rafmacalaba/data-use-mentions-extended huggingface dataset:
earmarked money to people in need. Harberger (1978) argues that free education, subsidized medical care; food and housing for the poor are all standard fixtures in today's world, in developed and developing countries alike. Attempts to convert such subsidies into their cash equivalent regardless of how the cash might be spent have everywhere faltered and failed. Harberger believes that this might reflect the value systems of our societies. For instance, he argues, a look at intra-family transfers shows that these seem to be aimed more at financing specific expenditures or "needs", rather than representing a systematic sharing of wealth through cash transfers. The success of initiatives to assist people that have suffered misfortune is another indication of the same phenomenon. Thereby, it is easier for low-income people to articulate their demands in terms of specific goods and services, given their receptivity in society, thanjustify demands for higher income.

Waddams Price (1999) believes that the public perceives utilities as services that are necessary for life, thereby challenging the idea that these are services that consumers freely choose whether to buy. Moreover, society seems to have a special regard for the needs of consumers living in rural and isolated areas, as illustrated by the strong impact of TV reports describing the isolation of remote areas of the country and how their inhabitants endure medical and other emergencies. Doble, Markou and Waddams Price (1998) report that 73% of respondents in a sample-survey of 1,685 people in the UK believe that water should be provided at a subsidized rate to households who are unable to afford it for themselves. The corresponding figures for electricity and telephony are 67% and 53%, respectively.

Sometimes the reasons underlying programs to subsidize certain goods or services are more debatable, but these are normally not made explicit. One such is paternalism: the _5_
We evaluate the performance of these methods using unit-level census data across seven developing countries in Africa and Asia: Burkina Faso, Madagascar, Malawi, Mozambique, Sri Lanka, Tanzania, and Vietnam. These countries were selected due to the availability of georeferenced census data. In Malawi and Tanzania we are able to extend the evaluation to include a headcount poverty measure in addition to the asset index, while in the other five countries we evaluate prediction of an asset index, similar to Chi et al. (2022) and Masaki et al. (2022).

The first main method we evaluate is empirical best predictions based on a nested error linear model, which we refer to as EBP. This estimation method comes from a long history of small area estimation in statistics. (Battese et al., 1988; Jiang and Lahiri, 2006; Molina and Rao, 2010). We also evaluate three newer machine learning methods: Cubist regression models (Quinlan et al., 1992; Wang and Witten, 1997), extreme gradient boosting (Chen and Guestrin, 2016) – more commonly known as XGBoost – and boosted regression forests, or BRF (Friedberg et al., 2020; Tibshirani et al., 2018). As a robustness check, we also evaluate another traditional method commonly used in small area estimation, the ELL method (Elbers et al., 2003).<sup>2</sup> These methods differ in their level of parsimony and transparency on the one hand, and their predictive accuracy on the other, and a key goal of this exercise is to better understand the terms of this trade-off in the context of welfare prediction. In each case, we specify models at the sub-area level, which in these contexts refers to highly disaggregated administrative areas akin to groups of villages. We then aggregate predictions to obtain estimates at the target area for each country.

For predictors, we use satellite-derived geospatial indicators that are available across much of the globe, meaning that the methods and data
# **Figure 7: STRI in Retailing (commercial presence; mode 3)**

<!-- Start of picture text --> GCC EAP SAR AFR MENA OECD LAC ECA<br>Note: 103 countries included.<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> However, the apparent openness in retailing should be viewed with caution. First, we find many of the developing countries do not have formal regulations for retail activities. In fact, the STRI database shows that in 23 percent of the countries a license is not required, and 30 percent of countries indicate there is no regulatory authority in retailing. It is not clear how far this lack of formal regulation translates into openness.

Furthermore, openness in retailing can be deceptive because, apart from explicit restrictions, there is evidence to suggest that a complicated multi-layered licensing process and idiosyncratic and behind-the-border restrictions hinder market entry. These measures are often buried in the procedures of obtaining licenses and in various requirements of administrative bureaucracy, all of which are hard to capture. For example, in the absence of a sector regulator, some countries require firms to obtain a license from a body whose board of directors consists of local trading enterprises and/or private sector representatives.

While there are few explicit barriers on ownership and licensing and few explicit quantitative limits on licenses, a number of countries impose economic needs tests (ENTs). These 31
example, using data from the Mexican Health and Migration Survey (HMS) taken in eleven communities in the state of San Luis Potosi, we find a strong and significant correlation between our measure of health knowledge and whether the mother has a good explanation for the causes of diarrhea.<sup>14</sup> Table 8 examines the impact of migration on the health knowledge of mothers. Columns 1 and 2 show that mothers in migrant households are found to have more health knowledge than mothers in non-migrant households. After

> 14 See <u>www.mexmah.com</u> for further description of this survey.

- 15 -
of education of plot manager|
|African Adult Male Equivalent|African Adult Male Equivalent household size|
|Dependency ratio|Dependency (child and adult) ratio|
|Distance to boma|Distance to district capital (Km)|
|Index of ownership of agricultural tools|1<sup>st</sup>principal component analysis of agricultural tools owned by the household|
|Index of ownership of durable good|1<sup>st</sup>principal component analysis of durable assets owned by the household|
|Annual mean rainfall|Avg 12-month total rainfall(mm) for July-June|
|Annual mean temperature|Annual mean temperature (<sup>o</sup>C*10)|

32
than) 7; ii) strong (weak) executive constraints if its average executive constraint index is greater or equal to (strictly lower than) 5; and iii) a democratic (autocratic) political system, if the average Polity2 index is greater or equal to (strictly lower than) 0. For example, the United States is coded as having a high level of political competition, with an average score of 9.8 over the sample period 1980 to 2018. We find that 77 countries have strong political competition and 88 have weak competition; 79 countries have strong executive constraints and 86 have weak ones; there are 111 democratic countries and 55 autocratic ones.

In section 3 we use alternative cutoff points for the various measures. We also use an alternative measure of democracy from Freedom House.

Data on education attainments are from Barro and Lee’s (2013) data set, obtained from the 2018 Update July 4. Structural reforms are from Ostry et al. (2009). The data set captures reforms along 6 dimensions: (i) domestic finance; (ii) capital account (iii) current account transactions; (iv) trade (v) agricultural regulation (vi) network reforms. Each of these indicators is normalized to take values between 0 and 1, where higher values denote more liberalization. In our analyses, the variables are organized into three groups using principal component analysis, where the first principal component is used in the analysis. Indicators (i),(v),(vi) are grouped as 'domestic' indices; indicators (ii),(iii),(iv) are termed 'international' indices. Finally, an aggregate index is used. Note that reforms are defined as changes in the indices.

We also use Doing Business data from the World Bank (2020) to capture regulatory reforms. Doing Business benchmarks aspects of business regulation based on work on the regulation of 7
baseline survey – see column (5). In the case that we include the variable without imputation – see column (6) – the estimated coefficient is slightly lower but the statistical significance does not change.

Finally, we also conduct robustness checks of the effects of survey mode on secondary outcomes after including other control variables that can proxy for income level. Specifically, we test if the lack of differences in the consistency of reporting sensitive information by survey mode changes after the inclusion of educational level as control variable. As we show in Table A5, differences are still statistically insignificant after including this proxy for income level.

# **7 Discussion and Concluding Remarks**

We provide empirical evidence on how a phone-based mode affects survey completion rates relative to a self-completion mode. Considering the rapid increase in access to digital technologies, this represents a key parameter for policy makers who confront a set of trade-offs when collecting data. We find that phonebased surveys increase response rates by 42 percentage points—even more if targeted to women and older generations. Considering the costs of data collection and the likelihood of failing to find a significant effect in underpowered studies, this result provides a key piece of information, especially in countries similar to El Salvador, where we ran this experiment.

Importantly, along with differences in completion rates, we also document differences in responses across groups. This can be relevant when collecting data on sensitive issues. For example, we observe that phone-based survey respondents were less likely to report a high level of stress. We find suggestive evidence that differences in stress level by mode of survey were due to differences in willingness to report as opposed to changes in the composition of the group that completed the survey. This is important if a research team is considering combining phone-based and self-completion surveys in some part of the datacollection process, and it wants to evaluate how much its decision can affect both the integrity of the data collected and its comparability with other sources of information.

Finally, policy makers would like to know the
<u>Table A.11: Robustness to Annual Data (DOLS and FMOLS)</u>

||VEC|OLS|DOLS-1|DOLS-2|FMOLS|
|---|---|---|---|---|---|
|Cointegration relation|||(HAC)|(HAC)||
|Terms of trade (tot)|1.681***|1.001**|1.174***|1.360**|0.996***|
|Productivity diff (tnt_ag_xchn)|0.642**|0.729**|0.607**|0.513*|0.721*|
|Reserves (resY)|0.644***|0.246**|0.457***|0.591***|0.335**|
|Trade openness (tradeY)|-2.025***|-1.158***|-1.531***|-1.766***|-1.213***|
|Constant|-13.661***|-4.736**|-8.537***|-11.239***|-6.474**|
|Lag Specification||||||
|Lags|1|0|-|-|-|
|Leads and lags|-|-|0|1|-|
|Cointegration Test||||||
|Johansen's cointegration rank|1|-|-|-|-|
|Hansen Parameter Instability 1/|-|-|0.131|0.145|0.512|
|(Probability)|-|-|(> 0.2)|(> 0.2)
br>given limited increase in emergency obstetric<br>care and quality of care constraints<br>|
|vouchers|f|f|
|3|Chiranjeevi Yojana,<br>Gujurat, India, 2005<br>onwards|Not assessed, but authors note that PNC is notincluded inthe<br>package||Not assessed|
|4)Safe Motherhood<br>Vouchers, Kenya,|Not assessed|Not assessed|
|5/RH Vouchers, Uganda||The proportion of diagnoses correctly treated ranged from 79% <br>for balanitis to98% forgonorrea.||Knowledge ofSTI symptoms increased 18<br>percent between the first and second years<br>(adjusted odds ratio, aOR=1.43; 95 percent<br>Cl=1.22-1.68). The prevalence of syphilis, as<br>measured bythe VDRLtest, decreased 42<br>percent between the two surveys<br>(aOR=0.63795 percent Cl=0.48-0.79). There was<br>a greater reduction in the prevalence of<br>syphilis among respondents who lived within<br>10 kilometers of a contracted facility (57%<br>decreaseversus20%forthosefurtheraway)|
strategy. We first clean the self-reported data, adopting an algorithm which keeps only observations that fulfill a battery of tests.<sup>25</sup> Then, we apply the PIM only to fill the gaps between the

> 21The Indonesian survey asks manufacturers about expenditures and quantities of PLN and non PLN electricity, gasoline, diesel, kerosene, coal, gas, LPG, lubricant, oil diesel, oil burn, charcoal, firewood, coke plus a category labelled “other fuels”. However, exception made for PLN, non-PLN electricity, diesel, gasoline and lubricants, all other sources have been included in “other fuels” in some years. Therefore, to minimize noise, we limit the analysis to the energy sources that have been separately identified in every year in the sample.

> 22We used the following standard conversion factors: 1 litre of Diesel corresponds to 10 kWh; gasoline: 9.1 kWh; lubricants: 11 kWh.

> 23A similar strategy can be found in Marin and Vona (2017).

24In particular, there is no a priori reason to believe that the quality of the self-reported capital stock the first year is necessarily better than the value in other years.

> 25The procedure is described in the appendix.

16
particular, to test whether a possible short-term reduction in quality of public good provision induced by reservation may be offset by the fact that this intervention exposes individuals, especially females, to new ideas and enhances their participation in local political decisions with beneficial effects in the long term.

# **2.3 Econometric approach**

To explore the issues discussed above, we use data from a nationally-representative panel survey of 233 villages in rural India conducted in 2008 by the National Council of Applied Economics (NCAER). In addition to its national coverage, this dataset, which is based on a long-running panel, has a number of desirable features. First, with information on the last three village-level elections, it provides information data on the reservation status of all panchayats elected after the provisions of the 73<sup>rd</sup> amendment were adopted in a state.<sup>8</sup> This allows us to test for long-term impact of reservations in a setting of decentralized decision-making. Table 1, which provides a breakdown of reservation by region, illustrates that out of the

> 8 While this includes three elections for most of the states, Bihar, Orissa, Punjab, Tamil Nadu and West Bengal held the earliest elections in 1992, before the amendment was adopted.

9
Policy Research Working Paper 11046

# **Abstract**

This paper investigates the role of gender bias in artificial intelligence–driven analyses of citizen participation, using data from the 2023 Latinobarómetro Survey. The paper proposes that gender bias—whether societal, data driven, or algorithmic—significantly affects civic engagement. Using machine learning, particularly decision trees, the analysis explores how self-reported societal bias (machismo norms) interacts with personal characteristics and circumstances to shape civic participation. The findings show that individuals with reportedly low levels of gender bias, who express political interest, have high levels of education, and align with left-wing views, are more likely to participate. The paper also explores different strategies to mitigate gender bias in both the data and the algorithms, demonstrating that gender bias remains a persistent factor even after applying corrective measures. Notably, lower machismo thresholds are required for participation in more egalitarian societies, with men needing to exhibit especially low machismo levels. Ultimately, the findings emphasize the importance of integrated strategies to tackle gender bias and increase participation, offering a framework for future studies to expand on nonlinear and complex social dynamics.

This paper is a product of the Social Development Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jcuesta@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<
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Kosack, Stephen and Fung, Archon, Does Transparency Improve Governance? (May 2014). Annual Review of Political Science, Vol. 17, pp. 65-87, 2014.

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Larreguy, Haracio A., John Marshall and James M. Snyder, Jr. "Revealing Malfeasance: How Local Media Facilitates Electoral Sanctioning of Mayors in Mexico." NBER Working Paper No. 20697 (2014).

Lathrop, Daniel, and Laurel Ruma (eds.). “Open Government: Transparency, Collaboration and Participation in Practice,” O'Reilly Media (February 2010).

Little, Eric, and Carolyn Logan. “The Quality of Democracy and Governance in Africa: New Results for AfroBarometer Round 4,” Working Paper 108, AfroBarometer Network (2008).

McKinney, James, Stephane Guidoin, and Paulina Marczak. “OGD Standards Inventory: Open Government Partnership OGD Working Group Standards Stream,” Open North (2015).

Myles, J., and J. Quagagno. “Political
greater.|Pradenas (2006)|

# **3 The Model**

To estimate the relationship between institutional fragmentation and economic performance of cities, measured through city productivity, we follow the two-step empirical approach devised by Ahrend et al. (2014). The authors warn about the importance of accounting for individual sorting of highly skilled individuals into cities when estimating productivity differentials across urban areas (Combes et al., 2011). This is necessary in order to account for the tendency of more talented individuals to colocate in cities that may lead to confounding agglomeration benefits with productivity increases from a more skilled workforce. Thus, in the first step, we use data from the Defense Meteorological Satellite Programs – Operational Linescan System (DSMP-OLS) nighttime lights (NTL) imagery to identify urban areas as well as survey-based micro-data for the period 2000-2014 to estimate productivity differentials across urban areas, net of individual and employment characteristics observables. The estimation on this first stage is then:
Data Appendix:

# A. Current Population Survey

We use the CPS Merged Outgoing Rotation Groups for years 1979 to 2002. Note that our analysis relies on the MORG copy prepared by the Center for Policy and Economic Research (CEPR). The CEPR MORG files are created from the National Bureau of Economic Research version of these data, and we rely on the processing performed by CEPR to produce consistent variables for wages, education, and other demographic characteristics of the MORG sample. Our sample includes wage/salary for workers ages 16 to 64 in current employment. Earnings weights, equal to the product of CPS sampling weights and hours worked in the prior week, are used in all calculations. Hourly wages are the logarithm of reported hourly earnings for those paid by the hour, and the logarithm of usual weekly earnings divided by usual weekly hours among salaried workers. Overtime, tips, and commissions are included in wages, and top-coded wages are computed by assuming a lognormal distribution for weekly earnings as described by Schmitt (2003). The calculated nominal hourly wage is converted to a real wage using the Consumer Price Index for 2006, and then trimmed to values between $1 and $100 per hour.

Source: Schmitt, John. 2003. “Creating a consistent hourly wage series from the Current Population Survey's Outgoing Rotation Group, 1979-2002.” Available for download at http://www.ceprdata.org/cps/cps_documentation.php.

# B. Bureau of Economic Analysis

Our data on offshoring is based on the most comprehensive available data and is based on firm-level surveys on US direct investment abroad, collected each year by the Bureau of Economic Analysis (BEA) of the US Department of Commerce. The BEA collects confidential data on the activities of US-based multinationals. Multinationals are defined a parent company, the US entity that made at least one direct investment in a foreign affiliate, defined as a foreign business enterprise. We use the data collected on majority-owned, non-bank foreign affiliates and non-bank US parents for
physical violence, emotional or psychological violence, sexual violence, or financial or economic violence (see World Bank 2015 for details). The mean value of the dummy variable equals 0.31, implying that on average in any given year about 31 percent of

> 6 In a number of instances the Women, Business, and the Law database shows the far‐reaching consequences of restrictions against women embedded in countries’ laws. For instance, it shows that the larger the number of discriminations against women found in the law, the lower the secondary school enrolment rates of girls relative to boys and the lower the female labor force participation rates.

10
*<u>gender, %</u>**

<!-- Start of picture text --> Agriculture 55.1 44.9<br>Information and Communication Technologies 53.8 46.2<br>Humanities and Arts 50.2 49.8<br>Health and Welfare 49.9 50.1<br>Education 49.0 51.0<br>Social Science, Business and Law 48.0 52.0<br>Science 47.7 52.3<br>Engineering, Manufacturing and Construction 46.3 53.7<br>Business, Administration and Law 44.9 55.1<br>Science, Technology, Engineering and Mathematics... 27.8 72.2<br>0.0 20.0 40.0 60.0 80.0 100.0<br>Female Male<br><!-- End of picture text --> _Source:_ WDI data retrieved May 2020. All data for 2015, except ‘Engineering, Manufacturing and Construction’ and ‘Education’, both datapoints for 2012.

This educational context translates into the current Sudanese labor force in which close to half (45 percent) of those out of the labor force are individuals with no education. This group represents the majority (52 percent) of the employed group. This probably explains why most of those engaged in agriculture have no education. Given that individuals with secondary and above secondary education represent only 26 percent of the labor force, of which 45 percent are unemployed while only 23 are percent employed, it suggests that the market demand is focused on low-skilled and low-paid work (Figure 13). There are stark gender differences: most women who are out of the labor force also appear to have no education (41 percent). The corresponding number for males is only 4 percent. But for all levels of educational attainment, the share of women out of the labor force