rafmacalaba/datause-encoder-data huggingface dataset:
# **Understanding the Links between Diet Quality, Malnutrition, and Economic Costs: An Evidence Review for LMICs**

Kendra Siekmans<sup>1</sup> , Patrizia Fracassi<sup>2</sup> , Tomoko Kato<sup>3</sup> , Ti Kian Seow<sup>4</sup> , Diana Carter<sup>5</sup> , Susan Horton<sup>6</sup> , Felipe Dizon<sup>7</sup> , and Kyoko Shibata Okamura<sup>8</sup>

**Keywords:** diet, undernutrition, overweight, obesity, non-communicable disease

**JEL classification:** I12 (Health Behavior), I18 (includes public health), Q18 (food policy)

1 Consultant, Food and Nutrition Division, FAO. Corresponding author: ksiekmans@nurturedev.com 2 Food and Nutrition Division, FAO. Patrizia.Fracassi@fao.org 3 Food and Nutrition Division, FAO. Tomoko.Kato@fao.org 4 Food and Nutrition Division, FAO. Tikian.Seow@fao.org 5 Food and Nutrition Division, FAO. Diana.Carter@fao.org 6 University of Waterloo. sehorton@uwaterloo.ca 7 Agriculture and Food, World Bank. fdizon@worldbank.org 8 Health and Nutrition, World Bank. kokamura@worldbank.org

## **Acknowledgments**

The authors would like to thank our colleagues from the ‘Estimating the economic costs of unhealthy diets project’ (under the World Bank ‘Repurposing Agrifood Public Policies and Support for Healthy Diets’ Programmatic Advisory Services and Analytics (PASA)), for their support of related work, including Erkan Ozcelik and Giles HanleyCook from FAO, and Hina Khan Sherwani from the World Bank. We thank Lora Iannotti, Mireya Vilar and Theresa McMenomy for their reviews of the paper. We thank Lynnette Neufeld, Bridget Holmes, Ramani WijesinhaBettoni and participants in a joint World Bank-FAO seminar on September 14, 2023, for helpful comments on earlier drafts. We thank Emily Meier for research assistance.
measures the amount of evaporation that would occur given sufficient water resources over the period 1950 to 2000. This variable is labeled _pet_ and plotted in Figure A2.3 (right panel).

In addition, we extract an aridity index and potential evapo-transpiration from models created by Trabucco and Zomer (2009). The models use data from WorldClim (Hijmans et al. 2005) as input parameters. The global mean Aridity index is Mean Annual Precipitation divided by Mean Annual Potential Evapo-Transpiration for the period from 1950 to 2000. High values of this index represent humid conditions, while low values represent arid conditions. A generalized climate classification scheme by UNEP (1997) suggests: hyper arid and arid are values less than 0.2, semi-arid has values 0.2-0.5 and dry sub-humid and humid have values above 0.5.<sup>19</sup>

# **Environment:**

The environment provides context to human settlements and surrounding economic activities. Land cover provides important information on the extent and type of human activity. We use the MODIS land cover products to select the crop and urban areas from the Annual International Geosphere-Biosphere Programme (IGBP) classification. These data area are the result of both supervised classifications of MODIS Terra and Aqua reflectance data and subsequent post-processing refinements for specific classes from prior knowledge and ancillary information. We select the crop and urban classes: _crop_ and _urb_ . The forest data are from Hansen et al. (2013) version 1.6 that provide both the stock of the forest from 2000, which is defined as “Tree canopy cover for year 2000, defined as canopy closure for all vegetation taller than 5m in height”, and forest loss, which is defined as “a stand-replacement disturbance (a change from a forest to non-forest state)”: _forloss_ and _forest_ .

To measure pollution, we use the fine particulate matter (PM 2.5) of air pollution data estimated from a model that excludes dust and sea-salt particles (van Donkelaar et al., 2015). The model uses geographically weighted regressions along with monitoring and satellite data, including: aerosol composition and land use information. We obtain the mean ( _pm25_ ), sum ( _pm25_ _~~s~~ um)_ and standard deviation ( _pm25_ _~~s~~ d_ ) for each administrative unit.

# **Socio-economic: Night time lights**

Night time lights (NTL) data, the visible light emitted from earth at night captured by satellites, are found to correlate strongly with population density and economic activity (e.g. Ebener et al., 2005; Henderson et al., 2012). The NTL high gain data come from the Defense Meteorological Satellite Program DMSP-OLS, which are made

> 19See Table 2 in CGIAR-CSI _Global Aridity Index (Global-Aridity)_ and _Global Potential EvapoTranspiration (Global-PET)_ Climate Database (2009)

46
Policy Research Working Paper 8741

# **Abstract**

Following a couple of decades of offshoring, the fear today is of reshoring. Using administrative data on Mexican exports by municipality, sector and destination from 2004 to 2014, this paper investigates how local labor markets in Mexico that are more exposed to automation in the U.S. through trade fared in exports and employment outcomes. The results show that an increase of one robot per thousand workers in the U.S. — about twice the increase observed between 2004–2014 — lowers growth in exports per worker from Mexico to the U.S. by 6.7 percent. Higher exposure to U.S. automation did not affect wage employment, nor manufacturing wage employment overall. Yet, the latter is the result of two counteracting forces. Exposure to U.S. automation reduced manufacturing wage employment in areas where occupations were initially more susceptible to being automated; but exposure increased manufacturing wage employment in other areas. Finally, the analysis also finds negative impacts of exposure to local automation on local labor market outcomes.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ research. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 7392

# **Abstract**

Wage inequality decreased significantly in the Russian Federation over the 2000s. The economic expansion experienced throughout the decade led to an improvement in social indicators, with a large reduction in poverty rates and an increase in higher education. In this context, wage inequality showed a sharp decline, with the Gini index on labor income decreasing by 18 percent between 2002 and 2012. Using data from the Russian Longitudinal Monitoring Survey, this paper documents the reduction in wage inequality and explores potential factors behind the trend. The analysis uses a decomposition technique proposed by Fortin, Lemieux, and Firpo (2011) to disentangle the main drivers behind changes in the wage distribution. The results suggest that wage structure effects are more important than composition effects for explaining changes in wage inequality. Institutional factors, such as minimum wage policies and changes in the returns to employment in different sectors and types of firms as well as the reduction of the skill premium, emerge as the most relevant factors for explaining changes in the wage structure.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lflopezcalva@ worldbank.org and jposadas@worldbank.org _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 1: Verification of Randomization by Gender**

||**Total n**<br>**obser**|**umber of**<br>**vations**|**Means**|**by treatment:**|<br>**Males**|**Means**|**by treatment: F**|<br>**emales**|
|---|---|---|---|---|---|---|---|---|
||**Male**|**Female**|**Control**|**Treatment**|**T-test**<br>**p-value**|**Control**|**Treatment**|**T-test**<br>**p-value**|
|Profits March 2005|188|182|4,735|4,763|0.958|2,819|2,866|0.908|
|Revenues March 2005|197|190|16,429|14,357|0.397|8,828|8,678|0.933|
|Total invested capital March 2005|197|190|153,020|176,984|0.547|112,952|143,889|0.214|
|Total invested capital excluding land|||||||||
|and buildings March 2005|197|190|33,665|29,719|0.292|20,638|20,734|0.978|
|Own hours worked March 2005|197|190|58.37|56.83|0.613|50.28|45.75|0.168|
|Family hours worked March 2005|197|190|12.61|13.93|0.719|27.78|42.24|0.541|
|Age of entrepreneur|197|190|43.52|42.17|0.427|40.26|41.78|0.340|
|Age of firm in years|197|188|8.99|11.15|0.172|8.08|8.79|0.631|
|Years of schooling of entrepreneur|197|190|8.20|8.65|0.308|9.79|9.19|0.182|
|Proportion whose father was an|||||||||
|entrepreneur|197|190|0.44|0.42|0.764|0.37|0.32|0.501|
|Number of household members<br>working in wage jobs|197|190|0.54|0.57|0.817|0.87|0.89|0.840|
|Household asset index|197|190|0.25|0.03|0.347|0.60|0.28|0.169|
|Number of Digits recalled in Digit|||||||||
|Span Recall test|179|178|5.86|5.90|0.833|5.61|5.74|0.512|
|Implied coefficient of relative risk<br>aversion from lottery game|197|189|0.28|0.44|0.517|-0.17|-0.07|0.615|
|P-value from Chi-squared(16)test ofj|oint sign|ificance:|||0.652|||0.781|

34

-  -
their own tax burden through sophisticated tax planning well before the operation. As a result, those tax incentives offered by the host country often lose their appeal and are ignored. In the extreme, it may even run counterproductive as these tax policies are considered to be nontransparent, insecure or bureaucratically infeasible.

There is evidence that political and institutional stability of the host country can boost foreign investors’ confidence in their investment. Our proxy of ICRG political stability risk index carries a positive sign across all models and is strongly significant under the fixed-effect model. This finding is consistent with the literature as the index assigns a higher score to those countries with political and financial stability, efficient institutional structure and strong law enforcement, all of which are conducive to FDI attraction.

Main telephone lines per thousand people as a proxy of host country’s infrastructure quality, is statistically insignificant and seems to carry the wrong sign. A possible reason lies in the fading importance of traditional communication technique in face of current innovations. Our positive coefficient on the accession dummy indicates that the fifth EU enlargement does help new members in the region to attract more FDI. Admission of CEECs to the EU can be considered a worldwide recognition of the country’s success in its political and economic reforms. As a result, foreign investors feel more comfortable investing in those markets with a favorable investment environment.

In general, we find in this paper no crowding out effect of China’s FDI to the Central and Eastern European countries. In some cases, China’s FDI seems even to help induce more FDI inflows to the region. Our results also show that among the 24
In the simple set-up we just described, it is easy to identify the set of welfare improving projects that the private sector should, but does not ...nd it pro...table to ...nance. Indeed, the private sector, which maximizes private returns, Ri<sup>P�c,...ndsitpro...tabletoinvestinaprojecti¤ 1 + xi�c,or</sup> However, since the private sector does not internalize the externality, �j, its participation constraint (1) is stricter than the condition that insures that the project is welfare improving. Such a condition can be written as 1 + xi + �j � c, or Our basic set-up is summarized in Figure 1a, where, setting c = 2<sup><u>3</u>,weordertheprojects’space</sup> according to the associated private sector’s e¢ ciency advantage and externalities, that is, in the (xi; �j) space. Region D, is the locus of the projects that are quintessentially private. They are characterized by high private sector e¢ ciency advantage (xi > <u>xP</u><sup>),andtheyalwayssatisfythe</sup> private sector participation constraint (1). We will disregard such projects in the remaining of the paper, as we will disregard the projects in region C, with xi < 0, where the public sector has an e¢ ciency advantage and, thus, it should always undertake them. Hence, the area of interest for policy makers, and the focus of this paper, is Region B, where xi 2 [0; x ~~P~~<sup>],and�</sup> j<sup>�</sup><sup><u>�</u></sup> ~~P~~<sup>.</sup> Projects that belong to such a region should, on welfare grounds, be undertaken by the private sector. Having disregarded regions C and D, the parameter space that is pertinent to the analysis is the one depicted in Figure 1b. In the ...gure, we split region B in two subregions, B<sup>0</sup> and B<sup>00</sup> . In Region B<sup>0</sup> , we have that and hence public investments are welfare improving. However, on e¢ ciency grounds, it would be preferable if projects were undertaken by the private sector. Instead, in region B00, condition (3) does not hold so that the only projects that are welfare improving are those undertaken by the private sector. This being said, since the private sector does not internalize the positive externality, and projects do not generate positive net returns–condition (1) is not met–, the private sector is not willing to invest in region B. Finally, projects in region A are not worth ...nancing by either the private or the public sector. However, we cannot ignore them altogether because, as we will see later, some of them could end up being ...nanced if the government is not able to target subsidies to speci...c projects.

# 3 How to ...ll the investment gap?

Before discussing issues related with the sequencing of interventions, it is useful to introduce the three di¤erent policies that, in our framework, can ...ll the investment gap: reforms, subsidies, and public investments, separately. We then compute the conditions under which each of them is 4
|||PPR Working Paper Series||Contact|
|---|---|---|---|---|
||Bla|Author|Date|orpaper|
|WPS266|Policy Changes that Encourage<br>Private Business Investment in<br>Colombia|Mansoor Dailami|August 1989|M. Raggambi<br>61696|
|WPS267|Issues in Income Tax Reform in<br>Developing Countries|Cheryl W. Gray|August 1989|N. Campbell<br>33769|
|WPS268|Shortcomings in the Market for<br>Developing Country Debt|John Wakeman-Linn|September 1989|S. King-Watson<br>33730|
|WPS269|Women in Development: Issues<br>for Economic and Sector Analysis|Women in Development<br>Division|August 1989|J. Lai<br>33753|
|WPS270|Fuelwood Stumpage: Financing<br>Renewable Energy for the<br>World's Other Half|Keith Openshaw<br>Charles Feinstein|September 1989|J. Mullan<br>33250|
|WPS271|The Industrial Labor Market and<br>Economic Performance in<br>Senegal: A Study of Enterprise<br>Ownership, Export Orientation, and<br>Government Regulation|Katherine Terrell<br>Jan Svejnar|||
|WPS272|Women's Changing Participation<br>in the Labor Force: A World Perspe|T. Paul Schultz<br>ctive|||
|WPS273|Population, Health, and Nutrition:<br>FY88 Annual Sector Review|Population and Human<br>Resources Department|September 1989|S. Ainsworth<br>31091|
|WPS274|The Demography of Zaire:<br>Review of Trends in Mortality and<br>Fertility|Miriam Schneidman|||
|WPS275|Revised Estimates and<br>Projections of International<br>Migration, 1980-2000|Fred Arnold|August 1989|S. Ainsworth<br>31091|
|WPS276|Improving Rural Wages in India|Shahidur R. Khandker|August 1989|B. Smith<br>35108|
|WPS277|The Effect of Formal Credit on<br>Output and Employment in Rural<br>India|Shahidur R. Khandker<br>Hans P. Binswanger|August 1989|B. Smith<br>35108|
|WPS2'78|Inflation and the Company Tax<br>Base Methods to Minimize<br>Inflation-induced Distortions|Anand Rajaram|September 1989|A. Bhalla<br>60359|
|WPS279|What Determines the Rate of<br>Growth and Technological Change|Paul M. Romer|||
# **-** **<u>Table 2. Average tariff on imports if virtual C is taxed at $50/ton CO2</u>**

|Exports<br>from:|Imports int|o:|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||BRA|CAN|CHN|E15|eit|IND|JPN|liy|MEX|RUS|USA|xhy|xmy|xx1|ZAF|Average|
|BRA|0.0%|3.4%|3.2%|3.2%|3.1%|2.8%|4.0%|2.8%|2.7%|2.6%|3.0%|3.9%|3.0%|3.7%|2.9%|3.1%|
|CAN|4.5%|0.0%|3.4%|3.4%|3.2%|3.7%|3.2%|3.4%|2.8%|2.8%|2.6%|3.8%|2.9%|3.6%|3.0%|2.9%|
|CHN|12.1%|10.5%|0.0%|10.5%|11.7%|13.4%|10.4%|11.0%|9.9%|10.0%|10.3%|11.0%|10.9%|11.1%|11.1%|10.7%|
|E15|1.6%|1.1%|1.1%|0.0%|1.1%|1.3%|1.2%|1.2%|1.1%|1.1%|1.2%|1.3%|1.1%|1.2%|1.2%|1.2%|
|eit|6.6%|4.1%|4.3%|4.0%|0.0%|5.1%|3.9%|4.5%|4.2%|4.4%|4.2%|5.2%|4.5%|4.6%|4.6%|4.2%|
|IND|8.3%|7.8%|9.2%|7.7%|8.9%|0.0%|6.8%|8.5%|8.1%|8.7%|7.9%|7.0%|7.9%|8.5%|5.3%|7.8%|
|JPN|1.4%|1.3%|1.5%|1.4%|1.5%|1.6%|0.0%|1.5%|1.4%|1.4%|1.2%|1.5%|1.4%|1.4%|1.3%|1.4%|
|liy|8.2%|5.4%|5.7%|5.0%|5.6%|6.1%|4.7%|0.0%|5.1%|4.9%|5.0%|5.3%|5.7%|6.1%|7.0%|5.3%|
|MEX|3.5%|2.1%|4.2%|4.0%|3.6%|10.8%|4.0%|4.9%|0.0%|4.1%|1.7%|4.6%|3.4%|4.0%|3.5%|2.3%|
|RUS|18.0%|14.3%|12.4%|11.8%|13.9%|12.8%|11.3%|15.0%|14.7%|0.0%|10.4%|14.5%|13.6%|14.0%|15.9%|12.6%|
|USA|3.3%|3.0%|3.1%|3.1%|3.4%|3.3%|3.0%|3.3%|2.8%|2.8%|0.0%|3.2%|2.9%|3.5%|3.2%|3.1%|
|xhy|3.3%|2.3%|2.2%|2.3%|2.6%|2.2%|2.0%|2.3%|2.2%|2.5%|2.0%|0.0%|2.2%|2.4%|2.5%|2.2%|
|xmy|6.3%|5.6%|5.0%|5.4%|5.8%|4.1%|4.1%|6.1%|5.3%|6.1%|4.5%|4.5%|0.0%|6.2%|5.1%|5.0%|
|xx1|2.5%|2.1%|2.1%|2.1%|2.1%|3.2%|2.2%|2.3%|1.8%|1.8%|2.0%|2.3%|2.1%|0.0%|2.7%|2.1%|
|ZAF|15.9%|10.1%|10.6%|9.8%|10.1%|11.5%|11.4%|9.0%|16.6%|7.9%|8.9%|12.4%|8.8%|10.2%|0.0%|9.9%|
|Average|4.2%|3.0%|2.7%|4.3%|2.9%|4.2%|4.2%|4.2%|3.4%|3.2%|3.1%|4.1%|3.2%|3.1%|3.4%||

Note: Average figures represent the trade-weighted average tariff faced by the exporting country (row) or the average tariff imposed by the importing country (column). Virtual carbon trade flows have been estimated using the BTIO approach. Source: authors 27
BRSS 2011-2012 (i.e., round 4), there are 91 new questions and a new section on Islamic banking, bringing the total number of questions to 361.<sup>5</sup> The final list of questions included in the fifth round of the BRSS reflects feedback from several banking experts, both within the World Bank and external, who suggested re-wording of questions from the previous round and the inclusion of new questions.

The questionnaire was distributed in March 2017 using the survey platform Qualtrics and addressed to the directors of banking supervision units or relevant officials within bank supervisory authorities. However, 30 agencies opted to submit a hard copy of the questionnaire. To limit coding errors, the survey team regularly communicated with the national authorities and clarified the intended meaning of the BRSS questions. The survey team checked each submission and followed up with the relevant agencies to clarify any issues arising from conflicting answers to various questions or consistency between responses in the current survey and the preceding one.<sup>6</sup> Because the data and questionnaire are available online, the discussion that follows presents results from a subset of questions focusing on capital regulation, market discipline, and supervisory monitoring. As alluded to above, these aspects of regulation and supervision have been identified as being crucial differences between countries that experienced a banking crisis during the GFC and countries that did not (Cihak et al., 2013). In Appendix II, we take a more comprehensive approach and report a list of questions organized by survey section where we identify regulatory and supervisory aspects that are different between high-income countries and emerging and developing economies (definitions of these groups appear at the beginning of Appendix II).

> 5 Just two questions have been discontinued, and a total of 12 questions do not match exactly between the two most recent surveys. Most of the questions have been re-drafted or changed to improve clarity and lessen measurement error concerns. 6 For instance, selecting the Basel II framework as capital framework in place during the period 2011-2016 and then reporting 2018 as the year of adoption of Basel II.

5
11

|Table 1: Income for 1988-1989 an<br>|d for the period 1|970 to 1989 (in U|S$ thousands)|
|---|---|---|---|
|[<br>Source of funds|1988|1989|1970-1989|
|<br>Australia|160.3|185.1|1,513.1|
|Bangladesh|_5.0_||_5.0_|
|<br>Canada|*||3,401.9|
|China|50.0|50.0|400.0|
|Cuba||4.0|22.6|
|Denmark|2,268.3|2,083.4|19,211.0|
|Famnily Health International (USA)|.|20S.0|205.0|
|<br>Finland|230.5|251.3|1,797.8|
|Ford Foundation (USA)||.|1,000.0|
|France|||6.5|
|Germany, Fed. Rep. of|1,339.4|719.4|6,288.1|
|<br>IDRC (Canada)|233.9|302.2|516.5|
|<br>India||79.3|456.0|
|Italy||.|259.8|
|<br>Kenya|||_0.5_|
|<br>Malaysia|||1.1|
|<br>Mexico|3.0|3.0|66.0|
|Netherlands|392.7|343.6|2,407.7|
|Nigeria|||51.2|
|Norway|1,923.1|1,965.1|32,736.6|
|<br>Pakistan|||5.0|
|Rockefeller Foundation (USA)|.|350.0|1,050.0|
|Sweden|2,548.2|2,280.7|80,692.5|
|Switzerland|<br>283.3|<br>.|577.5|
|Thailand|7.5|15.0|71.6|
|United Kingdom|5,966.5|3,573.7|34,838.1|
|<br>United States of America||<br>.|3,220.6|
|USSR (in kind)|46.3||46.3|
|<br>UNDP|.|56.5|56.5|
|UNFPA|3,250.0|3,500.0|27,040.0|
|WHO|||75.0|
|World Bank|3,000.0|2,000.0|5,008.3|
|Miscellaneous|33.3|1.0|36.0|
|Interest|449.0|661.3|9,412.4|
|Handling change for reagents|128.4|157.1|719.0|
|<br>Patents|||68.9|
|Sub-total|22,318.7|18,687.7|238,269.1|
|WHO Regular Budget and Special Account|649.1|649.1|8,825.0|
|UNFPA funds for country and inter-country||||
|projects|1,526.1|1,397.8|17,063.6|
|Total income|24,493.9*|20,734.6|264,157.7|

O of which $21,434.0 relates to 1988 and $3,059.9 to 1987. The lattci amount consists of two 1987 pledged contributions ($2,059,925 from the United Kingdom and $1.0 million from the World Bank) which arrived at the beginning of 1988 and therefore had to be taken up as 1988 income in WHO's Financial Report.

Source: See Fig. 1.