rafmacalaba/data-use-mentions-extended huggingface dataset:
Compared to media reports, academic studies provide a much more nuanced picture of how parental migration on left-behind children. The impacts on child outcomes are quite heterogenous, depending on the specific measures examined, child age (de Brauw and Mu, 2011) and gender (Zhang et al. 2015).

Studies of child health, nutrition, and education outcomes yield mixed results. Some studies find parental migration may benefit child nutrition, with lower risk of stunting, underweight, and wasting for young children of migrants (Shi et al., 2020). The timing of parental migration matters; children left behind during early childhood have slower growth rates in height and weight, especially boys (Zhang et al. 2015). Parental migration may also have positive impact on child academic performance, measured by standardized Chinese, Math, and English test scores (Bai et al., 2018; Chen et al., 2014). These studies argue that even though the positive findings might reflect selection if parents do not migrate when they expect their children’s education to suffer, the findings still suggest fewer costs attributable to parental migration. In addition, (Zhou et al., 2015) show left-behind children perform as well as or better than children living with both parents on nine health, nutrition, and education indicators, arguing that all children in rural China are vulnerable and the extra resources accessed by left-behind children should be expanded to all.

Contrary to these positive findings, some studies have found negative impacts on other health and education measures. For example, boys’ academic performance may suffer when both parents migrate (Zhou et al., 2014). Moreover, studies relying on school survey data do not consider school enrollment as an outcome, but evidence suggests that fathers’ migration timing and duration affect children’s probability of enrollment, with rural children of migrant fathers having lower enrollment rate (Wang, 2019). Considering both the positive remittance effect and the negative time effect, parental migration has an overall negative
highly significant. We could not use nominal or real effective exchange rates (either from IFS or WDI sources) due to missing observations for four of the sample countries considered in the second analysis; (ii) To capture the potential effects of macroeconomic policy on manufacturing production and export, we consider inflation and real interest rates. _Inflation_ was considered due to its potential negative effect associated with economic imbalances and distortions, as well as being a symptom of economic mismanagement. _Real interest rates_ were incorporated as a proxy for average productivity of capital, expecting that lower interest rates would boost investments, increase productive capacity, and exports. Neither inflation nor real interest rates entered significantly in the regressions.

(iii) As a proxy of industry concentration, we have used _economic density_ (US$/sq m), which is highly significant in all the regressions. Real GDP per capita, as a proxy for the level of development and income level of the country was tried but was mostly insignificant.

(iv) _Infrastructure_ has important consequences for an economy‟s export performance by lowering the transaction costs, facilitating diversification and giving rise to forces for agglomeration<sup>19</sup> . To evaluate the effect of infrastructure on exports, we considered a number of variables, including rail carrying capacity (tones), roads paved (km), electricity generated (gigawatts) and telephone mainlines per 1,000 people. Due to data constraints, telephone mainlines per 1,000 people was used in our analysis; however, it did not enter significantly.

(v) For the sectoral analysis, we have incorporated a variable to capture sector specific human capital contributions. Roberts and Tybout (1997b) find that the average wage is a better proxy for human capital than the skill ratio because it reflects variations in the quality of employees across firms. So we use the sectoral average wage, computed as total remuneration divided by total employment (data sourced from the UNIDO ISIC database). We expect this variable to affect positively
and a-half years after the debt relief program. The survey covered 2,897 households in four districts of the western Indian state of Gujarat and included detailed questions on household income, production, consumption and investment, as well as a number of questions on financial decisions and expectations. Households were identified from official beneficiary lists and considerable effort was spent to locate and interview as many borrowers as possible.

# **5.1 Bank Data and Sampling**

The sample frame was drawn from the official beneficiary lists published by banks and credit cooperatives. Bank lists typically included the name, village, pledged land, loan category, date of original disbursal, overdue principal and interest as of December 31, 2007, as well as the total amount eligible for debt relief under the program. This account level data was provided by the six largest commercial banks and the largest credit cooperative operating in the four survey districts. Together these banks account for 91% of all accounts that were eligible for the program in the survey districts and 87% of all debt relief accounts in the state. The sample frame was then restricted to accounts within a narrow band of _±._ 5 hectares around the 100% relief cutoff. This bandwidth was chosen following the cross-validation procedure proposed by Imbens and Kalyanaraman (2012).<sup>14</sup> The initial sample frame consisted of 5,554 accounts. This includes agricultural crop and investment loans, but excludes loans not directly related to agriculture, since these loans were not contingent on landholdings, so that the discontinuity induced by the program does not apply. The sample frame also omits previously restructured loans, since I observe neither the original loan size nor the vintage, or terms of restructuring for these loans. This restricts the set of loans covered to the roughly 70% of accounts for which landholding was determinant of debt relief qualification.<sup>15</sup> Descriptives for the population of qualifying loans in the four sample districts are reported in Table 2.
that draw lessons from successful customs modernization examples and reform initiatives. These reports are useful guidance for the recurring issues of current customs modernization initiatives.

_<u>Asia Pacific Economic Cooperation Tariff Database</u>_ Tariff rates for all 21 members of APEC .

The APEC Tariff Database is the Internet site with tariff information for the APEC member economies. After registration, you can access the following features:

- Tariff Chapter Listing Search

- Harmonized System Code or Tariff Heading Search

- Alphabetized Tariff Heading Listing Search

- Description Search

- Customs Guides and other related information (e.g. prohibitions, concessions, contacts, etc.)

- Global Search

The APEC Tariff Database is updated twice each year. Changes to tariff rates may occur between these update periods. These updates are dependent on the submission of data by the participating economies. Users are tariff related information such as Contacts, Prohibitions, Customs Guides, etc., provided by the member economies. Choose the one below that suits your

> 5 Armenia, Azerbaijan, Bulgaria, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Mongolia, Romania, Tajikistan, Turkmenistan, Turkey, Uzbekistan and Ukraine.

21 ____________________________________________________________________________________________________________
026|
||cons|0.899|0.007|-0.061|0.124|0.757|0.017|-0.031|0.028|
||x1|1.982|0.001|-0.079|0.150|2.214|-0.002|-0.039|0.051|
|UQR-Q90|x2|1.296|0.002|-0.060|0.136|2.321|0.001|-0.022|0.077|
||cons|1.778|-0.003|-0.093|0.141|2.499|-0.006|-0.031|0.057|

# **4. Wage inequality in Grenada**

This illustration focuses on an empirical application of our proposed method for the case of Grenada, focusing on the description of wage inequality trends in the country between 2013 and 2020 using the annual Labor Force Survey (LFS). This survey provides the only source of information that can be used to describe the status of the labor market and the distribution of labor income in the country.

One major limitation of this survey, however, is the collection of labor income data. Compared to standard household surveys or labor force surveys in most developed countries, labor income recorded in the LFS in Grenada is only available in brackets. Furthermore, there is a large proportion of the employed population who do not declare their labor income. Table 4 provides an overview of the labor income distribution across time.
|Other|14|0.0|0.5|
|---|---|---|---|
|CIP|N\A|N/A|N/A|
|**_Subsidies_**<sup>_c_</sup>|957|3.2|31.8|
|Electricity<sup>d</sup>|445|1.5|14.8|
|Water<sup>e</sup>|512|1.7|17.0|
|**_Other_**||||
|Contributory pensions|N\A|N\A|N\A|

Sources: Education- Ministry of Education budget (2018); Health - BOOST; Electricity - NEPCO sales (2019); NAF – Ministry of Social Development budget (2018); Bread compensation- Ministry of Finance, may include budget for other small components (2018); Zakat fund annual report. Water – WAJ. Notes: a and b. Total government spending = primary government spending plus interest payments on debt plus use of cash adjustment.

c. Subsidies are not included in the central government budget but are still government liabilities. For example, electricity losses incurred by NEPCO (National Electricity Production Company) have been financed not from the government’s budget but from debt raised and guaranteed by the government on behalf of NEPCO (World Bank 2012).

d. We estimate the total cost of both residential subsidies (JD 300m official estimate) and commercial tariff subsidies which go to consumers through cheaper final goods and services (JD 145m based on modeling of indirect consumption in HEIS survey; not an official estimate).

e. We estimate the total cost of both residential subsidies (JD 549m official estimate) less commercial tariff cross-subsidies which go to consumers through more expensive final goods and services (JD 37m based on modeling of indirect consumption in HEIS survey; not an official estimate)
, independent of SSA’s higher baseline poverty rates.

Two other issues need to be addressed before we can have reasonable confidence in the results presented so far. The first relates to surveys not adequately capturing rich households. One might argue that if richer households in Africa have higher consumption growth but are inadequately captured by household surveys, the lower passthrough between GDP growth and survey mean consumption growth in Africa may be due to sampling biases rather than a genuine lower effect of economic growth on household welfare.<sup>18</sup> Replicating the analysis by focusing on the passthrough between GDP growth and survey median consumption (which should be less affected by inadequate coverage of the rich) shows a similar pattern, with the interaction term between growth in GDP per capita and the SSA dummy being negative ( _-0.56_ ) and statistically significant with a p. value of _0.011_ . It is thus unlikely that under coverage of rich households can explain the lower passthrough between economic growth and household welfare in SSA. The second issue relates to the quality of national accounts. If lower income countries, many of which are in Africa, measure growth less well due to a variety of constraints, the associations between GDP growth and poverty reduction may be driven by quality issues. While it is not possible to completely dispel this concern, Angrist et al. (2021) do not find evidence that growth is on average measured less well in developing countries. We also tried to check the role of data quality by including the score on the World

> 16 Replicating this analysis on all 1,604 consecutive spells gives a similar result, with the passthrough between household consumption from national accounts and household consumption from surveys being much lower in SSA.

> 17 The concept of “semi-elasticities” has been proposed to address this, but arguably suffers from the reverse issue (a country with a baseline poverty rate of 9 percent cannot achieve a 10 percentage-point poverty reduction, while a poorer country with baseline poverty of 60 percent can)
comprehensive overview of this literature, at the same time highlighting the scarcity of studies that go beyond high-income countries (HICs) (Comunale and Manera, 2024).

Bridging this research gap, our study provides new evidence on the potential impacts of GenAI across labor markets in the Latin America and the Caribbean (LAC) region. Building on the approach developed by Gmyrek, Berg and Bescond (2023) – GBB hereafter – we provide new evidence on AI exposure between and within countries by leveraging harmonized household and labor force surveys for LAC from the World Bank (WB) and the International Labour Organization (ILO). By building on the comparative strengths of the datasets from both institutions, we develop a complete regional overview, accompanied by country-level estimates of the potential occupational exposure, with further breakdowns by detailed demographic and labor market characteristics.

An important contribution of this study is to provide a first attempt at adapting measures of jobs’ exposure to GenAI to the context of developing countries, where even workers in occupations that are generally expected to benefit from GenAI may not be able to reap its benefits due to poor access to digital infrastructure. We implement this adjustment by estimating measures of computer

> 2 In OECD’s survey of workers “ _four in five workers said that AI had improved their performance at work and three in five said it had increased their enjoyment of work_ ” (...) “ _Workers were also positive about the impact of AI on their physical and mental health, as well as its usefulness in decision making_ ” (OECD, 2023).

> 3 E.g., see Brynjolfsson et al., 2023; Hui et al., 2023, Beraja et al., 2023, Adams-Prassl et al., 2023.

2
>

> 1 The Sherman Act gave way to The Clayton Antitrust Act of 1914.

> 2 Notwithstanding the conceptual differences between income inequality and wealth inequality, the two terms are used interchangeably hereafter. In the present paper, we use a measure of wealth inequality. As such the focus of the paper is on latter. In practice, measures of income and wealth inequality are strongly correlated. What is more, data on top incomes using tax administration data are more readily available than top wealth.

> 3 For studies of wealth and income inequality in developing countries, see Banerjee and Piketty, 2005; Leigh and van der Eng, 2009; Freund, 2016; Lopez et al., 2016; and Assouad et al, 2018.

> 4 See the debate hosted by the Peterson Institute for International Economics:

<u>https://www.piie.com/events/combating-inequality-rethinking-policies-reduce-inequality-advanced-economies,</u> accessed May 16, 2020.

2
, maritime intermediation and other), professional services (accounting, auditing, legal services on foreign law, legal services on domestic law, legal advice and representation).<sup>9</sup>

> 5 Consumers must be understood in a broad manner, encompassing both individuals and firms, depending on the service concerned.

> 6 We do not take commitments made in FTAs or other economic integration agreements covering services trade as indication of an applied preferential policy. We mean by the latter policies that in practice treat different trading partners differently. Our experience shows that in many cases, commitments under FTAs have been implemented in a non-discriminatory manner and led either to the binding of an MFN-based status quo policy or to the elimination of specific restrictions for all trading partners. See Marchetti and Roy (2009).

> 7 Data on trade through mode 3, e.g. foreign affiliates trade, are not available for all countries.

> 8 - <u>https://www.oecd.org/trade/topics/services trade/</u>

> 9 At the time of writing, policy information in additional sectors including architecture, engineering, computer, construction, tourism, and health services is being collected for the seven CEFTA Parties, Jordan, and other African economies.

4
relationship describing the sectoral growth dynamics of Ecuador would be a surprising result.

To address the issue of stability of the cointegration relationship over time, we perform a recursive cointegration analysis, where the trace-statistics for the hypothesis of less than one cointegration vector is estimated for different sample periods.

Operationally, the data from 1965:Ql to 1982:Ql (roughly the first half of the sample) is used as a base period for the calculation of the first test statistic and the sample

**6** This result comes about by starting at the top of Table I and moving downwards until Ho cannot be rejected. As this is the case in the second row, the analysis maintains the Ho of zero cointegrating vectors, this implies the existence of exactly one cointegrating vector in the data.

The World Bank Latin American and Caribbean Regional Database contains data on annual real GDP from 1965-98 for Ecuador. Since this data base could have been used for our cointegration analysis, we perforn additionally a cointegration analysis using this data set (see Appendix 2). This allows us to contrast the results of a cointegration analysis based on annual data with the results of a cointegration analysis using a higher frequency. Since cointegration is a long-run property, the frequency of the data should not matter and we would expect to find similar results. As Appendix 2 shows, this appears to be the case.

9
the result of several forces, including increased efforts to attract them by policymakers as they recognize the central role of human capital in economic growth, positive spillovers generated by skill agglomeration, declines in transportation and communication costs, and rising pursuit of foreign education by young people. Among the resulting effects are the doubling of the share of the tertiary‐educated in the labor force and fierce competition among countries hoping to attract

> 1 The 2010 update was developed by the OECD, the World Bank and the International Migration Institute at Oxford University. The details of the databases, their construction, methodology and the main patterns are discussed in Arslan et al. (2014). There is a parallel DIOC‐E database for non‐OECD countries

> (http://www.oecd.org/els/mig/dioc.htm) described by Artuç et al. (2015).

> 2 To achieve harmonized and comparable series over the 20‐year period, we exclude Chile, Estonia, Israel, Slovenia, and South Korea from our analysis. Throughout this article, we use ‘OECD’ to refer to the 29 remaining members, irrespective of their date of joining the OECD.

4
return to education would necessarily be higher. That would make our assumption and all subsequent implications to be a clear underestimation of the potential real loss.

∆ _Earnings-per-year-per-studentc = (_ ∆ _LAYSc_ x x _Earningsc)_ ∆ _Earnings-per-yearc=_ N _x Ac x Uc x_ ∆ _(Earnings-per-year-per-student_ cc _c)_ RR ∆Life-time-earningscc _c =_ P (∆ _Earnings-per-year )_ cc,ii,tt Our calculations are described in the equations below, is the long-run expected returns to one-year-of-schooling, which is fixed at 8% for all countries as in the HCI; cc RR Where,

**Earningsc** is the mean nominal  monthly earnings of employees in 2017 PPP $,

**A** c is the Adult survival rate in country c – from Human Capital Index Database

**U** c is the Human Capital Utilization as per Pennings (2019) is the total number of students enrolled in pre-primary, primary, and secondary in country c from UIS Statistics, NNcc is the discount rate – assumed to be 3%, is years of working life that the change in earnings is experienced – 45 years **,** ii tt

# _Expected Earnings_

To calculate the earnings loss, we used the ILO database on monthly earnings of employees in 2017 PPP$. We have triangulated this information against both the countries average household GDP in 2017 PPP$ and the average total household welfare also in 2017 PPP$, these indicators were constructed from the WDI and GMD, respectively. We used the average household size in the latest household survey available in the GMD. Once this further adjustment was done, it was clear that for most countries (100) the ILO earnings data seemed plausible; in 35 countries we replaced the ILO earnings value by the World Bank JoIn database, and for the remaining 22 countries we used the average earnings of a specific income level as the proxy.

56
empirical evidence. Previous empirical studies also highlight that adding household assets helps to improve on poverty estimates, and Model 2, which adds asset and animal ownership to Model 1, is consistent with this evidence. However, adding more variables may lead to overfitting, resulting in less accurate welfare estimates. The results of Model 3 could be placed in this context.

# **V. Targeting Performance**

The imputed welfare estimates can be useful in evaluating ex post the inclusion/exclusion errors of the food assistance programs administered by the government and humanitarian organizations during 2016/17. The targeting strategy for food assistance was agreed to and implemented by the <mark>UNHCR, WFP, and the government agency in Chad responsible for refugees, the CNARR.</mark> We perform an analysis to show how accurately the current targeting strategy identifies poor households in terms of inclusion (leakage) and exclusion (undercoverage) errors. Both error types are important but from different perspectives. The inclusion error matters primarily from a budget perspective, as it represents a waste of resources. The exclusion error summarizes the program’s failure to cover households in need.

The current <mark>UNHCR/WFP/CNARR targeting approach relies o</mark> n the Food Consumption Score (FCS) generated by the WFP’s PDM surveys, which is a composite score based on dietary diversity, food consumption frequency, and the relative nutritional importance of different food items. As is the case for any index, the FCS is contingent on the selection of the food group weights as well as the food item thresholds, which are based on inherently subjective choices. Survey-tosurvey methods have been shown to outperform these types of index approaches, whereas the Dang et al. (2017) cross-imputation method has been shown to perform better than the proxy means testing also in refugee contexts (Dang and Verme 2019).<sup>10</sup> In light of the previous findings, we empirically evaluate how the UNHCR/WFP/