rafmacalaba/data-use-mentions-extended huggingface dataset:
# V.2 Estimation

Gravity equations are widely used to analyze the determinants of bilateral trade flows. Chen and Novy (2012) and Arvis et al. (2013) employ a gravity specification to analyze the determinants of bilateral trade costs in a cross-sectional dataset. The determinants of trade costs, as defined above, are estimated in a panel specification with time fixed effects, in line with the established literature (Moïsé, Orliac, and Minor 2011). The regression equation takes the following form: _TCijt_ = β1 _RTAijt_ + β2 _tariffijt_ + β3 _LSCIijt_ + β4 _LPIijt_ + β5 _Trade Policy Uncertaintyijt_ + β6 _Gravityij_ + η t + ε _ijt_ , (1) where, for any given country pair ij, bilateral trade costs _TC_ observed at time _t_ are regressed on a wide range of candidate drivers. These include membership in a regional trade agreement (RTA); sector-specific bilateral tariffs; shipping connectivity (UNCTAD’s Liner Shipping Connectivity Index, or LSCI) and logistics (the World Bank’s Logistics Performance Index, or LPI); a proxy for trade policy uncertainty; and standard gravity indicators (distance, a common language, and a common border). In line with Osnago, Piermartini, and Rocha (2018), trade policy uncertainty is defined as the gap between binding tariff commitments and applied tariffs. To ascertain the role of policies aimed at facilitating trade, indexes of logistic performance and maritime connectivity are included.

The model is estimated for the economy as a whole and for manufacturing separately. The regression uses bilateral trade data for 2007-18 for up to 23 advanced economies and 72 EMDEs for which data on trade costs and its determinants are available (table 1). The choice of variables in the panel is informed by Arvis et al. (2013), but also by findings from the discussion of the drivers of trade costs presented in the previous sections. Full details on data and sources are presented in annex A (table 2).

In the estimation results, all coefficients have signs and
|**Ta**|**ble 1: Dire**<br>**I**|**ction of So**<br>**MPORTS**|**uth Asia’**|**s Trade( **<br>**E**|**Constant**<br>**XPORTS**|**US$ Milli**|**on, 1995)**<br>**SHARE OF**|**TOTAL T**|**RADE**|
|---|---|---|---|---|---|---|---|---|---|
||**1981**|**1990**|**1998**|**1981**|**1990**|**1998**|**1981**|**1990**|**1998**|
|South Asia|618|733|3,293|556|837|2,814|3.2|2.4|5.0|
|East Asia|2,084|4,764|15,000|688|2,198|5,548|7.5|10.8|16.7|
|Other Developing|11,579|11,868|21,529|5,624|8,100|12,523|46.7|31.1|27.8|
|Industrial Countries|11,091|20,201|29,702|4,593|15,549|32,281|42.6|55.6|50.5|

Source: IMF Direction of Trade Statistics. Notes: Oil imports are excluded from the South Asia’s
event includes a brief summary and source of information coded by date, country pair (dyad), basin, and issue area. Each event is also coded according to

> 4 Such a measure provides a comprehensive assessment of hydropolitical relations in the basin which could potentially be inaccurate if one only considered conflictive or cooperative behavior alone. For the sake of completeness, however, we also ran separate regressions using only cooperative events and only conflictive events. Results from these regressions were not qualitatively different from those obtained using the combination of both types of events.

> 5 Other databases utilized in large-n studies pertaining to conflict and cooperation over water include the Correlates of War (used by Tir and Stinnett, 2012) and Issues Correlates of War (used by Brochmann and Hensel, 2009). Despite their merits, the Correlates of War database pertains to all forms of militarized conflicts (so the reason for the conflict may not necessarily be water related) and the Issues Correlates of War only pertains to conflicting claims (contentious events) and research is still underway to collect claims data for basins/countries in Asia, Africa, and parts of Europe.

6
Brazil – just like other major users – resort most often to antidumping measures (215 in total) vis-àvis 24 anti-subsidy measures and only 2 safeguard measures. Reviews of antidumping investigations started in 1997, and have involved 35 cases.

During the 1996-1999 period, 1997 excepted, the number of measures adopted shows an upward trend, giving rise to 27 cases in 1999 (Figure 3). This resulted from the currency appreciation which took place at the beginning of the Real Plan, during the second half of 1994, and which had a strong impact during the whole period.<sup>21</sup> Figure 3 Number of Antidumping Cases in Brazil: 1988-2003 <!-- Start of picture text --> Cases Cases, excluding reviews<br><!-- End of picture text --> <!-- Start of picture text --> Cases Cases, excluding reviews<br>30<br>Trade liberalization Fixed exchange rate Flexible exchange rate<br>25<br>20<br>15<br>10<br>5<br>0<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: DECOM, SECEX, MDIC Reports, several years. Author's calculations.

In 2000, once again in a flexible exchange rate context, the number of antidumping measures dropped substantially; if the cases under review are excluded, this downward trend is even more

> 21 In spite of the changes introduced in the exchange rate system in January 1999, it is possible that claims already submitted, based on data of previous periods, did not consider the devaluation as permanent.

21
accommodate at least some amount of surge in patients, though it is not possible to assess whether it will be enough given the extent of expected COVID-19 hospitalizations. In addition, isolation beds and intensive care unit (ICU) capacity are particularly important in the context of COVID-19, but this information is not available in the SDI surveys.

**Figure 2** : Counts of unoccupied inpatient beds by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Hospital Non-hospital Hospital Non-hospital Hospital Non-hospital<br>Kenya Sierra Leone Tanzania<br>Occupied beds Unoccupied beds<br>Inpatient beds<br><!-- End of picture text --> In an emergency, other beds in the facility (such as unused examination beds, maternity beds, etc.) could theoretically be repurposed for use as inpatient beds to accommodate high inpatient load. As shown in Figure 3, hospitals in particular may have large numbers of beds that could potentially be used for COVID19 inpatients, for example. An average hospital in Kenya has 14 beds that could potentially be repurposed, while this number is even higher elsewhere, 19 in Sierra Leone and 27 in Tanzania. Importantly, these crude estimates do not account for the need to maintain utilization of some beds for maternity and other crucial uses. However, they also provide an estimate of the extent to which hospitals may be able to reallocate to utilize their existing capacity.

8
# **2 Where do we stand? A brief review of the literature**

The literature on employment quality has evolved significantly in recent decades. In the case of developed countries, Jencks et al. (1988) were the first to develop a job desirability index for the United States, which considered both monetary and non-monetary aspects of jobs. Similarly, Mu ̃noz de Bustillo et al. (2011) comprehensively review different perspectives and approaches to evaluating workers’ wellbeing and job quality, constructing a job quality index for the European region. Additionally, OECD (2015) introduced a Job Quality Framework that considers earnings, labor market security, and the quality of the working environment as key dimensions for job evaluation. Furthermore, the International Labour Organization (ILO) presented a set of statistical indicators related to the Decent Work Agenda in 2008 and 2009. These indicators encompass fundamental dimensions such as labor standards, rights at work, employment opportunities, social protection, and social dialogue. These indicators allow for studying and monitoring labor markets in developing countries, including their informal sectors. However, job quality measurement in developing countries has been more limited due to data availability constraints, which hinder the construction and comparison of job quality indices across different countries.

In the context of developing countries, some studies have utilized the Alkire & Foster (2011) framework to measure job quality. For example, in the case of Latin America, Brummund et al. (2018), Senhbruch et al. (2020), and Apablaza et al. (2021) propose multidimensional measures of job quality based on this method. Brummund et al. (2018) employ the Socio-Economic Database for Latin America and the Caribbean (SEDLAC) for a subset of 15 countries between 1995 and 2012. The authors use income, health insurance, retirement benefits, tenure, job contract, job stability, and job satisfaction components to create a composite job quality index. One of their key findings is that Argentina, Brazil, Chile, Costa Rica
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1925 Half a Century of Development<br>Economics: A Review Based on<br>the_Handbook of Development_<br>_Economics_|Jean Waelbroeck|May 1998|J. Sweeney<br>31021|
|WPS1926 Do Budgets Really Matter?<br>Evidence from Public Spending<br>on Education and Health in Uganda|Emmanuel Ablo<br>Ritva Reinikka|June 1998|K. Rivera<br>34141|
|WPS1927 Revenue-productive Income Tax<br>Structures and Tax Reforms in<br>Emerging Market Economies:<br>Evidence from Bulgaria|Fareed M. A. Hassan|June 1998|A. Panton<br>85433|
|WPS1928 Combining Census and Survey Data<br>to Study Spatial Dimensions<br>of Poverty|Jesko Hentschel<br>Jean Olson Lanjouw<br>Peter Lanjouw<br>Javier Poggi|June 1998|P. Lanjouw<br>34529|
|WPS1929 A Database of World Infrastructure<br>Stocks, 1950-95|David Canning|June 1998|A. Abuzid<br>33348|
|WPS1930 The Main Determinants of Inflation in<br>Albania|Ilker Domac<br>Carlos Elbrit|June 1998|F. Lewis<br>82979|
|WPS1931 The Cost and Performance of Paid<br>Agricultural Extenion Services: The<br>Case of Agricultural Technology<br>Transfer in Nicaragua|Ariel Dinar<br>Gabriel Keynan|June 1998|F. Toppin<br>30450|
|WPS1932 Air Pollution and Health Effects:<br>A Study of Respiratory Illness<br>Among Children in Santiago, Chile|EBart D. Ostro<br>Gunnar S.
job attributes, since both are normal goods. Consequently, estimates of the hedonic wage equation will be biased in a direction opposite to the (true) compensating differential. Various approaches have been employed to deal with the problem, including the use of panel data (Duncan and Holmlund, 1983) and instrumental variables (Garen, 1988). While we lack panel data, participation in the safe sex training program provides us with a valuable instrumental variable that is able to correct the bias inherent in OLS estimates of effect of condom use on wages of sex work.

In September, 1992 the All India Institute of Public Health and Hygiene began a program in Sonagachi that attempted to provide basic health care facilities to sex workers and their families in Sonagachi while also educating them about HIV/AIDS and the methods to aid in its prevention. The HIV/AIDS information was communicated in a manner that proved to be both innovative and effective. Initially, a group of twelve sex workers were recruited to become peer educators. They were given intensive training on AIDS and other aspects of health care, provided with green coats to identify them as medical workers, and sent into the community to promote safe sex practices. The primary tool they employed for this purpose was a flip chart that used a series of pictures to explain the nature and progression of the HIV virus, its effect on the human immune system, and how the use of condoms was the most effective method of preventing the disease. They also carried condoms with them to distribute to the sex workers free of cost while demonstrating their proper use. Sex workers who wished to use condoms, subsequent to meeting the peer educator could pick them up for free from nearby locations, thus condoms were available in unlimited supply at zero cost.

The peer educators would contact about 40 to 50 sex workers every day and by the time the survey was conducted in 1993 about 53 per cent of them had been contacted in this manner. It is important to note that those who were contacted were
27

|University|0.000|0.000|0.009|0.094|0.025|
|---|---|---|---|---|---|
|**Lived in Dar es Salaam all their**||||||
|**life**|0.762|0.426|0.738|0.440|0.368|
|Marital status||||||
|Never married|0.704|0.457|0.738|0.440|0.202|
|Divorced/separated|0.259|0.438|0.226|0.419|0.203|
|Currently married|0.011|0.103|0.016|0.126|0.430|
|Widowed|0.014|0.119|0.011|0.103|0.598|
|Cohabiting|0.013|0.111|0.007|0.085|0.369|
|**Has at least one child**|0.780|0.414|0.707|0.455|0.005|
|**Neighborhood poverty ranking**||||||
|**(1-5)**|2.691|0.663|2.673|0.706|0.653|
|**Self-ranked social status (1-7)**|3.807|1.571|3.633|1.475|0.057|
|I**ncome**||||||
|Total income (30 days) Tsh - mean<br>|253742|196423|258666|382006|0.790|
|Income from sex work (30 days)||||||
|Tsh
the large difference in the magnitude of wages that migrants could expect to earn in these newer destinations in comparison to the “traditional” destinations that were mostly urban areas within Nepal and Indian cities. Although wage data are not available, this is somewhat manifest in the average value of transfers made by migrants from these regions. Statistics from the national living standards survey conducted in 1995 show that the average annual transfer made by a migrant to regions outside India was Rs.79,183. The corresponding figures for urban Nepal and India were Rs.15,294 and Rs.10,532 respectively.<sup>7</sup> At the macroeconomic level, the increase in remittance inflows resulting primarily from the diversification of international destinations has been dramatic. Official statistics show that remittance receipts increased from 5 percent of GDP in 1995 to about 14 percent in 2003 (see Figure 3) propelling Nepal to the fifth position in the list of top remittance receiving countries.

The second aspect of the diversification of destinations that is equally salient is that it weakened the importance of traditional social institutions and overall “social capital” on opportunities

> 7This point however needs to be caveated as remittances are just as much a function of the cost of living at the destination and the cost of migration itself as they are a function of the wages. Migrating to cities within Nepal - and to a large extent across the open border to India as well - is not very expensive in comparison to the costs of migrating to these newer international destinations. Acquisition of travel documents, visas as well as up-front facilitation fees payable to manpower agencies responsible for work placements often range between Rs. 55,000-Rs. 80,000 - or roughly the average amount migrants to these regions remit in a year. But since the labor contracts typically run for a 3-5 year period, a typical migrant can expect to recover this investment in roughly a quarter of his time abroad.

8
# **_HHS – NAS gaps, levels_**

With these being some key reasons for the observed gaps between national accounts and household surveys, we now turn to empirically estimating their magnitude. Following the approach of Ravallion (2003) and Deaton (2005), we calculate a set of ratios of the survey to national accounts means. By subtracting one from the ratio, we get a measure of the proportional gap, between the HHS mean and the NAS mean. A negative (positive) gap suggests the survey mean is lower (higher) than the corresponding national accounts mean.<sup>20</sup> The distribution of the gap in our sample of matched survey and national account means is illustrated in Figure 2. Both the unweighted histogram and the country-weighted density functions show that, on average, the gap is well below zero, indicating that survey means on average are lower than national accounts means. The gap is substantially larger with regards to GDP than with HFCE, moreover consumption shows larger variation than income, as reflected in the wider density functions seen for consumption surveys. We estimate the average gap for various subsamples and calculate standard errors clustered at the country level.<sup>21</sup> Table 1 shows the value of the gaps for various sub-samples, by region and survey type. Overall, our estimates suggest that survey means are 20 percent lower than national accounts means for HFCE. There is no statistically significant difference in the gap observed for consumption (22 percent) and income (20 percent). Notably, we find the gaps are substantially larger than the corresponding estimates from Ravallion (2003) and Deaton (2005). Deaton found a gap of 14 percent for consumption and 10 percent for income, while Ravallion estimated a gap of 33 percent for income and 7 percent for consumption, with the difference between income and

> 20 For example, a ratio of 0.9, gives a gap measure of -0.1, suggesting that the survey mean is
39 percent – graduated primary school, and over a quarter of those who completed primary school but not secondary school live on less than $3.10 per day. Fourth, the moderate poor, despite having similar profiles in terms of age and household composition, are considerably better educated and are less likely to work in the agriculture sector than the extreme poor. Finally, when conditioning on other observed characteristics, having two or fewer children, completing secondary education, and living in an urban area are strongly and positively associated with economic welfare within countries.

Taken together, these findings emphasize the central importance of policies and programs that benefit households in rural areas and those with large numbers of children in reducing extreme poverty. This includes not only direct support, but also efforts to reduce the future prevalence of these types of households, such as speeding the demographic transition by increasing female education levels. The results also suggest that continued improvement in educational attainment and quality, as well as more rapid urbanization and increased non-agricultural employment, can further hasten movement from extreme to moderate poverty.

The rest of this paper is organized as follows. Section 2 describes the data and the methodology used to harmonize and calculate poverty estimates across different national surveys. Section 3 reports and analyzes the demographic profiles of the poor in comparison with those of the nonpoor. Section 4 considers the robustness of key results to alternative line-up methods, spatial deflation, and varying samples due to missing data. Section 5 concludes.

# **2. Data and methodology**

# **2.1. Data**

The World Bank’s procedure for estimating global poverty rates is an extraordinarily dataintensive exercise. Global poverty estimates are derived from a collection of nationally representative survey data on household welfare – either income or consumption per capita – from 142 economies in the developing world. This collection of survey data is then combined with complementary data on population, inflation, real economic growth, and Purchasing Power Parity (PPP) exchange rates.

Estimating poverty rates for different
</sup> By design, these surveys are representative of L —that is the geographical areas delimited to capture households. Measures of informal businesses only require a few additional LL LL NN NN questions in these surveys, exploiting economies of scale in a cost-effective way.

However, such approaches are not necessarily well suited for representative estimates of informal businesses. The number of businesses and counts of individuals are not always mapped one-to-one; an informal business can be jointly operated across individuals, and one individual may operate several businesses. More fundamentally, L rarely accounts for the concentration LL NN

> 6 Mobility and timing issues are not unique to ACS but plague any attempt to sample informal businesses.

> 7 The most widely known of these is the World Bank’s Living Standards Measurement Survey (LSMS), which is run globally, in partnership with National Statistics Offices. 8 Note that this embeds a single-stage sampling approach, where only PSUs are selected.

> 9 An example of this is where excludes certain inaccessible areas (like a government or military installations). 10 Note the use of subscripts ‘LFS’ and ‘ES’ to denote, by shorthand, the Labor Force Survey and Establishment Survey methods. This notation is dropped from later discussions of the proposed method (Adaptive Cluster Sampling) NN for simplicity.

5
Our study also found that, as expected, there are few studies for children in LMICs that assess the contribution of diet to risk factors for NCDs. This is partly due to the limited research on older children and adolescents, when the most critical metabolic risk factors for a range of diseases are most likely to occur compared with younger children. Another reason for the limited research in this area might be due to the emerging nature of the nutrition transition, which is currently under explored also for the adult population in LMICs. It is also likely that there is less recognition of how excess consumption of unhealthy foods such as UPF contributes to nutrient deficiencies in LMICs, since there is a tendency to associate UPF consumption with overweight/obesity and NCDs (Scrinis 2020). In young children, unhealthy snack food and beverages can account for a significant proportion of total energy intake and the micronutrient dilution that results from high consumption of energy-dense but nutrient-poor foods may contribute to micronutrient deficiencies and poor growth outcomes (Pries, Filteau, and Ferguson 2019).

More research is needed to describe how dietary patterns interact with undernutrition (stunting, wasting and micronutrient deficiencies) and overweight/obesity and NCDs at individual, household and country level and in which direction they influence the economic effects. For example, recent evidence from a national survey in South Africa highlights the complexity of malnutrition and diet quality within households, where 18% of children were stunted and 70% of these children lived with an overweight or obese adult (Harper et al. 2022). While children <5 years and adolescents living in households with medium dietary diversity were more likely to be stunted than children in households with high dietary diversity, increased household dietary diversity was associated with increased risk of adult overweight and obesity. The contribution of diet to the growing double and triple burden of malnutrition in LMIC populations is not well-tested in the research available to date, with most SRM looking primarily at the role of diet in contributing to either undernutrition or overweight, obesity and diet-related NCDs.
**Figure 1:  Participation rates in MGNREGS are only weakly correlated with the incidence of poverty across the states of India**

<!-- Start of picture text --> .7<br>r=0.13<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>.0<br>10 15 20 25 30 35 40 45 50 55 60<br>Headcount index of rural poverty 2009/10<br>Participation rate in MGNREGS (share of rural households)<br><!-- End of picture text --> 20