rafmacalaba/data-use-mentions-extended huggingface dataset:
acceptable level of dietary adequacy for 11 micronutrients (Martin-Prevel et al. 2017). Diet diversity data have been collected for a sufficient number of years and countries such that there is a body of research linking these measures with nutritional status.

Research suggests that **MDD for young children (6-23 months)** is associated with micronutrient adequacy (Molani-Gol et al. 2023), improved linear growth in some contexts (Gassara and Chen 2021), and lower risk of anemia (Donkor et al. 2021; Kathuria et al. 2023; Molla et al. 2020), but there is no strong indication of links with overweight or obesity. Recently, **egg and flesh food consumption** (which is a component of MDD) was included as an indicator of nutrient-sufficient diets among young children 6-23 months (WHO and UNICEF 2021) and data are widely available for LMICs (UNICEF 2022). Observational studies suggest a strong association for consumption of animal-source foods (ASF) with improved linear growth (Headey, Hirvonen and Hoddinott 2018; Krasevec et al. 2017; Zaharia et al. 2021), while systematic reviews of controlled trials found mixed results (Eaton et al. 2019; Shapiro et al. 2019).

Data collection began more recently for **MDD for non-pregnant women of reproductive age (15-49 years).** MDD-W is now being collected through large-scale DHS surveys in at least 24 countries (although, at the time of writing, results were available for only a limited number) as well as through Gallup World Poll surveys using telephone-based interviews (2021-2022 results available for women 15-49 years for 55 countries plus Palestine). As for children, MDD-W has a strong correlation with nutrient adequacy of diets (Martin-Prevel et al. 2017) but dietary diversity (i.e., not focusing on MDD-W only) has not shown consistent relationship with overweight or obesity and NCDs (Salehi-Abargouei et al. 2016; Vadiveloo, Dixon and Parekh 2013, Verger et al. 2021).
kind of 'climate migration Kuznets curve'—we do not observe it, most likely because even relatively richer Nigerian farming households are still poor.

In terms of policy prescriptions, our findings suggest that, in this and similar contexts, any onesize-fits-all approach for the management of climate migration is doomed to fail. The different groups we identify call for targeted measures and group-specific programs and adaptation policies able to tackle the complex challenges stemming from climate-mobility interactions at multiple levels. We propose a methodology for group decomposition and threshold identification which is based on public, nationally-representative microdata available to policy makers and that can be easily tailored to different contexts. As such, this type of empirical approach for detecting 28
# **4. Sources and likely persistence of foreign investor demand**

The growing popularity of EM local currency debt is no mystery when its strong performance versus other major asset classes in recent years is considered (Figure 5).<sup>6</sup> Indeed, the basic long-term investment case for EM local currency debt—exposure to relatively high real yields, prospects for currency gains (more on this immediately below), and capital allocation to sounder sovereign balance sheets than those of many advanced economies—has not only weathered the crises of recent years, but arguably been strengthened by them.

From a multi-asset perspective, some allocation to EM local currency debt of course also confers portfolio diversification benefits. This holds even though, as a “risky asset”, the correlation of benchmark GBI-EM index returns to those of other asset classes has tended to be quite high (Figure 6). Inspecting historical correlations also reveals the close link between GBI-EM returns and EM currency returns (proxied using the JP Morgan ELMI+ index – an EM currency return index). Gaining exposure to EM currencies, which have shown secular appreciation trends, has likely been a major motivation for investors’ increased allocations to these countries’ domestic bonds.

But who exactly are these investors? Unfortunately, there are no comprehensive data on the current composition of the foreign investor base for EM local currency debt, nor is it straightforward to gauge its potential future size. However, some recent estimates are available from market participants.

**Figure 5** : **Risk, return and Sharpe ratios (2002-12)**<sup>**v**</sup>

<!-- Start of picture text --> 0% 50% 100% 150% 200%<br>MSCI-EM<br>2.00<br>Gold<br>1.97<br>Commod.<br>1.63<br>GBI-EM<br>1.28<br>
offer significant benefits in terms of accuracy or question numbers. On the other hand, the shortened questionnaire raises the risk of misreporting, as it concentrates both predictive power and enumerator attention on a few high-impact variables. A restriction to verifiable variables may mitigate this risk, but monitoring of response patterns would remain important to identify emerging subterfuge.

Beyond a more detailed assessment of financial and logistical aspects, additional applications with data from other countries would be helpful in assessing whether the early stopping algorithm can be a useful tool in other settings. The distributional impact in terms of unequal outcomes for different groups is another important aspect that would warrant further exploration. Appendix B outlines an illustrative group-level analysis which suggests that outcomes for pensioner households are similar for early stopping when compared with a standard PMT. Further reassurance would be gained by verification of estimation consistency for a full range of relevant vulnerable groups, as well as by a more extensive exploration of targeting fairness 30
/censusindia.gov.in/2011census/maps/administrative_maps/admmaps2011.html.</mark>

- O’Sullivan, A. and Strange, W.C., 2018. The emergence of coagglomeration. Journal of Economic Geography, 18(2), pp.293-317.

- Rathelot, R. and Sillard, P., 2008. The importance of local corporate taxes in business location decisions: Evidence from French micro data. The Economic Journal, 118(527), pp.499-514.

- Rohlin, S., Rosenthal, S.S. and Ross, A., 2014. Tax avoidance and business location in a state border model. Journal of Urban Economics, 83, pp.34-49.

- Shenoy, A., 2018. Regional development through place-based policies: Evidence from a spatial discontinuity. Journal of Development Economics, 130, pp.173-189.

- Singh, K. and Saluja, M.R., 2018. Input–Output Table for India 2013–2014: Based on the New Series of National Accounts Statistics and Supply and the Use Table. Margin: The Journal of Applied Economic Research, 12(2), pp.197-223.

Tillin, L., 2013. Remapping India: New states and their political origins. Hurst Publishers.

Unni, J. and Raveendran, G., 2006. Are the Results of the Economic Census Robust?. Economic and Political Weekly, pp.3558-3560.

Wang, J., 2013. The economic impact of special economic zones: Evidence from Chinese municipalities. Journal of development economics, 101, pp.133-147.

Wasylenko, M.J., 1997. Taxation and economic development: the state of the economic literature.

37
<br>13|11|
|Access to septic tanks|% pop|5|0|3<br>4|48|
|Access to improved latrines|% pop|18|18|21<br>27|34|
|Access to traditional latrines|% pop|39|8|5<br>8|7|
|Open defecation|% pop|38|74|72<br>62|11|
||||2000|2005<br>2009||
|Domestic water consumption|liter/capita/day|72|24|22<br>24|166|
|Revenue collection|% sales|93|102|91<br>91|100|
|Distribution losses|% production|34|20|26<br>28|27|
|Cost recovery|% total costs|56|49|57<br>75|81|
|Operating cost recovery|% operating costs|65|69|80<br>105|145|
|Labor costs|connections per employee|159|158|209<br>207|369|
|Total hidden costs as % of revenue|%|163|139|58<br>43|140|
|||2005|2008|Non-scarce<br>water<br>resources|Other<br>developing<br>regions|
|Average effective tariff|U.S. cents per m3|60|7|1<br>60–121|3–60|

_Source_ : Demographic and Health Survey and AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Access figures from Demographic and Health Surveys (1996 and 2006) and census (2002). _Note_ : A country is considered non-water-scarce if its renewable water resource per capita is greater than 3,000 cubic meter per year. — = data not available.

These
as a “gateway” into manufacturing for workers whose alternative is agriculture or the informal labor market (including domestic service, construction, and child care). The apparel industry is also a major source of formal employment for women who constitute a major share of the apparel employment in many countries.

Apparelis the key manufacturing industry in Cambodia and Sri Lanka. In Cambodia apparel exports accounted for 70 percent of total manufacturing exports, and the sector employed around 325,000 workers in 2008, which represented almost 30 percent of total industrial employment. In Sri Lanka the apparel sector contributed 40 percent of the country’s total exports and employed 270,000 workers, or 13 percent, of its industrial employment in 2008.<sup>5</sup> Female workers dominate the apparel sector employment in both countries, making up83 percent of apparel employment in Cambodia and 73 percent in Sri Lanka.<sup>6</sup> The goal of this paper is to analyze the post-MFA changes in female wages and working conditions, such as the use of child labor and working hours,in the apparel industry in Cambodia and Sri Lanka. The MFA phase-out was a major concern for both Cambodia and Sri Lanka because of the expected competition from cheaper Chinese products. Womenworkers

> 4On January 1, 1995, the MFA was replaced by the Agreement on Textiles and Clothing (ATC), which brought the MFA under the rules of the World Trade Organization (WTO). As a transitional instrument, the ATC established a timeframe to eliminate quotas and integrate clothing and textiles into the 1994 rules of the General Agreement on Tariffs and Trade (GATT).

> 5Authors’ calculations using data from World Development Indicators from 2009.

> 6Data from household surveys.

2
.68|

# A2.5 **Australia and New Zealand**

# A2.5.1 Australia Index: Australian All-share Index

||**AGL**|
|---|---|
|Five-year beta|0.38|
||_.25_|
||0.39|
||0.28|
||0.28|
||0.45|

# A2.5.2 New Zealand Index: NZ Capital 40 Index

||**Telecom Corp.**|
|---|---|
|Five-year beta|0.97|
|1990|n/a|
|1991|0.52|
|1992|0.60|
|1993|1.20|
|1994|1.06|

_Note:_ NZ Capital 40 Index is not available prior to 1991.

56
views and practices when it comes to women’s employment. On the surface, most Nepalis have a favorable outlook towards women’s jobs. Per a Survey of the Nepali People – a series of public opinion surveys of a nationally representative sample of Nepalis 18 years and older from all 7 provinces – over 90 percent of respondents disagree that women should not be encouraged to work outside their homes and a similar proportion also disagree that higher education is more important for a boy than for a girl child.<sup>7</sup> However, when asked about attitudes and practices that create an enabling environment for a woman to go out to work, the results are not so positive. Almost a quarter of ever- married women report that their husband is jealous or angry if they talk to other men. Similarly, a wife neglecting the children is reported by men and women as the most common circumstance justifying wife beating, followed by the wife going out without telling her husband.<sup>8</sup> Even though over 73 percent of the Nepalis believe that men have equal responsibility in raising children as their wives do,<sup>9</sup> girls and women disproportionately bear the burden of unpaid work in the household. Almost half of the girls (aged 5-17 years) are engaged in household chores as opposed to less than a fifth of the boys and almost all women are involved in caring for the family, irrespective of their labor market status.<sup>10</sup> Not surprisingly, “family and household responsibilities” is the most common reason stated by economically inactive Nepali women for not trying to find a job or to start a business, quitting the last job, or for working less than 40 hours a week ( **<u>Table A.1, Annex A</u>** ). While in the labor market, women are more likely to be unpaid family workers and less likely to be wage-employed, both
# **2.1.4 Fishery production**

Fish makes up approximately 17% of animal-sourced protein in the human diet worldwide (Mathiesen, A.<sup>`</sup> M., 2018). The fishery industry supports the livelihood of 12% of world population by creating 200 million jobs along its value chain. In the global trade system, 80 billion USD worth of fish is exported from developing countries and it plays a crucial role in promoting local economic development (Kelleher, Willmann, and Arnason, 2009).

We estimate both freshwater inland fisheries and marine production values using the FISHSTAT (FAO, 2009) data with a classification based on the fish production categories. The inland fishery production value is the result of disaggregating corresponding country level statistics in proportion to areas of inland water bodies in pixels. The distribution of inland water bodies is obtained from the ESA-CCI (Lamarche et al., 2017). Thus, the value of inland fishing production in each grid is calculated as follows: where _fishvali_ is the value of fishery production in pixel _i_ ; _freshvalx_ is the value of fresh fish production at the national level which is aggregated from FISHSTAT; _waterbodyi_ is the area of water bodies in pixel _i_ ; and _X_ is a set including all pixels _i_ that fall within the boundary of a nation _x_ .

The value of marine fisheries production is based on its proximity to fish landing ports weighted by a composite indicator of equal weight from the number of visits and sum of the vessel hold of fishing vessels. We use the port database from the World Port Index (National Geospatial-Intelligence Agency, 2019) and the number of port visits with a vessel hold of fishing vessels from Hosch et al. (2019) to create a composite variable as the prior based on the sum (for each port) of the number of visits (each event in the database) and total vessel hold at the port. The geographic coverage of the ports is calculated for each port using the minimum port
three months of the U.S. M1 is used to forecast the percent change in oil prices over the next three months. Global PMIs (composite and manufacturing), and China’s manufacturing PMI are used in levels and lagged _h_ months. In total, each commodity has six forecasted values for each horizon. The final forecast is an average of all statistically significant forecasts. Equations with nonsignificant results are discarded. The exercise is conducted with monthly data.

Monthly data are available for 2004-2022, constrained by the availability of China’s Manufacturing PMI. All data are from Haver Analytics.

# **3.3 Bayesian vector autoregression model**

A reduced-form Bayesian VAR model is used to forecast oil prices. In the estimation step, to ensure compatibility between prediction and scenario analysis, we impose sign-restrictions on the VAR model. The BVAR employs quarterly data for commodity production, commodity prices, and global real GDP growth. Forecasts are conditioned on realized data or the June 2022, _Global Economic Prospects_ report (GEP).

The BVAR model of the global oil or metal market is written as: 44 The BVAR model of the global oil or metal market is written as: 44 tt ii tt−ii tt YY = �AA YY + uu ii=11 where is the vector of endogenous variables, is a sequence of serially uncorrelated random vectors with mean zero and covariance matrix .. denotes the coefficient tt tt matrices. Endogenous variables are YY , uu , ]<sup>′</sup> , where , , denote the loguu ii differences of metal or oil productions, GDP growth rates and metal or oil prices.ΣΣ AA , ii= 11, 22, 33, 44<sup>12</sup> Since GDP tt tt tt tt tt tt tt growth is only available on a quarterly basis, the exercise is conducted with quarterly averages of YY = [ΔΔqq ΔΔyy pp ΔΔqq ΔΔyy pp monthly data. Other variables that have been used in forecasting commodity prices include indicators of
Development Index (HDI) in South Asian countries during 1990 and 2016. The authors find a positive and significant effect of mobile phone usage on human development. Namely, the findings suggest that a 1 percent increase in mobile phone penetration leads to a 0.12 percent increase in the HDI while internet penetration is not identified as a significant predictor.

Some studies point to the money-making effects of ICT adoption while others place them as a financial burden for the poorest, potentially increasing inequality. For example, Heeks (2010) analyzes various works that look at the relationship between ICT and poverty. The author highlights studies that find a positive association between ICT adoption and money-saving and money-making effects among the poorest (Abraham 2007; Levy et al. 2010). However, such findings are not clear when looking at different technologies. For instance, Scott et al. (2005) conducted a study on the economic impacts of mobile phone usage in India, Mozambique, and Tanzania and found that poor users of mobile services allocate a larger portion of their income towards mobile phones compared to higher income groups, indicating that mobile telephony represents a financial burden for the poorest. Additionally, they found that mobile phones are primarily seen as a means to save money rather than a source of income. Similarly, Malásquez et al. (2021) note that in countries such as Mauritania and Senegal lower welfare quintiles spend a higher share of their income on mobile services than the top quintiles. Furthermore, other studies associate mobile phones with an increase in inequality and poverty. For example, Mpogele et al. (2008) conduct a survey in rural areas of Tanzania to examine how people perceive the relationship between mobile phones and poverty; the results revealed that although people do not see direct financial benefits from mobile phone usage, there are indirect 13
systematic assessment of how country-specific initial conditions -- inequality, level of development and poverty line definition -- affect the poverty-reduction effectiveness of growth and income redistribution.

The paper’s approach is based on the use of a lognormal approximation to the size distribution of per capita income. We implement this approach in two stages. First, we perform empirical tests of lognormality of the observed distribution of income, using a large cross-country income and expenditure distribution dataset covering over 100 countries and 40 years. Our testing strategy is based on the comparison of the observed quintile shares of the distribution with their theoretical counterparts under the null hypothesis of lognormality.

The empirical tests, performed on the full data sample as well as a variety of subsamples, are very supportive of the lognormal approximation to the distribution of per capita income, but less so for per capita expenditure. In the former case, the null of lognormality cannot be rejected in any of the samples considered; in the latter case, it is consistently rejected. In both cases, however, the lognormal specification yields a very close approximation to the observed distribution.

Lognormality of the distribution of income allows us to derive, at the second stage of the analysis, some qualitative and quantitative implications for the relative roles of growth and inequality in poverty reduction under alternative initial conditions, using a variety of poverty measures.

Our conclusions can be summarized in four main points. First, inequality hampers poverty reduction, not only because of its negative impact on the growth elasticity of poverty (as stressed in the literature) but, in most scenarios, also because of its negative impact on the inequality elasticity of poverty. Second, for a given poverty line, the impact of growth on poverty is stronger in richer than in poorer countries, and hence the latter will find it harder than the former to achieve fast poverty reduction. Third, the share of the overall variance of poverty changes attributable to growth should be generally lower in richer and more unequal countries. And fourth, for given initial levels
insignificant impact of the program. The gap actually increased for the treatment and control groups that were followed over both studies, as is evident from the third panel of table 3.

To accommodate these differential changes in productivity levels over time, we used the panel data sets assembled for each study to re-estimate the Cobb-Douglas production functions in first differences, as in equation (2). Two specifications of value added were used; one reported by STPS, the other defined simply as value of production less the costs of intermediate inputs and materials. Production functions, estimated separately for each study, were augmented with a set of industry dummy variables to control for industry effects. For the second study, we included an indicator variable for 1995 to control for the potential negative productivity effects of the economic downturn; we also included 1991-93 data on the sample of firms from the first study that were followed over the 1993-95 period to see if a longer panel would change the estimates of the productivity impact of CIMO participation.

**Table 4. Production Function Estimates for 1991-1993**

||<br>|**Dependen**<br>**Log(Valu**<br>**1991-**<br>|**t variable:**<br>**e added)**<br>**1993**<br>||**Log(Pro**<br>|**Dependen**<br>**duction val**<br>**1991-**<br>|**t variable**<br>**ue - mate**<br>**1993**<br>|**:**<br>**rials cost )**<br>|
|---|---|---|---|---|---|---|---|
great‐circle arcs<sup>7</sup> were generated from port locations along the boundary of Exclusive Economic Zones. Within the Caribbean Basin, around Indonesia, and near land, connectivity to major ports was manually digitized using a map of Global Shipping Routes (ESRI, CIA, 2012).

When building our estimates of shipping time reductions that can be associated with the BRI, we need to enrich the current transportation network with planned projects. Compiling a list of transport infrastructure projects associated with the BRI is a delicate task since there is no one criterion that could be used to define what is or is not part of the initiative. In particular, there might be projects financed by loans from China that might not necessarily fall under the BRI, and there might be projects with non‐ Chinese funding that are still considered in some sources as being part of the BRI.

We start from the list of infrastructure projects developed by Reed and Trubetskoy (2018) and which considers projects respecting all three following criteria:

1. The project is explicitly mentioned in one of the following sources as being part of BRI: either document issued by a government (including government press agencies), or article in a major academic journal or global news source or a quote by a government official in a reputable news source.

2. The project is at least in the planning phase.

3. The project impacts travel between two cities of at least 300,000 inhabitants.

These criteria present the advantage of restricting our list to projects that have been indeed related to the BRI and to exclude some projects for which feasibility might be in question. For example, condition 2

> 7 Shortest distance between two points on the surface of a sphere, measured along the surface of the sphere.

6