rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9226

# **Abstract**

The growing importance of services trade for countries across the world is well-documented in the services and trade literatures. At the same time, regulatory and administrative barriers to the movement of service suppliers to deliver services internationally have resulted in very low shares of “Mode 4” trade in total services trade. Against this background, this paper conceptualizes an agenda for trade facilitation in services as it would apply to the movement of natural persons. It also provides descriptive statistical evidence from the Organisation for Economic Co-operation and Development’s Services Trade Restrictiveness Index database. The paper differentiates between regulatory measures that may improve transparency or facilitate access for service providers and those measures that impede Mode 4 trade as well as the administrative, financial, and economic costs of compliance with such measures.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 author may be contacted at igillson@worldbank.org or ssaez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and medium for the rest. The lower the correlation, the more informational value can be obtained from the SF approach to determine efficiency with respect to own benchmark.

In Table 5, we report the top 5 and bottom 5 countries according to OTE for each indicator. For illustration, let’s consider some specific examples. For access to finance, the SF model places Mongolia at the top 1, while the raw indicator places Norway as the best. The raw indicator is right in absolute terms, since almost 100% of the population in Norway has an account at a financial institution. However, Mongolia appears to be the country that is doing its best considering its structural characteristics: 85% of the population in Mongolia has an account at a financial institution, which is outstanding considering that Mongolia’s GDP per capita is only 2% of Norway’s and that Mongolia is the second country with lowest population density. Mongolia’s high OTE and ranking does not mean that it is better than, say, Norway. Rather, it tells us that given its structural characteristics, Mongolia is operating close to the frontier and does not have much slack (scope for improvement) regarding financial inclusion.

Consider another example. While for educational quality there is high correlation between the OTE and the raw indicator, inspection of the SF outcomes reveals some surprising results. The SF model places Vietnam as second while Korea is ranked seventh. However, according to the raw PISA indicators for reading performance, this second position is occupied by Korea, while Vietnam is number 17. The SF model tells us that Vietnam has been more efficient in reaching its frontier for educational quality considering that its overall level of development is way below Korea – Vietnam’s GDP per capita is only about 5% of Korea’s GDP per capita. Note again that we are not comparing the performance of one country with another; rather, we are comparing each country with its potential.

A third example relates to the indicator of financial depth
_Figure 4: Yield loss when electrical conductivity exceeds 100mS/m_ <!-- Start of picture text --> a. Mekong River Basin b. India c. Global<br>0% 0% 0%<br>‐2% ‐2% ‐2%<br>‐4% ‐4% ‐4%<br>‐6% ‐6% ‐6%<br>‐8% ‐8% ‐8%<br>‐10% ‐10% ‐10%<br>‐12% ‐12% ‐12%<br>‐14% ‐14% ‐14%<br>‐16% ‐16% ‐16%<br>‐18% ‐18%<br>‐18%<br>‐20% ‐20%<br>‐20%<br>30 percent 75 percent 90 percent 30 percent 75 percent 90 percent 30 percent 75 percent 90 percent<br>Cropland Threshold Cropland Threshold Cropland Threshold<br>Percentage Yield Loss Percentage Yield Loss Percentage Yield Loss<br><!-- End of picture text --> Notes: Figure shows results from estimating equation 1 in 3 different regions. Figure a (left) if in the Mekong River Basin, figure b (center) is in India, and figure c (right) is global using GEMStat data. In each region, coefficients from three different regressions are shown. The three regressions vary the restrictions on the share of cropland within a gridcell needed to be included in the regression. The left estimate includes all gridcells with at least 30 percent cropland in the year 2000. The mid and right estimate include gridcells with at least 75 percent and 90 percent cropland, respectively. The point estimate shown is the coefficient on a binary variable indicating observations where electrical conductivity in the upstream monitoring station exceeds 100mS/m. Error bars show the 95 percent confidence interval based on standard errors clustered at the gridcell
# **1. Introduction**

Artificial intelligence (AI) enables computers and machines to mimic human thinking, problemsolving, and social skills (Haenlein and Kaplan, 2019). We encounter AI in our daily lives through digital assistants, GPS navigation, self-driving cars, and generative tools like OpenAI’s ChatGPT. Many see AI as a catalyst for economic growth, impacting various sectors, from consumer goods to health care (Shrestha and Das, 2022). However, this promise also raises significant concerns, particularly regarding how AI can perpetuate societal inequalities and injustices linked to factors such as gender, race, ethnicity, or age. This paper specifically examines how bias in AI, rooted in social inequality, can hinder our understanding of citizen participation, a vital element of democracy.

While previous research has explored citizen participation, few studies have employed AI methods. The available evidence indicates that participation is a complex and nonlinear behavior that traditional linear statistical methods often oversimplify. For example, Pecorari and Cuesta (2024) highlight potential misinterpretations of participation patterns in Latin America. One of those is that women are generally less likely to engage in various forms of civic participation— such as signing petitions, or voting (which is not corroborated by data from Latinobarometro 2024).

Although the case for applying AI methods to study participation is compelling, little attention has been paid to how biases related to gender, gender identity, and sexual orientation impact research findings and the policy making that follows. We propose a two-part working hypothesis: first, that gender bias significantly affects our ability to predict citizen participation; and second, that all forms of gender bias—societal, data, and algorithmic—are crucial in shaping these predictions.

This paper contributes to the literature by identifying, assessing, and comparing different gender biases in the context of AI's analysis of social issues, rather than considering them separately as previous studies discussing AI biases have done. It is important to note that AI is closely linked to machine learning and
activity whereas demand-driven oil price increases are associated with rising activity, albeit often with weaker effects (Baumeister and Hamilton 2019; Cashin, Mohaddes, Raissi and Rassi 2014; Peersman and Van Robays 2012; Kilian 2009; Lippi and Nobili 2012; Aastveit, Bjørland, and Thorsrud 2015). This paper tackles a similar question but in a narrative approach which explicitly isolates episodes of large oil price collapses. It distinguishes oil price collapses since 1970 by their main sources and estimates their impact on growth in a local projections model. This approach is much closer to a natural experiment than the identification schemes used by previous studies that often relied on ad hoc assumptions.

Second, this paper focuses on emerging markets and developing economies (EMDEs) whereas most of the previous literature has restricted itself to advanced economies. Baumeister and Hamilton (2019); Baumeister and Peersman (2013); Kilian (2009); and Kilian and Murphy (2014) estimate the impact of oil demand and supply shocks on U.S. output in vector autoregression approaches. Kanzig (2019) and Lippi and Nobili (2012) extend these exercises to U.S. industrial production and Anzuini, Pagano and Pisani (2015) to a monthly U.S. activity index. Peersman and Van Robays (2012) extend these estimations to broader samples of predominantly advanced economies. Few studies include EMDEs at all and even those that do include only about a dozen large EMDEs (Caldara, Cavallo, and Iacoviello 2019; Mohaddes and Raissi 2019; Aastveit, Bjørland, and Thorsrud 2015; Cashin, Mohaddes, Raissi and Rassi 2014).

Yet, the impact of oil price swings might well be expected to be larger for EMDEs than for advanced economies. EMDEs are almost twice as energy intensive as advanced economies ( Figure 1 ). Almost one-quarter of EMDEs rely heavily on energy exports and another one-fifth of EMDEs rely heavily on agricultural exports, which are themselves highly energy intensive. Hence, this paper relies on data for the largest EMDE sample yet (153
for Romania). However, this empirical result reflects the sector’s marginalization after the almost completed massive privatization rather than an outcome of purposeful public policy. Still, even in these countries some income-rich tenants remain in the public stock and continue to pay very low “token” rents.

> 55  Data from the Lithuanian Household Survey in 2002 suggest an overrepresentation of both very poor and very rich tenants in the public stock. This is partly due to the relatively uniform distribution of public housing among urban and rural locations. However, anecdotal evidence for Vilnius suggests a concentration of poor clients even in a high-income region.

31
Consistent with what was observed through qualitative work conducted in the field as part of this impact evaluation, there is, however, indication of strong indirect effects of the program on school choice among boys. We find that families with daughters eligible to the FSSP tend to respond to the program by enrolling their sons in private schools. In fact, several econometric models show a statistically significant average increase in enrollment of boys in private schools of nearly 4 percentage points compared to a baseline level of 30 percent. Further analysis indicates that this increase in the share of male enrollment in private schools is largely driven by boys 6 to 12 years old who were sent to primary schools.<sup>34</sup> This behavioral response may be explained by reasons related to the supply, cost, and quality of private schools that offer primary-level education. First, there has been a significant increase in the number of private primary schools in rural and urban Punjab in the 2000s. Second, fees in private schools are often low and below the stipend provided by the FSSP (200 Rs. per month). In fact, a national census of private schools in 2000 found that the median rural private school charged 60 Rs. per month.<sup>35</sup> Third, there are also differences in quality; children who study in private schools score substantially higher in tests in all subjects (Andrabi and others 2006). Therefore, a potential indirect effect of the FSSP may introduce gender disparities in learning as more boys in eligible households are enrolled in better performing schools. More work, however, is needed to better understand this possible indirect effect.

# **7. Robustness Checks**

As eligibility to the FSSP was not assigned randomly, the analysis conducted in this paper has to rely on quasi-experimental techniques and their corresponding identifying assumptions. To assess the internal validity of this evaluation, we discuss the robustness of the results to a few issues that could contaminate the design of the evaluation. We start with
Mullainathan, 2016; Waldman _et al._ , 2020). Examples include availability bias<sup>7</sup> (Tversky and Kahneman, 1973; Karlan _et al._ , 2014; Brown _et al._ , 2018), scarcity<sup>8</sup> (Shah, Mullainathan and Shafir, 2012; Mani _et al._ , 2013; Lichand and Mani, 2020), satisficing<sup>9</sup> (Krosnick and Presser, 2010), and a large body of methodological research on the accuracy of farmers’ self-reports (Carletto, Savastano and Zezza, 2013; Carletto, Gourlay and Winters, 2015; Arthi _et al._ , 2018; Gourlay, Kilic and Lobell, 2019; Wollburg, Tiberti and Zezza, 2021).

# **3. Empirical Strategy and Data**

In exploring the suitability of survey data for disaster crop loss measurement, this paper will be guided by the methodology for damages and losses assessment in agriculture that was recently developed in Conforti, Markova and Tochkov (2020) for the FAO (henceforth also “the FAO methodology”). Complementing previously developed disaster impact assessment approaches by the United Nations Economic Commission for Latin America and the Caribbean (UN ECLAC, 2003) and the multi-agency Post Disaster Needs Assessment methodology (European Union, UN Development Group and World Bank, 2013), they provide a standardized methodology for measuring damages and losses due to disasters across the agricultural sub-sectors of livestock, forestry, aquaculture, fishery, and (this paper’s focus) crops. To measure the impact in the crop sector, the FAO methodology distinguishes between annual and perennial crops as well as damage (e.g. the destruction of stored inputs or stored crops) and loss (e.g. reductions in crop yields).

DL (Crops) = Annual crop prod. damage + Perennial crop prod. damage + Annual crop prod. loss + Perennial crop prod. loss + Crop assets damage
under-identification (though the instrument is statistically not weak).

> 40 This was done using OpenSourceMaps, which include coded data on land usage, each informal business can be coded according to the near-area surrounding where they were interviewed.  Using a 500m radius, an estimated 90% of informal businesses in Harare operate within 500m of a residential area. Only 27% of businesses are near a commercial or industrial area and a scant 16% are within half a kilometer of retail space, providing further support that the informal and formal sectors operate in segmented areas within Harare’s city limits.

(28)
current account balance if it is negative. The total investment is financed by the sum of all savings: TOTINV= ∑HSAVh h + GSAV+ ESAV+ EXR⋅FSAV+ WALRAS (37 `)` PQi ⋅QINVi = shareinvi ⋅TOTINV (38) where TOTINV is the total investment, WALRAS is the dummy variable, and shareinvi is the share of commodity i used as an investment in the total investment. The real GDP is calculated using the expenditure approach as follows: GDP= ∑(∑QHi h i,h + QGi+QINVi) + ∑QEi i −∑QMi i (39)

# **2.1.7 CO2 emissions and carbon tax**

CO2 emissions are calculated by multiplying fuel inputs in each production sector and final demand sectors by emission coefficients. CO2 emissions from sector i is calculated as: TCO2 = (∑QHh pp,h + QGpp+QINVpp + ∑QQi pp,i). coefpp (40) Where coefpp is CO2 emissions coefficient of petroleum products expressed in terms of ton of CO2 per unit monetary value of its consumption, carbon tax (CTAX) which is exogenous to the model, is converted to equivalent indirect tax ‘ctr’ as follows: ctrpp = CTAX. coefpp (41) This equivalent indirect tax rate is added to the existing indirect tax rate (indt). **2.2. Data** The main data source used for this study is the social accounting matrix (SAM) of Ethiopia. The SAM was built by the Policy Studies Institute of Ethiopia (PSI) using 2016 input-output tables and national accounts. It is available on request.<sup>6</sup> Other data needed are values for various parameters, such as elasticity of substitution used in CES functional forms used to model the 6 It can be argued that the analysis based on six-year-old data might not represent the economic reality of today in Ethiopia. However, SAMs are developed occasionally (such as at 5-year intervals) even in
in living standards over time, even when using consumption as the welfare indicator. So the lags in implementation have bearing on the performance of PMT in reducing current poverty.

We have panel data for a subset of our study countries, namely Ethiopia, Malawi, Nigeria, Tanzania and Uganda. By exploiting the panel data, we can introduce a 1 to 2 year lag in the implementation of PMT. The precise lags are one year for Uganda, and two years for the other countries.<sup>46</sup> In other words, we develop the PMT on the Round 1 survey data and then apply it in Round 2. If anything, our lags appear to be less than found in practice.<sup>47</sup> We consider two types of lags. In the first (Method 1), we take the regression parameters from Round 1, but use the covariates from the Round 2 data. Here there is no lag in the observations of the covariates; the lag is only due to the need to estimate the PMT scores. In the second (Method 2), we simply use the PMT score from Round 1, which we then compare to the survey data on consumptions in Round 2. The lag then applies to all aspects of the PMT method (both parameter estimates and covariate values).

The targeting errors obtained using Methods 1 and 2 are found in Tables 15 and 16, respectively. Comparing the results in Table 15 (top panel) with Table 5 we see that allowing for lags increases the targeting errors on average.<sup>48</sup> For the lower line, we now find that, on average, about half of those predicted to be poor are not in fact poor based on the survey data (a mean IER of 0.553, as compared to 0.481 from Table 5). Exclusion errors are also affected, though these errors rose less markedly. Using the Extended PMT we also find a substantial increase
information. We compare the performance of different groups of firms based on their exposure to the internationalization events. First, the _foreign listed group_ consists of firms listed outside mainland China, whose stocks were available to international investors for the entire sample period. Second, the _domestic listed group_ includes firms listed only in mainland China, whose stocks became available to international investors through the different internationalization events. Third, within the domestic listed group, the _connected group_ is the subgroup of firms whose stocks became accessible to international investors through the Stock Connect program and the incorporation into the MSCI Emerging Markets Index. Fourth, the _unconnected group_ refers to the remaining firms in the domestic listed group.

We systematically compare (i) domestic listed with foreign listed firms and (ii) connected with unconnected domestic listed firms. We emphasize the comparison between connected and unconnected firms because it is less subject to omitted variable bias and other identification concerns. The panel dataset enables us to examine yearly differences between the treatment and control groups over a long period.

We find that firms targeted by the post-2012 internationalization events substantially increased their equity issuance and investment activities relative to non-targeted firms. Domestic and foreign listed firms followed similar equity issuance patterns during 2000-13. But since the implementation of the Stock Connect in 2014, domestic listed firms, especially the connected ones, increased their equity issuances relative to the other firms. The difference in equity issuances between connected and unconnected firms peaked during 2015-17 and remained significant during the 2018-20 MSCI incorporation process. By 2020, the cumulative amount of equity raised (over initial assets) was 51 percentage points higher for connected firms than for unconnected firms with similar initial characteristics. Connected firms also increased their capital expenditures, acquisitions of other firms, research and development (R&D) expenditures, and short-term investments (including cash) relative to unconnected firms during 2014-20. We show that the rise in 2
19

# **Table 3. Bank Performance and Regulation in 2011, Survey IV**

|`------------------------------------`<br>`------------------------------------`|`--------------`<br>`Bank`<br>`Developmen`<br>`--------------`|`-------------`<br>`Interest`<br>`t Margin`<br>`-------------`|`--------------`<br>`Overhead`<br>`Costs`<br>`--------------`|`---------------------`<br>`Nonperforming`<br> <br>`Loans`<br>`---------------------`|
|---|---|---|---|---|
|<br>`Capital Regulatory Index`|<br>`-0.156**`<br>`(0.0669)`|<br>`0.00445`<br>`(0.00268)`|<br>`0
We assessed the external validity of our experiment and the representativeness of our sample by comparing them with the sample of the 2019 Household and Multipurposes Survey (EHPM), a nationally representative survey in El Salvador that is conducted through face-to-face interviews. Of the approximately 75,000 individuals interviewed in the EHPM, 15,680 (21 percent) meet our eligibility criteria (See Table A2 in the Appendix).<sup>17</sup> When we restrict the EHPM sample to individuals who meet our criteria, we find no meaningful differences in age, gender, and age of the oldest child with our sample (See Table A3 in the Appendix).<sup>18</sup> We also verify that the randomization of individuals in the Phone/WhatsApp intervention was not correlated with the characteristics and treatment assignment of the impact evaluation studied in Amaral et al. (2021). Therefore, we show in Figure A2 that there is no correlation across gender, age or mobile phone usage.

# **3 Data and Summary Statistics**

## **3.1 Data Sources**

We use four sources of data to assess the relative convenience of the two survey modes: administrative records provided by the survey firm, responses to the enrollment survey, responses to the midline survey, and responses to the parenting intervention’s baseline and endline surveys.<sup>19</sup> _Administrative records provided by the survey firm._ This data includes information on whether the individuals completed or did not complete the midline survey, the reason for non-completion (for those who did not complete it), and the number of reminder calls made to each person.

_Enrollment survey._ The enrollment survey included questions on respondent’s gender and age, mobile phone ownership status, whether they lived with and cared for a child eight years old or younger, and the

> 17The eligibility criteria are described in footnote 12.

> 18We only find differences between the EHPM and the survey-mode experiment samples