rafmacalaba/data-use-mentions-extended huggingface dataset:
,014<br>Sell Assets/Use 23%<br>Other Shocks 10,800 Savings/Rent Farm 25%<br>2,746<br>Livestock Loss 2,653 10,268 Increase Labor Supply 7%<br>13%<br>Robbery/Assult 2,086 10,030<br>9%<br>Fire 2,296 9,197 Borrowed Money 10%<br>Death of a hh. Member 7,364<br>1,612 16%<br>Other<br>10%<br>Floods/Drought 2,213 6,554<br>Crop Disease 1,511 5,351 Reduced Expenses orConsumption 3% 9%<br>Dwelling Damaged 1,4513,783<br>Kids Migrate or removed 4%<br>Severe Water Shortage 970 3,346 from School 1%<br>0 5000 10000 15000 0% 10% 20% 30% 40%<br>2014/15 2009<br>2014/15 2009<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

13
economies and 20 EMDEs. C. Shaded area indicates forecasts. GDP-weighted averages of production function-based potential TFP growth estimates. Sample includes 50 EMDEs.

D. Shaded area indicates forecasts. GDP-weighted averages of production function-based potential growth estimates. Sample includes EMDEs. Supportive policies assume that each country matches over the period 2018-27 its best historical ten-year improvement in educational attainment, schooling, life expectancy, female labor force participation, and investment.
.13%|2.39%|
|**Subsovereign Debt**||4.99%|1.31%|

Source: Instituto de Pesquisa Econômica Aplicada (IPEA)

## **Table A3.3: Canada**

|||<br>Pre-FRL<br> <br>|Post-FRL<br>|
|---|---|---|---|
|Provinces|Date|<br>_(Dt-1)/GSDP - (Dt-5)/GSDP_|_(Dt+5)/GDP - (Dt)/GSDP_|
|**Alberta**|1999|<br>-13.16%|-10.65%|
|**British Columbia**|2000|12.82%|-5.04%|
|**Nova Scotia**|2000|2.41%|-12.48%|
|**Ontario**|2004|-3.97%|-2.74%|
|**Newfoundland and Labrador**|2004|-23.81%|-26.54%|
|**New Brunswick**|2006|-6.04%|-0.26%|

Notes: 1) Pre-FRL data of Alberta only date back 4 years before the enactment of FRL. 2) To eliminate the impact of the recent financial crisis on our data set, our data stop at first half of 2008.

Source: Statistics Canada.
." Labor Economics **6** (179-202).

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# **Microdata Collection and Openness in the Middle East and North Africa (MENA): Introducing the MENA Microdata Access Indicator**

Uche Eseosa Ekhator-Mobayode<sup>*1</sup> and Johannes Hoogeveen<sup>*</sup>

**Key words** : National Statistical Offices, Statistical Indicators, Data Openness, Microdata, MENA

**JEL** : C18, H00, I00, O1, O53

> * MENA Poverty and Equity Global Practice. The authors would like to thank Henry Gannat and Federica Alfani for excellent research assistance. The authors would also like to thank Umar Serajuddin, Hai-Anh Dang, Brian Stacy and Daniel Mahler for providing suggestions on earlier drafts of this paper. Special thanks to several colleagues from the Poverty and Equity Global Practice at the World Bank who participated in the peer review process to validate results from the data collection exercise.

> 1 Corresponding author. Email: uekhator@worldbank.org
<!-- Start of picture text --> Comparing distribution of household/connection consumption from survey and admin data<br>(Admin billing data covers only JVVNL customers. NSS data restricted to districts served by JVVNL only)<br>Ww<br>Om<br>2<br>°<br>ron<br>o<br>no<br>3<br>°<br>=<br>&<br>se<br>JO°<br>o<br>c<br>Cc<br>O°<br>°<br>—fo}<br>—<br>Bw<br>E<br>3<br>c<br>—°<br>D<br>°<br>oO coo goclclUOCUCOUOUCUCOMUCcOrlmhCPrCcCPrmCcPrmCcCPrmUCcPrCmUCcUPWrCUCcC/]HT<br>© ] NO og+-gseeqeaeaea2agesesa9 i:<br>5 -Oo =w No WwN oOo ©w vTo vTuw) woOo wowo icO o } wo© ~Oo ~wD ©So oOw co?)o Soo wSo So woOo = ]} wO o So° 4<br>4¢46 d6 dF dC dg dC dC dB BC KFC oD dG dB dG dD dD TT FGFS YP FF EG<br>eweeegeesseesesssek88 eggssgssgsg<br>- N N oO oO<br>Number of Units consumed over 30 days (kWh)<br>GR Admin Data (2015-16) | ) Nss Consumption Expenditure (2011-12)<br><!-- End of picture text -->

The Admin Billing data provides the connection-wise consumption distribution. The NSS survey data provides household wise consumption distribution
, the predictive performance of the models stabilizes at the minimum sample size of around 1,000 observations for the target survey (close to 50 percent of the sample) and a similar

> 21 For example, a further increase in the size of the base survey from 20 percent to 60 percent (2,786 observations) does not appear to significantly improve the accuracy of the poverty estimates in all models, although there is a marginal improvement in Model 9 by bringing estimates closer to one standard error of the true poverty rate (the dark gray zone). Moreover, there is only a marginal difference between the behavior of Models 1 and 2 and that of Models 8 and 9, as Models 1 and 2 display less fluctuation starting from 844 observations (40 percent of the target survey), while Models 8 and 9 become more stable starting from 1,055 observations (50 percent of the target survey).

25
- [26] Fallon, P. R., & Layard, P. R. G. (1975). Capital ~~-s~~ kill complementarity, income distribution, and output accounting. Journal of Political Economy, 83(2), 279 ~~-3~~ 02.

- [27] Fitzenberger, B., & Kohn, K. (2006). Skill wage premia, employment, and cohort effects: are workers in Germany all of the same type?. University of Freiburg Working Paper.Freeman, R. (1976). The overeducated american. Academic Press, New York.

- [28] Freeman, R. B. (1979). The Effect of Demographic Factors on Age ~~-E~~ arnings Profiles. Journal of Human Resources, 14(3), 289 ~~-3~~ 18.

- [29] Gagliardi, L., & Lemos, S. (2016). Evidence on immigrants assimilation into recipi ~~-~~ ent labour markets using UK longitudinal data between 1981 and 2006. Journal of Economic Geography, 16(3), 547583.

- [30] Glitz, A., & Wissmann, D. (2016). Skill Premiums and the Supply of Young Workers in Germany. Working Paper, Humboldt University Berlin.

- [31] Goldin, C. and Katz, L. (2008). The Race Between Education and Technology, Cam ~~-~~ bridge, MA: Harvard University Press.

- [32] Hendricks, L. (2004). A Database of Mincerian Earnings Regressions. Available at http: //www.lhendricks.org/Mincer.htm.

- [33] Katz, L. F., & Murphy, K. M. (1992). Changes in relative wages, 1963 ~~-1~~ 987: supply and demand factors. Quarterly Journal of Economics, 107(1)
/sup><sup>_ρ_0,</sup><sup>_θ_</sup> 1<sup>_u_=</sup><sup>_ρ_1,and</sup><sup>_θ_</sup> 0<sup>_r_=</sup><sup>_ρ_0+</sup><sup>_ρ_2,</sup> _θ_ 1<sup>_r_=</sup><sup>_ρ_1+</sup><sup>_ρ_3.</sup> We use the pooled sample from four earlier rounds of the IFLS panel data to estimate equation (21) above.<sup>22</sup> The estimates of the regression equation (21) are reported in Table 4; we report estimates from three specifications : the first column contains the estimates of equation (21) without any controls, the second column controls for the number of children in

> 22IFLS 2007 (wave 4), IFLS 2000 (wave 3), IFLS 1997 (wave 2) and IFLS 1993 (wave 1).

20
multidimensional well-being indices proposed by Benjamin et al. (2014b) or from the total consumption approach (see Section 3.4.3). The limitation of this approach is that it assumes an infinite substitutability across dimensions and thus does not acknowledge the essentiality of well-being dimensions. As a result, it systematically makes exclusion errors on extremors. The intuition is easy to grasp in Figure 6, where extremor i<sup>′</sup> is (mistakenly) not identified as poor because her achievement vector is above the linear identification contour associated to such an achievement-based identification methodology (in red).

It would be interesting to study empirically the differences that these two solutions make when compared with a more traditional AF identification. For example, on could look at how well-being comparisons by the World Bank’s MPM would be affected by these solutions. In the remainder of this section, we shortly discuss how to quantify the impact of these solutions.

20
- **and Jan Bachmann** , “Roads to Peace? The Role of Infrastructure in Fragile and

- Conflict-Affected States,” 2017.

- **and** , “Infrastructural frontiers: Terrains of resistance at the material edge of the

- state,” _Geoforum_ , 2022.

- **, Judith Verweijen, Janvier Murairi, and Saidi Kubuya Batundi** , “Paths of authority, roads of resistance: Ambiguous rural infrastructure and slippery stabilization in eastern DR Congo,” _Geoforum_ , 2022, _133_ , 176–184.

- **Scott, James C.** , _The Art of Not Being Governed: An Anarchist History of Upland Southeast Asia_ , Yale University Press, 2009.

- **Stearns, Jason** , “Involution and symbiosis: the self-perpetuating conflict in the Democratic Republic of Congo,” _International Affairs_ , 2022, _98_ (3), 873–891.

- **Stoop, Nik, Marijke Verpoorten, and Peter Van Der Windt** , “Artisanal or Industrial Conflict Minerals? Evidence from Eastern Congo,” _World Development_ , 2019, _122_ , 660– 674.

- **Thegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Bulan, Ron Lester Durante, and Jayzon Mag-atas** , “Application of machine learning algorithms on satellite imagery for road quality monitoring: An alternative approach to road quality surveys,” _ADB Economics Working Papers Series_ , Dec 2022.

- **Tollefsen, Andreas Forø and Halvard Buhaug** , “Insurgency and Inaccessibility,” _International Studies Review_ , 2015, _17_ (1), 6–25.

- **Voigtl ̈ander, Nico and Hans-Joachim Voth** , “Highway to Hitler,” _American Economic Journal: Applied Economics_ , 2022.

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- *
>Percent 1995 2019 Percent 1995 2019<br>200 250<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0 0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br>s<br><!-- End of picture text --> Sources: Comtrade (database); ESCAP-World Bank Trade Costs Database; World Bank; World Trade Organization.

Note: EMDEs = emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Bilateral trade costs (as defined in the UNESCAP/World Bank database) measure the costs of a good traded internationally in excess of the same good traded domestically and are expressed as ad valorem tariff equivalent. Bilateral trade costs are aggregated into individual country measures using 2018 bilateral country exports shares from the Comtrade database. Regional and sectoral aggregates are averages of individual country measures. Bars show unweighted averages, whiskers show interquartile ranges. Sample in 1995 includes 33 advanced economies and 46 EMDEs (4 in EAP, 8 in ECA, 15 in LAC, 4 in MNA, 2 in SAR, and 13 in SSA). Sample in 2019 includes 23 advanced economies and 53 EMDEs (9 in EAP, 12 in ECA, 16 in LAC, 4 in MNA, 2 in SAR, and 10 in SSA).
on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 4The weights from the SDID approach are used in all survey data-based analyses at the individual level.

2
(Hoddinott and Kinsey, 2001; Wang, Kanji and Bandyopadhyay 2009; Bengtsson 2010; Portner 2010; and Skoufias and Vinha, 2012). We follow this last set of papers by using two rounds of the Nigeria DHS to assess the effect of rainfall shocks on child anthropometrics.

# **Weather and Child Health in Africa**

Not surprisingly, the existing literature suggests that weather variability has an impact on child health. Three recent papers have used the Demographic and Health Surveys to analyze the effect of weather fluctuations on child mortality rates in Africa. Wang et al. (2009) show that excess rainfall and extreme temperature increase under-five child mortality rates in 19 Sub-Saharan countries, although the magnitude of the reported impacts are rather small. Baird et al. (2010) find that excessive heat around the month of birth is predictive of an increased likelihood of death, particularly for newborns. However, the effect of rainfall is undetermined. While rainfall during the third trimester of pregnancy increases

> 1 The 2003 Nigeria Living Standard Survey (NLSS) -the last available consumption survey- does not provide the precise location of the sampled households, or even the Local Government Area (district) in which they reside.

3
economies, compared to medium-sized cities that might suffer from market access problems, lack of intermediate goods and infrastructure, and other impediments to grow fast. In the developed world this problem may be less severe, thus providing growth opportunities to medium-sized locations that are not present in India. Comparing the U.S. and India in the service sector, we indeed find that agglomeration economies peak for intermediate-sized locations in the U.S., whereas the large megacities are the winners in India. This finding is not common to all emerging economies. Although for want of high quality sectoral employment data at the local level we refrain from an in-depth study of China, our preliminary exploration suggests that China looks more similar to the U.S. in that decreasing returns dominate in high-density cities. The finding that “India is different”, because of both the failure of medium-density locations to grow faster and the importance of its service sector, justifies studying the spatial development of that country in further detail.

# **2. Data**

To study employment dynamics across space in India, a first issue is to decide on the level of spatial disaggregation at which we have reliable data. India is divided into 35 states (or union territories) and 640 districts. While certainly the quality of the data are more reliable at the state than at the district level, work on the U.S. by Desmet and Rossi-Hansberg (2009) shows that having a high degree of spatial disaggregation is important. Indeed, agglomeration economies and congestion effects may get lost at higher levels of aggregation, so that focusing on districts is better. In addition, having a broad distribution of places (going from small to intermediate to large) is also important, since previous work for the U.S. has shown that the scale-dependence of growth may be non-linear.

India does not collect comprehensive sectoral employment data at the district level. We therefore rely on micro-data from surveys. India runs two firm-level surveys, the Annual Survey of Industries (
denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆(Investme<br>|nt/Total Fix<br>|ed Asset)<br>||Investment/<br>|Total Fixed<br>|
|---|---|---|---|---|---|---|
|Shenzhen<br>firms only|Firms in<br>Metropolitan|<br>City<br>dummy|Propensity score<br>matching|Takeover|Panel data<br>regression|Pre-Trend<br>Test|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Titling Firm<br>0.411**|0.341***|0.352***|0.295*|0.297***|||
|(2.313)|(2.662)|(2.867)|(1.706)|(3.114)|||
|Takeover||||0.000***|||
|||||(3.156)|||
|Titling Firm * Takeover||||0.000|||
|||||(1.004)|||
|Post Dummy|||||0.004||
||||||(0.153)||
|Titling Firm
observations. Subannual and subnational geospatial data on land use and land cover transitions can help us understand the drivers, locations and trends in deforestation and degradation, which could in turn produce more sophisticated and effective KPIs. An SLB that targets deforestation due to cropland expansion may necessitate a different set of government entities than an SLB that targets deforestation due to urban expansion. The World Bank and the European Space Agency are currently exploring how SLBs may leverage new deforestation monitoring technologies with cloud-penetrating capabilities.

**Private capital mobilization** Multilateral development banks (MDBs) have a clear and essential role to play to mobilize private capital. The REACH framework aims at setting better performance incentives by accounting for what the issuer can be reasonably expected to achieve. The size of the step-ups or step-downs could then be scaled beyond the commonly used 25 basis points. However, while this sends strong signals about the commitment of both issuer and investor, the interest rate differentials may be difficult to stomach, especially when the market for sovereign SLBs is still nascent. MDBs can help derisk the transaction on either side through concessional finance or guarantees.21 Moreover, SPTs are often aligned with existing development operations and MDBs can provide technical assistance, capacity building and knowledge transfer to support issuers in reaching their goals. A strong coupling of incentives to performance, as in the REACH

> 20The SLBs discussed in the REACH framework could be seen as _floater_ -type SCDIs, which have a fixed principal and a variable coupon. For the SLB, the coupon would be linked to the benchmarked KPI, the state variable. 21In the context of contract theory, MDBs can help relax the participation constraints for issuers.

25
a unique mapping of the source countries where processing inputs are imported from and the destination countries of processed exports. This makes it possible to examine the role of both trade costs related to the import of inputs ( _upstream trade costs_ ) and trade costs related to the export of final goods ( _downstream trade costs_ ) on intra-GPN trade. Such mapping of GPNs cannot be conducted with regular trade data since imports are not necessarily used solely for export purposes, but can also be consumed domestically, as we explain in Section 2.

In Section 3, we identify three stylized facts that suggest that both upstream and downstream trade costs play an important role on China’s processing trade. First, China’s processing exports heavily rely on foreign inputs, with a relatively low share of the value made in China. According to a recent estimate by Koopman, Wang and Wei (2008), only 18% of China’s processing exports value is produced in China, while the remaining 82% consists of the value of imported processing inputs. Second, the average distance traveled by processing imports (import distance) is shorter than the average distance traveled by processing exports (export distance). In 2008, 75% of China’s processing imports originated from within the East Asian region, while 62% of the processing exports were destined to non-Asian OECD countries. Third, this spatial pattern is not consistent across processing locations. In a cross-section of 29 Chinese provinces, import distance is negatively correlated to export distance for most years between 1995 and 2008. In other words, locations in China that import their processing inputs from nearby tend to export their processed goods far away, and vice versa.<sup>3</sup> To explain the role of trade costs on intra-GPN trade, we in Section 4 develop a threecountry general-equilibrium trade model.<sup>4</sup> In the model, the world consists of three countries: _East_ (for advanced East Asian countries), _West_ (for Europe and
36***|
|||(0.13)|(0.30)|(0.29)|(0.54)|(0.31)|(0.48)|(0.68)|(0.17)|(0.31)|(0.18)|
||Constant|1.48***|1.17***|2.49***|1.98**|2.22***|2.90***|2.69***|3.79***|4.11***|4.22***|
|||(0.15)|(0.26)|(0.33)|(0.72)|(0.13)|(0.36)|(0.65)|(0.11)|(0.61)|(0.22)|
||N|58|59|72|51|41|54|33|55|34|47|
||R-sq|0.34|0.50|0.67|0.44|0.36|0.34|0.26|0.55|0.36|0.13|

_Sources_ : Authors’ calculations based on SEAD data for Malaysia.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224). See subsection 3.2. for details on the clustering.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 60
**Table 1: Enrollment by Grade, Children ages (6-10 years old), 2012**

||Public|Private|Other|total|Percent|
|---|---|---|---|---|---|
|Population 6-10 years||||7416|100%|
|Never attended||||3550|47.9%|
|Attended & unenrolled||||171|2.3%|
|Enrolled|3360|249|86|3695|49.8%|
|Currently enrolled:||||||
|_Katchi_* & Grade1|894|56|21|971|13.1%|
|Grade2|1079|63|29|1171|15.8%|
|Grade3|630|55|22|707|9.5%|
|Grade4|365|34|9|408|5.5%|
|Grade5|231|22|3|256|3.5%|
|_Katchi_* – Grade 5|3199|230|84|3513|47.4%|
|Grade 6 and above|161|19|2|182|2.5%|

_<mark>Source:</mark>_ <mark>Data from Sindh Baseline Household Survey, 2012.</mark> _<mark>Note: Katchi</mark>_ <mark>is the pre-primary grade in which children are admitted in a public primary school.</mark> Learning outcomes are still poor for enrolled children. At Grade 3, only 24 percent of students can read words while only 32 percent can perform basic subtraction. At Grade 5, only 19 percent of students can read full sentences while 35 percent can perform division (ASER, 2015). Gender disparities in learning are large and similar for both English and Math; boys outperform girls by 6 percentage points