rafmacalaba/data-use-mentions-extended huggingface dataset:
now in developing countries, researchers working with survey data should strongly consider implementing multi-way cluster robust standard errors.

Finally, there are two theoretically simple ways to reduce the design effects caused by the clustering of responses by interviewers. One is to simply expand the sample size using an expected interviewer design effect while keeping the number of respondents per interviewer constant. This approach is what is used to account for area cluster design effects. Another approach is to reduce the number of respondents per interviewer. In this analysis, all things being equal, a doubling of the number of interviewers from 20 to 40 would result in a reduction in the average design effect from 7.2 to 4. In both cases however there are limits to how many interviewers can be trained and managed without effects on data quality. Additionally, there may be limits to the number of high-quality interviewers available to work on a given survey. Nevertheless, survey implementors should increase the number of interviewers to the maximum number that can all be trained and managed well.

This paper provides some tentative evidence that interviewers with college degrees or higher, those with local knowledge of the survey area and those with experience conducting surveys, produce data with lower interviewer design effects. Future research could address interviewer characteristics associated with reduced interviewer design effects more robustly. Since the cost of interviewing interviewers during a survey is minimal, interviews of interviewers should become a common practice in survey implementation. Such practice would allow for meta studies to identify the relative importance of different interviewer characteristics for data quality.

# **References**

Brunton-Smith, Ian, P. Sturgis & G. Leckie. 2016. Detecting and understanding interviewer effects on survey data by using a cross-classified mixed effects location–scale model. _Journal of the Royal Statistical Society – Series A, Statistics in Society,_ 18(2): 551-568.

Caeyers, B., Chalmers, N. & De Weerdt, J. 2012. Improving consumption measurement and other survey data through CAPI: Evidence from a randomized experiment.
carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 The author would like to thank Bankscope for making the data on banks assets structure and portfolio available. This database was complemented by additional information on banks ownership structure and institutional arrangements provided by experts and officials from national and regional Central Banks. The author wishes to thank these officials for their support. The author is particularly thankful to Nihal Bayraktar, Antonin Dossou, Ibrahim Elbadawi, Barthélémy Kouézo, Raj Nallari and Jean-Claude Tchatchouang, for their valuable comments. The author also wishes to thank the anonymous referees for their comments on an earlier version of this paper.
and Young (database); GFS (database); International Monetary Fund; ISORA (database), McNabb, Danquah, and Tagem (2021); PWC (database); World Bank.

_Note_ : ATG = Antigua and Barbuda; BHR = Bahrain; BHS = The Bahamas; BRB = Barbados; CPV = Cabo Verde; EMDEs = emerging market and developing economies; FJI = Fiji; GRD = Grenada; GUY = Guyana; KIR = Kiribati; LCA = St. Lucia; MDV = Maldives; MNE = Montenegro; MUS = Mauritius; PLW = Palau; STP = São Tomé and Príncipe; SUR = Suriname; SWZ = Eswatini; SYC = Seychelles; VCT = St. Vincent and the Grenadines; VAT = Value-added tax; VUT = Vanuatu. A. Panel shows average composition of total revenue by major component in 34 small states and 97 other EMDEs for the period 2011-22.

B. Tax effort reflects the difference between actual and potential tax revenue. Tax potential estimates are latest available for each country based on the true random effects method reported in McNabb, Danquah, and Tagem (2021). Sample includes 14 small states, 8 small states commodity exporters, 6 small states commodity importers, 35 advanced economies, and 98 EMDEs excluding small states. C. Panel shows share of tax returns filed electronically, for different tax types. Based on a sample of up to 18 small states and 57 other EMDEs for 2021.

D. Panel shows standard VAT rates as of March 2024. EMDEs excluding small states include 81 economies.

43
including access to finance, corruption, infrastructure, crime, competition, labor, business environment obstacles, and performance. The comparability of data across countries as well as the high coverage is a rarity given that many survey data sets face issues of low country coverage, no comparability across countries, or both. The Enterprise Surveys restrict the universe of firms given the nature of the questionnaire. The universe excludes extractive industries, agriculture, informal firms (not registered), fully government-owned enterprises and micro firms with fewer than five full time employees.

> 2 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

> 3 For a complete list of the countries included in the analysis, please refer to Table A2 of the Appendix.

5
32 introducing export varieties into the GDP function. In exploiting the translog GDP function we are following Harrigan (1997), who hypothesized that export prices would differ across countries due to total factor productivity in exports. We have used the industry CES price indexes that differ across countries due to export variety, and enter as “price effects” into the GDP function and sectoral share equations. Estimating the share equations simultaneously with the GDP equation (transformed to become relative country productivity) allows us to identify and estimate the elasticity of substitution σ n between export varieties in each sector, and then infer the contribution of export variety to country productivity.

The resulting elasticity estimates range from a low of -0.02 in the electronics industry, to a high of –2 in the agriculture industry and the petroleum & plastics industry. Because these are the elasticity of substitution between _outputs_ (measuring the curvature of the concave production possibilities frontier), we have less intuition about the magnitude of the expected estimates than for inputs. But the ranking we have obtained seems reasonable, since there is the least substitution between export varieties in electronics, and the greatest substitution between varieties within agriculture and petroleum & plastics. In electronics, the estimate of -0.02 indicates that a 10% expansion of product varieties has the same effect as a 10/1.02 = 9.8% increase in prices, in terms of drawing resources into that sector. For agriculture and petroleum, however, a 10% increase in product variety has the same effect as a 10/3 = 3.3% rise in prices, since these products are more highly substitutable in terms of production.

We have treated export variety as an endogenous variable, and as instruments use those suggested by the work of Eaton and Kortum (2002) and Melitz (2003): tariffs, transport costs and distance. By using an over-identifying restriction test on the nonlinear system estimation, we have also been able to test the important exclusion restriction suggested by these models
_Table 8: Disaggregated trade flows between country 1 and country2 in sector GTAP_1_

|**Origin**|**Destination**|**GTAP Sector**|**HS Category**|**Τ**|**Trade Flow**|
|---|---|---|---|---|---|
|Country 1|Country 2|GTAP_1|HS_1|τ<sup>HS1</sup>|5|
|Country 1|Country 2|GTAP_1|HS_2|τ<sup>HS2</sup>|20|
|Country 1|Country 2|GTAP_1|HS_3|τ<sup>HS3</sup>|10|
|Country 2|Country 1|GTAP_1|HS_1|τ<sup>HS1</sup>|30|
|Country 2|Country 1|GTAP_1|HS_2|τ<sup>HS2</sup>|10|
|Country 2|Country 1|GTAP_1|HS_3|τ<sup>HS3</sup>|5|

For each directed pair of countries, we then build the “value of time” associated with the trade of GTAP_1 from the origin to the destination, by taking the trade‐weighted average of the underlying HS estimates from Hummels and Schaur (2013). Using the numerical example above, the value from country 1 to country 2 and from country 2 to country 1 are then computed as: To build the appropriate trade‐related weights, we use UN Comtrade data on trade flows which we aggregate at the 4‐digit or 2‐digit levels to construct sector and origin‐destination specific weights for the aggregation.<sup>15</sup> One particular issue when building the aggregation is the treatment of missing values. While Hummels and Schaur (2013) provides “value of time” estimates for many sectors at either HS2 or HS4 levels, the data also
where _F_<sup>_1j_</sup> is the Fisher index for region _j_ with region 1 as the reference area. This procedure is equivalent to taking a geometric mean of every possible combination of two regions. As discussed in Section 2.3, a multilateral price index can also be calculated by the CPD method.<sup>8</sup>

# _Choice of price data_

While the choice of price index calculation methods matters, the quality and types of price data used for the calculation may be even more important. There are three types of data commonly available for spatial price adjustment. The first type of price data is the market price survey data that are typically collected in parallel with an official household budget survey. The second type of price data that can be potentially used for spatial price adjustment is the CPI price data. The third type of price data is unit values that can be calculated based on the consumption module of official household budget surveys. Gaddis (2016) nicely summarizes the general characteristics of these price data from a poverty analysis perspective as in Table 1. We encourage readers to refer to Gaddis (2016) for the discussion, and only highlight here some key factors particularly relevant to spatial price adjustment for poverty measurement below.<sup>9</sup>

> 8 The GEKS procedure can also be applied to the Törnqvist index (Caves, Christensen, and Diewert 1982; Balk 2008). Another common approach to calculate a multilateral index is the Geary-Khamis method, which is used by the Penn World Table (Feenstra, Inklaar, and Timmer 2015) and the regional price parities in the United States (Aten 2017).

> 9 Also see Chapter V of United Nations Statistics Division (2005). Aside from these commonly available price data, there are other types of price data, such as the price data collected for the PPP calculation under the ICP, scanner data collected by private companies, and price inventories of specific goods and services (agricultural products, housing
, a grid cell � is classified as belonging to an urban cluster �

> 20 Although, as demonstrated by Day _et al._ (2017) for Sri Lanka, the approach can also be feasibly implemented using polygon data which correspond to sub-national administrative units.

> 21 In principle, the areas of two or more different agglomerations can overlap so that, in effect, they merge into a single agglomeration that contains multiple settlement points.  In this case, the number of “merged” agglomerations will be less than _J_ .  To avoid double-counting pixels, population should then be summed over the number of merged agglomerations to arrive at an estimate of the national urban share of the population.

> 22 Uchida and Nelson (2010) also report results from other combinations of assumed values for the three thresholds.

8
some road segments do not connect, and the GIS therefore assigns unnecessary detours. Importantly, network analysis assumes all travel happens over the road network and thus cannot factor off‐road travel. Interpolation is required to incorporate off‐road travel into network analysis calculations.

Network analysis can calculate travel in units of time or distance, offering an attractive package of analytical flexibility and communicative efficiency. Like cost time models, it can also handle trips spanning multiple destinations and optimize the order of visits. Therefore, where reliable data exist, it is a strong option for policy makers, analysts and researchers alike. For use cases like logistics planning or routing that must manage multiple travel destinations along established road networks it is arguably the default, preferred method.

Network analysis poorly fits our needs given the importance of off‐road travel and terrain in Nepal and the frequent inaccuracy of its roads geospatial data. In a preliminary analysis of options at the outset of this project the routes and times returned by network analysis over our road network were visibly incorrect even to researchers unfamiliar with the Nepali context.

Furthermore, we had the good fortune of inheriting a complete governmental roads data set from the Rural Access Index; such fortune is unlikely to repeat, and data availability would thus block updates to our eventual model. This limitation also applies to the cost time model but is less severe given the importance of terrain, land cover and other inputs in it, and the imperfect but easy to manage substitute of OpenStreetMap data. OpenStreetMap (OSM) is an ever‐growing open access, volunteer contributed global geographic data set and map, best summarized as “the Wikipedia of maps.” OSM data cannot be dropped so easily into a network analysis as it does not align perfectly with governmental roads data sets, requiring significant tedious labor to manually align both data sets on each update.

This is less important in our 30m x 30m grid where 1‐5 meters of separation between roadways will generally fit within a single cell and thus cause no impact.
He calculates that RSDP reduced transport costs and increased Woreda-level average yields. Kebede (2021) uses village-level data and finds that rural roads on average increased real agricultural income. Dercon et al. (2009) study a panel of 15 agricultural villages over a decade (1994–2004) and find that access to all-weather roads reduces poverty by 6.9 percentage points and also increases consumption growth markedly (by 16.3 percentage points). Atkin and Donaldson (2015) study trade costs in Ethiopia and Nigeria and find that the effect of distance on trade costs is about four times larger than in the United States. We add to this literature a focus on land use: beyond nighttime luminosity, we also use a recently released landcover panel dataset that allows us to track landcover at an annual frequency.<sup>2</sup> This allows us to go beyond the traditional use of nighttime lights as proxy for economic activity and also focus on the quantity of land allocated to agriculture or farm production (cropland area) versus urban uses throughout the country.

Second, we contribute to the broad literature on the effects of transportation infrastructure.<sup>3</sup> Our analysis is related to that of Mitnik et al. (2018), who use nighttime lights as an outcome with a two-way fixed effects approach and find that road rehabilitation in Haiti from 2004 to 2013 leads to increases of 6—26% in luminosity (corresponding to 0.5–2.1% increases in GDP). Similarly, BenYishay et al. (2018) evaluate road upgrades in the West Bank using panel data and find a 0.341 increase in absolute luminosity; given that baseline nighttime lights was 2.6, a 0.341 increase represents a significant increase. Storeygard (2016) constructs a panel of Sub-Saharan African cities using nighttime lights data and studies the effect of trade costs on economic activity. Asher and Novosad (2020) find effects of rural roads on non-farm employment but not on
/sup><sup>_>MCF_(</sup><sup>_Ki_)and</sup><sup>_MCF_(</sup><sup>_Li_</sup> _DE_<sup>)</sup><sup>_>MCF_(</sup><sup>_Li_).</sup> Two effects operate. Taxing an untaxed factor helps to restore prices to their undistorted levels, tending to lower the MCF. When the untaxed factors are inputs to taxed goods, the relative costs of these goods rise, the distortion associated with goods taxation increases and the MCF tends to be higher. The second of these effects is relatively stronger when the tax shock is concentrated on inputs to _domestic_ and _export_ goods.

Ignoring distributional issues, on average among the fictional tax instruments for taxing the informal sectors, the best way of raising money is by increasing taxes on _untaxed_ goods. The average _MCF_ ( _U_ ) is 0.87, lower than the average MCF elsewhere in the informal economy.

But governments may hesitate to increase taxes of the informal sector if they care about distributional issues and poor households are concentrated in production of _untaxed_ goods or they consume a lot of _untaxed_ goods. When distributional issues are considered, a promising part of the informal economy is _informal capital_ used to produce taxed goods: average _MCF_ ( _KDE_<sup>_i_) is 1.05.That is, imposing taxes</sup> on companies that produce taxed goods but do not pay company tax generally offers a lower cost of public funds than increasing existing taxes elsewhere in the formal economy. In many cases such companies have legal tax exemptions, which can be removed with low administrative expense.<sup>17</sup> Removing such exemptions has the 17In a survey of 197 businesses in Cameroon, Gauthier & Gersovitz (1997) report that 4 were legally exempt from sales tax, while 30 were legally exempt from the business profits tax. In Gauthier &
Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Qualitative approaches include methods such as belief networks, the Delphi procedure, fuzzy logic and expert systems, and text mining techniques (Behmiri and Manso 2013; Drachal 2016; Frey et al. 2009). This review of the commodity price forecasting literature focuses on quantitative methods for crude oil and metal commodity prices ( **Table 1** ).

## **2.1. Crude oil price forecasting**

The review of the literature on crude oil price forecasting draws on 40 studies in peer-reviewed journals, most of which examine West Texas Intermediate (WTI) prices and five of which examine

> 2 Diebold (2015), in a review of the use of the test of Diebold and Mariano (1995) argue that sometimes simpler approaches remain appropriate. Here, therefore, we have used both the comparisons of bias and root mean squared errors and the formal tests of Diebold and Mariano (1995) and Pesaran and Timmerman (2009).

> **3** There are caveats. First, the sample size of the BVAR is much smaller than the other approaches due to data availability. The BVAR in this paper uses the quarterly average of monthly data while the other methods use monthly data. Second, the BVAR predicts real prices, then inflates real values to nominal prices by actual inflation rates.

3
on each loan received by each firm, including information on the loan amount, interest rate, and loan maturity. We complement the bank data with unique data on the credit program we analyze, including detailed information on loan applications by firms (such as the amount requested) as well as banks’ decisions (such as whether a loan request is approved or rejected and the approved amount). These data allow us to measure selection and to disentangle supply and demand factors in the allocation of credit across the whole economy.

Second, we use confidential administrative tax records from Chile’s tax authority ( _Servicio de Impuestos Internos_ ). These data sets contain monthly, firm-level information including sales, materials expenditure, value added, number of workers, wage bill, net worth, age, industry, and municipality. These data allow us to construct measures of pre-pandemic firm attributes (such as productivity, measured as value added per worker) as well as firm performance during the pandemic, which we do with monthly sales during 2020. Third, we work with publicly available firm-level data on firms that use the employment program, a policy of interest for comparison purposes. These data are published monthly by the employment authority ( _Direcci ́on del Trabajo_ ) and contain the dates that each firm uses the employment program and the number of workers in each firm that participates in the program.

We merge these data sets using unique anonymous tax identification codes (IDs) of workers and firms that are common across sources. To secure the privacy of workers and firms, the codes are run by staff at the central bank that have the security clearance to access the full data. Furthermore, the Chilean tax authority requires all the results that are extracted and used in this paper to be calculated using at least 25 tax IDs. In sum, the merged data set allows us to study real and financial aspects of both the credit and the employment program covering the universe of firms. For most of the analysis, we use the 2018–2020
a statistically significant overall effect for rural districts; see bottom of table. The absence of test score declines for boys is arguably even more striking than for girls, given the more pronounced pattern of negative selection according to age found for boys in table 5.<sup>20</sup> A potential explanation for our results is that students may have reduced their effort on other exam subjects in order to focus on the required English and math sections. However, we find no decrease in the total number of exam subjects taken by students in response to the program, for either girls or boys. Nor do we find changes in the share of “easy” subjects taken in response to the program, where “easy” corresponds to a subject with a passing rate above the median in the pretreatment data. These results, reported in table S5 of the supplemental appendix, reveal no evidence of decreased student effort.

# _Spillover Effects on Boys_

Among the results presented thus far, the evidence of increased test taking for boys, with no corresponding decrease in achievement, in response to a program that targeted girls is perhaps the most intriguing. What explains this spillover effect? One possibility is that the scholarship alters constraints on human capital investment within a household. The opportunity to send a girl to school without incurring fees frees resources to send boys to school. Alternately, parental preferences for equivalent treatment of children, or a desire for boys to accompany their sisters, could induce a similar response.

In this subsection, we look for evidence of such intra-household spillovers in response to the program. We distinguish whether a secondary school-aged boy lives with younger or older girls, as we expect that the causes of spillovers might differ in the two cases. Spillovers from older girls to younger boys are more likely to reflect financial considerations, as liquidity-constrained households with older girls would gain relief earlier. We expect spillovers for nonfinancial reasons to be stronger when girls are younger, as parents would prefer older boys to accompany younger girls to school.
# **Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea**

_By_ Bo Pieter Johannes Andr ́ee and Utz Johann Pape<sup>∗</sup> JEL: C01, C14, C25, C53, O10.

Keywords: Inflation, Agriculture and Food Security, Food Price Analysis, Economic Shocks and Vulnerability, Macroeconomic Monitoring.

> ∗ Bo Pieter Johannes Andr ́ee, The World Bank, Development Economics, Data Group, can be contacted at bandree(at)worldbank.org. Utz Johann Pape, The World Bank, Poverty & Equity Global Practice, East Asia and Pacific, as well as University of G ̈ottingen, can be contacted at upape(at)worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

_1_