Burden of Female Protracted Displacement: Survey Evidence on Gendered Livelihoods in El Fasher, Darfur**

Wolfgang Stojetz<sup>[†]</sup> and Tilman Brück<sup>[‡]</sup>

**Keywords** : gender; forced displacement; internally displaced persons; livelihoods

## : D74; J16; J24; O15

The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

**Acknowledgments** : This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. Andrej Smirnov provided excellent research assistance.
or are broken (1=Yes)|0.33|(0.48)|
|Toys have moved to other ECED facility (1=Yes)|0.22|(0.42)|
|**What happened to the teachers?**|||
|No longer teaching|0.40|(0.36)|
|Teaching in other ECED center|0.26|(0.36)|
|Teaching in primary/secondary school|0.12|(0.21)|
|Don't know|0.11|(0.22)|
|Moved to other village|0.10|(0.17)|

Note: Data from 2016 Indonesia ECED Survey.<sup>*</sup> Reasons for playgroup closure are not mutually exclusive categories (multiple answers possible). N= 60

# **_Qualitative evidence_**

Interviews with former administrators and teachers from closed playgroups were also carried out to provide additional insight to the quantitative data presented in Table 1. Most described a chain of events that led to the closure of their playgroup. As one interviewee said, “ _When the project ended, we no longer had funds to pay the teachers and replace the books and toys that were damaged. So the teachers quit. And the students left. Nobody stepped up to keep the center.”_ Many of the project playgroups that eventually closed had tried to remain open for some time, for example, one former teacher described, _“After the project ended, the center continued to operate for a year. During this time, the center relied on funding from the village government. The center held a meeting with local parents, requesting them to pay tuition fees in order to maintain the center. But this was not successful.”_ Indeed, our quantitative data suggest that a strategy employed by many playgroups to sustain their programs was to charge fees. As shown in Appendix Table
In addition, drawing on responses from the household-level survey, we find that individual households increased their participation in land-cover maintenance activities. Panel B of **Table 7** presents program effects on work days.<sup>16</sup> For the full sample, we find statistically significant impacts (p<0.01) on total land-cover work, which includes both work that is paid for through wages and work that is not financially compensated. Transforming the estimated impact into days, land-cover work in treated communities increased by approximately 4.2 days for the earlier cohorts and 2.3 days for the later cohorts (about 2.7 days for the combined sample). These are large percentage changes compared to the means among controls of (2.4 and 3.8 days per household).

## _Heterogeneous Effects_

For the heterogeneous effects analysis, the research team re-classified the survey zones to better group states that had similar communities on the basis of characteristics measured by the surveys. These five regions were: North (Chihuahua and Durango states), North-east (Nuevo Leon and San Luis Potosi states), Center (Jalisco, Michoacan, and Puebla states), South (Chiapas and Oaxaca states), and Yucatan Peninsula (Campeche, Quintana Roo and Yucatan). We investigated possible impact heterogeneity by region ( **Appendix Table A6** ) and found that enrolled communities in the northeast states of Nuevo Leon and San Luis Potosi increased their land-cover management activities with the greatest magnitude. However, this region is also the one with the lowest baseline level among controls, suggesting perhaps higher effectiveness where activities were low to start. We also estimated impacts for communities whose parcels were at high risk of deforestation and did not find any significant differences in land-cover management activities ( **Appendix Table A7** ).

## _Robustness checks_

Our impact estimates for the land management index analysis are consistent across a series of robustness checks. Point estimates are robust to excluding state fixed-effects, and to
|No|No|Yes|Yes|
|Number ofnaicsindustries|257|257|257|257|257|257|257|
|Number of years|17|17|17|17|17|17|17|
|Year dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations (naics_×_ years)|4,369|4,369|4,369|4,369|4,369|4,369|4,369|
|_R_<sup>2</sup>|0.165|0.054|0.096|0.442|0.473|0.516|0.518|

_Notes:_ The dependent variable is the unweighted (count based) Duranton-Overman _K_ -density cdf at 50 kilometers distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. **Model 6** includes the following industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales). Last, our preferred specification,
of drought used in the IDB (2019) study. As is common in many studies of drought hazard, drought events are characterized using the three-month Reconnaissance Drought Index (RDI),<sup>8</sup> which is shown to replicate better the historical occurrence of “identified” drought events in Uruguay. This indicator tends to capture short duration agricultural droughts. A biological model of crop output and livestock pasture availability is then used to identify the loss of yield associated with drought conditions defined in this fashion. Water availability conditions that can lead to significant losses in production are different for different crops and for livestock. Short duration drought events in the summer have very negative implications for grain yields. Unlike crops, whose lifecycle covers only a few months, livestock can enter a period of compensatory growth if drought conditions reverse, which can partially make up for single acute drought losses. The climate model underlying IDB (2019) projects an increased occurrence of droughts during the summer months when rainfall is crucial for grain yields, but does not anticipate significant alterations in average precipitation patterns across various climate scenarios. Caution is, in any event, required to interpret these results, given the uncertainties in the projection of future precipitation patterns discussed in a previous section.

> 8 Droughts are defined as events where the normalized 3 month SDI is below -1.

14
disclosure and economic growth: What is the nexus? _World Development_ , _160_ , 106036.

- Hu, Y., & Yao, J. (2022). Illuminating economic growth. _Journal of Econometrics_ , _228_ (2), 359– 378.

- IMF. (2019). _Republic of Mozambique: Diagnostic report on transparency, governance, and corruption_ [IMF Country Report 19/276,]. IMF. <u>https://www.imf.org/en/Publications/CR/Issues/2019/08/23/Republic-of-MozambiqueDiagnostic-Report-on-Transparency-Governance-and-Corruption-48613</u>

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Janus, T. (2022). Democracy and statistical capacity. _Applied Economics Letters_ , 1–5.

- Jerven, M. (2013). _Poor numbers: How we are misled by African development statistics and what to do about it_ . Cornell University Press.

25
# **1. Introduction**

The current global financial crisis has not only shed doubts on the proper functioning of conventional “Western” banking, but has also increased the attention on Islamic banking.<sup>1</sup> Academics and policy makers alike point to the advantages of Shariah-compliant financial products, as the mismatch of short-term, on-sight demandable deposits contracts with long-term uncertain loan contracts is mitigated with equity elements. In addition, Sharia-compliant products are very attractive for segments of the population that demand financial services that are consistent with their religious beliefs. However, little academic evidence exists on the functioning of Islamic banks, as of yet.

This paper describes some of the most common Islamic banking products and links their structure to the theoretical literature on financial intermediation. Specifically, we discuss to which extent Islamic banking products affect the agency problems arising from information asymmetries between lender and borrower or investor and manager of funds. Second, we compare the business model, efficiency, asset quality and stability of Islamic banks and conventional banks, using an array of indicators constructed from balance sheet and income statement data. In separate regressions, we focus specifically on the relative performance of both bank groups during the recent crisis.

While there is a large practitioner literature on Islamic finance, in general, and specifically Islamic banking, there are few academic papers. Cihak and Hesse (2010) test for the stability of Islamic compared to conventional banks, while Errico and Farahbaksh (1998) and Solé (2007) discuss regulatory issues related to Islamic banking. This general dearth of academic work on Islamic finance stands in contrast with the increasing importance that Islamic banking has in many Muslim countries in Asia and in Africa. With this paper we hope to contribute to the emerging literature on this topic.

Sharia-compliant finance does not allow for the charging of interest payments (riba) as only goods and services are allowed to carry a price. On the other hand, Sharia-compliant

> 1 For just two examples, see Willem Buiter: <u>