rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Accurate poverty measurement is the prerequisite for policies aiming at reducing poverty. Yet, development practitioners face the typical challenges that the available household survey data underlying poverty estimates are either inadequate (e.g., do not offer nationally representative estimates) or outdated (e.g., do not offer timely estimates of poverty trends). Worse still, in the few countries where survey capacity is well established, data were known to turn out to exhibit varying degrees of incompatibilities over time due to changes in survey design (Deaton and Kozel, 2005). These data challenges could likely hinder effective policy implementation, especially for poorer countries with low statistical capacity (Devarajan, 2013; Jerven, 2019).<sup>1</sup> To address these challenges, alternative methods to obtain poverty estimates that rely on data imputation (instead of direct data collection through surveys) have become increasingly common (World Bank, 2021; Dang and Lanjouw, 2023).<sup>2</sup> Building on the seminal technique that imputes from a household consumption survey into a census to generate poverty maps (Elbers _et al.,_ 2003), recent studies have imputed from a household consumption survey into another survey to provide poverty estimates.<sup>3</sup> The central idea is to build an imputation model using appropriate predictor variables from an existing older consumption survey, which can be subsequently applied to the same variables in a more recent survey (that does not collect consumption data) to provide poverty estimates for the latter survey.

> 1 Serajuddin _et al._ (2015) show that over the period 2002-2011, of the 155 countries for which the World Bank monitors poverty data using the World Development Indicators (WDI) database, almost one-fifth (i.e., 28) have only one poverty data point and as many as 29 countries do not have any poverty data point in the same period. Furthermore, poorer countries have fewer
# Appendix B: Application to the World Bank’s global poverty and inequality measures

The method we developed lends itself to being used in the World Bank’s global poverty measures for the countries that do not have any survey data. For the purpose of calculating regional and global poverty headcounts, countries without a household survey at any point in time, or countries without national accounts data needed for year-to-year extrapolations, are currently assigned the population-weighted regional average poverty rate. This affects 55 economies (Table B.1).<sup>5</sup> A few countries, such as Afghanistan, Saudi Arabia, the República Bolivariana de Venezuela, and the Democratic People’s Republic of Korea, make up the largest part of the population without data, adding up to more than 200 million people across all the missing economies (Table B.2).

**Table B.1: Economies without household survey data at any point in time in PIP**

||Cuba|||
|---|---|---|---|
|Afghanistan|Curaçao|Kosovo|San Marino|
|American Samoa|Dominica|Kuwait|Saudi Arabia|
|Andorra|Equatorial Guinea|Libya|Singapore|
|Antigua and Barbuda|Eritrea|Liechtenstein|Sint Maarten (Dutch part)|
|Aruba|Faeroe Islands|Macao SAR, China|Somalia|
|Bahamas, The|French Polynesia|Monaco|South Sudan|
|Bahrain|Gibraltar|Nauru|St. Kitts and Nevis|
|Barbados|Greenland|New Caledonia|St. Martin (French part)|
|Bermuda|Grenada|New Zealand|St. Vincent and the Grenadines|
|British Virgin Islands|Guam|Northern Mariana Islands|Timor-Leste|
|Brunei Darussalam|Hong Kong SAR, China|Oman|Turks and Caicos Islands|
|Cambodia|Isle of Man|Palau|Venezuela, RB|
|Cayman Islands|Korea, Dem. People’s|Puerto Rico|
puzzles, such as the reduction in China’s involvement in global production sharing since accession to the WTO. We pay special attention to the question to what extent accession-related policies _per se_ generated this export growth. Our assessment based on export data and simulation results on the ex-ante accession-related effect on export volumes in the literature finds that accession must have increased China’s real export growth by at most 6 percentage points between 1997 and 2005. This effect is substantial, but not as large as suggested by the difference between the pre- and postaccession export growth rates in the 4 years before and after accession because of the influence of cyclical fluctuations related to the Asian financial crisis and the US dot-com crash which dampened export growth in the period before accession in 2001 and accelerated it afterwards. It is re-assuring that the ex-ante projections of the accession-related effects on export growth in simulation studies are clustered in the range between 3.5 and 6 percentage points. For various reasons, these projections underestimate the actual increase in export growth, which historical data imply is close to the upper end of this range.

21
# **3 Data and descriptive statistics**

We built a country-level panel dataset covering 150 countries for which price data was available over the following period: 2008-2021 for fixed broadband, and 2012-2021 for mobile broadband. Our dataset includes data on price, SMCs, competition, and regulation, as well as several variables used as controls in the econometric modeling. Definitions and sources of variables are described in Table 1. Summary statistics are reported in Table 2, while correlation matrices are presented in Tables 3 and 4 in the Appendix.

## **3.1 Price data**

Telecom markets host a variety of packages with different levels of usage allowance and attributes, and this can make the measurement of price challenging in this sector. Several approaches are considered in the literature to address this challenge. Studies like Nicolle et al. (2018) use a hedonic price model, whereby price is estimated as a function of the attributes of packages. As such, a focus can be set on the price of a particular attribute, for instance, data allowance or quality, and be used in the analysis. Such modeling requires comprehensive data on all packages offered by operators and the results can be dependent on the functional form of the model. Other studies like Genakos et al. (2018) rely on the price of a particular basket of usage. As part of this approach, baskets of usage are defined, and the price of the least expensive offer is tracked for each basket. In this paper, we use the basket approach which does not require the availability of comprehensive data on packages – a task that would have been challenging to complete for a study that involves more than 150 countries/markets.

Interestingly, the ITU has defined several baskets of broadband packages and tracked their price since 2008.<sup>11</sup> We focused on two baskets that are the most representative of recent trends in data usage: (i) a basket of fixed broadband packages enabling at least 5 Gigabytes of
# **Appendix B. Model inputs**

_Appendix B.1. Employment rates_ The exogenous employment rates are the same for households within a given income group. We calibrate the parameters χ _i_ as the fraction of employed workers in each income group using cross tabulations for the City of Cape Town as a whole in the Census 2011 data. For each income group, we calculate the distribution of educational attainment. We then use the distribution of employment status for each educational attainment to derive the average employment status of each income group. Table B.3 summarizes the values of χ _i_ .

|Income group<br>Parameterχ_i_|1<br>0.57|2<br>0.97|3<br>0.96|4<br>0.97|
|---|---|---|---|---|

Table B.3: Values of the employment rate parameter _Appendix B.2. Employment centers and transport costs_ We extract employment center locations, their composition, and transport times to these centers from the City of Cape Town transport model, as detailed below: _Employment centers._ We use the number of jobs per income group at the Transport Zone level, estimated by the City of Cape Town in 2015 to recover local wages ( _wic_ ). For simplicity and speed of computation, we restrict the employment locations to Transport Zones with more than 2 _,_ 500 jobs (185 job centers). Figure B.4 shows these employment centers and relative employment size by income group.

28
ties between people and nature (Klain et al., 2017). Our study is situated within the relational values framework, focusing on how human behaviors, cultural ties, and economic incentives impact conservation outcomes. Relational values guide both the design and analysis of our experiments. The demand-side experiment employs a culturally resonant video that connects pride in Congolese wildlife to traditional culinary heritage without wild meat. This video aims to strengthen people’s cultural bonds to wildlife, in order to shift consumer behavior through a renewed sense of stewardship and identity (West et al., 2018). Likewise, in the supply-side experiment, we tested how offering a culturally relevant alternative—Moambe Chicken—at a lower price could reduce wild meat consumption. This intervention respects existing foodrelated cultural practices while encouraging sustainable consumption, aligning with relational values that prioritize harmonious human-environment interactions (Schulz et al., 2017).

Our study offers practical insights for policymakers and conservation practitioners on designing culturally sensitive interventions that resonate with local values and traditions. By using actual consumption data and robust methodologies—including randomized control trials, pre-registration, and response bias measurement—we adhere to best practices in conservation research (Cisse et al., 2023). Internationally, the demand and supply-side interventions we evaluate could be tailored to urban areas beyond Kinshasa where wildlife consumption poses conservation challenges. Our study’s theoretical contribution to the relational values framework is demonstrating how economic incentives can be integrated with cultural values to effect behavioral change. By addressing the socio-cultural drivers of wild meat consumption, our work highlights the critical role of integrating relational values into conservation efforts to foster sustainable behaviors that are both ecologically beneficial and culturally acceptable.

5
nization of European standards, for instance through a political economy process, the estimators are not longer unbiased or consistent. I argue that _NHt_<sup>_i_is exogenous to trade flows for two reasons:</sup> First, the decision making process to create or to harmonize an European standard does not involve any international consultation with trade partners.<sup>27</sup> Second, U.S. firms have raised concerns on their inability to participate in the formation of EU standards, these claims are documented in the 2010 Report on Technical Barriers to Trade.<sup>28</sup>

# **4.2 The Extensive Margin of Trade**

While harmonization of product standards are important to increase U.S. export value to the EU, the theoretical model predicts that the extensive margin of trade (the number of exporting firms) accounts for this variation (hypothesis 1). I estimate the impact of falling trade costs on the probability that non-exporting firms to the EU become exporters to the EU via a logistic regression on my measure of changing trade costs, firm productivity and other firm characteristics. I use firms’ information from the 1992, 1997, and 2002 U.S. Censuses of Manufactures. I define the change in trade costs for census year _t_ as the log difference in tariffs over the preceding five years ( _△τt_<sup>_i_</sup> +5<sup>)andasthelogdifferenceonmymeasureofprod-</sup> uct standard heterogeneity over the preceding five years ( _△NHt_<sup>_i_</sup> +5<sup>).These regressions are given by:</sup> where _Et_ +5 is a dummy variable equal to one if a firm does not export to the EU in year _t_ and becomes an exporter to the EU in year _t_ + 5; _PRt_ is the firm’s revenue based labor productivity, and _Zt_ is a set of additional firm characteristics. Additional firm controls include size,
and a decrease in fertility rates.<sup>10</sup> Previous research in Indonesia has shown that women’s economic participation is hindered by marriage and childcare responsibilities (for example, Cameron et al. 2019, 2023).<sup>11</sup> To the extent that data on Indonesian social norms were available prior to our study, they suggest that norms in relation to women’s work are conservative. For example, Indonesian men have a similar tolerance to women working as Saudi Arabian men and are less tolerant than Indian men.<sup>12</sup> Data on attitudes to women’s work from the World Values Survey 2018 shows that 76% of Indonesian men agree with the statement that men have more right to a job than women, with women being only slightly (2 ppts) less likely to agree with this statement than men.<sup>13</sup> Women and men, even young adults, continue to strongly conform to social norms that emphasize women’s childcare and domestic responsibilities because women are perceived as being better at care-giving (YouGov and Investing in Women, 2020). In a qualitative study of 40 young adults in Greater Jakarta and Surabaya, Setyonaluri et al. (2021) found that such persistent social norms often stem from perceptions of kodrat, or God’s will when defining gender roles. Participants in their study saw women working for pay as positive, but only if it was done to support husbands and women did not ‘neglect’ their responsibilities at home.

As a result of our sample being comprised of respondents in large urban centers who have access to the internet, 81% of the women in our sample worked, significantly above the national participation rate of 53%.<sup>14</sup> However, only 53% of wives of male respondents worked.

Of the female respondents who were working, only 41% worked exclusively outside the home (45% of the wives of
as the further progress towards a Common External Tariff or any progress in the non-trade policy aspects of the agreement. These remain on the agenda for further research 2Globally there has been a long-term decline in the relative importance of the Mercosur countries. According to UN COMTRADE data, in 1950 member countries accounted for 4.6 percent of world exports, yet this figure fell to 1.4 percent in 1992. Since most of the countries ran substantial trade surpluses since the mid-1980s Mercosur's share of world imports is even smaller, i.e., 1.0 percent in 1992.

3A more detailed analysis of the annual trade data used in the construction of Table 1 strongly suggests that 1991 was the year that intra-Mercosur trade began its significant increase in relative importance. In June of 1991 Mercosur began to implement discriminatory tariff preferences on intra-Mercosur trade.
- 74 -

# Sweden

Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1990. Public education and health are the responsibility of non-central govemment. Military employment data include conscripts (31,600), but exclude personnel in paramilitary units, e.g., Coast Guards, Civil Defense and Voluntary Auxiliary organizations. GDP at market prices are from IMF Government Finance Statistics Yearbook, 1995 and relate to 1994. Consolidated Central Government wages and salaries are for 1994 and are taken from IMF Government Finance Statistics Yearbook, 1995.

Data on wages in manufacturing are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and referto 1993.

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1992.

# **Switzerland**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1985. Public education and health are the responsibility of non-central government. Data on military employment include paramilitary units, e.g., the Border Guard (4,300), and exclude personnel of certain units, e.g., the Civil Defense (480,000). Data on Wages In Manufacturing (monthly basis) are taken from the International Labor Office's Yearbook Of Labor Statistics 1995 and refer to 1993.

# **United Kingdom**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993.

Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country rofiles, OECD 1992 and relate to 1992. Public education is provided by State schools operated by local authorities, a mixed sector which
|_Table 1:_<br>_Tier_|_WASH Tiers_<br>_Water_|_Sanitation_|_Handwashing_|
|---|---|---|---|
|0|<sup>No supply/ Not</sup><br>functional|<br>No latrine/ Not<br>functional|No facility|
|1|Functional<br>hand‐pump|One functional<br>latrine|Facility with<br>water|
|2|Functional<br>piped water|Two functional<br>latrines|Facility<sup>1</sup>with<br>water and soap|
|3|‐<br>|Three functional<br>latrines|<br>‐|
|4|‐|Four functional<br>latrines|‐|

The poverty figures at the upazila level are derived from the predicted poverty estimates from Steele et al. (2017).<sup>4</sup> It uses overlapping data from i) traditional household surveys (DHS, HIES, 2010 and Census 2011); ii) remote sensing data such as night‐time lights, distance to roads, distance to closest urban settlements, climate variables; and iii) call detail records at varying spatial resolutions to estimate poverty rates using Bayesian geostatistical models (BGMs) (See Annex‐Figure 1 for a upazila level map with poverty estimates). The only other poverty estimate available to us at the time of analysis were the national estimates from Census 2011, which we deemed outdated to use alongside 2017 WASH data.

The upazilas level stunting estimates for children under five were taken from the _Small‐Area Estimation of Child Undernutrition in Bangladesh_ report by Haslett et al. (2014), Bangladesh Bureau of Statistics (BBS) and the World Food Programme (WFP). The estimates combine survey data from the Child and Mother Nutrition Survey of Bangladesh 2012 (CMNS) and the Health and Morbidity Status Survey
Haaland, V. F., Rege, M., & Votruba, M. (2013). _Nobody Home: The Effect of Maternal Labor Force Participation on Long-Term Child Outcomes_ (SSRN Scholarly Paper ID 2363912). Social Science Research Network. https://doi.org/10.2139/ssrn.2363912 Hubbard, M. N. (2009). _Effects of maternal employment and child care on the health of young children_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/304960202/abstract/4C8AF9FF2D534249PQ/1</u> James‐Burdumy, S. (2005). The Effect of Maternal Labor Force Participation on Child Development. _Journal of Labor Economics_ , _23_ (1), 177–211. https://doi.org/10.1086/425437 Mendolia, S. (2016). Maternal Working Hours and the Well-Being of Adolescent Children: Evidence from British Data. _Journal of Family and Economic Issues_ , _37_ (4), 566–580. https://doi.org/10.1007/s10834- <u>015-9480-1</u> Morrill, M. S. (2011). The effects of maternal employment on the health of school-age children. _Journal of Health Economics_ , _30_ (2), 240–257. https://doi.org/10.1016/j.jhealeco.2011.01.001 Pekkurnaz, D. (2014). _Dynamic analysis of maternal employment, child care, quality and early childhood development_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/1613182648/abstract/D6F7129E26342F5PQ/1</u> Pilkauskas, N. V., Brooks-Gunn, J., & Waldfogel, J. (2018). Maternal Employment Stability in Early
not report services revenues separately in their accounts due to differences in accounting standards. Industry-level supply-use tables suggest that selling services is still a comparatively limited phenomenon with only 5 percent of revenue in European Union (EU) manufacturing being generated from services (Figure 1). However, industry-level data on services output may not fully measure the extent to which manufacturers sell services. For example, industry-level data from 2011 suggests that services output contributes to about 11 percent output of manufacturing firms in Sweden, while firm-level analysis finds this share to have increased from 13.6 to 20.3 percent of total sales over the 1997-2006 period (Lodefalk 2013, 2015).<sup>7</sup> In France, industry-level figures in 2011 suggest a small contribution of services (2 percent), while firm-level data suggests that this share was close to 18 percent during 1997-2007 (Crozet and Milet, 2015). In the United Kingdom, Figure 1 suggests services share in total revenue to be around 7 percent, while a firm-level study finds this share to have increased from 5 percent in 1997 to slightly over 20 percent in 2007 (Breinlich et al., 2014).<sup>8</sup> In advanced countries, high-tech manufacturing seems to be leading this trend towards servitization. In France, the mechanical and electrical equipment industries are the most servitized with a service intensity of 5 percent, in contrast to food, beverages and tobacco, which have the lowest service intensity, close to 1 percent (Crozet and Milet).<sup>9</sup> In the EU, supply-use tables also suggest that the share of revenue generated from selling services is 6 percent in the more technology intensive industries compared to 3 and 4 percent in medium- and low-tech manufacturing. This can be understood in light of the Dachs et al. (2012) and

> 6 Neely et al. (2011) cover over 10,000 manufacturing firms with over 100 employees in 2007, 2009 and 2011.

> 7
consider the national average sales-tax rate.

- d. **Social Security** usually has regressive tax rates with several brackets.  UK is the only exception, since a high tax credit offsets the effect of regressiveness from the tax brackets.  The income elasticities of social security tax revenues, computed in a similar way to the personal income tax elasticities, range from 0.75-1.10.

4. **Gini Coefficients:** The Gini Coefficients were used to log-normalize of the labor earnings distribution for the computation of personal income tax and social security elasticities. Series on Gini coefficients were taken from the World Bank’s Povcal Net database for developing countries and national sources for high-income countries with the exception of France, Germany and Belgium, taken from Eurostat. Complementary data from the World Bank’s World Development Indicators were also been used to fill in missing observations and check for consistency. We interpolated remaining missing observations by a regression of the Gini coefficient on GDP for existing years. This allows for predictable shifts in the income distribution due to cyclical conditions to further inform the output elasticity of tax revenues.