rafmacalaba/data-use-mentions-extended huggingface dataset:
It is now well-established that the modern organized manufacturing sector exhibits unconditional convergence, contrary to other economic sectors (Rodrik 2014); that is, manufacturing industries converge to the global productivity frontier regardless of the condition of growth fundamentals. The question is whether Africa can generate a “growth miracle” based on the manufacturing industry. In a model that incorporates this unconditional convergence, structural change – the shift of labor from low productivity industries to high productivity industries- plays an essential role.

The level of employment shares across sectors in developing countries conditional on income level shows that African countries fit into a global pattern: shares of employment in the different sectors are what one would expect them to be. So just like elsewhere, structural change in sub-Saharan Africa has been through a decline in the share of labor employment in agriculture, the least productive sector in subSaharan economies. Unlike other developing countries, African countries (generally) have not seen a significant increase in the share of labor force employment in manufacturing—instead, the shift in employment share has been towards the services sector.

Thus African structural change is unlike the pattern exhibited by Asian and European industrializers: labor is currently largely shifting towards the services industry, which historically has been less productive than manufacturing in other countries. Additionally, the manufacturing sector is dominated by informal firms, which are less productive than the formal organized firms required for unconditional convergence. Rodrik (2014) notes that African countries are de-industrializing much faster than Asian and European countries did in the past. African countries thus need to assess what type of growth model is both suitable and feasible.<sup>**1**</sup>

> 1 Other literature has investigated the consumption side of African growth in recent decades. Using the <mark>Demographic and Health Survey (DHS) data, Young (2012) shows that since 1990, real material consumption in Sub-Saharan Africa has been rising at a rate of 3.4 to 3.7 percent per year. This is 3 to 4
innovation ecosystems that support sustainable AI adoption rather than merely seeking to close the divide.

countries facing similar structural challenges can exchange best practices and tailor their AI readiness strategies. By promoting knowledge-sharing and collaboration, the framework proposed here helps transcend income-based categorizations and instead fosters a more comprehensive AI development landscape. This peer-learning approach also supports policy experimentation, enabling nations to test scalable, cost-effective solutions before committing to large-scale deployments. By spotlighting overperformers across income groups, the methodology provides actionable insights into the pillars—whether regulatory, educational, or infrastructural—that drive AI overperformance.

In this study, the methodological decision to use the recent AIPI from the IMF instead of the AI Readiness Index developed by the private UK-based consulting firm Oxford Insights is based on several considerations. The IMF, as a globally recognized multilateral institution, offers a higher level of accountability, scrutiny, and transparency in the development of its products. Conversely, while the contributions of private entities like Oxford Insights are valuable, they do not operate under the same oversight or accountability. Moreover, the AI Readiness Index specifically assesses the preparedness of governments to deploy AI in public service delivery, whereas the AIPI provides a more comprehensive evaluation of how countries can effectively harness AI capabilities. That said, the choice of index does not significantly impact the broader contribution of this study, as the proposed methodology remains adaptable and replicable. The focus is on providing a transparent, data-driven approach that allows policy makers to assess their country’s AI preparedness relative to their economic complexity. By prioritizing a clear and simple framework, this study enables comparisons across different datasets and indices, reinforcing the robustness and utility of its findings regardless of the specific AI readiness index used.

Future research could also explore ways to improve the AIPI or similar indexes. For instance, our findings suggest that a significant number of East Asian countries—particularly those influenced by Confucian traditions—tend to overperform in AI preparedness regardless of their income levels, from Viet Nam to Singapore and China. This trend warrants further investigation
prices rising. Our main result is that cropyield elasticities with respect to fertilizer are 21–49% lower in GE than in PE, as a result of which the production decline in maize and rice are attenuated by 35% and 39% (respectively) in GE relative to PE. Intuitively, lower wages and higher output prices incentivize agricultural producers to expand labor input, partially offsetting the initial production losses from fertilizer price hikes. Our PE elasticities are very close in values to Carter et al. (2021) and Beaman et al. (2013), further validating the model. These results imply that extrapolations based on PE elasticities from small-scale experimental studies systematically overestimate the adverse effects of large-scale input shocks, underscoring the importance of GE mechanisms that extend well beyond the input market in shaping the aggregate consequences of nationwide supply disruptions.

Finally, we analyze how different features of developing countries that we model matter for welfare. Without non-homothetic preferences, the model underpredicts the size of the agricultural sector at baseline, leading to an underestimation of the ban’s negative effects on mobile workers. The subsidy program functions as an implicit income transfer scheme from mobile workers to farmers, so the model without subsidies overestimates ban-induced worker losses (and underestimates

> 10The existence of a small domestic fertilizer production sector ensures that, even under a full import ban, fertilizer use in agriculture does not fall to zero.

> 11The regional pattern of post-ban income losses predicted by the model replicates 82% of the corresponding pattern implied by nightlights data (7.7 p.p.), supporting the model’s validity.
national economic transactions into income and expenditure, consumption and savings, investment and the balance of payments. Monetarist developments of the same Keynesian system underlie the depository corporations survey (formerly known as the monetary survey - cf. the IMF’s Monetary and Financial Statistics Manual <u>http://www.imf.org/external/pubs/ft/mfs/manual/) which is the bedrock of central</u> banking data, and which classifies the assets and liabilities of banking and monetary institutions into monetary and credit aggregates. Collection of national data consistent with these two systems have allowed the evolving understanding of macroeconomic and monetary processes, as learnt from the experience of certain well-studied countries, to be verified and applied on a global basis thereby in particular offering an important technology transfer to developing countries.

International comparisons of living standards have also relied on unifying theoretical constructs. For example, the theory of price indexes based on utility theory underlying the purchasing power-parity calculations of the Penn World Tables and similar exercises taking into account international differences in the price of consumer goods thereby 4
# **6 Conclusion**

Environmental conditions play a crucial role in health. Vulnerable groups, such as children and the elderly, are particularly susceptible to the impacts of environmental hazards. This paper examines the impacts of three common environmental hazards, extreme temperature, extreme precipitation, and air pollution, in the context of Cambodia, a tropical country with expected increases in temperature and precipitation under most climate scenarios.

We combine the Demographic and Health Surveys (DHS) with satellite-based information on temperature, precipitation, and fine particulate matter ( _PM_ 2 _._ 5) to uncover the impact of environmental hazards on children’s diarrhea and cough. We find a positive impact of temperature on diarrhea and cough among children. Results also show a significant impact of extreme precipitation on diarrhea. Furthermore, our study demonstrates that pollution significantly impacts cough incidence. In Cambodia, out-of-pocket expenditure is high and amounts to 60.6% (WHO, 2023). As a consequence, the increase in morbidity due to weather shocks is likely to represent a significant cost for households that could be estimated in future work.

Simulations are employed to forecast the incidence of diarrhea in Cambodia under different climate and development scenarios. Our projections suggest that future diarrhea incidence among children could reach 19% by 2040 unless significant adaptation measures are taken. The acceleration in toilet ownership until 2021 has significantly decreased diarrhea incidence by at least 1.2-1.4 percentage points. However, results show that more remains to be done. Indeed, without further investments in water and sanitation or complementary climate adaptation, only a universal toilet ownership scenario would be sufficient to contain the increase in diarrhea incidence due to climate change by 2040. Hence, our findings underscore the need for proactive measures to mitigate the projected climate-driven increase in diarrhea incidence. More generally, there is a need to adopt comprehensive strategies to mitigate the health impacts of climate change in countries like Cambodia.

26
The inability of any of our models to replicate poverty rates in years 2004/05, 2009/10 and 2014/15 raises concerns whether this method correctly forecasts poverty in 2017/18.<sup>16</sup> This contrast with Newhouse and Vyas (2019) who validate their model by replicating poverty rates in rural areas in 2004/05.<sup>17</sup> Given these limitations, we explore another method to project poverty rates for India in the absence of survey data.

# **4. Pass-through method**

While we should expect that the growth of consumption in national accounts is positively correlated with growth of mean consumption measured from surveys, a substantial literature has found that growth in national accounts does not pass-through one-to-one to household surveys. The difference between these two growth rates is referred to as a pass-through rate. More precisely, and following Ravallion (2003), the pass-through rate is the coefficient estimate β in the regression of the growth in the survey mean, gsurvey, on the growth in national accounts, gNAS: where _i_ is a growth spell between two survey years, and the residual, ui, has mean zero. gsurvey is the growth rate of the survey welfare aggregate, here measured as household income or consumption expenditure per capita. gNAS is the real growth rate in national accounts, for which we consider either Gross Domestic Product (GDP) per capita or Household Final Consumption Expenditure (HFCE) per capita. The pass-through rate, β, captures the rate of growth in consumption that is passed through from national accounts to surveys. If β= 1, then mean consumption in the survey grows at the same rate as consumption in national accounts. Typically, the literature finds β< 1 (see more details below), which implies that mean consumption in the survey grows slower than the growth in national accounts.

The literature has discussed several channels for why there are systematic differences in growth of consumption as measured in national accounts and in the survey
to the public. Starting from version 8 different time series are reported for level and growth rate comparisons.<sup>22</sup> We take these concerns seriously and repeat the restricted structural break search using two very different data sets (in addition to the PWT 9). Specifically, we use the version 7 vintage of the PWT—before the computation of the chained series changed

> 20Another way of tackling this issue is to truncate the data and censor all spells lasting longer than a few, say five, years. Our results are robust to this type of truncation (at various points). We thank an anonymous referee for suggesting this test to us.

> 21 We are indebted to an anonymous referee for pointing this out to us.

> 22The PWT version 7 already offered a new series, _rdgpl_ 2, based on the growth rate of domestic absorption, as a response to the work of Johnson et al. (2013). We obtain very similar results when using this series instead of _rgdpch_ in the robustness checks.

29
Chong et al.

20 the novelty of this approach is that it provides strong evidence that anonymity and confidentiality of information technologies may be of great use in segments of the society in which keeping such anonymity is difficult to overcome.<sup>28</sup> The results on knowledge and attitudes are important because these two factors have been shown to be the strongest protective factors in preventing STIs, HIV, and pregnancy among teens (Kirby, Lepore, and Ryan 2007). Furthermore, recent research has documented the important role that social norms play in responsible sexual behavior (Munshi and Myaux 2005; Ashraf, Field, and Lee 2014). By changing knowledge and attitudes in youth attending school, sexual education can ultimately play a fundamental role in achieving desirable aggregate changes in sexual behavior.

A second contribution to the sexual health education literature is the focus on spillovers, through a twostage experimental design. The results indicate that spillovers from treated to untreated classrooms in the same school are negligible.

Lastly, the analysis provides strong indications that effects of the course were reinforced when treated individuals had larger percentages of their friend networks in treatment classrooms. The evidence is robust across a large set of sexual health attitude and knowledge indicators. In particular, the analysis found that students whose networks were more intensely treated had significant improvements in knowledge and attitudes, which the study interprets as social reinforcement effects or complementarities. These results demonstrate the positive externalities of the public provision of sex education: When an individual takes a sex education course, this decision has positive effects on sexual health outcomes among his or her close friends. This suggests that without collective action, there is an underprovision of sex education.

These results provide an optimistic assessment of the use of information technologies to generate improved sexual health outcomes among the youth. The cost-benefit analysis suggests that because Internetbased sexual health education programs are extremely low cost, their benefits in terms of STI reductions actually justify the costs.

# **References**

AIDS Control and Prevention Project (AIDSCAP)
# **Abstract**

The idea of social inclusion has garnered considerable attention, especially in the context of two recent developments: the Sustainable Development Goals and the heightened attention to inequality. This paper reviews the manner and extent to which social inclusion is addressed in the first 17 Systematic Country Diagnostics (SCDs), which are ex ante, country-level assessments conducted by the World Bank Group, ahead of the preparation of its Country Partnership Frameworks. In addition to this primary purpose, the paper fulfils three other purposes. It allows for a broader reflection on the value of the social inclusion construct in macro-level diagnostics; it takes the opportunity to develop and refine a methodology to assess social inclusion and finally, it positions the narrative on social inclusion into the ongoing discourse on poverty, shared prosperity, inequality and the thinking around the implementation of the Sustainable Development Goals. It is therefore, a refined articulation of the idea of social inclusion in the context of global epistemological shifts.

6
standard unemployment (search required), market definition for comparability across the various survey rounds. As for women, the unemployment rate in urban areas is found to be higher than the unemployment rate in rural areas in 2018, while the opposite was true in 2012. The higher female unemployment rate in rural areas compared to urban areas in 2012 is in line with the findings of El-Mallakh, Maurel, and Speciale (2018), who examined the impact of the 2011 revolution on intra-household differences in labor market outcomes between 2006 and 2012, and found that the gender gap in labor force participation decreased in the aftermath of the uprisings, as women started to actively search for employment, in particular women belonging to the poorest households.

The highly educated in Egypt are those with the highest risk of unemployment (Figure 6 and Figure 7). As shown in these figures, male and female unemployment rates are strikingly higher among highly educated individuals (those with secondary education and above).<sup>5</sup> Moreover, unemployment is also higher among the youth. Examining unemployment rates by age cohorts, Figure 8 and Figure 9 show that unemployment is highest among younger age groups and in particular among those aged between 15 and 24. These patterns are true for both men and women and suggest that unemployment in Egypt is especially pronounced at first entry in the labor market.

# **2.2 Descriptive statistics on unemployed individuals**

In this section, I present descriptive statistics on the sample of unemployed individuals. As presented in the introduction, the empirical analysis relies on panel data from the 2012 and 2018 rounds of the ELMPS. In 2018, the ELMPS tracks households and individuals that were previously interviewed in 2012, of which some were also interviewed in 1998 and 2006, while others belong to a new sample of individuals added in 2012. The total sample size in the panel is 13,763 households and 38,759 individuals.

The ELMPS has the advantage of providing rich information on Internet use. Individuals
, mattering only for older adults.

Overall, we find that the reason why women plot managers have lower harvest value and crop sales compared to their male counterparts is their concentration in food crops and lower use of both labor and non-labor inputs. Increasing fertilizer, pesticides and farming equipment use among women could help to substantially increase their revenues. Equipping women with the means to grow higher-value crops, like those that are exported (such as cotton), could further help to increase farm revenues.

# **4. Discussion and Recommendations**

The analysis above showed that <mark>the most important drivers of gender gaps across sectors are women’s lack of capital and male workers, lack of control over income and lower economic benefits from marriage. We also found sector-specific drivers, highlighting the importance of conducting this kind of analysis for policy prioritization.</mark> <mark>Rounding out our findings from the decomposition analysis, additional survey data provide further context and information on challenges that hamper gender equality. While we have discussed above that women in</mark> 15
America. 11 Value-added in USD is deflated to constant prices using US CPI-U, consistent with the treatment of trade values. Other controls in the UNIDO Industrial Statistics Database, such as wages, capital investment or number of enterprises, are not widely available for our sample countries.

12 We gratefully acknowledge concordance code available until 2019 on Peter Schott’s homepage https://faculty.som.yale.edu/peterschott/international-trade-data/.

13 Allowing us to compare (Washington) apples with (Washington) apples.

14 We follow Amiti and Khandelwal (2013) and trim the top and bottom 1% of prices by year. We estimate quality only for manufacturing sectors, and for differentiated sectors using the Rauch (1999) liberal classification. The results are robust to using conservative differentiated sector classification.

12
the credit bureau in countries where a bureau exists. Bouckaert and Degryse (2006) develop a model where banks benefit from strategically sharing only partial data on their borrowers. It is thus possible that banks use partial information sharing as a tool to maintain their informational rents after joining a credit bureau. In particular, conditional on a credit bureau being in place, banks that are protected from entry by new players may be more willing to share full information. We test the following hypothesis: H.4: Lower banking sector competition (high barriers to entry) is associated with higher coverage, depth, and transparency of information reported to the credit bureau in countries where a bureau exists.

In addition, banks with a large market share may be more reluctant to share full information with an existing credit bureau in an effort to preserve their market share, resulting in our last hypothesis: H.5: Lower bank concentration is associated with higher coverage, depth, and transparency of information reported to the credit bureau in countries where a bureau exists.

To the best of our knowledge, this paper is the first to test these hypotheses systematically for a large set of countries. Our empirical analysis is made possible by two recent and previously underexplored datasets, as described in the following section.

# **3. Data description**

# **3.1 Doing Business database on credit reporting institutions**

Our data on credit reporting institutions come from a survey conducted by the World Bank’s Doing Business team. This survey has been implemented yearly since 2003 and the Doing Business team uses it to generate their indicator on the ease of “Getting Credit” in countries around the world. In each country that is part of the exercise, the Doing Business survey always covers the credit registry if it exists and the largest credit bureau if more than one is in 7
91|1991/92|1992/93|1993/94|1994/95|1995/96|
|Annual cash cost (US$)|954.434|1.124.984|1.205,954|1.379.258|1.697,107|1.589.218|
|Cost percent of annual loain<br>volume|92|64|86|87|55|29|
|Cost per group credit (US$)|2.283|2.087|3.304|1.784|1.216|542|
|Cost per new group credit<br>(US$)|3.772<br>I|4.747|4.729|3.018|2.016|639|

a/ Costs exclude BI in-kind expenditures on thc program.

The costs detailed above effectively translate into very large subsidies for borrowers. To gauge the magnitude of this subsidy, this section provides rough estimates of the percent increase in the interest rate that banks would have to charge their PHBK clients if the banks were to maintain their current level of profits while fullv funding this program. This percent increase in the required interest rate is a lower-bound estimate of the Subsidy Dependency Index (SDI).<sup>46</sup> As Table 23 indicates, the program's subsidy dependency index follows trends in program costs. In fiscal 1990/9 1, banks would have had to increase their interest rate by approximately 628 percent to pay for the full cost of the program. Over the following 5 years. this figure declined sharply, and stood at 158 percent in fiscal 1995/96. While a 158 percent increase in the interest rate is still very high, it is impressive that this figure declined by almost one-half from fiscal 1994/95 to 1995/96 alone. To fund direct program costs, banks would have had to increase their interest rate

- **46