rafmacalaba/data-use-mentions-extended huggingface dataset:
business performance, and the Vietnam Labor Force Survey (LFS), which provides data on the intensive margin of household business performance.

Measures to curb the spread of the virus, such as mobility restriction, business closures, and border shutdowns, weighed heavily on businesses in developing economies. Many businesses were forced to close, and many did so permanently due to their inability to sustain prolonged shutdown-related losses.Household businesses, were highly vulnerable to the pandemic-related shocks given their limited resources, and likely suffered disproportionately.

We first estimate the impact of the pandemic on household businesses using an event study approach. Overall, we find substantial reduction across all household business performance indicators, including business suspension, business closure, work hours, and earnings. During April and May 2020—the period immediately following the imposition of a national lockdown, suspension and closure filings surged over three times pre-COVID-19 levels, and weekly work hours and monthly earnings declined by more than a quarter of their pre-pandemic levels. However, household business activity rebounded relatively quickly; both suspensions and closures reduced by over 50 percent in June, while work hours and earnings had made steady recovery by September.

We then show that the impact of the pandemic on household businesses is uneven across industry sectors and firm size. Consistent with our expectation, firms operating in transportation, accommodation, food, and entertainment industries, the sectors most vulnerable to social-distancing orders, witnessed the greatest reduction in business activities. Further, firms with larger revenue—i.e., those with the highest opportunity cost of staying open during lockdowns—were more likely to close permanently.

Further, we find that losses were larger in areas dominated by businesses relying on face-to-face interactions. We follow Avdiu and Nayyar (2020) and construct a commune-level face-to-face (F2F) index to highlight geographical differences across communes in Vietnam. F2F is a continuous measure that captures the average intensity of human-to-human interaction required to conduct business in a commune based on its existing sectoral composition of household businesses. An array of empirical estimates find
6 the average or unit cost of a payment. Up to some point, the larger is payment volume, the lower is the average cost of processing each payment.

Similarly, if the extra or marginal cost of processing one type of payment instrument is reduced as the volume of another payment instrument rises, then there will be economies of scope. When credit card payments over a network use the same terminals, communication links, or computers as debit card payments, the marginal cost of a credit card payment can fall as debit card volume rises (or vice versa). Similar economies of scope arise when ATM terminals use the same communication links as debit card transactions (as is more common). Scope economies exist when the total cost of processing two or more payment instruments together (jointly) is lower than the sum of the costs of processing the same volume of each instrument separately.6 (For a discussion of some empirical evidence of scope and scale economies see Annex 2).

# 4. **Demand for payment** services

The demand for a payment instrument is influenced by economic variables such as own price, the price and availability of substitutes, and user income. It is also influenced by such difficult to quantify influences as user convenience, acceptability by payees, and safety in use. For this and other reasons, the effect of price on the demand for payment instruments has been difficult to measure, although inferences can be made.

**_Substitution among payment instruments._** Due to a lack of extended time-series data within any one country, it has been necessary to infer the demand for and substitution among noncash payment instruments from pooled time-series cross-country analysis using data developed by the Bank for International Settlements. The relationships among the per person use of five non-cash payment instruments plus cash is illustrated in Table 1. Of the 15 pair-wise correlation coefficients, 11 are negative suggesting substitution.7 However, these substitution effects are weak since the largest negative correlation coefficient (or r) is -.
migration in Eastern Africa. In contrast, Di Falco et al. (2023) also use data from LSMS-supported longitudinal surveys conducted in Ethiopia, Malawi, Niger, Nigeria, and Uganda to analyze the effects of cumulative drought shocks on the migration decisions of rural households and find that a positive effect of multiple droughts accumulates over time. A study by Dillon et al. (2011) uses data from Northern Nigeria and finds that households respond to agricultural risk by sending males to migrate. None of these studies, however, carries out a systematic search for the drivers underlying the heterogeneity of climate (im)mobility outcomes, nor tries to disentangle the relationship between migration and local adaptation. Furthermore, a methodological limitation common to all these works is that they employ simple linear probability models, which not only impose restrictive functional forms, but by design can only estimate average treatment effects and check whether they differ across pre-specified subgroups; they do not allow for arbitrary and unrestricted treatment effect heterogeneity nor for nonlinear responses.

We provide three contributions to the existing research. The first is substantive: we produce datadriven evidence on the key channels that mediate the microeconomic relationship between climate shocks and mobility, providing empirical support for the hypothesis of climate immobility traps. This is a particularly relevant notion from a policy perspective as it implies the existence of climate-induced wealth and liquidity constraints. The evidence we document is in line with recent meso- and macro-evidence on the topic (Benveniste et al., 2022; Cattaneo & Peri, 2016; Peri & Sasahara, 2019) as well as with more general findings that the most vulnerable are often not those who migrate, as they are too credit-constrained and lack the necessary resources to do so (Cai, 2020; Dustmann & Okatenko, 2014). An increasing number of studies suggests that policy makers should be concerned about immobility as much as mobility (Findlay, 2012; Cattaneo et al., 2019). Indeed, it is now commonly acknowledged that too often the policy focus has been placed on
used land cover such as vegetation and built-up indices, however did not incorporate types of agriculture like cropland and livestock (Goldblatt, Heilmann, and Vaizman, 2019; Rashmin Gunasekera et al., 2015).

Other methods to estimate GDP at a local level take advantage of the lights at night dataset. Doll, Muller, and Morley (2006) and Elvidge et al. (2009) found nighttime lights to provide a uniform, consistent, and independent estimate for economic activity, and several other studies (e.g. Bundervoet, Maiyo, and Sanghi, 2015; Chen and Nordhaus, 2011; Eberenz et al., 2020; Ghosh et al., 2010; Henderson, Storeygard, and Weil, 2012; Wang, Sutton, and Qi, 2019) utilized this striking correlation between luminosity and economic activities to estimate economic output on the ground. While night light is a good reflection of economic activities in manufacturing and urban areas, night light data may not capture the agricultural activity as it requires areas to emit light. Bundervoet, Maiyo, and Sanghi (2015) suggest that agricultural indicators rather than rural population could improve the estimation of GDP given the importance of agriculture in many of the economies in their sample of Africa. Gibson et al. (2021) find that night time lights data are a poor predictor of economic activity in low density rural areas.

In this paper, we present a high resolution gridded Agricultural GDP (henceforth AgGDP) dataset that is produced through a spatial allocation model by distributing national 4
respondents to be interviewed on behalf of unavailable respondents, and (2) any respondent, whether proxy or self, to be interviewed in group, non-private settings. The Integrated Household Panel Survey (IHPS), on the other hand, was the third wave of a multi-topic, national, longitudinal household survey that was implemented from April 2016 to January 2017. The IHPS attempted to interview each adult household member in private, as part

> 4 The ICLS, which meets every five years, affects how country labor force surveys — guided by the ILO — are designed and undertaken.

> 5 Relevant SDG targets include, for example, Target 2.3 to double the agricultural productivity and incomes of smallscale food producers by 2030, through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment; as well as Target 8.3 to promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage formalization and growth of micro-, small- and mediumsized enterprises including through access to financial services.

> 6 The data, questionnaires and basic information document for the IHS4 2016/17 can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2936. The data, questionnaires and basic information document</u> for the IHPS 2016 can be accessed here: https://microdata.worldbank.org/index.php/catalog/2939. Both the IHS4 and IHPS were implemented with the technical and financial assistance from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The implementation of the individual interviews as part of the IHPS was made possible by the technical and financial assistance from the World Bank LSMS Plus (LSMS+) initiative, which aims to improve in IDA countries the availability and quality of individualdisaggregated survey data on asset ownership, work and employment and entrepreneurship. For more
survey.<sup>10</sup> In the first round, 3,295 observations completed interviews. This sampling frame is complemented using Random Digital Dialing (RDD) to ensure that all households that existed in 2020 and who had access to a phone are considered, regardless of whether they had a phone in 2015/16. Using RDD, we generate 93 million phone numbers among which a random subset of 5,000 numbers were sent an introductory SMS to confirm operational capability. 4,075 numbers were confirmed active out of which we successfully interviewed 763 households in the first wave. Based on the collected data, we classify respondents from the national samples into three groups: 1) existed in 2015/16 and did not change number; 2) existed in 2015/16 but changed number; 3) did not exist in 2015/16. The KIHBS households are representative of type-1 households while the RDD households are representative of type-1, 2 and 3 households. We merge the two samples by adjusting weights for KIHBS households and RDD type-1 households to account for the possibility of double-selection.<sup>11</sup> The refugee sample was constructed using four strata: Kakuma camp, Dadaab camp, Kalobeyei settlement and urban refugees. For refugees in Kakuma and Kalobeyei, sampling was based on previously conducted representative socioeconomic surveys (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020; UNHCR & World Bank, 2020), while for urban refugees and refugees living in Dadaab no such data was available, and sampling was based on phone numbers obtained from official UNHCR registration records (proGres). For each stratum, 1,000 phone numbers were drawn and sent an introductory SMS. From the list of active numbers 500 households per stratum and 375 replacements were drawn, from which 1,326 households were interviewed in total in the first wave, 1,687 in the second wave, 1,469 in the third wave, 1,357 in the fourth wave and 1,
The proportional change in household income is given by the share of cotton income, θ<sup>_h_</sup> _c_<sup>,</sup> multiplied by the proportional change in prices.

In section 1, we reviewed some evidence indicating that a full liberalization of agricultural markets along the lines of the Doha Round negotiations would bring about an increase in world cotton prices of around 12.7 percent. Similar results are obtained if we use the price increase predicted by Sumner (2004) of 11.6 percent. Using the data for 1998, we can estimate the average welfare effect given by equation (1). These averages, estimated again with a Fan regression, are shown in figure 6.5. We observe that the increase in prices would benefit farmers across the income distribution. The effects would range from over 0.75 to nearly 1.5 percent, at the bottom of the distribution, to roughly 0.5 percent at the top. The unconditional average gain would be around 1 percent of the initial household per capita income.

Figure 6.6 explores the regional differences in cotton gains. The solid line corresponds to the non-parametric average in the Central province; the broken line, to the non-parametric average in the Eastern province; and the dotted like, to the non-parametric averages in the Southern province. The figure shows that larger gains would take place in the Eastern province, particularly at the bottom of the distribution. In the Central province, the gains track the average gains in the Southern province at the bottom of the distribution; from the middle to the top, however, the gains in the Central province remain high, whereas the gains in the Southern province sharply decline with income.

These findings show that a 12.7 percent increase in the price of cotton would cause household income to increase, at most, by 1.5 percent (among the poorest households in the Eastern province, for instance). Although these are positive effects associated with welfare
14 ITN ownership. Indoor residual spraying (IRS) is another effective anti-malarial intervention, which could potentially be more prevalent in households that acquire ITNs. Unfortunately, only the 2008 DHS (and not the 2003 DHS) includes information on IRS, so it is not possible to include IRS in the Oaxaca-Blinder decomposition. However, even in 2008, the rate of IRS remains low: in the sample, just 9 percent of children overall and 11 percent of children in high malaria risk zones lived in households that had received IRS treatment. Additionally, in 2008, the correlation between ITN ownership and IRS was 0.00 overall and -0.03 in high malaria risk zones. The combination of the low prevalence of indoor spraying and lack of correlation with ITN indicate that changes in indoor spraying are unlikely to be driving the results presented here.

Another confounding factor is that between 2003 and 2008 there was an increase in the use of artemisinin combination therapy (ACT) for malaria treatment. In the 2003 DHS, among children who had a fever within the previous two weeks, 11 percent received sulfadoxine-pyrimethamine (SP), the recommended drug treatment at the time. In 2008, among the same category of children, 3 percent received SP and 8 percent received ACT. If ITN usage is correlated with access to ACT treatment, it is possible that part of the decline in mortality attributed to ITN usage is a consequence of increased availability of ACT.

Table 7 also presents results from a similar decomposition for the change in neonatal mortality, which shows a small, statistically insignificant increase. This decomposition is included for the sake of completeness and also as a modest falsification test of the main finding regarding ITN ownership in high malaria-risk zones. Given that malaria and ITN ownership are known not to have a substantial effect on neonatal mortality, we do not expect changes in ITN ownership to explain any part of the change in neonatal mortality. If ITN ownership did explain changes in neonatal mortality, that would suggest that the ITN ownership results for postneonatal
Figure A3: PMT performance and survey quarter (N=4,500 per quarter) <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=4,500) Q1 (N=4,500) Q1 (N=4,500) Q1 (N=4,500)<br>Annual (N=4,500) Annual (N=18,000) Annual (N=21,756)<br><!-- End of picture text -->

# Figure A4: PMT performance and survey quarter (N=1,500 per quarter)

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=1,000) Q2 (N=1,000) Q3 (N=1,000)<br>Annual (N=1,000) Annual (N=4,000) Annual (N=21,756)<br><!-- End of picture text --> Figure A5: PMT performance and survey quarter (N=250 per quarter) <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=250) Q2 (N=250) Q3 (N=250)<
minutes for 1 to 2 transfers. Overall, they prefer the commuter rail to bus or metro (but this may be biased by the perception of additional security).

The network data is used to assign each trip in the total market to a specific mode or combination of modes based on the user trade-offs between travel time, cost and modes obtained from the survey and on the characteristics of each model. This is done for the proportion of trips for each Origin-Destination pair that will travel by each mode of transportation. The specification of the modal choice model is a nested logit structure with the following mode combinations: (1) bus only, (2) metro only, (3) metro and bus, (4) commuter rail only, (5) Commuter rail and bus) and (6) a commuter rail, metro and bus option. The choice of mode is estimated separately for each trip, taking into account the distinct preferences of the person making the trip with respect to age, gender and other factor.

The model is calibrated to allow the reproduction of time and cost in 1998 for buses and metro and generates a commuter rail demand for each average week day assuming that commuter rail fares are set equal to the bus fares of 0.28 pesos/km. More specifically, the demand model estimates the probability that a person will choose a particular mode for their trip _for each of more than 90,000 public transportation trips._ This information is then used to estimate demand for each transportation mode for weekday demand. So for any pair of origin and destination, it computes the total trip time, the cost and the number of transfers and on that basis it estimated that the user will pick any one of the 6 travel options identified earlier. This week day demand is discounted for weekends. For the subway, this is done by applying a factor of 0.58 based on traffic counts. Annual demand is equal to weekday demand multiplied by a conversion factor for each mode as well
<!-- Start of picture text --> LIC LMIC UMIC HIC<br>100<br>80<br>60<br>40<br>20<br>Revenue, excluding grants (% GDP)<br>0<br><!-- End of picture text --> _Notes:_ The sample consists of 182 countries. The boxes represent the range of values between the 25<sup>th</sup> and 75<sup>th</sup> percentile (including the median), the ends of the whiskers represent the lower and upper adjacent value, and the dots represent outliers (see Tukey, 1977).

_Source:_ Authors’ analysis based on the variables and data sources described in Section III.

**Figure 5.** DEA Input Index (Measure of the Economic Strength and Capacity to Raise Revenues) by Income Group, 2016-2019

<!-- Start of picture text --> LIC<br>LMIC<br>UMIC<br>HIC<br>0.1 0.2 0.3 0.4<br>GDP per capita Share of Agriculture in GDP<br>Trade Openness Age Dependency Ratio<br><!-- End of picture text --> _Notes:_ The sample consists of 182 countries. Depicted values are normalized and aggregated as described in Section II. Details on the indicators and data sources used in the computation of the DEA input index are provided in Section III. _Source:_ Authors’ analysis based on the variables and data sources described in Section III.

_B. Frontier Analysis_ 13
translates into within-province inequality increasing from about twice higher than betweenprovince inequality in the early 2000s, to about three times higher than between-province inequality in the late 2010s. Thus, within-province inequality has become much more significant over time than between-province inequality.<sup>6</sup> There were significant differences in inequality levels within the provinces (Appendix A, Table A.1). Compared with the national average of 0.37, the Gini index was greater than 0.40 in Lao Cai, Dien Bien, and Lai Chau in the northern mountain region. In 2020, these provinces still had some of the remarkably high poverty rates (Lao Cai: 21.5%, Dien Bien 46% and Lai Chau: 36%). Inequality and poverty levels were similarly high in the central highlands region (e.g., Kom Tum and Gia Lai had Gini values of 0.41 and 0.39 and poverty rates of 17% and 27% respectively). On the other hand, inequality was lower in Mekong River delta with a Gini index of about 0.31 in Long An and Tien Giang (where poverty rates hover around 1%) and 0.28 in Hung Yen and Thai Binh in the Red river delta (where poverty rates range around 1%).

Finally, we also show in Table 1 the estimates of three Foster-Greer-Thorbecke _(_ FGT) indices of poverty (Foster _et al._ , 1984). The poverty rate (i.e., the headcount ratio) measures the incidence or the proportion of the poor in the population, the poverty gap measures the depth of poverty (i.e., the average income shortfall of the poor), whereas the poverty severity index (i.e., the squared poverty gap) takes into account inequality of the income distribution among the poor. All three measures are estimated using the national (expenditure-based) poverty line as well as the World Bank’s PPP $3.1
For example, the estimated poverty rate using Specification 1 and Sample 2 for the logit model is 53.1%, which is 1 percentage point larger than the corresponding figure of 51.8% for the empirical errors model (compared with the true poverty rate of 51.6%).

# **IV. Methodological Challenges in Other Contexts**

The data on Syrian refugees in Jordan that we analyze are of relatively high quality in the context of refugee populations. In this section, we discuss methodological challenges in other contexts where data quality may not be as good.

# **IV.1. Small Survey Sample Sizes**

One practically relevant question is how large the imputation sample should be to obtain accurate poverty estimates.<sup>11</sup> On the one hand, a large sample size can provide estimates with more accuracy and generally better statistical properties than a small sample size; but on the other hand, it is also more expensive and demands more logistical and technical resources to implement. A balance should be reached between these trade-offs. In most conflict situations, however, the logistical and technical constraints may pose especially severe challenges for data collection efforts.

Park and Dudycha (1974) offer some theoretical guidance on selecting the appropriate sample size for obtaining regression-based prediction estimates. In particular, we want to find the sample size _n_ such that

> 11 Note that this challenge of finding an appropriate sample size is in the context of predicted values based on regression models, which is different from calculating the sample sizes for other purposes, such as hypothesis testing. For the latter, see, e.g., Cohen (1998) for a textbook treatment.

16