rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 presents the share of each region's exports and imports in total world trade from the base data used in the model. Aggregation of individual economies into regions _for_ use in the model involved netting out trade among the combined economies, so that these data will not match data from other statistical sources on world trade volumes.<sup>8</sup> Overall, trade among the APEC regions in the model accounts for 70 percent of the total trade. with the rest of the world representing the rest.

Table 3 summarizes information on the sectoral structure of each region economy. Data are reported for base-year sectoral shares of output (column l), value added (2), final demand (3), imports (4) and exports (5). Columns 6-7 show the share of exports in output and the share of imports in demand. Columns 8-11 describe the functional income distribution for each sector.

These data clearly delineate differences in structure and international comparative advantage among ASEAN4, China, the Asian NIEs, and industrial countries such as Japan, the United States, and the European Union. ASEAN4 and China are more primary-intensive than the industrial countries, and their manufacturing sectors, especially the labor-intensive textile and apparel products, are relatively larger than in the advanced countries because of a smaller service sector. Japan, the European Union, and the United States are dominated by a large service sector with a much smaller labor-intensive manufacturing sector. Again, the Asia NIEs lie between China and ASEAN4 and the industrial countries.

Trade shares are also consistent with intuition about each region's international comparative advantage. For example, a labor-intensive industry, textile and apparel, constitutes 18 percent of ASEAN4 and 42 percent of China's total exports, while machinery and equipment, which is capital and technology-intensive, makes up about 45 and 38 percent of their total imports respectively. The reverse is true for Japan and the United States. The Asia NIEs are in between, with a lower textile export share 8 For example, the tigures for ASEAN4 exclude trade among these tour economies; similarly, the rest of world figures include only trade between the rest of world and other regions in our model, not among the many countries lumped together in our rest of world aggregate.

_Lewvis, Robinson, and Wang_ 8
our design. In Section 5, we present the results quantifying the extent to which _well-being comparisons_ are altered when accounting for non-monetary dimensions.

# **Section 4.1: Data sources and the Nigerian context**

We compare well-being across the 36 states of Nigeria in 2019. As we explain below, this is a conservative context to probe the answer to question 1.

## **Data sources**

We rely on two main data sources: the 2018-19 Nigeria Living Standards Survey (NLSS) and the 2018 Nigeria Demographic and Health Survey (DHS). The NLSS 2018-19 is a large-scale household survey, focusing on measuring living conditions of the population, collected by the National Bureau of Statistics of Nigeria (NBS) between September of 2018 and October of 2019. The NLSS questionnaire includes wideranging modules, covering demographic indicators, education, health, labor, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators (NLSS Report 2020). The additional data source, the 2018 NDHS is a survey implemented by the National Population Commission (NPC), whose data collection took place from 14 August to 29 December 2018. It provides up-to-date estimates of basic demographic and health indicators, especially, nutrition data relying on records of anthropometry measurements of children under 5 and women between 15 and 49. We complement these databases with mortality information taken from Human Development Indices for the UNDP Nigeria Human Development Report 2016.

## **Monetary poverty is high and heterogeneously distributed across Nigerian states**

Nigeria provides a conservative context for our purpose. Answering question 1 requires contrasting wellbeing comparisons obtained from the monetary dimension alone with well-being comparisons obtained from a multidimensional indicator aggregating the monetary and the non-monetary dimensions. As we show below, Nigeria has a high monetary poverty rate and this rate is very heterogeneous across the 36 Nigerian states. Accounting for non-monetary dimensions is less likely to reverse well-being comparisons in a context where the monetary dimension already provides a stark contrast across states.

Monetary poverty was high in Nigeria in 2019. That year, some 40.5 percent of Nigerians had their monetary aggregate below the national poverty line. Monetary poverty was also distributed unequally across Nigeria in 2019. That year, five states had less than 10 percent monetary poverty while two states had more than 85 percent. The map shown in Figure 4 illustrates this heterogeneity across geographical units called “zones”, which are groups of several states. The map shows the heterogeneity in poverty rates across the six zones of Nigeria. Each of the three zones in the south have less monetary poverty than each of the three zones in the north. Monetary poverty in the South-West zone is about 10 percent while monetary poverty in the North-East zone is about 70 percent.

17
**Figure 19: Relationship between Outcomes and Full-Cost Recovery and Financial Operating Cost Recovery**

<!-- Start of picture text --> b) Financial Operating Cost Recovery and<br>a) Full-Cost Recovery and T&D Losses<br>T&D Losses<br>45% 45%<br>40% India -Odisha 40% India -Odisha<br>35% 35%<br>30% 30%<br>Uganda Uganda<br>25% 25%<br>Senegal Pakistan Senegal Pakistan<br>20% Tanzania 20% Tanzania<br>Tajikistan Kenya Tajikistan Kenya<br>15% 15%<br>R2 = 0.0119 R2 = 0.2466<br>10% 10%<br>Egypt, Arab Vietnam Vietnam Egypt, Arab<br>5% Rep. Peru PhilippinesColombia 5% PhilippinesRep. Peru Colombia<br>0% 0%<br>50% 70% 90% 110% 80% 100% 120% 140%<br>Full-Cost Recovery Financial Operating Cost Recovery (Cash Collected)<br>c) Full-Cost Recovery and d) Financial Operating Cost Recovery and<br>Number of Outages per Month Number of Outages per Month<br>80 80<br>Pakistan Pakistan<br>70 70<br>60 60<br>India -Odisha India -Odisha<br>50 50<br>40 40<br>30 30<br>20 20<br>R2 = 0.0021 R2 = 0.0564<br>10 TanzaniaSenegal U... 10 TanzaniaSenegal Kenya<br>Egypt, Arab Tajikistan Kenya Philippines Tajikistan Egypt, Arab Uganda<br>0 Rep. Vietnam Peru Colo mbia 0 Vietnam Philippines Rep. Peru Colombia<br>50% 70% 90% 110% 80% 100% 120% 140%<br>Full-Cost Recovery Financial Operating Cost Recovery (Cash Collected)<br>e) Full-Cost Recovery and f) Financial Operating Cost Recovery and<br>Security of Supply Indicator Security of Supply Indicator<br>250% 250%<br>Uganda Uganda<br>Egypt, Arab Egypt, Arab<br>200% Rep. Kenya 200% Kenya Rep. R2 = 0.1828<br>150% 150%<br>Peru Peru<br>Vietnam Colombia<br>R2 = 0.0009 Vietnam Colombia<br>100% 100%<br>Senegal Senegal<br>50% Tanzania Philippines 50% Tanzania Philippines<br>Pakistan Pakistan<br>0% 0%<br>50% 70% 90% 110% 80% 100% 120% 140%<br>Full-Cost Recovery Financial Operating Cost Recovery (Cash Collected)<br>T&D Losses T&D Losses<br>Number of Outages per Month Number of Outages per Month<br>Security of Supply Indicator Security of Supply Indicator<br><!-- End of picture text --> _Note:_ Graphics only show subset of cases for which data is available. _Source:_ World Bank staff.

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that facilities-based providers of basic telecom services must operate as a joint venture with a foreign equity limit of 49 percent.

**Figure 3: STRI for Telecommunications - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

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# **Retail Distribution**

No country made TPP commitments that went beyond its applied policies. Many countries – Australia, Chile, Japan, Mexico, New Zealand, Peru and United States -maintain no explicit restrictions and their TPP countries reflect that. Four countries made TPP commitments that went beyond Doha offers and the rest maintained what they already offered during the Doha negotiations. Chile and Brunei did not include the retail sector in their Uruguay Round commitments or Doha offer, but in the TPP, these countries have placed no limitations on retailing.

Most countries’ TPP commitments reflect their best commitments under their PTA, except that Malaysia and Singapore’s TPP commitments are more restrictive than their AFAS commitments. Malaysia’s TPP commitment lists several limitations: for example, foreigners are not allowed to operate supermarkets or mini markets and no less than 30 per cent of the equity in hypermarkets, superstores and convenience stores is to be held by the Bumiputera (i.e. native Malay).<sup>15</sup> Singapore

> 15 Other restrictions include the following: All distributive trade companies with foreign equity are required to appoint Bumiputera director/directors.  Hypermarkets, superstores, convenience stores and departmental stores are required to allocate 30 percent of the Stock Keeping Units displayed on the shelf space for Bumiputera SME goods and products in each outlet within 3 years. One hypermarket will be allowed for every 250,000 residents and one superstore for every 200,000 residents. Foreigners are not permitted to provide wholesale and distribution services for fabrics and apparels of batik, motor vehicles including motorcycles and scooters, passenger cars and commercial vehicles (excluding automotive components and parts of these vehicles) (Annex I of Malaysia, p24).

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# VIII.2 Issues for Further Research.

Many issues raised in tl-.s paper require further research. I conclude with a brief mention of seven that I believe are among the most pressing from the point of view of policy. First, undernutrition. More evidence based on anthropometric indicators is required given difficulties with the intakerequirements comparisons. As yet we have little systematic information to tell whether the central problem is one of under or malnutrition. Empirical studies of food consumption are also required, in particular, econometric estimates of the price and income elasticities of nutrients. As noted, there are no estimates of these elasticities for Mexico. Yet it is important to determine whether results obtained for other countries (e.g. Behrman and Deolalikar (1987) for India) carry over into Mexico, given differences in diet composition and consumer behavior across countries. Vill increasing income improve nutrition or will more efforts have to be channeled to education and improvements of the health environment?

Two, the line of extreme-poverty. The method used in the paper to set x relied on minimum cost diets constructed by linear programming methods. But as I already remarked, this can potentiall **y** ield overestimates. One promising line of research is to apply the Greer-Thorbecke (1986a,b) methodology and calculate z with respect to a caloric level rather than monetary income, allowing consumers to choose any diet given preferences and prices. These estimates would provide alternative values for Pj(.) and T(.), which can serve as a check on the results obtained in this paper. A second line of research is to obtain disaggregated information on the components of own-consumption, so that households can be ranked by total food expenditures. This in turn will allow to identify the extremely-poor through a direct comparison of f(yf) with the monetary cost of the nutritional basket.

Three, intra-household inequality. As I remarked, little is known about this issue in Mexico, which can potentially be very important given that children are over represented among the extremely-poor. Detailed household studies are required for this, but mention should also be made of a technique recently developed by Deaton (1989) that tests for such inequality using only IES data. Implementation of this technique seems relatively straightforward.

Four, targeted benefits for the extremely-poor. Research is needed to determine appropriate foods. What is the income elasticity of demand for
corresponding import record. Conversely, the reported imports of both the intermediate countries (excluding the first one) and the destination will also lack a corresponding mirror. Carrère and Grigoriou (2015) estimated that as much as 23% of recorded trade flows on the aggregate level do not have a corresponding mirror. However, the relative effect of re-export reporting on partner misattribution in mirror data remains difficult to assess. Although the rising role of re-exporting in merchandise trade and its effect on reported discrepancies has been documented for the largest re-exporting hubs - Hong Kong SAR, China; Singapore; and the Netherlands (Ferrantino & Zhi Wang, 2008; Fortanier, 2016; Jones et al., 2020), there is no comprehensive methodology that considers re-exporting on a global scale. The challenge of tracking re-exports and goods undergoing processing is further complicated by the non-uniform application of re-export reporting guidelines across countries, with 118 reporters having never reported re-exports to the UN Comtrade system (Jones et al., 2020).

Therefore, structural constraints, such as differences in valuation methods, result in many inconsistencies in reported trade. However, it simultaneously suggests that the magnitudes of many observed discrepancies cannot be attributed to logistic constraints alone. Therefore, a growing number of studies have turned to country and bilateral characteristics to explain the recorded gap in reporting.

Besides logistic reasons, discrepancies in international trade reporting can also be non-logistic, i.e., they are often a deliberate misevaluation or misclassification for evading import tariffs (Cantens, 2015 and Demir and Javorcik, 2020) and export taxes (Chalendard et al., 2020), minimizing corporate profit and/or turnover tax, and other non-tariff measures (Kee and Nicita, 2022). The misclassification and misinvoicing of products have been shown to be substantial (Carrère & Grigoriou, 2015), with 77% of all bilateral trade flows reported by both partners at the aggregate level and only 47% of bilateral trade flows reported by both partners at the six-digit level. Larger discrepancies in disaggregated products have long been observed (Rozanski & Yeats, 1994) and are often attributed to human error (misclassification) or deliberate misreporting.

An important question then is how to infer whether the observed discrepancy is “expected,” i.e., implied by structural reasons, as opposed to deliberate, i.e., caused by factors that are not purely logistic in nature. If some discrepancies are structurally expected, a dependable reporter is not one without any discrepancies, but one that exhibits discrepancies that are most likely to 6
health improvements for the remaining adults in emigrant households.<sup>26</sup> The point estimates also suggest lowered blood pressure, but this effect is insignificant.

In related work (Stillman et al., 2009), we have found migration to improve the mental health of the Tongans who move to New Zealand under the PAC. The last column of Table 8 shows that their remaining family members do not receive the same improvement. The MHI-5 ranges from 5 to 25, with higher scores indicating better mental health. The point estimates thus suggest that, if anything, migration lowered the mental health of remaining family members, although this effect is insignificant.

The two individual characteristics for which evidence of selection was greatest in Table 3 were mental health and currently studying. Stayer household members in ballot loser households have better mental health than individuals who would move if the household had been successful in the ballot, and also better mental health than non-applicants. The point estimates in Panel C of Table 6 reflect this selectivity, with the results that do not control for migrant selectivity indicating that migration improves mental health of those left behind, whereas the experimental estimate suggests it lowers mental health. However, these differences are not statistically significant. The currently studying variable is an example where controlling for individual characteristics eliminates selection bias – here we are controlling for years of education, which likely corrects for much of the selectivity in whether an individual is currently studying.

# **6.2. Children**

Table 9 examines the impacts of migration of other household members on the education and health of children aged 17 and under. Recall that the migrants here are typically aunts and uncles of these children27 and their parents are not the ones migrating. For this reason we might expect less of the potential negative effects of parental absence on education and health, and that the main channel through which migration would affect these children is through income effects. Tonga has good basic education and health services, and is ranked by the UN as high in terms of human development, with an adult literacy rate of 98.2 percent.<sup>28</sup> As such, liquidity constraints appear unlikely to be of large importance in

> 26 These health improvements have occurred along with a decline in household economic resources, suggesting that one pathway is that less food leads to less over-nutrition but behavioural change could also be a factor (e.g. more walking if migrants sold the family car). Evidence from developed countries suggests that people adopt healthier lifestyles when income declines, such as during recessions (Ruhm, 2005).

> 27 72% are classified as “other relative” in terms of their relation to the principal applicant, and 23% are a sibling of the principal applicant.

> 28 <u>http://palaceoffice.gov.to/content/view/124/95/</u> [accessed December 18, 2007].

23
Table 8

# Robustness to Set of Instruments Two Instruments

||Poverty|Exports|& Markets|Poverty|& Exports|
|---|---|---|---|---|---|
||& markets<br>(Model 3)|Share land<br>(Model 3)|Share income<br>(Model 3)|Share land<br>(Model 3)|Share income<br>(Model3)|
|IV – Linear Model|_−_0_._26|0.15|0.13|_−_1_._45|_−_1_._80|
||(0_._11)<sup>_∗∗_</sup>|(0_._06)<sup>_∗∗_</sup>|(0_._05)<sup>_∗∗_</sup>|(0_._89)<sup>_∗_</sup>|(1_._19)|
||0.21|0.19|0.15|0.21|0.21|
|IV – Probit/Tobit|_−_0_._32|0.13|0.24|_−_1_._86|_−_2_._15|
||(0_._013)<sup>_∗∗_</sup>|(0_._024)<sup>_∗∗∗_</sup>|(0_._047)<sup>_∗∗∗_</sup>|(0_._90)<sup>_∗∗_</sup>|(1_._02)<sup>_∗∗_</sup>|
||0.18|0.28|0.26|0.18|0.15|
|Test of Instruments||||||
|F-test (frst stage)|11.49|11.19|9.61|8.43|6.84|
|p-value|0.00|0.00|0.00|0.00|0.00|
|_R_<sup>2 </sup>(frst stage)|0.71|0.71|0.71|0.21|0.16|
|Test of Overidentifcation||||||
|_χ_<sup>2</sup>|0.178|0.773|0.765|0.290|0.029|
|P-value|0.673|0.379|0.382|0.591|0.865|
|Observations|6734|6554|6606|6554|6606|

Note: Instrumental variable estimates using two instruments, the out-of-district cost of transportation to Kampala and the lagged export market density in 1995. Poverty is defined as a dichotomous variable of whether the household is poor or not. Export participation is measured with the share of land devoted to export crops (coffee, tea, cotton, pineapples, passion fruit) and with the share of income generated by sales of these crops. The linear model is estimated with IV and the IV-Probit model is estimated with control function methods. Standard errors (within parenthesis) are cluster-corrected. Based on the Uganda National Household Survey 1995/96 and 1999/2000.

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