rafmacalaba/data-use-mentions-extended huggingface dataset:
1,651 Theil indexes from 141 countries (see Annex 1 Table 2 for details). The average Theil is 5.5, the median 3.8 and the standard deviation 6.4. In about ten percent of observations inter-sectoral wage differences are minimal with Theils less than 1. Many of these cases include developed countries (Nordic countries, the Netherlands) but also Algeria, Cuba, Iran, and until the mid-1980s China.<sup>25</sup> Table 2 shows simple correlations between different inequality measures from the two databases. We have three inequality statistics from the OWW database (Gini coefficient, standard deviation and absolute mean deviation from the median) and only one from UTIP (Theil coefficient). Different inequality statistics from the OWW database are obviously strongly correlated (see the shaded cells). The correlation between Theil index from UTIP and Gini from the inter-occupation inequality is much less—around 0.4 (see also Figure 2). Still it shows that higher skill premium is associated with greater inter-sectoral inequality. The correlation between the changes in the two measures (which we shall be using in our regressions) is virtually zero however.

> 25 It will be noticed that we do not use Gini coefficient here (although we would have linked, for a more direct comparison with OWW dataset to do so). The reason is that UTIP  database does not provide individual mean industrial wages which would allow us to calculate different inequality measures. The authors only provide the “finished” statistic, that is the Theil index, and not the underlying data. This is not the case with the OWW database where individual occupational wages by country/year are available and one can thus calculate various inequality indexes.

27
|5 19%|2.3%|3.7%|
|2%||||||||
||2008|2009|2010|2011|2012|2013|2014|
||—@4U|SD<br>—@|55USD|—@8US|D<br>—e1|0USD||

<!-- Start of picture text --> 8<br>3 17%<br>co 11.3%<br>= 10.2% 9.5%<br>12% 9-2% 7.995 5-34 7.9% .<br>7% 3.9% 4.8% 3.4% 2.7% 5 19% 2.3% 3.7%<br>2%<br>2008 2009 2010 2011 2012 2013 2014<br>—@4USD —@55USD —@8USD —e10USD<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>28 ya<br>o ie,<br>o6<br>g<br>3.4<br>=<br>=<br>a<br>oO 2 Z<br>0 aa<br>0 4 5 10 15<br>daily expenditure per capita in $ 2011 PPP<br><!-- End of picture text -->
- 13 -

**Figure 8. Average monthly precipitation for the past 10 years**

<!-- Start of picture text --> 1200<br>Siraha<br>Makwanpur<br>1000 Banke<br>800<br>600<br>400<br>200<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Monthly precipitation (mm)<br><!-- End of picture text --> Source: Authors’ calculation based on data from the Ministry of Environment, Nepal.

<!-- Start of picture text --> Figure 9. Average monthly precipitation<br>Average monthly precipitation<br>in 2000-2009 (mm)<br>73<br>74 - 136<br>137 - 173<br>174 - 208<br>209 - 329<br>330 - 426<br><!-- End of picture text --> <!-- Start of picture text --> .<br><!-- End of picture text --> Given these backgrounds, road procurement costs vary significantly across districts, though the absolute levels remain low by global standards. The average unit cost of road upgrading is about NRs1.6 million or $23,000 per km (Table 1), which is far below the range of unit costs supported by the literature.<sup>6</sup> This is mainly because of the project specifications. Our sampled roads are mostly unpaved, gravel roads with simple surface treatment, if any. If an additional surface material, such as otta seals, is added, the project cost increases by $3,0004,000 per km. Still, it is relatively cheap.

Normalized bids, which are bid prices relative to engineering cost estimates, vary from 0.55 in Dhanusa to 1.05 in Syangja (Figure 10
. In addition, we examine whether there is heterogeneity in program impacts by location (urban and rural) and by the gender of child. Finally, this paper explores possible indirect effects on the human capital of older children (18 years and older) who by the rules of the program are ineligible to the transfer but who reside in households with younger participant children.

Identification of program impacts is derived from two different empirical strategies that use a panel of household surveys, a census of the poor and administrative data from 3
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Among the road corridors that serve landlocked countries, one finds significant cost differences. There is a strong negative correlation between lower traffic volumes and cost. As traffic increases, the unit cost of transport (per tonne) decreases (figure 2.5). Indeed, unit costs can vary by a factor of one to three—from $0.05 to $0.15 per tonne-km—depending on the level of traffic along different segments of the corridor. Freight tariffs start to fall once traffic volume exceeds 1,000 vehicles per day.

**Figure 2.5 Relationship between traffic volumes and unit costs of transport**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000 y = -5007.1x + 1893.2 Series1<br>R2 = 0.1127<br>Linear (Series1)<br>500<br>0<br>0.00 0.05 0.10 0.15 0.20<br>Costs per tonne<br>Weighted average annual traffic<br><!-- End of picture text --> _Source:_ Derived from preliminary data in Nathan 2010. The analysis is based on data from East and southern Africa.

The costs and times of moving imported goods along various corridors in southern Africa are well aligned with observed traffic patterns. An analysis of the time required for imports to reach a landlocked capital ranged from 400 hours to more than 800 hours (figure 2.6a). Around 50 to 80 percent of the time required can be attributed to inefficient operations at ports (based on information from 2006–07; figure 2.6b). In the cases of Durban and Dar es Salaam, some of the inefficiency can be traced to excessive demand for port services, which slows freight clearance. Imports encounter long dwell times, and ships endure long waits for berths. Beira is
17 survey we employ a three-way city size breakdown, distinguishing between cities larger than 500,000 persons, large towns with a population between 100,000 and 500,000, and towns with fewer than 100,000 inhabitants. For reasons of data availability our index captures only food and housing price differentials. The reference basket employed in our Laspeyres index is based on the consumption patterns of the second quintile of the national urban per capita consumption distribution.

Table 6 presents our Laspeyres spatial price index based on the cost of food and housing in urban Brazil. Relative to the reference region of metropolitan São Paulo, the cost of living in other regions and city size categories of Brazil is generally lower, often a good deal lower. In the regions of the North, South and Center West there is evidence of lower cost of living as conurbations become smaller. This pattern is less clear-cut in the North East, where the cost of living appears particularly high in large towns, and the South East, where those living in small towns appear to face the highest cost of living in the region (outside metropolitan São Paulo).

We next take the price indices reported in Table 6 and apply them to the smallarea based estimates of per capita consumption for each household in the population census. We then re-calculate poverty rates across region and city-size categories. Does adjustment for cost of living in the Brazil data overturn our conclusion that urban poverty in smaller towns is significantly higher than in the large and metropolitan areas? Table 7 indicates that correcting for price variation does attenuate the “gradient” between poverty and city size somewhat, but is far from sufficient to negate or overturn our broad finding. In Brazil, it remains the case that the incidence of poverty in the smallest towns is roughly three times higher than in Metropolitan centers.

Our second rubustness check investigates whether the finding of a negative gradient between poverty and city-size is somehow an artifact of the focus, in this paper, on income poverty as opposed to a broader conceptualization of deprivation
Policy Research Working Paper 10070

# **Abstract**

As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes. Skills profiles in low- and middle-income countries are typically derived from data collected in the United States, which is known to inaccurately reflect their occupational skills. This paper uses online job postings data from Malaysia to identify the digital, cognitive, and socioemotional skills required for digital and non-digital occupations. The skills profiles for each occupation are then merged with labor force survey data from Cambodia, Malaysia, Thailand, and Vietnam to sketch skills profiles of the workforces in these countries. Using descriptive statistics and linear probability model regressions, the paper finds evidence that highly digital occupations require not only digital skills, but also cognitive and socioemotional skills. Similarly, virtually all occupations, regardless of the digital intensity of the job, require some basic or intermediate digital skills. Pairwise correlations and a factor analysis confirm the complementarity between digital skills and different subsets of cognitive and socioemotional skills. The data also confirm that, even with the excitement about the digital revolution, the bulk of employment in Southeast Asia is in low- (around two-thirds) or medium-digital (around onethird) occupations. Only between 1 and 5 percent of jobs are highly digital in the four countries studied. These findings suggest that as education and training systems adapt to teach basic digital skills, they will need to continue to foster cognitive and socioemotional skills.

This paper is a product of the Social Protection and Jobs Global Practice. 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 wcunningham@worldbank.org.

_The Policy Research Working Paper Series
discrepancy with higher survey estimates. On average, over three months (97.9 days) pass until administrative reports reach the coverage estimated from our baseline survey data estimates. These lags would have to exceed six months (182 days) on average in our sample of five Sub-Saharan African countries. Of note, they would still have to be substantial even when we account for respondent selection effects in the survey data (38 days in Uganda, 63 days in Burkina Faso, 102 days in Malawi).

# **Denominator issues (under- or overestimating target population size)**

We also find evidence for denominator issues in the administrative data (Table 10). To analyze this, we compare administrative coverage figures, expressed in percent of the total population, between the Our World in Data (OWID) COVID-19 Vaccination data set (the administrative data source for our study, Mathieu et al. 2021) and the WHO’s COVID-19 vaccination dashboard (WHO 2020b). In most cases, the total number of people vaccinated at a given date coincides exactly between both data source. This allows us to compare reported coverage as a total population share for the same date and for the same figure of total number of people vaccinated in the OWID and WHO data. Any discrepancies we detect between vaccination rates (expressed in percent of the population) thus reflect differences in the denominator, that is, the size of the population. We can make this comparison for 19 LMICs.

**Table 10: Potential Numerator and Denominator Issues in Administrative Data**

|**Issue**|**Metric**|**Countries**|**Mean**|**Max**|**Min**|
|---|---|---|---|---|---|
||Average frequency of<br>|36 LMICs|2.4|175|1|
||reporting|5
# _5.5.3. Survey Design_

All previous results are weighted using household sampling weights. Table 8 tests the robustness of results to different survey design. Columns one through three show estimates using noweights, while columns four through six use individual sampling weights (household sampling weights times household size). Panel A shows results for Chile, panel B for Nicaragua, and panel C for Peru. Predictions are based on the third specification explained in section 3.4. Results suggest that the technique performs well irrespective of survey design. True mobility confidence interval lies within or overlaps upper and lower bound estimates of mobility irrespectively of the weights used.

# **8. Conclusion**

Recently, there has been growing interest among policy makers and researchers to study intragenerational mobility in and out of poverty. Panel datasets constitute the most appropriate information source for the study of mobility. Unfortunately, such surveys are rare and, where they exist, generally span only a few years and follow a relatively small number of households or individuals over time. To overcome this limitation, there is also a growing literature which studies intra-generational mobility by means of cross-sectional surveys via the application of “pseudo-panel” methods. A new method proposed by Lanjouw et al. (2011), which largely relies on insights from poverty-mapping techniques (Elbers et al, 2003), yields lower and upper bound estimates of mobility using cross sectional surveys. The significant advantage of this “synthetic panel” method is that it imposes fewer restrictions and structural assumptions than the earlier literature on pseudo-panels.

20
**Figure 13** : Closeness of CA’s total intra-regional exports value to the Unconstrained Expectations Frontier

<!-- Start of picture text --> Expectations Frontier<br>Costa Rica<br>Honduras<br><!-- End of picture text --> Source: World Bank calculations based on trade flows data from WITS, LCSSD Economics Unit (2010) In terms of the closeness to the frontier of expected exports by separate export types, Grains and Steel products are particularly strong examples of the two countries being at very different levels of “adjacency performance” (Figure 14). In line with the results shown in Figure 10, Costa Rica’s is close to the level of the region’s best performer, while Honduras is much more distant from the benchmark.

**Figure 14** : Intra-regional Observed Exports vs. Projected Exports, assuming that Costa Rica and Honduras’ Trade Behavior follows Central America’s own “Best Adjacency Performance"

<!-- Start of picture text --> Expectations Frontier<br>1.0<br>0.9<br>Costa Rica<br>0.8<br>0.7<br>0.6<br>0.5<br>Honduras<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Total Exports Grains (Volume) Steel (Volume) Proc. Food (Volume)<br>(Value)<br><!-- End of picture text --> Source: World Bank LCSSD Economics Unit calculations based on WITS data, 2010 In turn, when projecting the potential increase in total absolute intra-regional exports under three different scenarios—each of which represents a type of improvement in the region’s “trade performance”—estimations show that intra-regional trade could be 33-percent higher if the effect of adjacency between each
)|[0.94]|0.70|(0.46)|254|
|Applied for Loan (=1)|0.11*|(0.07)|[0.56]|0.17|(0.37)|1,422|
|Applied and Took Loan (=1)|0.06|(0.07)|[0.94]|0.16|(0.36)|1,422|
|Applied but Did Not Take Loan (=1)|0.06***|(0.02)|[0.00]|0.01|(0.10)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified financial participation outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: 30 JD Saved? reports an indicator for whether the focus respondent had at least 30 JD in personal savings to draw from in case of an emergency, regardless of whether it is in a bank. Loans received reports the total quantity of formal and informal loans received by the FR in the past year. Interest rate on last loan taken reflects the self reported interest rate on the FR’s most recent loan in the past 12 months. FRs were allowed to report interest as dinars/[day/week/month/year] or percent/[day/week/month/year] or as a flat rate (common in Islamic finance). Interest reported in dinars was translated to percent using the most recent loan principal. Interest reported as a flat rate was translated to percent by calculating<sup>_<u>f</u>latinterestrate_</sup> _principal_ /loan period. The loan period is calculated as repayment date of the loan minus the survey date. (Note that this is not ideal since
2003 PICS survey, Serbian managers were invited to rate 18 dimensions of the investment climate in terms of whether they were important obstacles to doing business. These dimensions were rated from 0 (factor is no problem at all) to 4 (factor is a major obstacle). Economic policy uncertainty was considered the biggest obstacle (Figure 5), which is understandable in light of the assassination of Prime Minister Djindjic in March 2003, not long before the survey took place. The factor that managers believed presented the least problems was access to land, a finding that is not as surprising at it first appears, for reasons discussed below.

13
Table 1: The fraction of messages from return and IDP areas by source for each dataset.

||`Location `|`Mention`|`s`|||`Return `|`Date`|
|---|---|---|---|---|---|---|---|
||Total|% IDP|% Returnee|% Neither|% Both|Total|%Pre|
|Total|2,360,559|5%|16%|62%|17%|769,745|53%|
|Telegram|1,264,871|5%|16%|64%|15%|391,356|57%|
|Facebook|676,580|4%|15%|60%|21%|241,964|46%|
|Twitter|419,108|8%|15%|58%|19%|136,425|56%|

the messages from locations where return occurred during the time of data collection 53% (411,694 messages) are from pre-return and 47% are from areas post-return.

We also compiled a set of groups which mention location information in the name of the group or group description (the `Researcher Coded` dataset). Such groups include @National.Defense.in.maharda on Facebook, @newsmanbij on Twitter and @saraqib2017n on Telegram. We use this smaller set of 173,906 messages as a robustness check for our results since it includes information we may be missing by using messages with location mentions. For example, in sales messages individuals might not include their location if they are already posting in a group where the location is apparent, and thus such messages would not appear in the `Location Mentions` and `Return Date` datasets but would in the `Researcher Coded` dataset. For the image analysis we used all images
population threshold (as measured in the most recent population census).<sup>14</sup> The reason for this is that transfers were given out universally in treatment villages, and so our partner NGO chose smaller villages to be able to preserve their liquidity.

In Liberia, we implemented the project in two waves: a smaller first wave (90 villages), which had its endline in late 2020; and a bigger second wave (210 villages), which had its endline in September-November 2021. Most of our ACASI protocols were developed, tested, and refined over the course of the Wave 1 endline. Therefore, this sample is excluded from our results, and our results for Liberia are restricted to Wave 2 only. In Malawi, all 300 villages were enrolled at once and the endline was in April-July 2021.<sup>15</sup> In both countries, we attempted to enroll 10 households per village into data collection for program evaluation, though in some cases we were only able to enroll fewer households.<sup>16</sup> Surveys were targeted at female heads of households, i.e., either the spouse or partner of the male head for dual-headed households or the solo head for female-headed households<sup>17</sup> (male heads of dual-headed households were interviewed only when the female was not present, and could not be reached within a few days of the baseline study. When the male head was interviewed, the IPV module was not asked).

# **2.2 Questionnaire Design and ACASI Experiment**

## **Measuring Intimate Partner Violence**

To measure IPV, we employed WHO’s standard Violence Against Women module.<sup>18</sup> The questionnaire includes 20 questions about experience with specific forms of violence, over a time period of 12 months prior to the survey. Following the literature, we group these questions into four

> 14In Malawi, the upper threshold was 100 households
USBOC<br>1985|
|Wake Island|Apr 70|<br>2 F|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Watlis and<br>Futuna|Mar69|<br>9 F|USBOC 1985|--|-|
|Western Sahara|Dec 70|<br>76 F|UNPVSR 4/87|--|--|
|Western<br>Samoa|Nov 81|<br>156 F|U.N. 1984 assessment|Based on USBOC 1985|USBOC 1985|
|Yemen Arab Rep|. Feb 86|<br>9274 F|Bank projection<br>from|Bank assessment<br>of World|Bank assessment<br>of World|
||||6/80 census|Fertility<br>Survey|Fertility<br>Survey|
productivity deadlock in the state, including its colonial history (Banerjee, 2004; Banerjee and Iyer, 2005) and the incumbent land tenure system (Bhaduri, 1973; Prasad, 1974).

While yield levels remain low in Bihar, there has been an uptick in productivity growth over the last 15 years or so. The yields of all three major crops – rice, wheat, and maize, have doubled during the 2005-2017 period. The main objective of the paper is to examine whether these impressive gains observed at the state level have been widespread and have helped in reducing disparities in land productivities across the districts. Analyzing regional inequality in yields is important for several reasons. First, the crossdistrict differences are significant. For rice and wheat, the yield in most productive district was 7–8 times that of the least productive district in 2005 (Government of Bihar, 2010). Hence, the burden of low yields is felt differently, with the situation being more concerning in some districts than others. Second, the districts lie in different agro-ecological zones with variations in agronomic endowments. As such, the principal drivers of low productivity might vary across districts, and policies that contribute to productivity growth in some may fail to address the challenges faced by others. Thus, a convergence result will support continuing the ongoing efforts as far as regional aspects of agricultural policy are concerned. On the other hand, the absence of convergence will call for fine-tuning the policy to correct the situation in laggard districts. With this motivation, I test whether a convergence in district yields accompanies the aggregate yield improvement. To test the convergence hypothesis, I collate a district-level database of both annual production and area under cultivation for the main crops using information from multiple annual surveys. A valuable feature of this study is that I examine convergence in yields for the three main crops, which usually account for more than 90 percent of the gross own area at the district level. This crop-specific approach provides further richness to policy guidance. If inequality persists for a
is no longer statistically significant (column 8). The number of informal credit sources listed in the village-level survey shows a decline in Jeevika areas (Panel B, column 7). This is driven by fewer friends and relatives listed as sources of credit (column 8); there is no significant impact on the number of professional moneylenders or shopkeepers (column 9).

Figure 1 illustrates the distribution of monthly informal lender rates reported at the village level by treatment group pre- and post-intervention. The 2% lending rate offered by Jeevika is barely represented, providing confidence that the estimated impact of the program on this outcome is not driven by misclassification of SHG loans as informal loans.

> 13 Controls in this case are village-level means of the household-level baseline variables included in the models with household-level outcomes. For villages with no rate recorded at baseline, the mean rate at the panchayat (16 observations) or district (2 observations) level is used.