rafmacalaba/data-use-mentions-extended huggingface dataset:
.000|0.001|
|Monthlyconsump|tionper capita|||||||||
|6 months|-22.004|0.000|0.001|-15.903|0.002|0.003|-9.885|0.011|0.004|
|12 months|-19.553|0.000|0.001|-13.987|0.003|0.003|-6.301|0.057|0.013|
|18 months|-45.651|0.000|0.001|-33.246|0.000|0.001|-23.930|0.000|0.001|
|Food Consumptio|n Score(standa|rdized)||||||||
|6 months|0.162|0.000|0.001|0.207|0.000|0.001|0.261|0.000|0.001|
|12 months|0.135|0.000|0.001|0.195|0.000|0.001|0.254|0.000|0.001|
|18 months|0.063|0.011|0.006|0.143|0.002|0.003|0.239|0.000|0.001|
|Reduced CopingS<br>|trategies Index<br>|(inverted<br>|and standa<br>|rdized)<br>||||||
|6 months|0.158|0.000|0.001|0.205|0.000|0.001|0.247|0.000|0.001|
|12 months|0.016|0.610|0.220|0.076
countries, the wealth distribution changes are not uniform, suggesting that certain areas are benefiting more from economic growth while others are falling behind. This wealth disparity could provide insights into economic policies, development programs, or external factors such as climate impacts that have influenced these changes.

## **Performance on prediction of city-level wealth**

The Landsat-based wealth prediction models above produced wealth maps with a spatial resolution of 4.8 km. For some applications, such as targeting aid within urban areas, finerresolution wealth maps are of interest. To that end, we utilized household- level census data from two cities to test wealth prediction using high-resolution satellite imagery (PlanetScope and SkySat). Following the same settings with the above two subsections, we evaluate the CNN, transformer, and XGBoost models, as shown in Figure 4a. The CNN and Transformer models consistently perform better than XGBoost (with or without geospatial features) across both cities. This highlights the importance of deep visual representation from high-resolution satellite imagery for city-level wealth prediction, which constitutes the main gap between deep learning–based models and XGBoost. Our transformer model outperforms the CNN by a noticeable margin in Blantyre, especially when utilizing the full dataset, demonstrating greater scalability with increased data while also achieving comparable performance to the CNN in Lilongwe. Across both cities, all models exhibit significant improvements as the training data fraction increases; however, performance gains begin to plateau after approximately 25%-50% of the data, resulting in diminishing gains beyond that threshold.

We compare two kinds of commonly used proprietary highresolution, multi-spectral satellite imagery, i.e., PlanetScope (3m) and SkySat (0.5m), as shown in Figure 4b. The results indicate that SkySat consistently outperforms PlanetScope across various training data fractions in both cities. Both sensors capture 4-band (red, green, blue, and near-infrared) satellite imagery, but they differ in spatial resolution and swath width. This highlights the importance of urban spatial detail in accurately measuring wealth using our transformer
inconsistencies and produce a final dataset at the firm-by-shareholder level, including the participation of each shareholder. Whenever direct shareholders are corporations, we apply an iterative process to recover individual shareholders and their shares through up to five levels of ownership. This is a static dataset, reflecting the most updated data available to the tax authority as of early 2023.

We then use information on corporate income tax (CIT) declarations to compute firms’ profits every year and assign the resulting profits to their shareholders using our static shareholder registry. In recent years, we observe approximately 30,000 corporations filing income taxes and over 50,000 identifiable domestic shareholders to whom we assign their share in corporate profits.

7
Estimation Challenges**

Several estimation challenges emerge when attempting to estimate income-source and consumption responses to trade-driven price changes. One concerns price data. In practice, the relevant surveys ask households to report total expenditures by type of good or service, and they are also asked to report the number of units purchased. Analysts can then use these variables to compute unit values, the ratio of expenditures over the number of units. But unit values are not equal to prices for two main reasons. First, unless households maintain detailed records, self-reported expenditures and quantities are likely to suffer from measurement errors. Second, each category of goods or services probably contains many varieties, which might differ in terms of quality. Hence unit values, even if they were perfectly reported by the households, might reflect quality differences that vary systematically across types of households, and it is common to find positive partial correlations between expenditures and unit values in cross sectional estimations (see Deaton 1997; Lederman, Lichand and Fajnzylber 2011).

Fortunately, the literature has developed econometric estimations methods to deal with the measurement errors and the quality-valuation challenges discussed above. A full discussion of these approaches goes well beyond the scope of this short note. However, a bit of intuition might help busy operational staff and policymakers to think about the economics underlying the estimation challenges. An approach developed by Deaton (see Deaton 1988 and references in Deaton 1997) is to estimate a system of three equations: one for the determinants of unit values without location fixed effects, one for expenditures and one for income, both with location fixed effects (added by Porto 2005). The errors from those estimations can be used to correct for measurement errors and quality valuation, because

> 5 World Bank staff has online access to these surveys via DDP.

6
2020. “The pandemic and political order.” _Foreign Affairs,_ July/August.

- Galasso, V., V. Pons, P. Profeta, M. Becher, S. Brouard, M. Foucault. 2020. “Gender differences in COVID-19 related attitudes and behavior: Evidence from a panel survey in eight OECD countries.” NBER Working Paper No. 27359.

- Glaeser, E.L., G.Z. Jin, B.T. Leyden, M. Luca. 2020. “Learning from deregulation: The asymmetric impact of lockdown and reopening on risky behavior during COVID-19.” NBER Working Paper 27650.

- Greenstone, M. and V. Nigam. 2020. “Does social distancing matter?” University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-26.

- Gorodnichenko, Y., and Roland, G. (2011). “Which dimensions of culture matter for long run growth?” _American Economic Review_ , 101(3), 492-98.

- Gorodnichenko, Y., and Roland, G. (2015). “Culture, institutions and democratization.” National Bureau of Economic Research Working Paper 21117.

- Gorodnichenko, Y., and Roland, G. (2017). “Culture, institutions, and the wealth of nations.” _Review of_

22
Correlated Effects (CCE) estimator by Pesaran (2006) and a novel panel dataset that spans 133 countries over 20 years. Our findings suggest that, at the global level, transport fuel prices are significantly negatively correlated with air quality. Coefficient estimates for gasoline and diesel show a 22.2- and 31.1microgram per cubic meter (μg/m<sup>3</sup> ) reduction in PM2.5 concentration for each US$1 increase in price, respectively. Coal prices, on the other hand, seem to have no significant effect. We discuss several reasons why the relationship between fuel price and air pollution may not be as pronounced as between price and consumption, including technological modernization, dedicated pollution control measures such as air filters, regulation, and timescales for effects to materialize.

The remainder of this study is structured as follows. Section 2 provides an overview of existing literature on air pollution and fossil fuel prices. Section 3 summarizes the data sources and analytical methods used (including selection of the CCE estimator). Section 4 presents the results, and section 5 discusses policy implications.

2
Peters, P. E. 2010. ""Our Daughters Inherit Our Land, but Our Sons Use Their Wives' Fields": Matrilineal-Matrilocal Land Tenure and the New Land Policy in Malawi." _Journal of Eastern African Studies_ , **_4_** (1), 179-199.

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Sesmero, J., J. Ricker-Gilbert and A. Cook. 2018. "How Do African Farm Households Respond to Changes in Current and Past Weather Patterns? A Structural Panel Data Analysis from Malawi." _American Journal of Agricultural Economics_ , **_100_** (1), 115144.

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van Donge, J. K. 1999. "Law and Order as a Development
*<br>**ion**|0.00|0.01|0.01|0.03|0.07|0.10|0.13|0.17|0.17|0.17|0.17|
|**Health**|0.00|0.01|0.02|0.03|0.08|0.12|0.14|0.19|0.20|0.20|0.20|
|**Other**<br>**Servic**<br>**es**|0.00|0.02|0.05|0.10|0.25|0.36|0.44|0.60|0.60|0.61|0.62|

_Table B. 4. Annual spending associated with investments and households’ consumption under the Nicosia Tram Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0
Fertility in the short term is strongly correlated with the number of children previously born. Also the sex of children matters: One additional son (SONNUMSHORT) changes the fertility rate in the short term by factor exp(-0.05) = 0.95, while one additional daughter (DAUGHTNUMSHORT) changes the fertility rate by factor exp(-0.08) = 0.92. Surprisingly, education (PRIMEDU, SECEDU) does not have a significant effect on fertility in the early post-war period. This result fits empirical evidence from other early post-war economies where education played a minor role in driving post-war socio-economic outcomes at the micro-level (e.g. Bozzoli and Brück 2009). In line with expectations, household wealth (WEALTHFUL) significantly decreases fertility. Being currently in union (INUNION) and living in a more densely populated province (PODENSITY) significantly increases fertility in the short term. Child mortality at the district level (DCHILDMORT) – one of the few exogenous measures of the health infrastructure that can be constructed from RDHS data – does not significantly affect fertility. Women living in cities (URBAN) do not have
have the same well-being. Such _irregularity of consumption_ has implications for welfare analysis even when survey measurements are not misreported (as discussed by Kay et al., 1984, and Deaton and Grosh, 2000, among others). In addition, our analysis allows for survey errors from any of two broad sources. The first source is _infrequency of acquisitions_ , which arises because cash outlays occurred during the interview week may present extreme values resulting from no acquisition or positive acquisition of infrequently purchased items.<sup>2</sup> The second source of survey errors arises because of _misreporting_ , which is the object of a voluminous literature spanning both developed and developing countries (see Deaton and Zaidi, 2002, and Carroll et al., 2015). Our investigation disentangles the relative contribution of any of the above channels in both diary and recall data.

We leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012. The survey ran continuously for one year and was administered to approximately 25,000 households across the country. All households filled out a 7-day diary on their acquisitions with the assistance of enumerators during regular visits. One-third of households were randomized to an additional survey module, administered before the diary, that asked them to recall food consumption in the last 7 days. The size of the experimental sample, approximately 8 _,_ 000 households, is of a different order of magnitude compared to other studies on consumption measurement in developing countries. The household diary employed was of the standard acquisition type, with one respondent recording on behalf of the household. Diaries were filled out by the same person

> 2If stocks are observed, one can impute consumption from diaries by measuring stock inflows and outflows (as in Beegle et al., 2012). However, the measurement of stocks is costly, prone to additional measurement concerns (Sharp et al., 2019), and seldom available in empirical work.

2
financial support from other entrepreneurs; and 4) Sharing of tools, inputs, equipment or employees with other entrepreneurs.</mark>

- **<mark>Work‐life balance (WLB)</mark>** <mark>was measured using the composite scores of work‐life balance as measured by the 8‐item Work‐life Balance Questionnaire. WLB is described in the current study as the balance that an individual requires between the time allocated for work and other aspects of life.</mark>

- **<mark>Counterproductive work behavior (CWB)</mark>** <mark>was measured using the composite score of counterproductive work behaviors as measured by 4 counterproductive work behavior questions. CWB is non‐productive behavior of entrepreneurs that goes against the legitimate interests of an organization. As such, it can harm organizations or people in organizations, including employees and clients, customers. Entrepreneurs were asked whether they thought they should have stayed home, had angry outbursts, complained about insignificant things at work, or thought of leaving the business in the previous three months.</mark>

Business Performance Variables: Sales, asset turnover, employee turnover etc.

Since improvements in mediating variables and business performance are likely to take longer to manifest, these outcome variables will only be measured at endline.

28
6285|7076|7014|7014|8323|
|Adj R2|||0.075|0.103|0.087|0.26|0.237|0.257|

_Note:_ Estimates are obtained using (5) (columns 1 to 3) and (6) (columns 4 to 6). Estimates in Panel A separate between the effect of ∆ _τzt_<sup>_DD_</sup> for good’s whose tariffs were phased-out (class B,C,C+) and the rest. Standard errors, in parentheses, are clustered at HS-2 product * ** *** level, _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.

# **2.5 Anticipation and Storability**

Measuring storability for a wide range of products is not a straightforward task and no off-the-shelf measure is readily available. To make progress we follow Alessandria et al. (2010a) and proxy it by considering the lumpiness of a goods’ trade flows.<sup>20</sup> In the model we present in section 4, more storable goods with lower depreciation rates are characterized higher inventory-sales ratios and more infrequent orders. We build on this insight and relate the goods inventory intensity with its observed trade lumpiness.<sup>21</sup> We measure trade lumpiness using the Herfindahl–Hirschman index of monthly concentration of the annual imports, calculated as follows:

> 20Alessandria et al. (2010a) also use a direct measure of a product’s physical depreciation and obsolescence rate but only for a limited number of goods and at more aggregate levels than HS-6.

> 21Lumpiness in trade flows can be explained by lumpiness of demand or the usage of inventories. We take the second view. The importance of inventories in international trade has been widely documented as we emphasized in the introduction.

12
<mark>to MI), M</mark> L approaches follow a data mining philosophy and build the imputation model using variables that maximize predictive power from the training sample. This imputation model is then applied to the estimation sample. In this study, we employ several ML techniques, namely LASSO, Elastic Net, and Random Forest, and use the base survey as the training sample and the target survey as the estimation sample.

We show the estimation results for imputation from NPS 2020/21 to the three treatment arms in Appendix A, Tables A.18 to A.20.<sup>19</sup> The MI estimates perform well for 19 out of 23 imputation models for the three treatment arms, where two to four estimates fall within one standard error of the true rates (with the PMM technique obtaining somewhat greater accuracy than the linear model). The ML estimates perform worst, yielding accurate estimates for only 2 to 3 imputation models when imputing to TAs 1 and 2 and yielding inaccurate estimates when imputing into TA 3. In contrast, our proposed imputation method performs well for 20 out of 23 imputation models for the three treatment arms, providing 17 estimates within one standard error of the true rates.

# **5. Further extentions**

Next, we examine how the imputation model performance is impacted by the size of the base and target survey samples. Given the comparability of the data on the poverty predictors across different treatment arms, we pool the TA 1, TA 2, and TA 3 samples to form an expanded target survey sample and investigate the implications of varying the target survey sample size for the predictive accuracy of competing imputation models.<sup>20</sup>

> 19 The final selected variables and goodness-of-fit statistics for the machine learning estimates are shown in Appendix A, Tables A.21 to A.23. 20 We show additional estimation results when imputing into varying sample sizes of TA1 in Appendix A, Figure A.3.

24
# **I Introduction**

This paper explains the methodology and results of a survey to survey imputation exercise that was used to generate headcount poverty estimates for India. India was selected as a pilot case because of its size and importance, as well as the lack of recent data on household living standards. The latest survey-based estimates of poverty are from 2011-12. The most recent nationally representative survey that could be used to impute poverty into a later year is the NSSO expenditure on services and durables survey of 2014-15. The resulting poverty estimates from the imputation informed the 2015 poverty estimates, which are the most recent that are currently available, marking the first time that the World Bank used this type of method as an input into its global and regional poverty estimates.

Understanding trends in poverty in India is not only vital to Indian policy makers to measure changes in the welfare of the poor, but also has major implications for regional and global estimates of the prevalence of poverty. In 2013, an estimate of the number of extreme poor in India, defined as those consuming less than $1.90 per day per person, numbered 250 million<sup>1</sup> . This means that India accounted for over a quarter of the global total of 783 million extreme poor. Although Nigeria has now likely passed India as the nation with the largest number of extremely poor persons, accurately monitoring India’s progress in the fight against extreme poverty remains critical to assess progress towards the goal of reducing poverty to less than 3% by 2030.

The second motivation for undertaking a modeling exercise is that the typical method used by the World Bank to adjust poverty to a later year tends to overestimate the pace of poverty decline. When estimating global and regional poverty rates, the World Bank usually adjusts or “lines up” survey data from a variety of years to a common year, by scaling up the measured levels of household per capita consumption to account for growth in the intervening years. For example, to “
LFS data 2003-2008 and CASE 2006 survey database.

The resulting general trend estimates for 2003 -2004 from probit models for shadow workers (including employers) are presented on Chart 1 to better illustrate the results obtained. Only results of the probit models are presented for the sake of clarity, as the results did not differ substantially between logit and probit models‘ shadow employment proxies.

All estimations, where explanatory variables are used to obtain the proxy for shadow employment, show that the share of shadow employment was increasing between 2003 and 2006, and after 2006 it started to decrease. On the other hand, the results of estimations without the contract variable show something else – increasing share of shadow workers in total employment over the entire period. These two are the qualitatively different results but these differences seem to be relatively easy to explain.

**Chart 1 – Estimated general trends for shadow workers (probit models)**

<!-- Start of picture text --> 4.0% 1.7%<br>1.7%<br>3.9%<br>1.6%<br>3.8% 1.6%<br>3.7% 1.6% Shadow Workers -probit/ basic<br>1.6% Shadow Workers -probit/ basic/no<br>3.6% contract<br>1.6% Shadow Workers -probit/ narrow (right<br>axis)<br>3.5% 1.5%<br>1.5%<br>3.4%<br>1.5%<br>3.3% 1.5%<br>2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->
equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 32