rafmacalaba/data-use-mentions-extended huggingface dataset:
Krashinsky , H. (2007) “The impact of social mobility and within-family learning on voter preferences: Evidence from a sample of twins”, Journal of Public Economics 91:97–116 Kreider, B. and J.V. Pepper (2007), “Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors,” _Journal of the American Statistical Association_ , 102(478), 432-441.

Loprest P, Rupp K, Sandell SH (1995) “Gender, Disabilities, and Employment in the Health and Retirement Study”. Journal of Human Resources, 30, supplement: S293-S318.

Neumark, D. (1999) “Biases in twin estimates of the return to schooling”, Economics of Education Review, 18:143–148 Oreopoulos, P., Stabile M., Walld, R. and Roos, LL, (2008) “Short-, Medium-, and Long-Term Consequences of Poor Infant Health An Analysis Using Siblings and Twins”, The Journal of Human Resources, XLIII(1):88138 Rocco, L. and Suhrcke, M. (2008) “Microeconomic consequences of non-communicable diseases at the individual or household level, with a focus on labor market outcomes”. Presented at the WHO Consultation “Methods for identifying the economic consequences of diseases and injuries”, 12 - 13 June, Venice Suhrcke, M., Rocco, L., McKee, M. (2007) “Health: a vital investment for economic development in eastern Europe and central Asia”. European Observatory on Health Care Systems, (London) 2007 WHO (2005) “Preventing CHRONIC DISEASES a vital investment” WHO Press, Geneva Wilson, S.E. (2001) “Work and the accommodation of chronic illness: A re-examination of the health-labor supply relationship”, Applied Economics, 33: 1139-1156 Wooldridge, J.M. (2002) “Econometric analysis of cross section and panel data”, The MIT Press 23
percentile) and the rich (99<sup>th</sup> percentile) as well as the time-trend of total income inequality (bottom-right panel) over the 50-year period. Low- and middle-income households have experienced a visibly different trend in income growth rates compared to top-income households. The last decade denotes an outlier arguably due to the 20072010 global financial crisis. Between 1970 and 2000 the poor and the middle-class have seen their growth rates decline while the rich have seen their growth rates increase.

> 11 Employment data come from the Current Employment Statistics of the Bureau of Labor Statistics ( _U.S. Department of Labor_ : http://www.bls.gov/data/#employment). ‘Welfare’ expenditures are collected from the U.S. Census Bureau, Annual Survey of State and Local Government Finances and Census of Governments (yearly data): https://www.census.gov/govs/local/historical_data.html. Fertility is obtained from the Vital Statistics of the United States: <u>http://www.cdc.gov/nchs/products/vsus.htm.</u>

8
# **II. Methodology and Data**

Empirical Model and Methodology

## **Statistical matching techniques, also known under the term data fusion, require at least one donor**

**and recipient dataset and overlapping variables.** Traditionally, in statistical matching, there is a donor dataset that contains information one wants to add to a second dataset lacking this information (here denoted as variable C). The second dataset often goes by the name recipient dataset and has information on a second variable of interest not included in the donor dataset (here denoted as variable I). Moreover, for statistical matching techniques to work, there needs to be a set of overlapping variables X, also often called matching variables. Overlapping (or matching) variables are variables that are included in both the recipient and donor datasets. Consequently, they facilitate the matching of both datasets.<sup>7</sup> Ideally, one would have a third dataset, which has information on all variables under consideration. This dataset is often called auxiliary data. If there is no such data available, then the data fusion relies on the assumption of conditional independence (Donatiello et al., 2016b). The assumption of conditional independence means that the overlapping variables X fully explain the relationship between C and I, a strong assumption that often does not hold in practice (D’Orazio et al., 2006).

## **There are several possibilities to address the assumption of conditional dependence in the absence**

**of auxiliary data (Donatiello et al., 2016b).** First, previous estimates on the relationship between C and I might be available (for example, correlation coefficients between C and I). Is it then possible to use these estimates to avoid the assumption of conditional independence. In the absence of such data, one can rely on at least one matching variable that is highly correlated with the target variables. If X and C are perfectly correlated, we
list of the 230 most commonly performed procedures in US community hospitals, published by the Agency for Health care Research and Quality, we selected 15 procedures for price comparison purposes. These procedures are listed in Appendix 1 along with data on patient volume, and summary statistics such as mean charges, mean length of hospital stay, and surgical mortality rates.

# _US prices_

Hospital mean and median “billed charges” were available for each procedure in the Health care Cost and Utilization Project (H-CUP, hereafter) Nationwide Inpatient Sample

> 21 The five radiologists working for Wipro Ltd., and based in India, who interpret 30 CT scans a day for Massachusetts General Hospital, are an example of such cross-border trade in health services that does not require movement of the patient to the foreign country.

> 22  The criteria were developed after consultations with Drs Grizzard & Sorock at the World Bank Health Services Department.

16
of risk (risk-induced -RI), and particularly to classify those that are climate risk-induced (CRI) or idiosyncratic risk-induced (IRI). Our model is a mixed/hybrid approach from the two main ways of operationalizing vulnerability to poverty described by Skoufias and Baez (2023), which are the “multilevel or hierarchical” approach and the “simulation” approach. The multilevel or hierarchical approach does not use any explicit information on community-level climate or weather shocks. Instead, it decomposes the error term into a covariate (community level) and an idiosyncratic component. Unlike the multilevel approach, the simulation approach uses concrete information on the incidence and intensity of the aggregate and idiosyncratic shocks experienced by households. The approach in this paper essentially introduces explicit information on climate and weather within a multilevel model. The climate is characterized by the historical means of rainfall and temperature data from the geographical area where the household is located. Additionally, recent weather-related events, such as natural disasters, impact the variability of unobserved shocks at the community level. We use household-level data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), along with various natural disaster and extreme weather data sources, such as EM-DAT, ThinkHazard! Platform, and Climate Change Knowledge Portal (CCKP). Additionally, we incorporate drought indexes, such as the Standardized Precipitation-Evapotranspiration Index (SPEI), into our analysis.

We find that Guatemala and Honduras have the highest incidence of vulnerability to poverty, followed by Colombia and Peru. Conversely, Uruguay and Chile exhibit the lowest incidence. Our findings suggest that out of the approximately 184 million people vulnerable to poverty in the region, 109.5 million are PI, and around 74.5 million are RI, where almost 14.5 million are CRI. By examining the spatial distribution of the vulnerable households, we can identify their location within the countries and gain a better understanding of their exposure to natural hazards to design more efficient policies targeting for the most vulnerable populations.

The rest of the paper is
To study the empirical Chenery-Syrquin relationship, we use agricultural GDP and income per capita expressed in 1995 US$ obtained from the World Development Indicators 2003 of the World Bank.<sup>9</sup> The data cover the period 1960-2002. Figures 1 is a scatter plot of agriculture’s GDP share as a function of income per capita. Fitted regression lines are drawn for the whole sample and for LAC. The two sub-samples do not seem to have different β ’s . However, this graph is inappropriate for examining international heterogeneity in the β ’s because it does not control for long-run heterogeneity in agricultural GDP shares and GDP per capita.

Table 1 presents econometric estimates of β across several specifications and estimations methods. All estimations use annual data. The first column presents the estimate of β derived from the pooled data. Columns (2)-(4) present estimates from country fixed-effects regressions, whereas columns (5)-(10) present results from two-stage regressions with fixed effects (Baltagi 2002). Consequently all the estimates of β under columns (2)-(10) correspond to the effect of development (increases in the log GDP per capita) on agriculture’s GDP share over time, within countries. The instrumental variables for the IV regressions are the 5-15 years lagged observations of log GDP per capita. The p-value of the Sargan test for the validity of the instruments are listed toward the bottom of Table 1. The null hypothesis of the Sargan test is that the instruments are not correlated with the errors, and thus a p-value above 0.05 indicates that the we cannot reject the null and thus the instruments are valid. This is the case for regressions (6) – (10). In all of them we observe a negative and significant β .

Models (3)-(4) and (6)-(8) also include a time trend, which is also statistically significant. Moreover, regression (8) includes dummy variables identifying high-income
the international trade literature. For example, Muendler and Becker (2010) assess how the wage differential between home and abroad affects German MNCs’ demand for labor and find significant labor substitution between headquarters and foreign affiliates at both the intensive and extensive margins;<sup>6</sup> Harrison and McMillan (2011) use US firm-level data to identify the labor substitution channel such that offshoring to low-wage countries substitutes for domestic employment.<sup>7</sup> Here, we investigate the labor substitution channel through MNCs’ internal networks (i.e., the intensive margin), but break new ground on this important channel by exploiting unanticipated labor market policy changes to identify the mechanism.<sup>8</sup>

> 6Muendler and Becker (2010) categorized two critical margins of multinational labor substitution whereby an MNC’s labor demand responds to international wage differentials (1) at the extensive margin, when the MNC enters a foreign market; and (2) at the intensive margin, when the MNC operates through existing foreign affiliates.

> 7Harrison and McMillan (2011) also explored a complementary effect such that for firms that do significantly different tasks at home and abroad, foreign and domestic employment are complements. They found that the overall effect is a small decline in manufacturing employment.

8Several other earlier studies failed to find any impact on home employment through MNC operations 4
# **Box 1. Occupation and skill categorizations using machine learning**

By processing text information in job posts and jobseekers’ CVs, we identify their occupations and associated skill categories. Our text processing method basically follows the one developed by Atalay et al. (2018). By estimating a continuous bag of words (CBOW) model, which uses machine learning, job titles and descriptions in job posts are mapped to, respectively, ISCO08 occupations and skill categories. The main output of a CBOW model is to find synonyms for input words and phrases. That is, once a model is estimated, it tells how similar two words are to each other. The model judges the similarity between two words based upon whether these two words are used in “similar” contexts. For example, the model concludes that _realtor_ and _real estate agent_ are synonyms of one another if they tend to be used near similar words, like _sales_ , _purchase_ , _buildings_ , and _land_ .

In our text processing, each job title in the online job data is mapped to the ICSO08 occupation title that our CBOW model finds is the most similar to the job title. A jobseeker’s ISCO08 occupation is identified by finding the ISCO08 occupation title that is most similar to the jobseeker’s previous job title. Skill requirements in job posts are identified in a similar manner. We first adopt a set of keywords for each skill category from previous studies, namely Deming and Kahn (2018) and Spitz-Oener (2006), then modify these keywords in the context of the Pakistan online job portal by applying a CBOW model, and lastly look at whether a job <u>description includes any of these keywords.</u> We also identify required skill categories by applying a machine learning method that captures skill-related words in job descriptions. Table 3 presents what percentage of jobs require each skill category. Nonroutine analytic skills are much more frequently required than routine cognitive skills. Nonroutine interactive skills and soft skills—namely social, character,
- The IRA distribution formula and tax assignments that lead to vertical and horizontal imbalances in LGUs’ resource bases – absence of equalization, mismatch with expenditure assignments;

- The tax assignments that favor one type of LGUs – cities – while depriving other levels (especially provinces) of adequate tax bases.

The combination of the current IRA distribution formula and the tax assignments exacerbates the vertical imbalance in revenue assignments across levels of LGUs as well as mismatch between devolved service delivery responsibilities and LGUs’ fiscal capacities.

On the expenditure side, the incomplete implementation of the legally mandated devolution, thanks to “loopholes” in the LGC and some national government agencies’ reluctance to fully devolve certain functions, has created confused situations of expenditure assignments and murky accountability. The unclear de facto assignments of expenditure responsibilities and the distorted revenue assignments weaken the LGUs’ incentives to cooperate with each other, and this tendency is accentuated by the hyper fragmentation of the LGU jurisdictions.

A recently completed Bank report on local service delivery in the Philippines provides rare empirical data on the skewed resource allocations among different levels of LGUs and clear mismatch between expenditure assignments and resource bases even after taking the IRA into account. An in-depth review of spending data from several case study LGUs revealed the troubling patterns of expenditure allocation such as one province spending more than 2.5 times as much on health on a per capita basis than another province in the sample, or one city receiving 1.5 times as much in total revenues (including the IRA transfer) than another city in the sample, resulting in the former being able to spend 1.5 times more on roads and 1.7 times more on health, again on a per capita basis, than the latter (World Bank 2011). Relative levels of spending are only proxies of the quality and adequacy of service delivery, and it is conceivable that some LGUs may make use of limited resources more effectively than others. Nonetheless the wide disparities in revenue assignments across LGUs are likely to be correlated with the adequacy of
**_<u>________________________________________________________</u>_**

The Excel workbook provides these data both at a regional level and by pilot site. It is based on the methodology described in details in the <u>TTFSE Manual. (Please see Annex F</u> for definitions) The methodology entails measurement of processing times at pilot sites, usually on a monthly basis, and of the overall Customs performance on an annual basis. Performance at pilot sites are measured by the National Customs Administrations of the countries concerned with support of US Customs consultants under the TTFSE program.

_-_ _<u>Global Express Association Country Market and Customs Barriers Reports</u>_ The Global Express Association represents the international express delivery companies that serve over 215 countries, carrying over 30 million packages each day, all of them guaranteed to be delivered within specified time frames. The report is a summary of information about 150 countries based on a detailed questionnaire. Questions asked on market access restrictions include restrictions on entry to the express delivery sector, foreign investment, and the use of local services. Questions asked on customs restrictions are categorized into transparency, customs efficiency, and post-release processes.

# _<u>CSCMP: Annual US State of Logistics Report</u>_

Delivered each June by Rosalyn Wilson, _The US State of Logistics Report_ is published annually by The Council of Supply Chain Management (CSCMP), formerly called the Council of Logistics Management (CLM), which is the association for individuals involved in supply chain management.

Total logistics cost normally includes trade and internal logistics. This often is cited as 10% of GDP for advanced countries, and 20% or more for developing countries. This report is a compilation of statistics about transportation and business logistics, and includes statistics on logistics costs/GDP and inventories.

The 16<sup>th</sup> annual report published in June 2005 is titled _Security Report Card—Not Making the Grade._ _–_ _<
minimizes the standard error of a target variable while explicitly accounting for the stratified clustered sample design.<sup>16</sup> The standard Neyman allocation rule provides an optimal allocation of PSUs when the sample follows a stratified random sample design (i.e. squared root of the design effect of _DEFTh_ =1 for each stratum _h_ ), and when finite sample correction is zero (i.e. fh=0). For stratified clustered samples, the standard Neyman allocation requires modification to achieve an optimal allocation. The modified Neyman allocation rule is given by the following expression: where _h_ is a sub-stratum or stratum within a district; ��∗ is the optimal sample allocation in sub-stratum _h_ ; _n_ is the total number of households in a district; ��� is the population share in each sub-stratum _h_ ; ��� is the standard deviation of the target variable of interest; DEFT is the square root of the design effect of the target variable of interest; and �1 ���� is a finite sample correction factor. For the sample design of the HIES 2016, we assume finite sample correction is zero.<sup>17</sup> All parameters required to estimate the optimal allocation of PSUs across sub-strata are estimated based on the HIES 2010 data. Since the cluster size in the HIES 2016/17 is identical to the cluster size used in the HIES 2010 (20 households), the intra-cluster correlation coefficients used to estimate the _DEFTs_ can be computed directly from the HIES 2010 data.<sup>18</sup> After deriving the modified Neyman allocation to produce reliable poverty statistics at the district level, the following five additional adjustments are made to achieve the other two goals

> 16 The Neyman allocation rule is derived from an optimization problem consisting in minimizing the variance of a target variable subject to a pre-determined fixed sample size. The full derivation of the modified Neyman allocation rule used for the allocation of PSUs in the HIES 2016/17 is presented in
145|
|(Percent share in the total jobs)|(22.5%)|(25.9%)|(12.8%)|(18.5%)|
|Manufacturing establishments with|||||
|fewer than 4 employees|4,145|3,651|44,158|319,277|
|(Percent share in manufacturing)|(58.2%)|(64.5%)|(55.6%)|(49.0%)|
|Manufacturing jobs in establishments<br>with fewer than 4 employees|9,953|8,584|106,193|765,822|
|(Percentsharein manufacturing)|(13.5%)|(19.7%)|(9.7%)|(6.9%)|

_Source_ : Statistics Bureau, Jigyosho.kigyo tokei chosa houkoku (Survey on Business Establishments and Enterprises in 2001. Tokyo. 2002.

78
(Ferreira et al. 2011). As often pointed out, when the set of observed circumstances is limited, the degree of underestimation is large and lower bound estimates of IOp end up being of limited use for policy (Kanbur & Wagstaff 2014).

3 The negative relationship between IOp and growth has been documented using variation over time and across regions of the U.S. (Marrero et al. 2013, Bradbury et al. 2016) and in Europe (Carranza 2020). Evidence including developing countries is less conclusive and constrained by data limitations (Ferreira et al. 2018). 4 In another noteworthy attempt, Ferreira et al. (2018) estimate IOp across 75 countries between 1980-2005, including 22 in Sub-Saharan Africa using data from the Demographic and Health Surveys (DHS). However, there are two large disadvantages to using the DHS to estimate IOp: welfare is measured using a wealth index because household consumption is not available, and parental information such as parents’ education is unavailable. Their IOp estimates in SSA range from 1-39 percent, though excluding parental information likely leads to substantial downward bias. A World Bank report (2022a) implemented an IOp analysis in 5 countries in Southern Africa, though data limitations meant it was also unable to consider parental characteristics. This report found that IOp ranged from 15-26 percent, and increased to 46 percent in South Africa after race was included as a circumstance. Other studies examine drivers of inequality more generally – for example, Tetteh-Baah et al. (2024) use the DHS to calculate horizontal inequality in various outcomes across Africa based on location, ethnicity, gender, and religion – but they often do not include income or consumption as an outcome or incorporate information on parental characteristics.

3
percent of GDP per year.<sup>4,5</sup> There is growing evidence that cognitive, motor and socio-emotional deficits in early childhood, as well as schooling and learning outcomes later in life, are mediated through the brain’s architecture as it develops in an environment cut off from adequate nutrition, stimulation and care. The vast majority of neural development occurs between conception and infancy and the neural circuits that underpin key functions such as seeing and hearing, speech production and higher cognitive ability are established before the age of two.<sup>6</sup> Key processes such as the creation of neurons, the growth of axons and dendrites, synapse formation and pruning as well as myelination are dependent on key micronutrients.<sup>7</sup> New studies that use neuroimaging technologies to measure brain size and structure show alarming differences in brain architecture associated with chronic malnutrition and sensory deprivation.<sup>8</sup> While the Philippines has an excellent survey program that routinely monitors the nutritional status of children, there is limited published empirical evidence on the size, direction, interdependence and relative importance of stunting’s risk factors and interventions. The objective of this paper is to help fill that knowledge gap by conducting a systematic analysis of potential drivers of stunting. The paper begins with a discussion of the data used for the analysis, and then reviews the broad patterns of stunting in the Philippines and across countries. It next describes a conceptual framework for understanding undernutrition and presents the analysis and discussion.

# **2 Data**

The analysis in this report relies on the nationally representative 2015 National Nutrition Survey (NNS) conducted by the Department of Science and Technology’s Food and Nutrition Research Institute (DOST-FNRI). The 2015 NNS is an ‘updating’ survey, meaning that it was implemented in the years intervening the more comprehensive NNS surveys which are conducted every 5 years. The 2015 NNS 2