rafmacalaba/data-use-mentions-extended huggingface dataset:
, firm size, ownership type, and previous experience with electricity outages—which made companies less sensitive to price increases, likely due to their familiarity with managing energy and input shortages.

Andre et al. (2023) analyze firm-level data from 21 OECD countries (1995–2020) and show that energy price shocks impact firm productivity through immediate cost increases and longer-term adjustments. The study highlights sectoral and firm-level heterogeneity, finding that energy price hikes initially reduce productivity, particularly in energy-intensive sectors and firms under financial constraints. However, medium-term productivity improvements often follow as firms adapt, typically through increased investment. These findings align with the "pollution haven" hypothesis, which suggests that higher energy costs erode competitiveness and may drive firms to relocate (Copeland & Taylor 2004), and the Porter hypothesis, which posits that higher carbon prices can enhance productivity by incentivizing efficiency and innovation (Porter & van der Linde 1995). This dual perspective underscores the nuanced impact of energy price shocks on firm outcomes, blending short-term losses with potential long-term gains.

The recent energy price surge, triggered by the strong economic recovery following COVID-19 and the Russian invasion of Ukraine, has sparked renewed interest in the subject. Battistini et al. (2022) focus on EU economies during 2020-2022 and highlight a few interesting aspects. Energy price shocks disproportionately affect firms based on their energy dependence and hedging strategies. Energyintensive sectors are particularly hard-hit, facing greater financial stress and profitability challenges due to heightened costs and supply chain disruptions. Spiking energy prices have triggered a significant 2
Sector Management (PSM) projects and<br>do they have differential effects on PSM vs. non-PSM projects?|<br>Political economy factors are key predictors of how the World Bank’s PSM projects<br>perform. These factors distinctly predict PSM project performance vs. non-PSM project<br>performance, suggesting PSM projects are particularly sensitive to the political context.|
|Briggs, 2020|Yes (Table<br>A1.2 and<br>Text)|Do the findings on development project success that focus on<br>World Bank data generalize across multiple donors?|Similar factors seem to drive project success across donors. For all donors, country-level<br>factors end up explaining only about 15% of the variation in project outcomes. All but<br>one of the donors with a large sample of projects in the database show a negative<br>relationship between project duration and outcome ratings. All donors found the same<br>country-time periods relatively easy or hard, and had more successfully rated projects in<br>recipient countries with faster growth.|

31
pension system provisions and contribution rates (and other forms of financial saving in the case of housing affordability) and saving for old age.

The paper proceeds as follows: Section 2 reviews the existing literature. Section 3 describes the data and presents the descriptive statistics for saving for old age around the world. Section 4 presents the empirical analysis of saving for old age, in terms of empirical strategy and estimation results with sets of individual, macroeconomic and pension-system characteristics. Section 5 concludes.

# **2. Review of existing literature**

Research has long shown that retirees suffer from a variety of economic hardships, even in developed economies that enjoy relatively strong pension systems. In their study of the United States, Bernheim, et al. (2001) show that consumption drops rapidly at the time of retirement, and decreases even more post-retirement. Using UK data, Bardasi et al. (2000) report that on average, economic well-being declines sharply in the years preceding retirement, and continues to do so through retirement and the years following it,<sup>1</sup> with women facing a swifter decline than men. Among adults in Germany, satisfaction with current household income decreases substantially upon retirement, while satisfaction with free time increases, according to Bonsang and Klein (2011). Retirement has an overall negative effect on life satisfaction, with larger effects for involuntary retirement, and insignificant effects for voluntary retirement.

High levels of poverty among the elderly compound the negative economic consequences of a retirement crisis. In the US, Clark and Quinn (1999) report that the elderly remain disproportionally represented among the near poor and certain older Americans still face substantial risk of economic deprivation, even though their average economic well-being has significantly improved compared to older generations (also see Sabelhaus and Manchester, 1995). Crystal and Shea (1990) also show that resources among the elderly are distributed even more unequally than among the rest of the population.

> 1 For evidence against the negative impact of retirement on well-being,
to qualify could be referred first, with larger numbers being referred later when budgets expand.

# 4.2 Who Are the Likely Eligible? A Profile

This sub-section profiles households from the CLCI legacy database that would be deemed eligible by the PPMT. The profile focuses on eligible households under the targeting of the bottom 18 percent nationally – the national poverty rate in 2017/18 before the adjustment for SWIFT non-coverage and likeliest coverage criteria. In particular, we analyze households that are eligible at 90 percent of confidence level, which we referred to as households with “High Likelihood of Eligibility (HLE)”. Analysis at 70 percent confidence, referred to as “Moderate Likelihood of Eligibility (MLE)” is discussed in Annex B. As reported in Table 7, households under HLE account for 24.3 percent of the legacy database, while 42.3 percent are eligible under the MLE scenario. Annex B also provides the profile of the households that are deemed ineligible even under the largest program and least confidence scenario explored in the paper i.e. poorest 35 percent nationally at 50 percent confidence level for comparison.

Among the households with the highest likelihood of cross-model eligibility (90 percent confidence level; hereafter referred to as the HLE sample), a third are concentrated in Mosul, followed by other large urban centers such as Tikrit and Ramadi, followed by smaller urban centers and more rural areas affected by conflict such as Shirqat, Tel Afar, and Baiji. Not surprisingly, this is similar to the distribution in CLCI database that has greater coverage in these areas, which also have larger population sizes.

15
estimates of years of schooling of the cohort of 25- to 29-year-olds for S�, Schoellman’s estimates of labor market returns for Q�, and his preferred value of 0.5 for η. The right panel of Figure 5.7 shows that there is broad agreement between the two measures (excluding just three outliers, the correlation coefficient between the two measures is 0.84, and the rank correlation is 0.85).

# **_5.3 Quality adjustments based on grade-to-grade test-scores increases_**

Third, we can test the plausibility of _LAYS_ by comparing its findings with those of other efforts to adjust schooling for quality. Beyond _LAYS_ and Schoellman’s returns-based approach, there are alternative ways to adjust years of schooling for quality to allow cross-country comparison. One relevant recent study that (like _LAYS_ ) relies on measured test scores to do this is Kaarsens (2014), which derives measures of education quality across countries using multiple rounds of TIMSS (1995-2011). The analysis exploits the fact that in 1995, students were tested in Grades 3 and 4 at the primary level and at Grades 7 and 8 at the secondary level.<sup>24</sup> It uses these data to calibrate a model that simultaneously estimates overall average annual test score growth (that is, the average changes from Grades 3 to 4, and from 7 to 8), along with “education quality,” which is (essentially) a country-specific fixed effect calculated after controlling for the overall average test-score growth.<sup>25</sup> The approach then normalizes the quality measure relative to the United States. To more directly compare the approach to the _LAYS_ approach, we renormalize the “education quality” estimates so they are expressed relative to Singapore’s; we then recalculate _LAYS_ by multiplying that relative quality measure by the Barro-Lee average years of schooling of the cohort of 25- to 29-year-olds.

The left panel of Figure 5.8 compares Kaarsen’s (renormalized)
is 3.6.<sup>8</sup> The SHWALITA team also conducted a market price survey in each village, in conjunction with the household survey. Markets are relatively dense in Tanzania: 97% of study households live within 10 kilometers of the nearest market, and the median distance is 1.15 km. For 42 food items (10 of which meet the criteria for inclusion in this study), enumerators visited the village market and recorded the most common units in which each item was sold. They precisely measured the unit in kilograms or liters, and noted the price. Unit prices were

> 7Helpfully, 98.7% of purchases recorded in units other than kilograms or liters were recorded as integer values. Most of the decimal entries in kilograms or liters stem from unit conversions, not from respondents being forced to convert non-standard units into kilograms or liters on the spot.

> 8Additional descriptive statistics for the survey households are provided in Appendix S1.

12
# **1 Introduction**

According to the _United Nations Refugee Agency (UNHCR, 2019)_ , 70.8 million people are currently forcibly displaced worldwide, due to a variety of reasons including armed conflict, economic deprivation and environmental disasters.<sup>1</sup> These events entail the loss of many lives and huge human suffering among those directly affected. In addition, neighboring countries are also affected because, willingly or not, they end up hosting a large part of the displaced populations.

It is important to study the economic consequences of receiving large inflows of forcibly displaced migrants. Identifying which groups in the hosting regions are bearing the largest economic burden, and adopting policies to alleviate it is crucial in order to avoid backlash and foster integration. Obtaining and analyzing data in a timely fashion poses an important challenge to researchers and policy makers because the typical sources, population censuses and labor force surveys, are ill-suited to collect information pertaining to recently arrived migrants that are still in the process of settling down. As a result, researchers have to develop new strategies to address these shortcomings by using new data sources.<sup>2</sup> Our focus in this paper is on the Venezuelan exodus that started around year 2015. As of September 2019, it is estimated that more than 4.3 million Venezuelans have left the country since in the last 5 years. About 80% of these migrants have settled in Latin America and the Caribbean, with Colombia (about 1.5 million) and Peru (about 1 million) as the main destinations.

Our analysis focuses on Ecuador, which started receiving significant inflows of Venezuelans around 2016 and is currently hosting over 400,000 Venezuelan migrants (as of 2019). More specifically, our main goals are to analyze the determinants of the location choices of Venezuelan migrants within Ecuador and how they might affect the labor markets of the host regions. Crucially, we employ novel data based on mobile phone records to measure the geographical distribution of
<!-- Start of picture text --> 5<br>Be<br>8<br>2<br>-<br>g<br>E<br>Bot<br>=<br>7<br>3<br>a<br>%<br>‘Ape2020 May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>Months from March 2020<br>© Young <25 years old © Adult >=25 years old<br>‘Note: Standard errors clustered on survey wave<br><!-- End of picture text --> <!-- Start of picture text --> gCS<br>a<br>a<br>5<br>an<br>E<br>es<br>&<br>Ey<br>Bo<br>§<br>uy<br>%<br>é2 4<br>‘Ape-2020 May-2020 Jum-2020 Jul2020 Aug-2020MonthsSep-2020 fromOct-2020 MarchNov-2020 2020Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>—®— Young <25 yearsold ——®— Adult >=25 years old<br>‘Note Standard eors clustered on suvey nave<br><!-- End of picture text --> <!-- Start of picture text --> a<br>g*<br>[EP<br>:<br>an<br>f<br>os<br>2<br>5<br>aa<br>=<br>‘Ape2020 May-2020 Jun-2020 Jul-2020 Aug-2020MonthsSep-2020 fromOct-2020MarchNov-20202020Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>—®— Young <25 yearsold ——®— Adult >=25 years old<br>‘ote Standard enorschsteredon survey wave<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>go7<br>E<
portfolio choices of institutional investors over time and across asset classes.

# **3. Data**

We use data from two main sources. First, we use information from the Global Pension Statistics Project administered by the OECD which covers funded and private pensions components of pension systems across the world. Between 2002 to 2019, the data set provides country-year coverage of pension assets, including data on domestic and international holdings disaggregated by asset class (e.g., equity vs. fixed income assets). In recent years, the GPS data set has added new countries to its coverage. We use an unbalanced panel of 77 countries for a total of 853 country-year pairs. Second, we collect country data on financial and economic development from the World Development Indicators (WDI). We use information on financial integration, measured as foreign direct investment inflows, currency account, and the existence of a common currency (e.g., for euro zone countries). Other variables capture capital market development, such as the total market capitalization of the domestic stock market and trading volume, as well as economic development, including GDP per capita and GDP growth (Table A.1 includes a list of countries and summary statistics for each variable).<sup>8</sup> 8 We also collect, for a subsample of countries in Latin America –Chile, Colombia, and Peru – detailed portfolio information from local pension supervisory authorities. The data set includes comprehensive security-level information covering the entire portfolio composition of pension funds in these countries. The data set is useful as it provides a clear picture of the type of assets that pension fund managers are using to access international markets, and to compare how these relate to domestic security selection.
**Figure 14. Use of innovation inputs vs innovation rates across developing East Asian countries (percent of all firms)**

<!-- Start of picture text --> 50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Cambodia Indonesia Lao Malaysia Philippines Thailand Vietnam Total<br>R&D technology license trainning Any innovation (product OR process)<br><!-- End of picture text --> Source: Own elaboration based on ES data Note: tabulations are adjusted by sampling weights

# **Box 2. How R&D activities, training activities and acquisition of technology licenses affect the probability to innovate across developing East Asian countries**

To assess how the overall probability to innovate is influenced by innovation inputs, the logit model applied in Box 1 is estimated again, now with R&D, technology license and training activities as additional (dummy) explanatory variables. Again, parameters are estimated by maximum likelihood using sampling weights and standard errors are clustered by country. Table A3 in the Annex presents – for each type of innovation output - the odds ratio of each innovation input. Firms that perform R&D activities are more likely to innovate when compared with those that do not perform; the probability of introducing product or process innovation among firms that perform R&D activities is 7 times higher when compared with those that do not perform, and this applies to all types of innovation activities except for firms that have introduced an innovation product that is new to the market. Training activities for innovation is another relevant innovation input: firms that provide formal training for the development of new products and processes are almost 3
# T-able 1: Linking PPAs to policy development work

||**_PPA work_**|**_Broader wo_**<br>**_PPA work t_**<br>**_formulation_**|**_rk which helped to link_**<br>**_o national policy_**<br>|
|---|---|---|---|
|During<br>1998|- Ftundingfor PPAs agreed with DFID<br>-EarlyPPA work and pilot activities<br>- Commitmenit by PPA partner agencies<br>l<br>J<br>.<br>.|- Background<br>assessment, sec<br>resources and<br>poverty assess<br>- (and 1997) co<br>t~~~~~~~~(V|work in planning the poverty<br>uring donor commitment of<br>donor commitment to a shared<br>ment<br>llection of household survey data<br>LSS)|
|Jan 1999|WB and PPA partner agencies meet to discuss<br>scope of research and methodology|Link to global<br>research confir|"Consultations of the Poor"<br>med|
|Feb 1999|- PPA partner agencies join Poverty Working<br>Group asmembers<br>- World Bank travels to PPA study sites to request<br>local authoritypermission<br>for studies|Poverty Worki<br>- including PP|ng Group established and workplan<br>As - agreed|
|Marcli -<br>April|xsa~<br>1999<br>Ail|March: first me<br>to discuss plan<br>~~~~~~~~~~<br>on quant<br>~~~~~~~~~~<br>if|eting of Poverty Working Group
consumption by food items. For example, in the ten-group version, according to the FBDG (see Annex 4), 30 grams of egg was assigned to per AME per day. In both HIES 2000 and 2016, egg category consists of hen egg, duck egg, and other eggs; for an adult male whose actual intake of egg category is 50 percent hen egg and 50 percent duck egg, the FBDG-scaled egg consumption of this dietary pattern will be 15 grams of hen egg and 15 grams duck egg. Consequently, foods that would belong in a specific food group but where 0 grams was consumed, as per HIES, would not be included in the FBDG-scaled diet. Similarly, if only a very small amount of an HIES item was consumed, it would also contribute only a very small amount to that food group’s scaled up (or down) ‘FBDG-scaled’ diet. After scaling quantities consumed to the FBDG recommendations, macro- and micronutrient intake were calculated using the same method as in the actual dietary pattern accordingly.

The EAT-Lancet-scaled diet has the same modeling process as FBDG-scaled diet. Preferences as per HIES were maintained with assigned quantity for each food group. The EAT-Lancet-scaled diet differs from the FBDG-scaled diet in recommended quantities (see Annex 5) and food groupings.

# **2.3 Environmental cost**

Additional to analyzing quantities and nutrient intake, we calculated total GHGe from five sources — land-use change (LUC) CO2e (CO2 equivalents) footprints of feed palm for terrestrial animals, LUC of feed soy for terrestrial animals, LUC of soy and palm oils for human consumption, LUC for pasture expansion, and other CO2e footprints excluding LUC. To calculate GHGe, two pieces of information are needed: 1) item specific (and ideally, country specific) GHG emission footprints; 2) carcass weight for the quantities of food consumed.

5
technical inspectorates.

The EU announced a new strategy for the region in 2018, which has heightened the need to examine the effects of integration between CEFTA and the EU. The new EU strategy for the Western Balkans countries (i.e. CEFTA countries minus Moldova) provided a target accession date of 2025 for the next batch of EU expansion, but this target date may be delayed given the 2020 COVID-19 pandemic. Given the current accession status, it is likely Serbia and Montenegro will be first two CEFTA countries to join the EU, with North Macedonia possibly being the next country. There are, however, many challenges to the reforms needed in the EU acquis and Grieveson, Grubler and Holzner (2018) concludes that the target date is ambitious and serves more as an incentive for reforms.

The CEFTA countries also recognize that further integration amongst themselves can help their integration with the EU. After the EU, other CEFTA countries represent the next largest market for imports and exports for CEFTA countries. The CEFTA only covers market access for all industrial goods and most agricultural goods, but has recently expanded to include trade facilitation and services liberalization.<sup>12</sup> In 2017, the CEFTA countries endorsed a regional action plan to promote economic integration amongst themselves, with the view to encourage economic convergence with the EU. For example, the trade measures in the action plan adopt trade facilitation measures and data systems that conform to EU standards. Most relevant for our paper is an action to investigate the impacts of harmonizing their MFN tariff regimes with the EU’s common external tariff regimes as a method to encourage more integration between CEFTA and EU.

Despite the importance and need to evaluate the benefits of integration for CEFTA countries, data availability has limited any analysis on the topic. Databases such as the WIOD or GTAP, which are widely used for general equilibrium counterfactual analysis such as the one performed here, do not contain data for the CEFTA countries. The databases generally list the CEFTA countries in regional aggregates.
significantly from reduced VAT rates. On close consideration, this is not surprising: Chile and New Zealand both have very few, if any, reduced rates in comparison to the majority of countries covered in the study. Hungary and Latvia also have relatively few reduced rates, and importantly the vast majority of food products are subject to the standard rate.<sup>24</sup> Second, higher spending households in these – and presumably other – countries spend a greater proportion of their total expenditure on items that are either untaxed or exempt from tax (for example, financial services, international air travel).

Turning to the Reynolds-Smolensky results, it follows logically that the 22 countries with a positive Kakwani index, also have a positive Reynolds-Smolensky redistribution index – showing that inequality has fallen in these countries. Conversely, the four countries with a negative Kakwani index, also have a negative Reynolds-Smolensky index – indicating an increase in inequality. However, in all cases the redistributive effect is very low, ranging from 0.0082 in Belgium to -0.0025 in New Zealand. Redistribution is low despite average tax rates typically being above 11%, further emphasizing the very low degree of progressivity (or regressivity) in the VAT. This highlights that even in countries with the most extensive use of reduced VAT rates and exemptions, the VAT can be expected to have very little impact on redistribution.

> 24 Note that while Latvia applied the standard VAT rate to all food products except baby food in 2010 (the year modelled), it now also applies a reduced VAT rate to some fresh fruit and vegetables. Hungary now also applies a reduced VAT rate to a wider range of basic food products than in 2010.

15
Tables 1 and 2, column 3). This indicates mild over‐sizing, arising from autocorrelation in the error terms generated by the moving average transformation.

Overall, three conclusions emerge. First, on model choice, the stochastic trend model is slightly preferred. It is more reliable in its statistical inference. It is also more parsimonious in its data use, which the analysis above has abstracted from.<sup>14</sup> Second, on the matter of whether there is

> 14 It remains that the MAD procedure uses up twelve monthly observations in estimating the trend. The estimates reported in Table 2 relay on 5,10,20 and 40 years of detrended data equivalent to 6, 11, 21

12