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

In the last decade, Mali has become the epicenter of one of the world’s worst civil conflicts (ACLED, 2020), generating more than 346,000 internally displaced peoples (IDPs) (UNHCR, 2020). Real and perceived inequalities between and within communities have helped fuel this conflict (Pezard & Shurkin, 2015). The plight of IDPs in Mali is severe, which has been recognized and they are actively aided by national and international organizations. Hoogeveen, Rossi, and Sansone (2019), which studies the dynamics of intentions to return after the early part of Mali’s conflict subsided, shows that some but not all IDPs intend to return home. IDPs fleeing this conflict and locating in other Malian towns potentially affect social cohesion by changing economic conditions including wealth, poverty, and inequality among the population in host communities. Policymakers dealing with an internal displacement crisis are often challenged to mitigate shocks caused by a sudden inflow of people on host populations, while providing adequate support for incoming displaced peoples. If IDPs foster increased inequality or negatively affect local wealth, then they may affect social cohesion in ways that exacerbate the conflict. This work tries to answer how does the arrival of IDPs in host communities in Mali affect wealth, consumption, poverty, and inequality?

We analyze the effects of IDP presence and numbers on consumption, poverty, and inequality of host communities in Mali. We use comprehensive nationwide household survey data on over 36,000 households from 2013 - 2019 combined with data on IDP populations to estimate econometric models of the effects of IDP presence and numbers. Our modeling strategy uses multiple econometric methods, difference-in-difference, instrumental variable, and propensity score matching to provide the most robust results to concerns about the endogenous location choices of IDPs.

A now burgeoning literature analyzes the economic impact of displaced peoples on host communities, see Verme and Schuettler (2020) for a systematic review. While the literature review finds a low probability of a decline in well-being for
Polanyi, Karl, 1944, _The Great Transformation: The Political and Economic Origins of Our Time_ , Farrar & Rinehart, New York Pradhan, M. and M. Ravallion. 2000. Measuring poverty using qualitative perceptions of consumption adequacy, _Review of Economics and Statistics_ , 82(3).

<mark>Rao, Vijayendra. 2023. "Can Economics Become More Reflexive? Exploring the Potential of Mixed Methods." In A. Deshpande (edited)</mark> _Handbook on Economics of Discrimination and Affirmative Action_ <mark>, pp. 1-27. Singapore: Springer Nature Singapore.</mark> Ravallion, Martin and Shaohua Chen, 2003, “Measuring pro-poor growth, _Economics Letters_ , Volume 78, Issue 1, Pp 93-99, Ravallion, Martin. 2012. Poor, or just feeling poor? On using subjective data in measuring poverty, World Bank Policy Research Paper No. 5968.

________________. 2019a. “Has Malaysia virtually eliminated poverty?” _The Edge_ , 29 January.

________________. 2019b. "Ethnic Inequality and Poverty in Malaysia Since 1969," NBER Working Papers 25640, National Bureau of Economic Research, Inc.

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low-income cities such as Kinshasa and Mumbai are also lower than their naïve population densities, with the correction making more of a difference for specific neighborhoods that have taller structures. While the FAPD makes intuitive sense, more work is needed to get better estimates of building heights. The DLR WSF3D data make a first contribution to such measurement. There are many measurement errors in the data coming from the ability of satellite sensors to measure height as well as noise introduced in the aggregation of the data collected at 12mx12m resolution. However, analysis of these data for the city of Dhaka shows remarkable correlation with predictions of heights based on productivity measures estimated through a general equilibrium urban model (Sturm, Takeda and Venables 2021b).

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in 2019. That study is not restricted to companies complying with the GDPR alone, but surveys companies across the world, including many in the United States. Research conducted by the Ponemon Institute in 2019 on behalf of international law firm McDermott Will & Emery (MW&E) focused on GDPR compliance found substantially higher figures: an average 2019 budget of $13.6 million for GDPR activities, a slight increase from $13.2 million in 2018.<sup>69</sup> The different results suggest the great variation in expenditures for compliance, depending on firm size, industry, types of activities, geography, perceived risks of operations, and risk tolerance. For the very large companies that make up the FTSE 100 stock index, estimates for GDPR compliance for 2018 range from an average of $84 million for banks, $26 million for technology and telecommunications firms, to $6 million for industrial goods and services firms.<sup>70</sup> A high percentage of the costs (between a fifth and a half, depending on the study) are associated with hiring of privacy compliance personnel. Technology also accounts for a significant portion (between 12 to 17 percent, depending on study) of GDPR privacy expenses. Outside consultants and lawyers accounted for another 19 to 24 percent, again depending on the study. One study concluded that GDPR compliance required extensive person-hours in meetings; DataGrail estimates that the average company spent 2,100 hours in GDPR preparation meetings and that enterprises staffed with 1,000 or more employees could have spent over 9,000 hours in such meetings.<sup>71</sup> Notably, despite these expenditures, most respondents (62% in the IAPP/EY study) believed their privacy budget was insufficient to meet their data protection obligations.<sup>72</sup> The IAPP/EY study surveyed 370 respondents predominately composed of organizations headquartered in the U.S. (39%), European Union (33%
Lastly, the education policy literature reports the importance of community support in sustaining reform efforts. In particular, support from local stakeholders is a strong positive predictor of successful institutionalization of school reforms (Bryk et al. 2010).

This paper considers each of these factors together rather than individually. As such, it contributes not only to the literature on sustainability of development projects but also offers operational lessons for project designers. We leverage rich panel data on playgroups in rural Indonesia to demonstrate how characteristics of the playgroups during the funded project period predict their sustainability several years after donor funding ends. This is an improvement from existing research on sustainability in health and education, which typically uses cross-sectional data (Scheirer and Dearing 2011). While the focus of this paper is on the sustainability of services established under an early childhood education project, the findings also provide lessons and insights for designing other types of community-based development projects.

We present six key findings in this paper. First, we show that the vast majority (86%) of playgroups established under the project remained open three years after project funding ended. In particular, those that remained open balanced the reduction in donor funding by introducing student fees. Both our survey data and qualitative interview data show that the playgroups that closed were those that struggled to find the financial and human resources needed to continue operating.

Second, we show that the playgroups that allocated a larger share of expenditure to teacher salaries during the project period were significantly more likely to remain in operation after the project ended. We find evidence that playgroups reallocated their resources towards teacher salaries by cutting down on supplementary services such as weekly food programs.

Third, we find that the quality of the project playgroups – as measured by direct observation of the playgroup – is a key predictor of sustainability. We find that such observations capture elements of the quality of these playgroups that are predictive of sustainability, over and above what can be predicted from teachers’ education and teaching experiences alone.

Fourth, we argue that the presence
educational attainment and become employed more easily. Conversely, as women become more educated and enter the labor market, fertility rates tend to fall. This scenario thus considers this two-way relationship.

The final alternative scenario ( _all interventions_ ) considers the case when there a country can achieve all the individual demographic dividend enhancing interventions together: educational convergence, accelerated productivity growth, greater mobilization of savings, improvements in employment rates, and closing the gender gap in the labor market.

For all scenarios, changes in the poverty headcount rate from the GIDD can be decomposed into three sources. The first source is the change in poverty due to the changes in employment, skill premia, and income that would have occurred had there been no additional income growth or distribution shifts – the poverty reduction effect without demographic effects. The second source of poverty reduction is the mechanical change in the distribution of households due to demographic change. This is based on the reweighting of the household surveys, and captures the impact of there being fewer (or more) households with certain demographic characteristics that in the benchmark survey data are found to be poorer (or richer). This can be referred to as the demographic composition effect. The third source of poverty reduction is the additional change in income and the income distribution that may occur when economies are able to realize their demographic dividends by absorbing the additional entrants into the working-age cohort and by increasing savings and investment due to declining child dependency ratios.

# **4. ANALYSIS**

The rising working-age population shares in SACU economies can be beneficial for Botswana, Lesotho, Namibia, and Swaziland, although to varying degrees, considering the magnitude of the possible contribution of demographic change to future growth (Figure 5). The scenario analysis framework attributes half of the real GDP per capita growth for Lesotho and Swaziland to demographic change. The average annual growth rate is 1.2 percent (versus 2.6 in the baseline) if the labor supply only grows at the same rate as the total population – keeping
09. This appears to provide additional evidence of the fundamental problem in the more recent surveys: the gap widens when the error is introduced (2002/03) and stabilizes thereafter, as the error distribution does not change much between 2002/03 and 2008/09 (see Table 1).

One possible objection to these findings is that differences between 1996/97 and the two later surveys are not necessarily related to measurement error and might be interpreted as merely a consequence of fixing the basket in 1996/97, with the gap between the revised and official PLs widening because of changes in the baskets themselves, i.e. substitution.<sup>32</sup> However, this argument is not entirely convincing: when constructing national-basket PLs with baskets that change every year (see Section 3.2 and Appendix) the resulting values are still closer to our revised PLs, suggesting that the inter-temporal effect of basket substitution is minimal. The gap is therefore either capturing some spatial basketsubstitution effect or, as we argue, is a product of the interaction between location-specific PLs and systematic measurement errors in the data.

Finally, the comparison reveals another interesting phenomenon: the growth rate of the poverty lines. This provides, for each of the 13 regions, a measure of the change in the cost of living exactly at the poverty line. By design, this particular measure of inflation is most relevant for the poor, as the contents of the food bundle reflect items that are consumed by the poor and regarded as necessary for basic survival. Although the PLs themselves appear to be significantly impacted by measurement error, their rate of change over time is consistent with the available price data. While the rates of PL growth

> 31 , where = the standard deviation and = the mean. Lower CV values mean less variability.

> 32 Revised PLs are constructed by fixing the basket at national level in 1997, therefore the whole inter-temporal variation is explained by price changes only.

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**Figure 12 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on EV exports**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>0.07 100<br>0.06<br>80<br>0.05<br>0.04 60<br>0.03 40<br>0.02<br>20<br>0.01<br>0.00 0<br>Morocco Vietnam Türkiye Ukraine El Salvador Serbia Bosnia and Jordan Ecuador Argentina<br>Herzegovina<br>Percent Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS. _Note:_ EV data is based on HS code 870380. Sector and economic exposure are export of EVs to three economies divided by exports of EVs to world and GDP, respectively. Mexico’s EV exports to three markets accounts for 82 percent of its EV exports to world and 24 percent of GDP, respectively.

## **3.2.3 Renewables and components**

**EU, US, and China policies on renewables are likely to increase demand for renewable energy tech and components.** The renewable energy tech and components sector (solar and wind products) is most exposed in Bosnia and Herzegovina, North Macedonia, Serbia, Tunisia, and Cambodia, with more than 80 percent of this sector’s exports going to the EU, US or China (Figure 13). In these countries the overall economy is exposed to the policies as renewable energy tech and components exports represent between 3 and 4 percent of GDP.

**Figure 13 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on exports of renewable technology and components (solar and wind)**

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### facturing.

The dominance of the pharmaceutical sector is likely due, in part, to the JA. Because sectors that rely on sea-shipped inputs are largely missing from PR’s industrial mix, a sector that uses air transport for most inputs and outputs appears more dominant. But the pharmaceutical sector’s dominance is also the result of other U.S. policies, notably the "Possession Tax Credit" (commonly known as Section 936). This policy gave favorable tax credits to firms with intangible assets (such as patents) locating activities in PR. While Section 936 was phased out by 2006, the dominance of the pharmaceutical sector in PR is clearly a legacy of the policy. Pharmaceuticals account for a large share of PR’s trade (20.9 percent of imports and 74.0 percent of exports). We exclude trade in pharmaceutical products from our regressions to avoid attributing to the JA the lingering effects of Section 936. Our CV calculations assume JA tariff-equivalents of zero on everything purchased and sold by the pharmaceutical sector.

# **3 Data**

We exploit three main sources of data. Comprehensive data on ship ports-of-call in the Caribbean illustrate the relative supplies of different types of vessels in Caribbean shipping markets. Data on PR’s imports – from both foreign and U.S. mainland sources - are used in an empirical gravity model. Once we have estimated tariff-equivalent trade costs attributable to the JA, we match them to data on final expenditures from a Puerto Rican input-output table. In this section we describe these three data sets, as well as some ancillary data that we use in our estimation.

## **3.1 Port of call data**

In order to better understand the relative and absolute supplies of different shipping services to PR, we purchased comprehensive data on freight vessels’ ports of call 8
# **2  MEASURING SERVICES TRADE RESTRICTIVENESS**

The method used to generate the STRI is an updated version of the STRI developed by the World Bank a few years ago In updating the World Bank STRI, the main principles were maintained but several improvements were introduced; namely the use of an algorithm and an automatized aggregation method to construct the STRI. Introducing these technical improvements removes the subjective bias that may have existed in the previous World Bank STRI. The improved methodology will be briefly described below, and more information may be found in Borchert et al. (2019).

The construction of the STRI follows the same principal steps as in Borchert et al. (2012):

- 1) the **selection** of key restrictions entering the STRI; 2) the **determination** of the level of restrictiveness of individual measures (or bundles of measures if they are conceptually intertwined, which we call ‘synthetic measures’); and

- 3) the **aggregation** of measures into indices at the sector-mode level, sector level, and economy level, respectively.

# **2.1  Selection**

While the Services Trade Policy Database (STPD) contains a large number of measures, the index is based upon a subset of key restrictions to trade in services, approximately 115 regulations and policies. The choice of key restrictions entering the quantification exercise was driven by a number of considerations: 1) to cover the most significant restrictions – this was based on the previous analysis conducted by the World Bank as well as expert advice; 2) to respond to recent regulatory developments perceived as having a discernible impact on trade in services, e.g. screening policies, restrictions relating to cross-border data flows; 3) to ensure comparability with the 2008-2011 World Bank exercise; and 4) to adapt to – and be consistent with – the coverage of measures in the OECD database, which served as a fundamental source of information for economies covered by both the STPD and the OECD
estimation models for both were based on same available data, and only in the case of underweight, a satisfying model was found. Nevertheless, wasting and underweight have a significant pairwise correlation of 0.52 in the 2014 EMHDS survey. Figure 2 illustrates, in a scatter diagram, the partial correlation with notable noise between underweight and wasting. As such, one can consider underweight a proxy, albeit an imperfect proxy, for wasting. Stunting, on the other hand, has no correlation to wasting, while there is a high correlation between underweight and stunting (Figure 2).

**Fig 2. Z-score correlation between underweight and wasting, and stunting and wasting**

<!-- Start of picture text -->
-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Weight-for-length/height Z-score Weight-for-length/height Z-score Weight for age Z-score (Underweight)<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>Height for age Z-score (Stunting) Height for age Z-score (Stunting)<br>-6 -6 -6<br><!-- End of picture text -->

# **Are woredas a useful targeting mechanism?**

As pointed out in the introduction, existing literature points to weaknesses in data for geographical targeting, and our estimates fill a current data gap. However, is woreda targeting using SAE the most efficient targeting mechanism? Given available data, a complete analysis of this question is not possible, but woreda targeting can be compared to some other alternatives. A data-driven national targeting strategy requires data for the entire nation that is correlated with undernutrition. The profile of undernutrition, based on correlates is well-documented [9,14,30] and useful,
# **2.2 Identifying unreliable observations: 2002/03 and 2008/09**

In order to cope with measurement errors in consumption and with the ‘error-poverty line interaction’ (described in Section 3, below), we utilize a method that preserves most of the original information while correcting for only manifestly inaccurate observations. Our approach is to carefully screen all consumption data and define specific criteria by which to determine their degree of reliability.<sup>9</sup> In a second stage, the unreliable observations are re-estimated using out-of-sample predictions from the 1996/97 coefficients model (see Section 2.3, below). Since we also identified clear signs of measurement errors in 2002/03, after using this methodology for the 2008/09 data we apply it to the earlier survey as well.

The original consumption data are ranked and compared to a wealth index.<sup>10</sup> Observations that are below the poverty line (the 54th percentile of the income distribution), but above the 70th wealth-index percentile, or observations above the poverty line but below the 30th wealth-index percentile, are singled out as ‘suspicious’.<sup>11</sup> Figure 2 illustrates the definition of these two observation sets: one set of households (A) is considered poor based on the original consumption data, but is classified as non-poor based on the wealth index; the other set of households (B) is considered non-poor based on the original consumption data, but is classified as poor based on the wealth index.

**Figure 2: Observation Sets A and B**

<!-- Start of picture text --> B: Non-poor based on consumption<br>data/Poor based on wealth data<br>NON-POOR<br>Consumption Poverty line<br>POOR A : Poor based on consumption<br>data/Non-poor based on wealth data<br><!-- End of picture text -->

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The finding that the process of direct democracy increases villagers‘ satisfaction is consistent with Olken (2010). However, our finding that selection and prioritization procedures affect allocation outcomes differs from Olken (2010), who finds no effect of procedure on the choice of general projects, but does observe some differences between procedures in project location.<sup>20</sup> Although this difference might be driven by the differences in sample size between the two studies,<sup>21</sup> there are also reasons to believe that the differences in results are driven by differences in the local context. Two differences in context can be particularly important in explaining the differences between the results of the two studies.

First, Indonesian villages generally consist of several hamlets separated by as much as two kilometers, whereas Afghan villages generally consist of a single hamlet. As a result, project location is highly salient in the selection process in Indonesian villages, but less important in Afghan villages. In Afghanistan, project type is thus more likely to be the main source of disagreement between villagers in the allocation process. This is confirmed by monitoring data which indicates that only 19 percent of villagers consider location to be the most important consideration in project choice, and by the absence of a correlation between this share and the geographical size of the village.<sup>22</sup> Second, Indonesian villages receive funding for general projects periodically, which allows for ̳logrolling‘ and inter-temporal trade between different interest groups within a village, so that each part of the village gets its most preferred projects in turn. Repeated interactions can sustain such intertemporal trade under both procedures, which make the analysis of a single instance of selection quite misleading. However, a women‘s project included in the sample studied by Olken (2010) was considered a one-shot event, which is comparable with the situation in Afghanistan where no commitments for follow-up grants were made to villagers. In both cases, the analysis reveals that, absent opportunity for inter-temporal