rafmacalaba/data-use-mentions-extended huggingface dataset:
to drive through all the streets of the sampled sub-counties. The mobile monitors were also active 24 hours a day and produced an average of 30 (median 31) measurements an hour for a total of 119,011 in our sampled sub-counties.<sup>18</sup> By moving across space, the mobile monitors allow us to measure the spatial variation in pollution at a highly granular level within the city. By virtue of being fixed in one location, stationary monitors allow us to precisely measure the time variation in pollution. In Section 5, we describe how we use both types of measurements in our empirical approach.

**Checks and descriptives.** We present two descriptives about our pollution data. First, in Appendix Figure A15, we show that the average PM2.5 readings of mobile and stationary monitors in the same sub-county are positively correlated (the correlation is 0.34, significant at the 1% level). This positive correlation reassures us about the internal validity of our measurements.<sup>19</sup> Second, in Figure 1, we report average and median pollution readings by hour of the day, from both stationary and mobile monitors. The figure reveals that: (i) the stationary and mobile measurements track each other closely, which reassures us about the quality of our data; (ii) average levels of PM2.5 are high in our sample, oscillating between 30-90 micrograms per cubic meters, which lines up well with the average of 50 micrograms per cubic meter reported by the World Bank in 2017 and mentioned in Section 2; (iii) there is a strong cyclicality in pollution within the day with peaks at rush-hour in both mornings and evenings, which indicates that, as expected, the main source of pollution in these urban areas is road traffic rather than economic activity, something that we explore further and confirm again in Section 6. This hourly pattern is robust: we reach the same conclusion if we use the stationary or mobile readings,
|High-low education ratio|-0.622***|-|-0.179|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.002|-|0.000|-|
|Services value added share|0.081*|-|0.071|-|
|Manufacturing value added share|-0.043|-|0.068|-|
|Agriculture value added share|-0.087|-|-0.110**|-|
|Log labor force|-|-6.538**|-|-17.058***|
|Log gross fixed capital formation|-|3.433***|-|7.237***|
|Log exports of goods and services|-|2.348**|-|3.519***|
|Log REER|-|5.852***|-|2.742|
|World oil price index|-|0.068***|-|0.066***|
|Worldmetalpriceindex|-|0.016|-|0.010|
|Observations|1015|1034|873|873|
|R2|0.060|0.392|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The results show that higher levels of resource dependence are associated with lower levels of income inequality among countries in the higher-income group, with no negative effect on GDP per capita. Conversely, higher levels of resource dependence are associated with greater income inequality and lower GDP per capita in lower-income countries. Again, the estimated results of the non-recursive model, summarized in Table 9, confirm those
_Cognition (%)_|||||
|Score=11|10.27|10.02|10.53|0.817|
|Score in [8, 11)|32.32|25.18|40.00|<0.001|
|Score in [0, 8)|23.95|22.98|25.00|0.508|
|_Disease (%)_|||||
|Minor illness|46.51|47.68|45.26|0.498|
|Major illness|30.42|31.30|29.47|0.579|
|CES-D score|8.52|8.81|8.21|0.090|
|ADL or IADL disability (%)|51.08|58.92|42.63|0.090|
|Observations|789|409|380||

Note: 1) Sample are main respondent parents with children no younger than 25, and older than 60 or spouse older than 60. 2) P-values are from t-test of the coresident and non-coresident groups.

25
.3967|0.3911|
|**_Included controls_**||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education, occupation,<br>other income sources, land ownership)|_No_|_No_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|
|Household fixed effects|_No_|_No_|_No_|

For our second investigation, we correlate welfare and refugee presence interacted with an indicator equal to one if the household moves to a particular main source of income. The results then suggest that households which changed from their initial main source of income to commercial farming and wage employment likely benefit more from the influx of refugees (Table 9).<sup>22</sup> Overall, these results suggest that households respond to the market dynamics created by the influx of refugees by switching to commercial farming in order to increase their welfare. Given our descriptive statistics (Table 1), we should nonetheless acknowledge than less than 1 percent of the households in refugeehosting areas generate their income mainly from commercial farming. While such a coping strategy certainly offers large pay-offs, it remains limited in scope.

Table 9: OLS: Effects of refugee presence on household welfare disaggregated by change in main source of income, with - <u>analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of welfare (<br>|consumption aggregate<br>equivalent)<br>|per adult<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|0.0183|0.0183|0.0175|
|
# Inclusive Statistics: Human Development and Disability Indicators in Low- and Middle-Income Countries

Sophie Mitra, Jaclyn Yap, Justine Hervé and Wei Chen

## 1. **Introduction**

The Sustainable Development Goals (SDGs) endorsed by the United Nations member states have the mission to “leave no one behind”. Monitoring the SDGs is critical. The Inclusive Data Charter signed by the World Bank and other global data partners in 2018 aims to mobilize political support to improve the quality, quantity, availability and financing of disaggregated data to support the ambition of the 2030 Agenda for Sustainable Development of leaving no one behind. More data are required to highlight different situations of various socioeconomic and geographic groups including persons with disabilities.

The lack of data and statistics on disability is in part responsible for the lack of attention to disability in development, but there has been growing awareness on the need for data on disability and improved tools to collect data on disability in an internationally comparable manner. The report by the High-Level Panel on the Post-2015 Development Agenda repeatedly refers to disability, including in the call to end extreme poverty and “leave no one behind” and in the call for a “data revolution”: “data gathered will need to be disaggregated by gender, geography, disability, and other categories, to make sure that no group is left behind.” Disability has been included in several SDG targets and as a cross-cutting issue in the 2030 Agenda for Sustainable Development. As argued in the United Nations report (2019), “efforts need to be stepped up to ensure that the goals and targets will be achieved for persons with disabilities too, in line with the Convention on the Rights of Persons with Disabilities (CRPD)”.

Using 24 population censuses and general household surveys for 21 low- and middle-income countries (LMICs) that have internationally comparable disability questions, this paper disaggregates human development indicators across disability status to assess the situation of persons with disabilities and disability related inequalities and facilitate future monitoring. We aim to contribute
systems are purely cognitive phenomena in which aggregates of individuals come to develop “common information” and shared “models” of external causation. The notion that the formation and expression of an individual’s beliefs requires a shared set of background meanings, and that coordinated social practices give rise to shared background meanings, are foreign to this account. Finally, despite their repeated emphasis on the role of beliefs in history, they provide almost no direct evidence that beliefs have, in fact, played a causal role in the development and maintenance of open access orders. At one point, when arguing that even mature natural states, such as Korea and Venezuela, may not exhibit societal beliefs consistent with open access, they point to polls showing that many individuals in those countries would accept military coups in certain circumstances and believe that authoritarians 16
internet, and (iii) to perform a rich analysis of heterogeneities, as national household surveys have data on income, wages and other variables. The analysis based on national household surveys allows a more granular measure of the potential impact of the shock while also capturing asymmetries within countries.

# **4.1. Methodology and data**

School closures implied a severe interruption in the learning process. Unfortunately, it is extremely difficult to estimate the long run impact of this interruption on the human capital stock given the proximity of the shock and the data at hand. NLT provide first estimates on the long run effect of school closures based on two main simplifying assumptions: (i) the human capital stock, which is relevant as a determinant of future earnings, is well approximated by years of formal education, and (ii) the loss of human capital can be approximated by the share of the school year in which the learning process was interrupted. While the first assumption is standard in part of the literature ( _e_ . _g_ . Black and Devereux, 2011), the second follows the contributions of Abadzie _et al_ . (2009) and Adda (2016).<sup>6</sup> The human capital loss could be lower if successful compensating measures are taken in the future, but on the other hand, they could be larger if interruptions make the process of learning more difficult to resume and lead to cumulative learning losses over time.<sup>7</sup> Following NLT, the loss of education (as a share of the school year) for individual _i_ in country _c_ is defined as:

> 6 An argument against the first assumption would be that years of schooling do not capture differences in educational quality that lead to substantial differences in skill levels. Since we do not have information on cognitive abilities, we use only years of education here. As the estimates on the negative impact of the pandemic on enrollment in private education included in Section 3 suggest, not considering
prices, and fuel prices. The model is estimated across three types of carriers – truck-load, less-than-truck load, and specialized – with the estimated rate being the weighted average by value of the three by commodity (see Brown, 2015, for a detailed discussion of the data and model). Because prices are measured on a quarterly basis, fixed and variable costs are to time.<sup>7</sup> Prices to are permitted vary through charged shippers predicted by commodity using their average tonnage for a 500 kilometers trip.<sup>8</sup> The final predicted price, _R_<sup>�</sup> _c_ , _t_ , is the weighted average by value of the three carrier types by commodity and year. While, as will become apparent, we only use predictions from 2008, the entire period is used in order to bring as much information to bear on the cross-sectional estimates by commodity. Predictions from the model closely match observed annual prices on aggregate.

Finally, the predicted prices _R_<sup>�</sup> _c_ , _t_ are converted to ad valorem trucking rates _T_<sup>�</sup> _c_ , _t_ by measuring the value of shipments. Since the value of shipments are not reported, they have to be estimated by multiplying the average tonnage shipped for each commodity by their respective value tonne derived from an trade in 2008. The per ‘experiment export file’ produced only ad valorem estimate at the commodity level in 2008 is _Tc_ ,2008 = _R_<sup>�</sup> _c_ ,2008/ _Vc_ ,2008, where _Vc_ ,2008 is the estimated value of the commodity for the average tonnage shipped in 2008. Using an industry-commodity concordance, the ad valorem transportation rates in 2008 for commodities _Tc_ ,2008 are aggregated to an industry basis _Ti_ ,2008.

Finally, to generate a time series, yearly trucking industry price indices **P** trans, _t_ and manufacturing industry price indices **P
**2. Improving tender design by allowing more time for bidding** . Short advertisement periods can lead to a lower level of competition and suboptimal tender performance, while they are also often corruptly abused to exclude non-connected bidders.

**Recommendation** : Limit the use of short advertisement periods so that potential bidders have adequate time to submit their proposals.

**Implementation** : Policy makers should directly influence advertisement periods by setting minimum required lengths and closing loopholes for short periods. Moreover, advertisement periods can be indirectly influenced by training contracting authorities on the importance of high-quality tender designs. Furthermore, guidance for suppliers on efficient preparation of tender documentation can help smaller companies compete. Related to these interventions, making full use of e-attestations (that is, the electronic storage and verification of certificates and other official documents) also allows to cut down the administrative burden of bidding and hence enables bidders to submit quality bids in a relatively short time frame. The implementation of such improvements in tender design could be closely monitored using real-time public procurement data and a dashboard of the type advocated earlier.

**3. Reducing the use of non-competitive types of procedures** . After 2016, there has been a significant increase in corruption risks due to the more frequent use of non-open procedure types in Bulgaria. Closed procedures, such as direct negotiations and awarding, significantly increase prices and often lead to contract implementation delays. One of the widely used corruption techniques is applying nonopen procedure types in an unjustified way, for example, splitting contracts so that the value of each part is below the threshold of the required open procedure. Although this practice does not necessarily aim at corruption rent extraction—institutions might use it to decrease the administrative burden by avoiding a difficult procedure—it certainly weakens competition and transparency and hence increases the risk of corruption. The implementation of such requirements could be closely monitored using real-time public procurement data and a dashboard of the type advocated earlier.

**Recommendation** : Increase the share of open procedures
|0.025|0.026|0.001|
|Residual|0.356|0.253|-0.103|
|Peru||||
|Total Variance|0.653|0.501|-0.152|
|Variance in sector-occupation-formal status|0.185|0.133|-0.052|
|Variance in skill-groups|0.044|0.024|-0.019|
|2*Cov(sector-occupation-formal status, experience-education)|0.044|0.038|-0.006|
|Residual|0.380|0.305|-0.075|
|El Salvador||||
|Total Variance|0.633|0.411|-0.222|
|Variance in sector-occupation-formal status|0.239|0.126|-0.113|
|Variance in skill-groups|0.021|0.016|-0.005|
|2*Cov(sector-occupation-formal status, experience-education)<br>Residual|0.019<br>0.354|0.031<br>0.238|0.012<br>-0.117|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note:_ Total wage variance is the variance of the log hourly wage in the household surveys data set. Variance in skill-groups is the variance of the log real hourly wage that is explained by a full set of gender, education, and experience dummies, and their interaction. The variance in sector-occupation-formal status is the variance of the log real hourly wage that is explained by a full set of sector-occupation-formal status dummies.63 AKM = Abowd, Kramarz, and Margolis (1999
products, there are still significant deviations, particularly in the number of GDD produced by the products. In Malawi, MERRA-2 produces the fewest GDD but in Niger it produces the most. In most countries, CPC is in agreement with ERA5 but in Tanzania CPC disagrees with both ERA5 and MERRA-2. Based on these changes in the ordering of EO products across countries, we conclude that researchers’ selection of EO product cannot be country-agnostic.

# **4 Analysis Plan**

A common feature of research on measurement error is that the researchers have a measure of objective truth. They can trace the boundaries of a plot of land (Abay et al., 2019), they can weigh the quantity of harvest (Lobell et al., 2020), they can determine the exact location of a household (Michler et al., 2022). In our setting, we lack an objective measure of the truth regarding what the weather was like for a given household in a given year. And, given the paucity of gauge station data on the Continent, the typical gold standard for weather data does not exist. This lack of an objective measure for quantifying mismeasure in EO weather data informs how we implemented our research design. First, we developed a pre-analysis plan and registered it at Open Science Framework (Michler et al., 2019). While pre-analysis plans have become common in experimental economics, they are still relatively uncommon for binding researchers’ hands when using observational data 14
Table 5: Total Factor Productivity Growth: Average in 1950-1995 and Change During Periods of Reform(%), Pooled OLS and Fixed Effects with Five-Year Averages and Annual Data (Percent)

|Recessions Dummy|None||DREC1|_DREC2|||
|---|---|---|---|---|---|---|
|Method / Period|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|
|Pooled OLS<br>5-Year Averages(*)|-0.8<br>(0.003)|1.3<br>(0.000)|_<br>_||-||
|Fixed Effects<br>5-Year Averages()|-0.9<br>(0.001)|1.5<br>(0.000)|||||
|Pooled OLS<br>Annual Data|-1.0<br>(0.000)|1.5<br>(0.000)|1.1<br>(0.000)|0.6<br>(0.007)|2.0<br>(0.000)|0.9<br>(0.000)|
|Fixed Effects<br>Annual Data|-1.2<br>(0.000)|1.7<br>(0.000)|1.0<br>(0.000)|0.9<br>(0.001)|1.9<br>(0.000)|1.0<br>(0.000)|

Notes: -- Not available; (*) The average for the period 1950-95 is the coefficient on the
tries (GCC members and Southeast Asian countries), emigration from Bangladesh is temporary by design. Acquisition of citizenship or permanent residency in the destination countries is effectively impossible, irrespective of migrants’ occupation, education, nationality, or duration of stay (Wahba 2015; Fargues 2011; Fargues and De Bel-Air 2015). Low-skilled migrants’ stay is strictly conditional on a valid employment contract. These contracts are typically of fixed duration and tied to a specific employer, but contract renewals are possible to extend duration, again conditional on the continued agreement of the employer. The initial duration of the work permit for low-skilled migrants is one year in Malaysia, and two years in Singapore, and, in both countries, these can be renewed for a total duration of up to 10 years. There is no explicit cap on total duration of stay for migrants in the GCC countries. However, because their residence at the destination is conditional on holding a contract, migrants cannot stay after they have retired. Similarly, a job loss or the expiration of the contract means mandatory and automatic return to the home country. Furthermore, lowskilled migrants are almost never allowed to take their families with them. This generates additional costs to migrating, and further incentives to return.

In addition to the high levels of international migration from Bangladesh, internal ruralurban migration is also widespread. Although transportation costs have been shown to be important (Bryan, Chowdhury, and Mobarak 2014), the financial constraints for overseas migrants are considerably tighter, and complemented by strictly enforced legal restrictions.

# **3 Data**

## **3.1 The Bangladesh Return Migrant Survey (BRMS)**

The primary data source in this paper is a newly collected dataset with unique information on full employment histories, migration expenditures, expectations and demographic characteristics of migrants. The Bangladesh Return Migrant Survey (BRMS), conducted by the World Bank in 2019, consists of a sample of 5,000 temporary migrants who had returned from employment overseas to Bangladesh at the
after running gradient boosting with all covariates on each sample and averaging each covariate’s importance across the 500 samples. We find that all of the top 10 covariates are census-based, with only five geo-referenced covariates entering the top 20.

Figure 4 presents MSE and bias estimates for various models across different “poverty quantiles.” That is, we order all municipalities by their true poverty rates, divide them into 50 quantiles, and calculate the average MSE and bias for each quantile. For the sake of simplicity, we only present results for two machine-learning models – the municipality-level models using only geo-referenced and census-based covariates – and select traditional methods. Panel (a) presents the MSE results and panel (b) presents the bias results. In both plots, gradient boosting with census-based covariates performs quite similarly to the “gold standard” unit-level model across all quantiles. However, gradient boosting with geo-referenced covariates again departs considerably from this benchmark, particularly at the lowest and highest quantiles. The bias results are particularly notable: the model with geo-referenced covariates systematically underestimates poverty at the lowest quantiles and overestimates poverty at the highest quantiles. The standard implementation thus understates the spatial distribution of poverty.

An additional consideration is how the methods perform for predictions which are out of sample. Because the experiment consists of 500 samples taken from the census data, a given municipality may be present in one of the samples and may be absent in others. Consequently, to evaluate out of sample properties we rank municipalities by their likelihood of selection across the 500 samples. The likelihood of being present ranges from 0.09 to 1, with a median of 0.48 and a mean of 0.53. Among the 20 percent least likely to be sampled municipalities, the gradient boosting model with GIS-based covariates is the worst performing in terms of MSE followed closely by unit-context models (Figure 5). In terms of bias among the 20 percent of municipalities that are least likely to be sampled, the gradient boosting model with the geo-referenced covariates shows very wide