rafmacalaba/datause-encoder-data huggingface dataset:
living in a gas-flaring host community is associated with being hypertensive (Maduka and Tobin-West, 2017). Gobo et al. (2009) compared medical records in two communities with a long history of flaring with one with no flaring and found more frequent disease such as asthma, cough, breathing difficulty, eye/skin irritation in flaring communities. Sojinu et al. (2010) study distribution of polycyclic aromatic hydrocarbons (PAHs) in 44 soil sediment samples from rivers and canals adjoining oil and gas producing areas in parts of the Niger-Delta; PAHs from pyrogenic sources were present predominantly in surface soils. Yet while gas flaring associated with oil exploration work in the Delta affects surface soils, Sojinu and colleagues conclude that except for two locations, the observed contamination level from these sites was not a serious threat to the ecosystem.

In summary, there has been no large-scale examination of the impact of gas flaring on human health in Nigeria even with a 60-year history of routine gas flaring. Prior evidence is limited to the type of studies discussed above which focused on a handful of communities. Our study overcomes some of these data limitations by examining the association between flaring and health outcomes using linked georeferenced data on all flaring locations from satellite observations (including offshore flare sites) and pooled cross-sectional child health information from Demographic and Health Surveys (DHS).

# 3. Data and Methods

## Data

We use data from two sources: the first is satellite observation on gas flaring locations and estimates of flare volumes. These come from the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the _Suomi_ satellite. Launched in 2012, the VIIRS sensors can detect heat emitted by gas flares through the collection of shortwave and near-infrared data at night, recording peak radiant emissions from flares. Elvidge, et al. (2016) present a methodology for estimating the volume of gas flared at each site with the accuracy of the flared gas volume estimates rated at ±9.5%. These data and methods have been used to identify global flaring locations and to estimate global gas flaring volumes (World Bank, 2020). A publicly available website: www.gasflaretracker.ng has the geographic coordinates of each flaring point in Nigeria as well as monthly estimates of the flare volume from each location.

Our child health data come from the 2013 and 2018 Nigerian Demographic Health Survey (DHS). These nationally representative cross-sectional surveys have demographic and health details for women aged (15-49) and for children aged (0-5). A stratified, two-stage cluster design is used, with the geocoordinates of each cluster reported with some displacement for confidentiality reasons.<sup>7</sup> The DHS collects information on women’s birth histories and child health characteristics including self-reported

> 7 The displacement is up to 2km for urban clusters and 5km for rural clusters, with 1 percent of rural clusters displaced up to 10km.

6
# **Design of a Multi-Stage Stratified Sample for Poverty and Welfare Monitoring with Multiple Objectives: A Bangladesh Case Study**

Faizuddin Ahmed Dipankar Roy Monica Yanez-Pagans Nobuo Yoshida

**JEL Classification:** C81, C83, I32

**Keywords:** stratified clustered sampling, household surveys, poverty measurement

> *Corresponding author: Nobuo Yoshida (nyoshida@worldbank.org), The World Bank. Faizuddin Ahmed (fahmed3@worldbank.org) and Monica Yanez-Pagans (myanezpagans@worldbank.org) are also at The World Bank and Dipankar Roy (dipankar.roy@bbs.gov.bd) is at the Bangladesh Bureau of Statistics. The authors are very grateful to Juan Munoz and Kristen Himelein for their advice and suggestions during the preparation of this paper. All errors and mistakes remaining in this paper are solely ours.
# **1 Introduction**

Among all the socioeconomic indicators developed during the last two centuries, the share of the population living in poverty has become one of the most studied and monitored (Ravallion 2016, chap. 1). A good poverty measurement and profiling identifies the poor and targets and monitors all interventions designed to benefit the most disadvantaged groups in society. Thus, there is an increasing interest from academics and policy makers to know the current poverty status at the country, regional, and global levels to design policy solutions that alleviate the situation of the less well-off.<sup>1</sup> Nonetheless, due to data collection and processing time, poverty indicators are generally released with one or more years of lag. This delay makes it challenging to provide real-time poverty analysis and to strengthen the dialogue on the implementation of policies to mitigate the poverty impact of shocks like economic crisis or natural disasters.<sup>2</sup> Departing from the basic intuition that the performance of poverty depends on the evolution of national welfare aggregates like the GDP per capita, it is expected that poverty forecasting relies on the accuracy of forecasted GDP. That is, if the forecasted GDP were off, the forecasted poverty would be off as well. However, the main difference between nowcasting and forecasting poverty is that the GDP per capita is actual for the former, while it is forecasted for the latter. By validating three poverty forecasting methods with actual GDP information, this paper bridges the gap of knowledge between the most recent poverty numbers available and the current calendar year. Additionally, it provides empirical evidence for the reliability of such methods to enlighten the current situation of poverty, rather than its performance in the future.

Generally speaking, poverty forecasting provides a series of possible, future GDP scenarios whose poverty outcomes are determined by the poverty forecasting method. That is, poverty forecasting is an attempt to understand the performance of poverty if the GDP scenario assumptions are met and the poverty forecasting method is accurate (Karver, Kenny, and Sumner

> 1 For instance, extreme poverty reduction was among the most important indicators in the Millennium Development Goals (MDGs), while the World Bank recently adopted the goal of ending extreme poverty at the global level by 2030. The World Bank measures global overall poverty as life under a US$1.9 per person per day in 2011 Purchasing Power Parity (PPP) (Ferreira et al. 2016, Dean and Prydz 2015). Similarly, the World Bank estimates the regional overall poverty in the LAC region as having an income below the US$4 per person per day poverty line in 2005 PPPs (Gasparini et al. 2013, Castaneda et al. 2016).

> 2 For instance, the latest global poverty estimates produced by the World Bank are from 2013, while the most recent Latin American and the Caribbean regional estimates are from 2014 (see Castaneda et al. 2016 for the 2014 LAC regional estimates).

2
15 are an omitted variable of importance to both the full period (1977-87) and sub-period (1981-87) contemporaneous regressions.<sup>2 4</sup> A third concern with the earlier interpretation is the parameterizing of the effects of minimum wage fixing on unskilled employment in Mexico. In arriving at an implied elasticity of unskilled employment with respect to the minimum wage in the range of -.55 to greater than -1.0, I have generalized about the distribution of affected _individuals_ based on information about the distribution of _firms_ that pay wages at or near the minimum. It may be equally reasonable to think of _all_ unskilled workers as potential minimum wage workers and base calculations of impact on estimates that exclude the unskilled wage. Columns (5) and (6) of Table 8A re-estimate the basic employment equations from Table 8 omitting the unskilled wage. The coefficient estimates suggest implied minimum wage elasticities as high as .45, but much smaller than the distribution adjusted interpretation given to the coefficient estimates in the previous section.

As a final issue, columns (7) and (8) of the table balance the panel data by dropping firms missing data over the 1981 to 1987 period. Presumably this has the effect of isolating only the largest and most stable firms, and has the advantage of comparability with the Mexico data, which has been balanced by design. Given the existence of sizable firm-size wage effects in the Colombia data,<sup>25</sup> the balancing should reduce the estimated minimum wage elasticity. As is clear from the table, balancing the panel in this way reduces the number of observations by over 50 percent, and produces unstable coefficient estimates of the minimum wage impact on low wage employment.

The most serious threat to the estimated minimum wage elasticities from the original Table 8 regressions arises due to the fact that over the 1977-87 period -- the entire sample period covered by the Colombia data -- the contemporaneous effects of minimum wages on employment are statistically insignificant. However, the fact that input prices matter is not surprising given the Table 8 results and the likelihood of some correlation between input prices and the minimum wage, and in any case, the omission

> 2 4 Lagged minimum wage effects remain significant in the 1981-87 specifications omitting other input prices. Energy prices and materials prices are p-substitutes to unskilled labor and presumably are correlated positively with the contemporaneous minimum wage.

> 2 5 There is as much as a 60% firm-size differential between the top 1/5 sized firms and the bottom 1/5 sized firms in the Colombia data.
_Table 2. Balance of the sample at the baseline, education variables_

|T Tests of Differe|ncesin<br>|means betwe|entreat<br>|ment and co|ntrolvill<br>|ages at base|line|
|---|---|---|---|---|---|---|---|
||-1<br>Contr|ol|-2<br>Treate|d|-3<br>Total||t-test<br>Differences<br>in means|
||N|Mean/SE|N|Mean/SE|N|Mean/SE|(1)-(2)|
|Age (5 to 15)|5369|9.136<br>[0.041]|5490|9.074<br>[0.040]|10859|9.105<br>[0.029]|0.061|
|Household size|5369|9.452<br>[0.062]|5490|9.025<br>[0.055]|10859|9.236<br>[0.041]|0.427***|
|Girl|5361|0.482<br>[0.007]|5481|0.474<br>[0.007]|10842|0.478<br>[0.005]|0.008|
|Enrolled|5321|0.418<br>[0.007]|5449|0.402<br>[0.007]|10770|0.410<br>[0.005]|0.016*|
|Can Read|2144|0.676<br>[0.010]|2136|0.679<br>[0.010]|4280|0.677<br>[0.007]|-0.003|
|Raven score|5369|3.946<br>[0.045]|5490|3.855<br>[0.047]|10859|3.900<br>[0.033]|0.092|
|Math score|5369|4.063<br>[0.055]|5490|4.151<br>[0.054]|10859|4.107<br>[0.038]|-0.089|
|French score|4971|0.489<br>[0.013]|5043|0.454<br>[0.013]|10014|0.471<br>[0.009]|0.034*|
|ethnicity 1|5369|0.480|5490|0.483|10859|0.481|-0.003|
|||[0.007]||[0.007]||[0.005]||
|ethnicity 2|5369|0.462<br>[0.007]|5490|0.483<br>[0.007]|10859|0.473<br>[0.005]|-0.021**|
|ethnicity 3|5369|0.012<br>[0.001]|5490|0.011<br>[0.001]|10859|0.012<br>[0.001]|0.001|
|ethnicity 4<br>The value displaye|5369<br>d for t-tes|0.046<br>[0.003]<br>ts are the diff|5490<br>erences i|0.023<br>[0.002]<br>n the means|10859<br>across the|0.034<br>[0.002]<br>groups.|0.023***|

***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Source: Education modules, baseline survey.

26
Our results indicate a pronounced cost effect in West Africa (significantly lower unit costs than the other regions), and the same wide dispersion of unit costs by work type that we have already observed in Table 8. The results suggest that, once we control for country characteristics, unit costs for upgrading projects are significantly different only for two types: New two-lane highway construction and widening and reconstruction. On the other hand, unit costs remain highly differentiated for maintenance categories. As in Table 8, grading is lowest-cost in this category and surface treatment resurfacing highest-cost, ceteris paribus.

Using the results in Table 12 and our country database, we project benchmark unit cost estimates for each country in the network. We also subtract and add standard projection errors to the benchmark projections to obtain low- and high-range projections. We use country-specific measures for rainfall intensity, percent paved and income per capita. For work types, we use the same mix that characterizes the projects in the ROCKS database. We project unit costs for upgrading and maintenance separately. For upgrading, we use each relevant work type’s share in 170 ROCKS upgrading projects as the multiplicative weight for its estimated dummy variable parameter in Table 12. For maintenance, we use each work type’s share in 300 ROCKS maintenance projects as the multiplicative weight for its parameter.

Complete unit cost results are included in Appendix Tables A4 (upgrading) and A5 (maintenance). We summarize the results by work category and region in Table 13, which indicates substantial regional variation in unit costs. For upgrading, the median unit cost is $167,700/km; regional costs vary from $135,000 /km to $418,000/km. Within regions, there is also wide variation. The maintenance results replicate this 31
indicators for the crisis period, we are effectively controlling for the general severity of the crisis in each country.

We are also interested in the link between the interaction of financial dependence and financial development on the one hand and the difference in real growth in value added between the cr.isie ,pr:nd **_"nd_ thp** nr,-.ric ipriond **non** the **nthpr,khane Ti** _an_ in **altrrn,tivp** enpAfirnt.nn **nfmnroPl** (1), we therefore use the difference in real growtn in value added between the crisis period and the precrisis period as a dependent variable.

_ARVAGRU = Ci +NDj + Aj *SHAREij +p82 *FDi *EDj +e£_ (2) where _AR VAGR,_ is the difference in real growth in value added of sectorj in country i between the crisis period and the pre-crisis period. In other words, _AR VA GRi = R VA GRU Crais - R VA GRU,pre-crisis_ X where _RVAORiS,_ the re1 Qronwth in v21uenrl2ddedr nfePr-tnr _i_ **in** nniintrv _i_ **Am-rinc** thr s4is **npri.od** and _RVAGRy,pre-Crisij iS_ the real growth in value added of sectorj in country i during the pre-crisis period. To avoid potential endogeneity problems, we use the pre-crisis levels of share in value added and our proxies for financial development.

B101ecause +r **ar** - + **...** she i .fion FDi- * _EDjL -a_ the real growth in value added of sectorj in country _i, R VAGR 0,_ we also estimate the following model:

**_iVA_** _=_ **_Ci_** _+ lNL/j + p *_ + _i * HighEDj +_ **_ji_** _(3)_

8
context. This section calibrates a TrESt model to a particular urban setting, allowing us to weigh enumeration costs against estimation accuracy, and thereby maximize a policy objective. The following rudimentary policy simulation suggests scope for substantially improved targeting outcomes.

Three basic solutions for the cost-accuracy trade-off are apparent. One is maximum accuracy regardless of cost. Implicitly, the standard PMT falls into this category as it optimizes the model’s predictive power regardless of questionnaire length. The other extreme would be the minimum cost solution of a core questionnaire. In many cases, the optimal solution may lie at a point where cost and accuracy are balanced to achieve a certain policy objective. Programme incidence, defined as the share of intended beneficiaries in the covered population, is a realistic objective, and in this case study we optimize for it on the assumption of a fixed budget. Accordingly, we select the TrESt model specification that yields the highest feasible incidence by weighing the extent of survey coverage against overall accuracy.

The setting we simulate is DKI Jakarta, Indonesia’s capital city region that had about 10.5 million inhabitants within a densely populated urban environment. Information shared by the local government, which is charged with updating the social registry, suggests that enumerators who administer the current targeting questionnaire survey collect information from 10 to 20 households per day, and that each questionnaire takes between 15 and 30 minutes per household (personal communication, September 2021). Taking the midpoints of these ranges implies that the average travel time between households is 10 minutes. Based on the number of items that make up the core questionnaires and the implied travel time, the fixed and maximum variable times for each interview both amount to circa 16 minutes<sup>14</sup> . Assuming that each of the optional questions takes the same amount of time, variable time is reduced to the proportion of grouped questions out of the full questionnaire total.

The programme we simulate is the PBI-JKN subsidized health insurance programme intended to cover 40% of the population nationally, but which the DKI government has extended to 51% of households. Rounding to 50% to align with our simulations, and adjusting household figures accordingly, SUSENAS data indicate an incidence of 62.8% in 2019. A simplifying assumption at baseline is that all households which are included in the social registry receive the programme, as full coverage of all the surveyed households mirrors actual practice at national level, where the DTKS social registry contained the same 40% share of households that the programme aimed to cover [Pahlevi, 2019].

We simulate the impact of a shorter questionnaire by separating households into consumption deciles and splitting each decile into a surveyed and

> 14Based on 26 core items out of a total of 112 ungrouped variables, and rounding up to the full minute.

26
greater than remittances received by migrant households (which are net of expenditures in the city). According to the China Family Panel Surveys (CFPS) for 2014, 2016, and 2018 survey, remittances received by rural households decreased over the five years, with about 16,000 yuan on average in 2014, and 15,000 in 2018 and the remittance share of household income also declined from 38% in 2014 to 33% in 2018 (Figure 10). This difference reflects the rising costs of living in cities, and a reduction in earnings available for family members in rural areas.

Finally, the share of migrants who return to the village is stable over time. Table 1 presents the share of return migrants in the village population and among total migrants, respectively. Return migrants as a share of the village population were 7.73% during the 1991-2000 period and 7.41% during 2013-2018. About one in five migrants (20%) return to the village and this share decreased slightly from 20.3% during the 1991-2000 period to 19.3% during the 2013-2018 period. Overall, Table 1 shows that the share of return migrants is stable over time, indicating urban off-farm work is a robust and stable source of employment for rural labor. This is consistent with what we know about the very limited impact of the global financial crisis on off-farm employment in China: many of the long-term laid-off migrant workers were rehired within a year after the crisis, even though two-thirds returned to family farms in the immediate wake of the 2008-2009 crisis (Huang et al., 2011). As return migrants are thought to be more entrepreneurial than non-migrants (Zhao 2002; Démurger and Xu 2011) and also more politically active (Lu et al. 2017), some have posited that return migrants may revitalize economies and contribute to social change in rural China. Given that there is no sign of a significant share of migrants returning, such expectations may not be fulfilled in the short and medium term.

# **5. Potential Concerns for the Future: Vulnerabilities Associated with Out-Migration**

Migration has proven to be beneficial for growth in household incomes and appears to benefit poorer households through indirect effects associated with improved risk-coping and shifts in activities within villages. However, significant outflows of migrants may contribute to future problems in two ways. The first is the erosion of local capacity to raise funds for investing in public goods. The second is the longterm consequences of family separation, which may affect children’s human capital formation and the well-being of China’s elderly, particularly if they become infirm. Evidence presented below highlighting these concerns points to policy areas that warrant attention.

**Public Goods Provision.** Apart from positive effects on private investment as migration eases credit constraints, migration may also affect public goods investment, which in turn may also have influence long-term growth and accumulation of human capital. Studies of migration in other settings note various

19
is information on the cost of subsidies as a share of GDP in 2007 and 2009. During the period 2007–2009, national food prices increased substantially,<sup>10</sup> but food subsidies mostly declined or stagnated, except in Egypt where they increased.<sup>11</sup> We illustrate this situation on Figure 2 assuming that a country is a price taker in international food markets and faces a perfectly elastic import supply curve.<sup>12</sup> We assume also that the objective of the government is to keep domestic prices and imports of food as stable as possible via price subsidies on imported food.<sup>13</sup> If there is a shock to the world food price, resulting in a jump in the world price from _P_<sup>_W_</sup> up to _P_<sup>_W’_</sup> , the government will have to double the subsidy paid per unit of imported food in order to keep imports at levels prevailing at the old international prices _P_<sup>_W_</sup> .

**Figure 2. Impact of Subsidies on Domestic Food Prices**

<!-- Start of picture text --> P W‟<br>MS‟<br>P W s s<br>MS<br>s<br>P<br>MD<br>M‟ M<br><!-- End of picture text --> As discussed above doubling of subsidies will be difficult in countries with limited fiscal space, implying that domestic prices _P_ might move up. In our example,

> 10 Food CPI increased by 20 percent in Jordan, 44 percent in Egypt, 21 percent in Syria, and 8 percent in Morocco between 2007 and 2009.

> 11 According to World Bank data, during the period 2007–09, food subsidies declined as a share of GDP from 1.8 percent to 0.8 percent in Jordan, and from 2.1 percent to 1.4 percent in Syria. Food subsidies remained at 0.7 percent in Morocco, and increased from 1.3 percent to 2.0 percent in Egypt. 12 Note the analysis does not change if we assume that the country can affect prices and is facing an upward sloping import supply curve.

> 13 Commitment to price stability is signaled by the prevalence of price controls in MENA on certain types of food products. Price controls can be viewed as flexible subsidies that accommodate perfectly fluctuations in the price. According to Ortiz _et al._ (2011), Algeria, Morocco, Jordan, and Djibouti use also price controls on certain food products. Some of these use both price controls and price subsidies.

-14-
higher level of government spending has been sustained, the harder _a prior.i,_ it will be to reverse. Thus, the government faces policy reversal co_-v (e.g., disinvestment costs for a project already started, labor firing costs) which a _priori_ increase with the level of total expenditure (investment) and therefore increase with the time span of the commodity boom. However, this effect could theoretically become inuxistent for an economy in which the commodity boom has 13sted sufficiently long so that either it is at full capacity and firing costs are small (i.e., overemployment) and/or the marginal investment projects have a lower internal rate of return than the projects undertaken at the onset of the commodity bo'-. This is unlikely, excluding perhaps some oil exporters, to be the case for the majority of developing countriea.

The lir.ited disinvestment or policy reversal effect, expressed as a constraint on government spending, can, for example, be written as

# which corresponds to a polity reversal interpretation.' I;

> 9Equation (3) means that there is pressure on the government whenever it decreases the level of real government spending (w - 0). w >0 can be **justified** If policy revsrsal **is** defined In terms of real government spending **per** capita.

'OA limited disinvestment interpretatlon can, for example, be wrltten **as** _Gc+,_ 2 p _wte#-l_ which is *qualvalen **t** o imposing a lower bound on investment.