rafmacalaba/data-use-mentions-extended huggingface dataset:
addition to greater demographic pressures and an ongoing structural transformation of the economy can strengthen the rationale for policy action.

Given these reasons that might justify government involvement, the key question is then how should the government get involved in order to ensure these policies have higher chances of being successful?

# **What Are the Most Promising Avenues for Government Involvement in Job Training and Job Search?**

Jobseekers may struggle to find jobs for three main reasons. First, there may be a shortage of firms wishing to hire them. This may reflect a lack of overall labor demand in the economy, in which case the appropriate policy actions will be in the area of private sector development policy. In some cases, it may also reflect discrimination, with firms not willing to hire individuals with certain characteristics. Second, jobseekers may struggle because they lack the skills and experience needed for jobs. Job training and internships and apprenticeships can then be used to help overcome this problem. Finally, even if jobs are available and individuals have the right skills, they may struggle to find and match with employers who want their labor, in which case job search assistance can be useful.

It is difficult to find systematic evidence to assess the relative importance of these three reasons, and the answer will almost surely vary with context. Our sense is that in many cases the largest issue is a lack of labor demand for jobs, and especially “good” jobs, and that policies to help firms to grow and demand more labor need to be a primary part of any jobs policy solution (although it is not the topic of this paper). Data from the Mexican National Survey of Occupation and Employment (ENOE) provides one data point, asking unemployed individuals why they are not looking for work: 4.6 times as many individuals say it is because they think there are no jobs available near them than say it is because they lack the skills or experience needed for jobs. Lack of experience and knowledge of where to look for
Since there are no cross-product terms between the factors, the variance in inflation attributable to the global factor is: The variance shares due to the group factors and idiosyncratic terms are calculated using a similar approach.

# 2.2. Data

The analysis is based on annual inflation data for 99 countries, 25 advanced economies and 74 EMDEs (including 16 LICs), for 1970-2017. In the benchmark estimation, inflation is measured as annual headline CPI inflation and output growth.<sup>8</sup> To analyze the extent of synchronization in multiple measures of inflation, the database is augmented with core CPI inflation, PPI inflation, import price inflation, and GDP deflator growth for a subset of 38 countries (of which 13 are EMDEs). The list of the countries is presented in Table 1.

# 3. Inflation synchronization

# 3.1. Synchronization: Global and group factors

The model identifies a global inflation factor that, as expected, registers sharp movements around oil price spikes and global recessions (Figure 2). Within a year of an average global recession (such recessions having occurred in 1975, 1982, 1991, and 2009) and during the average oil price plunge (these having occurred in 1986, 1990-91, 1997-98, 2001, 2008, and 2014-16), annual global inflation fell by 0.5 and 0.2 percentage point, respectively, below its long-term trend. Conversely, in the average year preceding a global recession, global inflation was almost 2 percentage points above trend. The global factor moved in tandem with median inflation across countries.<sup>9</sup> The advanced economy and EMDE factors also exhibited common (although more muted) movements with their respective group-specific median inflation rates.

During 1970-2017, the global inflation factor accounted for a sizable share of within-country inflation variance in advanced economies and EMDEs (Figure 3; Table 2). In the median country, the global factor accounted for 12 percent of inflation variation, but its role varied widely across and
goods included in the consumption basket, as well as the sources of income. In the notation of Winters et al. (2004), the first order approximation of the welfare effect of a price change caused by a trade shock is written as:

# (1)

W represents household welfare, subscript “j” identifies a good, q stands for the household’s income generated by the sale of good j, and c is the household’s consumption of the good. The price change of good j is denoted by delta p. The summation tells us that we are summing the effects from price changes of all goods affected by the trade shock and that are consumed or sold by the household. Regardless of notation, the intuition is obvious: an increase in the price of good that is sold by the household but not consumed would result in an increase in welfare, whereas an increase in the price of a good that is not sold but is consumed would have the opposite effect.

The appeal of this simple framework for rural settings is obvious, because such households are often both producers and consumers of the same agricultural commodities. Indeed, this simple framework has been applied in World Bank studies of the effects of agricultural commodity price increases on poverty.

Two excellent examples of operational research that followed this framework are Wodon et al. (2008), which analyzed the agricultural commodity price boom of 2006-07 with African household data, and Ferreira et al. (2011), which focused on Brazil out of concern over the agricultural price boom of 2010. What makes these two examples notable is the acknowledgement of necessary caveats related to limitations of this approach. More specifically, both studies state that they are focusing only on the first order effects of the price changes and ignoring long-term considerations related to household decisions to change their consumption and income patterns. Wodon et al. (2008) in fact ignore even short run changes in income, whereas Ferreira et al. (2011) allow incomes of agricultural workers to
highlights the fundamental role of digital infrastructure availability and quality as the key supplyside barriers to generative AI usage. Column (3) shows that trade openness, service sector share of GDP, and digitally delivered service exports in total exports are all positively and significantly associated with ChatGPT usage. Column (4) reports positive and significant coefficients of human capital variables: dummy for English speaking country, literacy rate, percentage of the population with intermediate digital skill.<sup>7</sup> We choose intermediate digital skills as ChatGPT is most relevant for workers with such skills.

Column (5) includes all three sets of variables together and shows similar and robust results as the previous columns. The coefficient of log GDP per capita becomes weaker as we include more factors into regressions. It turns insignificant and close to zero in Column (5). Together with the increased adjusted R squared from Column (1) to Column (5), this pattern indicates that the correlation between income level and ChatGPT usage can be broken down into our identified three areas.

The comparison with Google yields additional insights. Specialization in digitally deliverable services, white collar workers, English fluency and digital skills are highly correlated with ChatGPT traffic but not Google traffic. This corroborates our previous findings that ChatGPT is by and large used by white collar workers in tradable, knowledge-intensive industries.

The strong correlation between country-level English fluency and ChatGPT usage can be attributed to several interconnected factors. Primarily, ChatGPT’s training on predominantly English-language data results in better performance and user experience in English. This is compounded by the internet’s English-centric content and the tech industry’s use of English as a lingua franca. Countries with higher English fluency often have education systems emphasizing technology and digital literacy, fostering a more tech-savvy population primed for AI adoption. Early adoption patterns, initially driven by English-language marketing and outsourcing demand emanating from advanced English-speaking countries, may have set a precedent for ongoing usage trends. Additionally, many professional fields that benefit from ChatGPT, such as
Future empirical analysis would benefit from using annual data on the SPI. A panel data set of SPI would allow researchers to refine the estimates of demand for and supply of data by controlling for country-specific, unobservable effects that might be correlated with statistical performance.

The results of our findings should be treated as the first step of this analysis. More in-depth research that relies on detailed country data is required to understand the interplay between the supply of and demand for data in determining the capacity of an NSS. We hope this paper will stimulate research on the optimal path for improving statistical capacity in developing countries.

21
> Note: Gross Tax Revenue totals 13.1 percent of GDP in 2010. Source: IMF (2011a).

20. **Less than 2 percent of Colombia’s adult population pay income taxes on declared income, in part, because of a very high threshold for personal income taxation.**<sup>**13**</sup> Among the 1.1 million Colombians who submitted an income tax declaration in 2010, only 640,000 people paid income tax in a adult population of about 37 million, according to the Colombian Tax Authority (DIAN). This is partly explained by the fact that Colombia has the highest threshold in the region. Individuals have to earn three times the average household income per

> 12 Missing data for top earners in household surveys imply that the reported Gini coefficient is artificially low.  Top incomes represent a small share of the population, but a very significant share of total income and total taxes paid, as discussed by Atkinson _et al_ (2011).  Household survey data, such as that used in Table 1, generally does not capture the top earning individuals owing to the way in which surveys are designed (often with a view to understanding the lower end of the income distribution) and the difficulties of sampling top earners.  Future analysis could focus on combining income tax data with household survey data to understand this effect better in the case of Colombia. 13 This number rises to 12.4 percent if the retentions of independent workers are included.  The number of tax payers is compared with the adult population, reflecting the fact that pensioners also form part of the universe of potential tax payers.

8
implementing the carbon tax in 2030. To check sensitivity to international commodity price and carbon tax rate assumptions, we also apply CPAT assuming international energy prices are lower at the time of the introduction of the carbon tax in 2024 as well as a CEPR scenario with a carbon tax rate of US$12.5/ton CO2e.

Finally, we touch upon why governments are oftentimes reluctant to introduce carbon pricing or phase out fossil fuel subsidies and provide some suggestions for the successful implementation of a reform like the CEPR. This also links to the international debate on who should share the burden of mitigating climate change. Some might suggest that it is unfair to impose carbon taxes on developing economies. After all, most of them emit but a small fraction of existing GHGs into the atmosphere in per capita terms, even as they face the most severe challenges from climate change. However, an increasing body of evidence (Hallegatte, 2022; Wollburg et al., 2023) shows that there are various direct benefits to adopting a well-designed green transition strategy to ward off climate damages. Our paper contributes to this literature.

To our knowledge, this is also the first analysis that quantifies economic and distributional implications of climate mitigation policies for the South Asia region as a whole. We contribute to the literature by using updated household survey data from economies that have not been considered for this type of analysis in the past. Similar studies either focus disproportionately on large emitters (IMF, 2019b; Parry, Mylonas, and Vernon, 2019; 2021) or lack granular coverage of South Asia (Alonso and Kilpatrick, 2022). Ohlendorf et al. (2021) use an ordered Probit meta-analysis framework to examine 53 empirical studies in 39 (mostly advanced) economies and find that, in the less wealthy economies of their sample, the likelihood of progressive distributional outcomes following the introduction of mitigation policies is higher. For developing economies, the literature is sparse. A notable study on Latin American and Caribbean economies finds that up to 30 percent of
|Population|POP|Population ages 15-64 (% of total)|World Bank (WDI)|
|---|---|---|---|
|Governance|VA_PER_R<br>NK|Voice and accountability|World Bank (WGI)|
|Public spend|PSPEND|Public spending on education (% of GDP)|World Bank (WDI)|
|Infrastructure 1|IS_ROD_P<br>AVED|Roads paved (% of total roads)|World Bank (WDI)|
|Infrastructure 2|EG_ELC_H<br>YRO_ZS|Electricity production from hydroelectric (% of total)|World Bank (WDI)|
|Wealthdummuy_|WEALTH|Dummy variable based on the decomposition of<br>(lower=0, lower middle=1, upper=2) wealth level|Author compute|
|Conflictdummy_|CONFLICT|Dummy variable based on the decomposition of<br>(Conflict=1 versus absence of conflict=0) countries|Author compute|
|Saheldummy_|SAHEL|Dummy variable based on the decomposition of<br>(Sahel=0 versus Forest=1) countries|Author compute|
|Legaldummy_|LEGAL|Dummy variable based on the decomposition of (French<br>law=0 versus Common law=1)countries|Author compute|

WDI: World Bank Development Indicators. FDSD: Financial Development and Structure Database.

_Source_ : Authors’ construction.

**Table A.4: Mortgage Depth Data Sources**

|Country|Region|No. of Obs|Period|Source|
|---|---|---|---|---|
|**Botswana**|Sub-Saharan Africa|1|2009-2009|Other|
|**Burkina Faso**|Sub-Saharan Africa|5|2006-2010|Central Bank|
|**Burundi**|Sub-Saharan Africa|5|2007-2011|Central Bank of Burundi|
|**Cameroon**|
forward, are:

- The use of specialized sampling techniques as proposed by De Brauw and Carletto (2012) remains the most effective solution to the issue. This essentially consists in **oversampling** areas with a high prevalence of migrants which, however, poses challenges due to the limited availability of adequate sampling frames, particularly in developing contexts. This is yet another area where the **integration of household surveys with other data sources** can bring significant advantages by considerably cutting down the costs associated with more traditional updating methods (i.e., door-to-door listing operations). For example, using multi-source frames or leveraging auxiliary information from nonconventional sources (e.g., Earth Observations and geospatial data) can facilitate the implementation of alternative sampling designs that can be used to oversample migrant households (United Nations Statistical Commission, 2021). There are already quite a few examples of studies trying to link survey data with digital trace data to recruit difficult-to-reach respondents (Iannelli et

> 33 Alternative approaches to be tested can include the adoption of roster of moves for shorter recall periods, or relying on household roster updates to capture household members’ moves in-between waves.

> 34 For an application and further practical reflections on the ‘rare event’ issues in household surveys, see subsection 4.1.

21
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**Note** : Each bar is a weighted average of countries, weighting by total workers. Firm size categories for Chile, Costa Rica, Uruguay, Europe, Asia, and the United States are categorized as 1-9 and 10 or more. Panel B: rather than cut off at 50, Argentina cuts at 40 and Costa Rica at 30. Panel C: For Brazil, Chile, and Uruguay, 51-100 versus 100+ split is imputed using the average distribution across those two categories in the rest of LATAM. **Sources** : Authors’ calculations using employment or household surveys representative of the labor force. Details in the Appendix.

The other two panels of Figure 1 present the business size distribution for more disaggregate size bins. Care must be exercised when comparing panels since we lose progressively more countries as we use more disaggregate size bins, but this more disaggregate comparison sheds light on interesting differences across regions. First, in LATAM, the vast majority (27% out of 36%) of workers in businesses of 1-10 employees fall in the 1-4 workers category, while more prosperous 8
Horn, Reinhart and Trebesch (2021) warn that Chinese loans to developing countries collateralized against natural resource revenues have the potential to become a threat to debt sustainability due to the size of such deals and their lack of transparency. The lack of transparency is highlighted by the authors’ estimate that 50% percent of Chinese lending (overall, not just RBLs) is missing from official statistics.

Some studies have focused on the role of China. Brautigam and Gallagher (2014) provide a systematic empirical overview of Chinese commodity-backed loans (with data on loans until 2011) and discuss some key features of these loans. Sanderson and Forsythe (2012) analyze these deals from the perspective of China’s development banks. Meidan (2016) analyzes their importance in terms of China’s access to oil resources.

Another group of relevant studies focus on the resolution of debt repayment problems in cases where there is collateralized lending; and two papers look at the outcome of renegotiating collateralized debt extended by China. In the case of Gardner et al. (2020) the borrower is the Republic of Congo, while Acker et al. (2020) look at loans taken by Angola and Chad. Both papers contrast collateralized loans with uncollateralized loans.

There has been very little work on the socio-economic impact of projects financed via RBLs. Fieldwork by Tang and Shen in Ghana found that the Bui Hydropower Project in Ghana, financed via an RBL backed by cocoa proceeds, “has improved local urban households’ access to electricity and increased their ownership of electric appliances” (Tang & Shen, 2020).

The World Bank and IMF, among other international financial institutions, have analyzed collateralized loans in order to formulate their own recommendations. The IMF-WB (2020) concludes that collateralized transactions “can be beneficial to a developing country borrower under a range of circumstances”, but also points to risks and discourages this practice when the proceeds are not spent on assets which can be used to repay the loans, when loans are excessively large and when the
# 6.3 Officials believe that more money is needed but are skeptical about effectiveness.

The gap between officials’ preference for education spending and their perception about current spending on education is also larger in the region than in other surveyed countries (Figure 7). Indonesia is an outlier in EAP, as official data perfectly match officials’ estimates and the preferred level of spending is only 2 percentage points higher.<sup>18</sup> In Lao PDR, the Philippines, and Vietnam, officials overestimate public spending substantially while still believing that more resources should be spent. The magnitude of the difference between actual spending and desired spending is about 12 percentage points, which is significantly larger than in non-EAP countries where this same gap is only about 9 percentage points. The average estimated level would require a 31 percent increase in spending, while the average desired level of spending would require a 79 percent increase in actual education spending in surveyed EAP countries.

Even in a country such as Lao PDR, where financing was identified as a major constraint, officials commonly believe that additional money would not improve learning (Figure 17). There is considerable variation in the opinions of officials on whether additional resources for education will improve learning. If a system does not have the capacity to spend resources effectively, it would make sense that additional financing would not improve student learning.

_Figure 17: Share of officials who believe that more money will_ **_not_** _improve learning._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>PHL IDN VNM MNG LAO Non-EAP<br>Strongly agrees Agrees Does not know Disagrees Strongly disagrees<br>money will not improve learning<br>Share of officials believeing that more<br
As a simple way to understand the intuition behind the index and as a test of the validity of the index, we assess correlations of the country-level _AHI_ with macro-level data using national income and infant mortality rate (Figure 2), variables that are not part of the index creation. The _AHI_ is highly correlated with both concepts. The _AHI_ is low in low-income countries, while higher- and middle-income countries exhibit higher scores on the _AHI_ , consistent with the positive relationship established for the underlying dimensions of the AHI in the literature (cf. Section 3.2). Likewise, countries with a higher score on the _AHI_ exhibit lower infant mortality rates. This provides evidence for external validity of the _AHI_ as clear links have been established by literature on adequate housing conditions and health outcomes (e.g., Galiani et al. 2005; Jalan & Ravallion 2003; Nabassaga et al. 2019; Jamison et al. 1993). Ultimately, the _AHI_ as well as data on the adequacy dimensions from this paper are meant to allow further research on such relationships.

**Figure 2: Correlation of** **_Adequate Housing Index_ with GDP per capita and Infant Mortality**

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