rafmacalaba/datause-encoder-data huggingface dataset:
# **_Big Exporters and Importers_**

**Table 8: The 20 Largest Agricultural Exporters and Importers in World Markets, 1990/91-2006/07**

||Agricultur|al Exports ($|million)|World|Market Shar|e (%)|
|---|---|---|---|---|---|---|
|Exporter|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|
|United States|52,403|77,157|105,951|14.0|13.6|10.5|
|Netherlands|30,867|35,180|63,717|8.2|6.2|6.3|
|Germany|21,464|29,473|62,956|5.7|5.2|6.2|
|France|32,621|35,210|61,491|8.7|6.2|6.1|
|Canada|20,348|37,705|47,438|5.4|6.6|4.7|
|Brazil|9,944|19,610|45,511|2.7|3.4|4.5|
|China|8,023|19,543|36,849|2.1|3.4|3.7|
|Spain|10,070|17,739|33,777|2.7|3.1|3.4|
|Italy|11,728|16,191|31,923|3.1|2.8|3.2|
|Argentina|7,152|12,919|27,944|1.9|2.3|2.8|
|Belgium|12,650|14,422|27,667|3.4|2.5|2.7|
|United Kingdom|12,492|16,623|24,752|3.3|2.9|2.5|
|Australia|10,706|18,478|24,386|2.9|3.2|2.4|
|Indonesia|4,439|10,026|22,335|1.2|1.8|2.2|
|Thailand|7,577|12,996|21,591|2.0|2.3|2.1|
|Russian Federation|..|8,587|19,693|0.0|1.5|2.0|
|Malaysia|8,809|8,477|17,925|2.4|1.5|1.8|
|Denmark|10,309|10,900|17,479|2.8|1.9|1.7|
|Mexico|4,140|8,783|15,436|1.1|1.5|1.5|
|Chile|3,619|7,730|15,403|1.0|1.4|1.5|
|Allabove countries|279,363|417,748|724,223|74.6|73.4|71.8|
||Agricult|ural Imports (|$million)|World|Market Shar|e (%)|
|Importer|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|
|United States|31,412|61,895|96,471|9.2|11.8|9.9|
|Germany|37,323|42,647|75,708|11.0|8.1|7.8|
|United Kingdom|22,918|29,223|55,423|6.7|5.6|5.7|
|Japan|37,250|47,097|54,561|10.9|9.0|5.6|
|China|4,833|18,086|52,299|1.4|3.4|5.4|
|France|23,182|27,494|49,423|6.8|5.2|5.1|
|Italy|25,963|26,576|48,741|7.6|5.1|5.0|
|Netherlands|20,180|25,054|48,120|5.9|4.8|5.0|
|Spain|9,593|15,430|31,040|2.8|2.9|3.2|
|Belgium|15,632|16,534|29,284|4.6|3.1|3.0|
|Canada|8,678|15,193|25,199|2.5|2.9|2.6|
|Russian Federation|..|9,336|24,407|0.0|1.8|2.5|
|Mexico|4,548|10,385|18,096|1.3|2.0|1.9|
|Korea, Rep.|6,953|10,607|17,190|2.0|2.0|1.8|
|<br>Hong Kong (China)|5,137|9,471|11,969|1.5|1.8|1.2|
|Austria|2,861|5,119|11,114|0.8|1.0|1.1|
|Denmark|4,200|5,370|10,814|1.2|1.0|1.1|
|Sweden|3,331|4,943|10,351|1.0|0.9|1.1|
|Poland|1,569|3,333|10,243|0.5|0.6|1.1|
|Taiwan, China|5,221|6,766|9,644|1.5|1.3|1.0|
|All above countries|270,783|390,558|690,099|79.6|74.3|71.0|

Note: Agriculture is defined as SITC (0+1+2+4-27-28) in Revision 3. Source: Based on mirror data from UN COMTRADE Statistics.

Table 8 shows the imports and exports of the top 20 countries in terms of sizes of imports and exports. This table also shows the shares of these top 20 countries in total exports and imports over the last two decades. Few things stand out. First, consistent with our earlier findings, most of the large importers and exporters are industrial countries. USA is the world’s largest importer and exporter. Among the top 10 exporters, there are 3 developing countries, China, Brazil, and Argentina. Among the top 10 importers, there is only one developing country, namely China.

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rado would fall from 800 to 400, and increased contraception would account for the remaining reduction. The second program is a more limited effort in a very poor area with limited health care infrastructure and a weak transportation system. This effort would cost $0.48 per capita and would prevent about 20% of maternal deaths. The maternal mortality ratio would fall from 1,000 to 950, and there would be a small increase in the contraceptive prevalence rate.

Annexes discuss several alternative configurations of Safe Motherhood programs, as well as presenting evidence from programs in Bangladesh, Brazil, Ghana, India, and China.

Maine, D. Safe Motherhood Programs: Options and Issues, Center for Population and Family Health, Columbia University, 1991.

This publication is a combination of a useful compendium of Safe Motherhood information in chartbook form, and a discussion of the analytical model of maternal mortality developed at Columbia. It documents in a straightforward manner the extent of the problem of maternal deaths and presents a model of causes of maternal ill-health and related interventions. Several possible program options are discussed, and a simple cost- effectiveness exercise is included. Details of cost assumptions are discussed in the text of this report.

Among its conclusions is the following estimated cost per death prevented by various programs:

|TBA training|$17,250|
|---|---|
|TBA new training|11,500|
|Prenatal care|17,692|
|Family planning|5,750|
|Health centers|4,098|
|Health ceniters and<br>urban hospital|6,014|
|Health Centers and<br>rumral hospitals|3,735|

Mooney, G. and Creese, A., Cost and Cost-Effeciveness Analysis of Health Interventions, World Bank, 1990.

This work, a chapter in the World Bank's Health Sector Priorities Review, provides a general discussion of the approach to cost and cost-effectiveness analysis for developing country health programs. It emphasizes a number of the principal issues in this type of analysis, including a focus on marginal costs, identification and discounting of benefits, the importance of opportunity costs, equity considerations, and problems of uncertainty. The paper makes a strong plea for setting health sector priorities "on the margin." Regional Plan of Action for the Reduction of Maternal Mortality in tlhe Americas, Pan American Health Organization, Washington, D.C., Sept. 1990.

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**CNAPTBR IV: TIE ROLE OF TRhINING IN WDP**

4.1 If there is one way of describing the "methodology" involved in the growth of WDP, it is "training". The concept of training in WDP is seen in the broadest sense, as an exercise that generates experiences which alter women's perception of themselves and society's image of them. This is in contrast to schemes like the Development of Women and Children in Rural Areas (DWCRA) and Training of Rural Youth in self employment (TRYSEM)<sup>7</sup> where training is envisaged as an exercise in skill endowment.

4.2 Typically, training for Sathins or Prachetas is an intense 20-25 day get-together. It is held in an atmosphere charged with tension and emotion that leads to a catharsis in the final stages. Far from being lectures on cleanliness, nutrition and child-care, the training sessions "experiment with the possibility of creating a climate of questioning, reflecting, sharing, choosing, seeking and discovering" (IDS and IDARA, 1986, pg.13). The topics for discussion and brainstorming include drought, environment, health, water, crimes against women, laws relating to women, and women's inequality in all its forms. An attempt is made by the trainers to bring out women's views on these issues and to get them to ask questions.

4.3 The objectives of those charged with training the Sathins and Prachetas are essentially to:
wage rigidity? Although there is no heterogeneity in the models presented above, it surely exists in the data. In light of this fact, it seems reasonable to assume that there is some rigidity, especially at the bottom of the wage distribution, but perhaps not at the top. Not only is this statement sensible, it is also supported by the data.

An increase in the enforcement of mandated benefits in the formal sector leads to reduction in formal wages. If that is the case then it is likely that _v>_ 1 (workers put a higher value on mandated benefits than firms do). The minimum wage may prevent formal wages from falling at the bottom of the wage distribution.

There is an increase in the share of individuals who are non-employed which, given that _v_ >1, can be probably attributed to downward wage rigidity (as shown in section 3, in a model without wage rigidity, _v_ >1 implies that increased enforcement should lead to less nonemployment). On the one hand, at the bottom of the formal wage distribution wages cannot adjust, and some employees are likely to be dismissed. On the other hand, if the fall in wages is smaller than the increase in the value of other formal job benefits (because of wage rigidity, or because the value workers put on benefits is higher than their cost to the firm), formal jobs become more attractive, inducing self-employed workers to search for work in the formal sector, in spite of a higher risk of unemployment.<sup>32</sup> If these jobs are at the bottom of the distribution of formal wages, it is natural that those workers leaving the self-employment sector have low levels of schooling (which would explain our empirical results on schooling).

If the cost of mandated benefits to the firm is below the value they have to workers (e.g., tax-free job severance payments, health and safety on the job), then an increase in mandated benefits could explain a rise in formal sector employment, if wages are fully flexible. Labor supply to the formal sector would increase since this sector becomes more attractive, and labor demand would also increase if there were a large enough decline in wages. The contraction in the supply of self-employed workers causes an increase in their wages. Formal sector employment could also increase due to worker registration (through direct action of labor inspectors, or indirectly through a deterrent effect).

> 32 Using data from IPEA, we can compute the number of people searching for a job in each city, by subtracting the number of employed from the number of active individuals. The problem with this measure is that we cannot distinguish search in the formal and in the informal sector. If we regress the (log) number of individuals searching on enforcement (instrumented) we estimate that a one standard deviation increase in enforcement increases total search by 1/3 of a standard deviation.

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Reddy C S and Manak S (2005) Self-Help Groups: A Keystone of Microfinance in India – Women Empowerment and Social Security, Hyderabad, APMAS Robeyns, I., 2006. The capability approach in practice. _Journal of Political Philosophy_ , 14(3), pp.351-376.

Roche, J.M., 2013. Monitoring progress in child poverty reduction: methodological insights and illustration to the case study of Bangladesh. _Social Indicators Research_ , 112(2), pp.363-390.

Rosenbaum, P.R. and Rubin, D.B., 1983. The Central Role of the Propensity Score in Observational Studies for Causal Effects _, Biometrica_ , 70, 41-55. Mathematical Reviews (MathSciNet): MR742974 Zentralblatt MATH, 522.

Saha, S., Annear, P.L. and Pathak, S., 2013. The effect of Self-Help Groups on access to maternal health services: evidence from rural India. _International Journal for Equity in Health_ , 12(1), p.36.

Seebohm, P., Chaudhary, S., Boyce, M., Elkan, R., Avis, M. and Munn-Giddings, C., 2013. The contribution of self-help/mutual aid groups to mental well-being. _Health & Social Care in the Community_ , 21(4), pp.391-401.

Sen, A., 1985. _Capabilities and commodities_ . Amsterdam and New York: NorthHolland.

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10 import volumes on GDP, in Section V we study the relationship between the individual components of imports and GDP.

A well-known limitation of this empirical approach is that it presumes that world income is an exogenous variable that has contributed to the increasing levels of world trade, when in fact both are endogenous variables that are co-determined. These regressions should, therefore, be seen only as useful reduced-form specifications that illustrate the changing correlation between trade and income, which is the main focus of this paper. They do not, however, capture the complex and structural relationship between these two variables and its deeper determinants.

# **A.   Trade and Income in 1970-2013: Annual Data**

In this subsection, we begin looking at the trade-income relationship using annual data for the period 1970-2013. While the global trade slowdown is seen as a recent phenomenon, investigating a sufficiently long time period is essential to detect structural changes that may have occurred in the relationship between trade and income. The results for this period are presented in Table 1. The next subsection will take a closer look at the dynamics of the trade and income data before and after the Great Recession.

For the entire sample, the long-run elasticity (- δ/γ) is 1.7, but the response of trade with respect to income differs considerably across the three periods. In the period 1986-2000, a 1 percent increase in world real GDP is associated with a 2.2 percent increase in the volume of world trade. This elasticity is substantially higher than in the preceding period (1970-1985) and in the subsequent period (2000-2013), for both of which the trade elasticity was 1.3. Using a non-linear Wald test to assess the presence of structural changes in the sample, we find that there is a significant structural break in the long-run trade-income relationship in the
thereby also benefiting local host communities.

It is somewhat unclear how successful Uganda’s integration policy has been, because only a handful of studies has analyzed how the presence of refugees has affected welfare outcomes in host communities. With the exception of Kreibaum (2016), these studies have focused on general welfare (income and consumption) rather than service delivery. Other studies explore environmental outcomes. Based on focus group discussions in host communities near the Nakivale refugee settlement, Ronald (2020) reports that host communities are concerned about environmental degradation. Similarly, based on stakeholder interviews, IRRI (2019) also reports tensions over natural resources, especially around the allegation that refugees engage in illegal logging.<sup>10</sup> Zhu et al. (2016) assess the impacts of World Food Program aid within a 15 km radius of two refugee settlements in Uganda. They find that the average refugee household receiving cash food assistance increases the annual real income in the local economy. Here, locals around the settlements benefited from aid provided to refugees because on average, they are in a better position to increase their supply of goods and services as the local demand rises. Kreibaum (2016) corroborate the finding that host communities near refugee settlements in Uganda have relatively higher consumption levels using data on three south-western districts, though the effect is small in magnitude. d’Errico et al. (2021) find that the presence of refugees has only modest effects on local households’ consumption levels. The authors attribute this finding _not_ to an increase in demand for local produce, but to greater participation by host households in paid employment in aid agencies, and to the resulting increase in wage incomes. However, the effects they observe are small, and concentrated in areas very near refugee settlements.<sup>11</sup> Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of refugee presence. They measure refugee presence at the district level (refugee share of the district population) and focus on the south-western districts between 2002 and 2010. Thus, by including all of Uganda at the parish-year level as well as the post-2014 influx, we extend their analysis. In sum, our study is the first to measure the effects of refugee presence on service delivery outcomes in Ugandan host communities.

> 10See Gianvenuti, Jalal and Kirule (2020) for more details.

> 11Note that both Zhu et al. (2016) and d’Errico et al. (2021) use original cross-sectional surveys. Without pre-treatment data, it is harder for them to make causal claims.

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nationally funded, often come with strings attached and raise questions of “ownership” by the local authorities which can later create problems with poor O&M performance; (c) public‐private partnerships or private responsibility and funding for the project, which have a checkered record in developing countries; (d) land related taxes and betterment charges, which have worked successfully in some countries, including the land readjustment scheme in South Korea (see above), but require considerable administrative capacity and political will, and (e) local taxes and user charges, which may be able to fund the O&M costs, but rarely will cover outright the upfront capital costs of urban infrastructure investments.

Based on his review of actual practice around the globe, Alm (2010) concludes that there is no simple principle or model of expenditure and revenue assignment for urban infrastructure and that despite decades of practical experience and academic research a number of gaps remain in our knowledge about urban infrastructure finance:

1. <u>Data: What is the extent of municipal infrastructure spending? What are the</u> funding mechanisms, including municipal borrowing? What are the assets municipal governments have which could be mobilized to fund infrastructure investments (e.g., through sale of public land assets, or through capture of land value increments on private land)?

2. <u>Municipal borrowing: How can the long‐run borrowing capacity of local</u> governments be strengthened? How can national capital markets be developed for municipal borrowing, including development of rating agencies? Are there proven institutional solutions that can help raise funds for urban infrastructure, such as the creation of Urban Development Funds?<sup>17</sup> 3. <u>Capital grants: What is the impact of such grants on local investment</u> decisions? How should they be designed for optimal investment decisions? How to ensure “ownership” for O&M by the local authorities?

4. <u>Cost recovery through user charges: What is the contribution of user charges</u> to capital and O&M finance so far? How can their contribution be strengthened as part of an effort to strengthen the financial viability and efficiency of urban infrastructure?

5. <u>Distributional effects: What is the impact on different income groups, and</u> especially on the poor, from different funding schemes? How can infrastructure financing be structured to ensure affordable and effective service provision for the poor?

6. <u>Governance and administrative capacity: What are the current prevailing</u> governance practices and administrative capacities of local and (where appropriate) national authorities for assuring effective urban infrastructure service provision? How can they be strengthened? This covers such issues as the appropriate assignment of expenditure and revenue responsibility to different levels of government, the strengthening of tax administration and

> 17 For at least partial answers to these questions, see Annez et al. (2008).

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Table 5 shows that of 51,976 GGUs, 1,131 units in the top 5 deciles account for 50% of global methane emissions. For these high-priority GGUs, we simplify the monitoring problem by confining our exercise to emissions-intensive (5-decile) EDGAR cells in the set of 21,057 cells that account for 50% of global methane emissions (Table 4). Within each GGU, we compute the convex hull that includes all EDGAR 5-decile cells in the unit, construct a 20-km buffer around the hull, and intersect the buffered polygon with the GGU boundaries to define the sampling area for Sentinel-5P observations. This generates sampling areas for 1,001 high-priority GGUs.<sup>10</sup> Figure 5 maps these units, revealing significant representation for on- and offshore GGUs in all world regions.

# **4.  Sample Data**

We have constructed our atmospheric CH4 concentration database from observations by the ESA’s Sentinel-5P platform that typically number over 200,000 per day. We locate each observation in a 5-km global grid comprising 25,920,000 cells. The dominant component in each observation is the global stock of atmospheric CH4 molecules that have accumulated during the past decade. The second component is seasonal, determined by natural processes over the annual cycle. The third component is geographic: Emitted CH4 molecules tend to remain within their latitude bands of origin for significant periods before full global mixing.

The fourth observation component is determined by local anthropogenic CH4 emissions, which remain near their point of origin long enough to be distinguished from the three “background” components. We term this the “concentration anomaly” since it measures the local deviation from the global background CH4 concentration. As previously noted, its primary sources are emissions from agriculture, fossil fuel production and waste disposal.

Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and a current spatial resolution of 5.5 x 3.5 km. We use the S5P L2 Offline georeferenced measures of XCH4 (the column average dry air mixing ratio of methane<sup>11</sup> ), corrected for bias associated with XCH4 dependence on surface albedo.<sup>12</sup> Recent global research by Sha et al. (2021) has shown that S5P CH4 measures correspond very closely to independent measures taken at ground stations maintained by TCCON (Total Carbon Column Observing Network).<sup>13</sup> Sentinel-5P’s bias-corrected XCH4 data exhibit a difference of

> 10 The remaining 5-decile GGUs do not contain any 5-decile EDGAR cells.

> 11 The total atmospheric column between the surface and the top of the atmosphere normalized to the corresponding dry air column.

> 12 For more technical details, see the documentation at

> https://sentinel.esa.int/documents/247904/3541451/Sentinel-5P-Methane-Product-Readme-File

> 13 Current TCCON sites are located in the United States, China, Canada, Germany, Poland, France, Japan, Australia, New Zealand, the Republic of Korea, Réunion, and Ascension Island.

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