rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>CPTPP as more countries request to join, and the launching of the African Continental Free Trade Area (AfCFTA) that aims to boost intra-African trade, are examples of a trend towards more regional or – in the case of the CPTPP – plurilateral integration. Along the same lines, Fajgelbaum at al (2022) show that while the trade war between the US and China reduced trade in the targeted product categories between these two countries, it boosted trade between third countries and the rest of the world in these products. The countries that exhibited the largest export growth to the rest of the world were countries with expansive trade agreements. So, the trade conflict between the world’s two largest economies did not simply cause reallocation of global trade flows, it also generated new trade opportunities for other countries. Therefore, it seems that outside the US, the picture is more nuanced with many countries striving to take advantage of the new opportunities potentially created by the reversal of US policy.</mark> <mark>Note however, that one of the most assertive recent moves of the US, the export restrictions in the semiconductor sector targeting China, require the cooperation of other countries to succeed. Because most of the semiconductor chip manufacturing takes place in countries other than the US, using however to a large extent US software and US machinery, the new export controls require third country chip manufacturers to obtain export licenses from the US in order to export their products to China. The US implemented this policy change unilaterally, , putting allies in a difficult position as they faced a choice between going along with the export restrictions or--- if they wished to continue exporting to China---foregoing access to US technology. This use by the US of economic interdependence to force other countries to go along with its policies has been termed “weaponizing interdependence.”</mark><sup>8</sup> <mark>It suggests that no matter what other countries’ preferences and intentions
# **A Data Description**

# **A.A Characterizing Refugees in Tajikistan**

Figure A.1. Age Distribution <!-- Start of picture text --> 80+<br>75-79<br>70-74<br>Male 65-69 Female<br>60-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>1-4<br><1<br>600 450 300 150 0 150 300 450 600<br>Total Number<br><!-- End of picture text --> _Notes:_ Source: Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions.

48
to show their dynamics. Following the standard procedure, we calculated the height‐for‐age z scores for children aged 0 to 12 and the weight‐for‐age z scores for children aged 0 to 10. In the panel analysis, we use the sample of children aged 0‐6 in 2007 (thus, they are aged 3‐9 in 2010).

The surveys collected consumption and expenditure data using the following formats. For food consumption, which is of our primary interest, respondents were asked to do a one‐week recall to provide information, for each commodity, on quantity consumed and, if purchased, the price and how much of the total consumption is from own production or as gift. The 2007 round used different units of quantity but they were converted into kilogram when the data were processed. The 2010 round recorded quantities all in kilograms when households were interviewed (if necessary, converted in the field). The analysis uses price per kilogram. From the above data, we computed the proportion of calories produced by own production. Two measures were constructed to represent main foods and staple food (see Figures 4a and 4b, respectively). The main foods include (i) staple, (ii) fish, meat, tofu and tempe, (iii) beans (pulses), (iv) vegetables and fruits, and (v) milk and eggs.

Price indices were constructed from the food consumption data to understand the general trend of food price changes. We computed the Laspeyres and Paasche indices using the 2007 and 11
# **2.4 Model Results**

Our econometric results are presented in Table 1. The table includes results for alternative estimators that incorporate different assumptions about the structure of the stochastic error term (Ɛit) in the model. These techniques produce the same point estimates for model parameters, but their differing estimates of standard errors (and the accompanying t-statistics) may lead to very different inferences about the statistical significance of model variables. We replicate the point estimates in columns (1) – (3) to aid interpretation of the t-statistics. We include results for standard nonlinear (NL) regression, NL with robust standard errors (SE) and NL with SE adjusted for 3,074 clusters defined by level-1 administrative units (states, provinces, etc.) for the 190 countries in the regression database.

As the table shows, all results have the expected signs. The mean anomaly for satellite-observed CO2 concentrations in a 25-km grid cell is positively related to direct emissions from industry and fires; wind-displaced emissions from industry and fires in neighboring areas; and population-related emissions. The marginal impact of population is positively related to heating needs (heating degree days), cooling needs (cooling degree days) and income per capita, and declines with the interaction of subway length and scale. All variables meet classical significance tests in all three regressions, with the exception of cooling degree days in the cluster-adjusted regression.

# **3.  Alternative Templates for CO2 Emissions Tracking**

As noted in the introduction, recent research indicates that satellite-based observations can support an objective, spatially-referenced system for tracking CO2 trends in local areas and regions. Prefiltering by the method of Hakkarainen et al. (2019) or similar methods enables measurement of CO2 concentration anomalies -- the locally determined components of atmospheric concentrations. Temporal considerations are also important because satellite-based measurements, like observations from ground-based monitors, include random components that hinder short-period trend identification. At the present state of the art, trend estimates for periods shorter than a year seem problematic.

7
. 227-236.

- Prud’homme, Remy (1995). “The Dangers of Decentralization”, _World Bank Research Observer_ 10(2), pp. 201-220.

- Rose-Ackerman, Susan (1978). _Corruption: A Study in Political Economy_ , New York: Academic Press.

- Schiavo-Campo, Salvatore, Giulio de Tommaso, and Amitabha Mukherjee (1997). “An International Statistical Survey of Government Employment and Wages”, _World Bank Policy Research Working Paper_ 1806, Washington, DC: The World Bank

- Schleifer, Andrei and Robert Vishny, (1993). “Corruption”, _Quarterly Journal of Economics_ 108(3), pp.599-617.

- Seabright, Paul (1996). “Accountability and Decentralisation in Government: An Incomplete Contracts Model”, _European Economic Review_ 40(1), pp. 61-89.

- Shah, Anwar and Mark Schacter (2004). "Combating Corruption: Look Before You Leap," _Finance and Development_ , December 2004: pp. 40 - 43.

- Shah, Anwar, Theresa Thompson, and Heng-fu Zou, 2004, “The Impact of Decentralization on Service Delivery, Corruption, Fiscal Management and Growth in Developing and Emerging Market Economies: A Synthesis of Empirical Evidence _,” CESifo Dice Report_ , _A Quarterly Journal for Institutional Comparisons, Vol. 2 (Spring), pp. 10-14._

27
protests against the electricity crisis and repeated power cuts. In Sudan, demonstrations in 2019, here again youth-led, resulted in the removal of Omar al-Bashir.

Protests reflect the broader attachment of African citizens to democratic values. Many protests have in common the defense of existing forms of democracy, however flawed, and a strong commitment to democratic norms and forms, and in particular the right of expression. The analysis of Afrobarometer survey data across 37 countries surveyed in 2021/2023 shows that over two-thirds (68%) of African citizens expressed a preference for democracy over any other form of government. The figure is higher when respondents are presented with specific alternatives; 83% reject one-man rule, 79% reject oneparty rule and 68% reject military rule.<sup>35</sup> As stated by Afrobarometer (2023), “Africans want more democracy, but their leaders are still not listening”. In this regard many protests turn on the legitimacy of public authority; but by the same token such dissent and perceived illegitimacy can also propel popular support for the military, and even for coups. It is noteworthy that the extent of citizens’ opposition to military rule has declined by 10 percentage points over the past decade and that over half (53%) of respondents report being willing to endorse military interventions if elected leaders abuse their power.

> 32 Brockmeyer, M. Fox, J. 2015, _Assessing the Evidence: the effectiveness and impact of public governance oriented multistakeholder initiatives,_ London: Transparency and Accountability Initiative.

> 33 Kinda, Harouna and Thiombiano, Noel. 2024. “Does transparency matter? Evaluating the Impacts of the Extractive Industries Transparency Initiative (EITI) on Deforestation in Resource-rich Developing Countries”, _World Development 173_ (forthcoming). 34 See IEG (2019), Manyika et al., (2020), Bussolo (2018).

> 35 <u>https://www.afrobarometer.org/online-data-analysis/. See also</u> <mark>
0.01|Number of Smart Phones (Wave 1)|1.08|1.13|0.05|
||(0.38)|(0.37)|(0.04)||(1.17)|(1.24)|(0.12)|
|Owns Household Business|0.27|0.31|0.04|PhilHealth Insurance (Wave 1)|0.69|0.83|0.14***|
||(0.45)|(0.46)|(0.04)||(0.46)|(0.38)|(0.04)|
|Owns Farm|0.10|0.07|-0.03|Has Bank Account (Wave 1)|0.20|0.83|0.63***|
||(0.30)|(0.26)|(0.03)||(0.40)|(0.38)|(0.04)|
|Observations|216|222|438|Observations|216|222|438|

Notes: Standard deviations (in columns 1 and 2) and standard errors (in column 3) reported in parentheses. * p _<_ 0 _._ 1, ** p _<_ 0 _._ 05, *** p _<_ 0 _._ 01.

9
GVC exposure index developed by Arvis et al. (forthcoming). Niger has the most exposed sector, with about one-third of its direct and indirect exports of petroleum, chemical and non-metallic mineral products focused on the EU, followed by Bosnia and Herzegovina, and Tunisia.

**Figure 4 Developing countries with largest GVC exposure to the EU: Petroleum, Chemical and NonMetallic Mineral Products (percent of domestic products output)**

<!-- Start of picture text --> 35<br>Direct export to EU Indirect export to EU<br> 30<br> 25<br> 20<br> 15<br> 10<br> 5<br> -<br>Niger Bosnia and Tunisia Libya Belarus North Morocco Albania Türkiye Sri Lanka<br>Herzegovina Macedonia<br><!-- End of picture text --> _Source:_ Arvis et al. (forthcoming) and authors’ calculations.

> 18 GVC exposure data is not disaggregated at the fossil fuel level.

14
|(Respondents: 370 privacy<br>professionals from the<br>IAPP database located in<br>U.S. and E.U.)|Salary and travel (2018)<br>Salary and travel (2019)|47% of privacy budget<br>50% of privacy budget|
|---|---|---|
|**Paul Hastings, 2017**<br>Rdt 100 FTSE|Additional staff (U.K.)|$263,600 – $524,700|
|(esponens:<br>350 & 100 Fortune 500<br>companies)|Additional staff (U.S.)|$501,000 - $1M|

_<u>Note: Data figures have been converted from Euros to U.S. Dollar using XE’s currency converter and rounded.</u>_ Data from MW&E’s study reported that almost half of the organizations represented (48%) are either in the process or expecting to hire an average of almost four additional employees to provide ongoing assistance with the GDPR.<sup>86</sup> Despite the expected increase for some, 38% of organizations in the research group believe their organization lacks the human resources to fulfill their obligations and sustain GDPR compliance in 2019.<sup>87</sup>

# Privacy Team Responsibilities Reported to IAPP-EY, 2019

<!-- Start of picture text --> Privacy-related legal counsel (internal) 62%<br>Assuring proper cross-border data transfer 66%<br>Privacy-related vendor management 69%<br>Data inventory & mapping 74%<br>Privacy related investigations 77%<br>Design and implementation of privacy controls 81%<br>Compliance with the GDPR 83%<br>Incident response 85%<br>Performing privacy impact assessments 85%<br>Following legislative developments 85%<br>Company awareness
**Figure 2:** Gain in GDP from closing the gender employment gap (%) for each country in MENA— comparing GEGI (long run), short-run (fixed capital) and the LTGM-NR (by 2050)

<!-- Start of picture text --> Aggregate capital does not respond (SR)<br>2050 LTGM<br>Basic GEGI (Aggregate capital fully adjusts) 86<br>79<br>74<br>70<br>68<br>63 63<br>56 56<br>54<br>49 49 48<br>44 46 45 45 43<br>41 41<br>35 37 33 34 36 34 35 37 36<br>29 27 28 30 30<br>2225 23 25 25 22 23<br>19 20 21<br>15<br>12 14 12<br>10<br>7 9 7 6 5 8 7<br>2<br>QAT ARE BHR KWT OMN LBY SAU DJI MAR TUN LBN EGY DZA IRN PSE JOR SYR IRQ YEM<br>100<br>80<br>60<br>40<br>Percentage points increase<br>20<br>0<br><!-- End of picture text --> Note: The blue lines are the Gender Employment Gap Index (GEGI) from Pennings (2022) (ARE, BHR, KWT, OMN, QAT and SAU are adjusted for unequal male-female populations). The green lines are the 2050 gains in output per capita from closing the gender gap by 2050 obtained calibrating the LTGM/LTGM-NR. The red lines are the GEGI adjusted by the labor shares from PWT 10 (or 0.5 if this number is below 0.5 and it is a non-resource rich economy).

increase in GDPPC.

# **3. Transitional gains to 2050 using the Long Term Growth Model (LTGM)**

The analysis
. In the latter case, one can investigate parameter heterogeneity for country groups.

Another possibility is that national GDP numbers are inflated – due to GDP rebasing or creative national accounting – or otherwise mismeasured. Comparing GDP data to alternative proxies of economic activity (e.g., nightlight) can be informative to substantiate this possibility.

A third possibility is that growth dynamics during periods of large residuals are driven by cyclical factors. Neoclassical growth models focus on the role of fundamental long-run growth correlates, as opposed to cyclical factors. The next subsection will give an example of such cyclical deviations.

# **6.4 Crises and Growth Underperformance**

The example of Ghana in Geiger et al. (2019) is instructive to see how deviations between model and actually observed growth rates reflect cyclical effects and can be used to assess potential growth vulnerabilities. Their model only predicts a growth rate of 1.1% for the early-2010s but actual growth was almost 10%. As is noted in their paper, actual growth mainly reflected cyclical factors (oil discoveries, commodity prices, loose fiscal stance) and was not sufficiently backed up by relevant long-run growth correlates that feature in the model. This can be a warning sign for growth to run out of steam – as actually happened in the case of Ghana, which defaulted on its external debt in 2023. Using filtering techniques that separate growth into a trend and cyclical component can be useful to detect such cyclical deviations from trend. Moreover, it could be a warning sign for vulnerabilities to build up if developments in credit/GDP are unusually large, particularly if paired with large government consumption and foreign financing (e.g., fast increases in FDI or other capital inflows, or an increasing current account deficit).

A separate case concerns longer-lasting periods when growth considerably underperforms in a country, with growth rates hovering around or below 0. On top of the steps suggested under 6.3, one could look at levels of main growth correlates (see 5.
Refugees (UNHCR).

## **3.1. ESS Data**

The individual- and household-level information comes from the ESS data. The ESS is a three round panel survey, at the individual and household levels. The first round (ESS1) was collected in 2011/12 and contains 3,969 sample households from 333 rural and small urban enumeration areas (EAs)<sup>10</sup> in 10 of the 11 administrative regions of Ethiopia.<sup>11</sup> Of these sample households,

> 9Figures 1 and 2 shall only serve as an exposition to the existence of a large spatial difference in within-location (within-village/EA) temporal changes in refugee intensity. They shall not be interpreted as implying the location of refugee settlement sites located in any one region are closer to all points (sample EAs) in that region compared with the points (sample EAs) in other regions. As an example, some of the largest refugee-hosting camps that are located in the Somali region are closer to a large part of the Oromia region than more than half of the Somali region.

> 10EA is a sub-village geographic unit with 150-200 households. On average, CSA subdivides each village into about 4 EAs. Despite this, I use the word village in the paper to refer to EA.

> 11Small urban centers have a population of fewer than 10,000 persons in the 2007 PHC (CSA and World Bank, 2015).

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_International Journal of Hydrogen Energy_ , 46(14), 9553-9566. <u>https://doi.org/10.1016/j.ijhydene.2020.12.100</u>

- Li, S.-h., Guo, L.-j., Cao, C.-g., & Li, C.-f. (2021c). Integrated assessment of carbon footprint, energy budget and net ecosystem economic efficiency from rice fields under different tillage modes in central China. _Journal of Cleaner Production_ , 295, 126398. <u>https://doi.org/https://doi.org/10.1016/j.jclepro.2021.126398</u>

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The State Statistical Committee (SSC) of Azerbaijan has carried out a regular household budget survey since independence in 1991. However, the survey design which it inherited from the Soviet period did not allow it to supply reliable data on living standards. Since 1999 the SSC, in collaboration with a group of international technical experts, has been working on improving the HBS design in order to produce nationally representative data (Marnie, Lapins and Zaidi 2001). In designing the new survey, the team has attempted to rectify the faults of the old HBS, while limiting the additional human and financial resources required, since the survey has to be sustainable for the future. The 2001 Household Budget Survey was the first year of a newly designed survey. During the first year the SSC received technical assistance both from the World Bank and the Asian Development Bank (ADB) to further improve the methodology (SPPRED 2004). As a result of this further collaboration the way in which information on consumption expenditure was collected changed, and as a result the values of consumption obtained from the 2001 and 2002 surveys are not comparable. The 2002 Household Budget Survey is now considered as the baseline for future poverty monitoring (SPPRED 2004). We therefore use the 2002 HBS for our imputation in the census<sup>3</sup> .

However, using the 2002 HBS creates a series of problems for the practical implementation of this methodology due to the fact that between the year of census data collection (1999) and the 2002 HBS, Azerbaijan has experienced a sharp drop in fertility levels with the Total Fertility Rate (TFR) decreasing from around 2 children in 1999 to 1.8 children per women in 2001 (UNICEF 2003). For this reason we have had to modify our measure of

> 3 The World Bank is currently undertaking a Programmatic Poverty Assessment in Azerbaijan. The Azerbaijan Programmatic Poverty Assessment is a three-year program comprising technical assistance, capacity building and analytical work to be carried out jointly with Azeri counterparts. The technical assistance includes
Taiwan’s development and poverty reduction efforts mirrored those of Korea both in their nature and timing. Korea’s efforts, e.g., in land reforms, were in part motivated by developments in communist North Korea. China may have motivated Taiwan’s reforms even more. Taiwan began with a phased but comprehensive land reform between 1949 and 1963, which involved an organized transfer of ownership to the tillers. By 1965, more than 90 percent of the land was tilled by tenants. Agricultural productivity increased quickly, putting a big dent in extreme poverty.

While the population more than doubled between 1950 and 2000 (the population is about 23 million today), massive investments in education, health, and family planning resulted in declining fertility, rising school attainment, and greater longevity. An aggressive policy to attract foreign direct investment (FDI) resulted in Taiwan becoming a major exporter by the 1990s. By 2005, agriculture was just 3 percent of GDP and the island was home to a third of the world’s computer industry. Despite minimal spending on social welfare, extreme poverty appeared to have been eliminated.

Because statistics on poverty have only been maintained since 1981, it is impossible to be sure. Even today, Taiwan’s social welfare spending is less than 4 percent of GDP, less than half the ratio in South Korea and one‐fifth the average in the OECD. Welfare recipients are fewer than 2 percent of the population and get less than $150 a month.

# **_Malaysia and Thailand_**

Malaysia followed South Korea and Taiwan with a lag of about a decade. Malaysia is unusual in East Asia in that it has had an official poverty line since 1970, when the New Economic Policy was introduced as a response to social unrest. Steps to increase agricultural productivity and attract manufacturing‐related foreign direct investment were accompanied by programs to provide health, housing, and other services in rural areas. As in other countries, rural poverty rates are higher in Malaysia: three