rafmacalaba/datause-encoder-data huggingface dataset:
workers. Indeed, 19 of these firms employ more than 1,000 workers.<sup>3</sup> Of these employees, 74 percent are engaged in apparel production. In absolute terms, the number of employees in the parks is still relatively small. However, since 2015, employment in industrial parks increased by 78 percent, comparable to what we saw in Vietnam over the period 1990-2000.

All but three of the new industrial parks are owned by the Ethiopian government: two of the other owners are Chinese and one is Bangladeshi. Almost all the firms in these parks are foreign-owned; 79 percent are owned by Asian investors, with China in the lead at 66 percent, 12.4 percent are owned or partially owned by Ethiopian investors, and 8.6 percent are owned by EU and UK investors.

Within Ethiopia’s industrial parks, 83 percent of employment is in firms with more than 1,000 employees. Women account for 74 percent of total employment; firm managers tend to hire young female workers who have completed a 10<sup>th</sup> grade education. These women typically work 8 hours a day for 6 days a week plus an average of two hours overtime each day bringing the average hours worked per week to 60. Ultimately, the sustainability of these parks depends in part on working conditions in the parks. Using a phone survey of firm managers, Meyer et al. (2021) find that the base salary in most of the firms in these industrial parks exceeds the cost of basic needs as measured by the local poverty line. When bonuses, overtime pay, incentive payments and in-kind benefits are included, total compensation is roughly four times the cost of basic needs. However, they also find that 21 percent of firms in the industrial parks report paying a base wage below the local poverty line. Based on the information in the report, it is not clear how far below the poverty line these wages fall and what happens to total compensation for these workers when non-wage benefits are included.

A unique feature of many of the foreign firms in Vietnam is their sheer size. For example, in 2017, there were 756 foreign-owned firms with more than 1,000 employees while in Ethiopia, this number is only 21. Moreover, there were 125 foreign firms with more than 5,000 employees in Vietnam while there are no firms of this size in Ethiopia’s manufacturing sector. This difference does not apply only to foreign firms, although the “huge” firms are more prevalent among foreign-owned enterprises and in the labor-intensive sectors of apparel and footwear. We call this the case of the missing huge firms.

> 3 A list of Ethiopia’s industrial parks, with some basic data on when they started, number of firms and workers, and share of workers who are female and/or in the garment industry, is available in the Appendix. Our evidence is consistent with that of Meyer et al. (2021).

13
could be interpreted as a proxy for dissemination efforts and client engagement, capturing travel costs, printing costs, etc. In fact, we find that reports which have higher other costs tend to receive more downloads, conditional on being downloaded. Third, we identified reports that were published in another type of documents, such as a working paper or a book chapter. We find that the reports were downloaded at a significantly greater rate in their new incarnation. For example, one policy report was cited twice within our dataset, but when later published as a working paper with a new title, it was cited over 50 times.

# **_Citations_**

**A standard measure of an index of research output is the h-index which is highest for reports on Latin America and the Caribbean followed by the Africa region.** The h-index is a measure on the impact of research output.  First proposed in Hirsch (2005), the h-index was meant to quantify an individual’s scientific output for applications such as faculty recruitment, granting tenure, and awarding grants.  An h-index value of x means that the author has published x items, each of which has been cited at least x times.  It assesses both the productivity and influence of research.   As citations tend to increase the longer a study is published, the h-index tends to rise with years of publications. Figure 8 shows the h- indices for the regional VPUs.

**Figure 8: H-Indices for the Regions**

<!-- Start of picture text --> AFR = 7.00<br>60<br>EAP = 7.50<br>ECA = 4.75<br>50<br>LCR = 8.63<br>40 MNA = 3.00<br>SAR = 6.00<br>30<br>20<br>10<br>0<br>0 5 10 15 20 25 30<br>Rank of Policy Report<br>Source: Google Scholar and World Bank Documents & Reports.<br>Note: The x-axis ranks individual policy reports by the number of citations. The H-index of<br>a region is the value of the distance between the x-axis and the region's intersection with<br>the 45° line.<br>Citations<br><!-- End of picture text -->

**Contrary to downloads, costs are not a significant determinant of citations (Table D).** A more expensive report is not more likely to be cited or receive a higher number of citations, conditional on being cited. However, under some specifications, cross support related costs significantly increase the probability of being cited. The year dummies, which were all significant and positive, indicated that policy reports that have been disclosed for a longer period of time are cited more frequently. Reports prepared by network anchors such as HDN and PREM as well as by the Africa and LCR regions tend to

18
Mobility 3.1 Methodology This paper analyzes “ladder questions”, where persons are asked to rank themselves with regard to economic and power status, both for now and before the violence. ® The scale ranges from 1 to 9, with 9 indicating “richest” or “most powerful”, respectively. How does past status relate to current status? For example, were those at the bottom of the economic or power scale today also at the bottom in 1999? Investigating this issue requires linking information on the 1999 status with the current assessment. This can be most conveniently done with the help of transition matrices. They capture “intra-distributional” dynamics, and reveal differences in upward and downward mobility. More formally, let be a quadratic transition matrix of order K capturing the change in economic status. States are ranked from worst to best, and the top four steps are collapsed into one state, reducing the number of steps from nine to five. For example, element pj, refers to the probability that an individual at the lowest step in 1999 progresses one step in 2001. Denote by Fi§99 and Fo, the 1999 and 2001 density functions of economic status, represented as column vectors with five elements. The transition matrix P” satisfies Fi), = Fijj,P” where each row adds up to unity P”1 = 1).

How does one interpret these matrices of K? transition probabilities? Similar to poverty and inequality measurements, various approaches have been developed to analysis the structure of mobility as captured by transition matrices (Bartholomew (1982)). The simplest indicators are scalar summary measures of overall mobility, like the immobility measure [;,, which gives the share of individuals jumping less than k number of steps,? or the jump measure J,, which equals the absolute value of the average step change between 1999 and 2001, to be calculated for each state s and the transition matrix as a whole. Such measures treat upward and downward changes symmetrically, even though higher steps are preferred to lower steps. By contrast, the upward mobility measure U, gives the value of the average step at 2001, again calculated for each row and the matrix as a whole.

8 Tn aseries of papers, Ravallion and Lokshin (Ravallion and Lokshin (2001), Ravallion and Lokshin (2002), and Lokshin and Ravallion (2002)) have analysed identical ladder questions using Russian household survey data. ® is no larger than the maximum number of states minus one. is linked to the Shorrocks axiom for transition matrices (Shorrocks (1978)): shifting probability mass from the diagonals to off-diagonals increases mobility.
61

# REFERENCES AND BIBLIOGRAPHY

Akin, J.S., Guilkey D.E., Popkin, B.M. and Smith K.M. "The Impact of Federal Transfer Programs on the Nutrient Intake of Elderly Individuals". The Journal of Human Resources. (Regents of the University of Wisconsin System 1985).

Akin, J.S., Griffin, C.C., Guilkey, D.K. and Popkin, B.M. "The Demand for Primary Health Care Services in the Bicol Region of the Philippines". Econonomic Development and Cultural Chane . 34(4). July 1986.

Austin, J,E. and Zeitlin, M.F. Nutrition Intervention inDeveloping_Countries: An Overview. (Cambridge, Mass.Oelgeschlager, Gunn and Hain, 1981).

Austin, J.E. "Urban Malnutrition: Problem Assessment and Intervention Guidelines". Submitted to World Bank by Harvard University.

Barnum, H.N., and Squire, L. Model of an Agricultural Household. Johns Hopkins Univesity Press. 1979.

Basta, S.S. and Church, A. Iron Deficiency Anemia and the Productivity of Adult Males in Indonesia. Washington, D.C. World Bank Staff Working Paper, No. 175, 1974.

Becker, G.S., "The Theory of Allocation of Time" The Economic Journal. 75. Sept. 1965.

Ben Zion, U., Chernichovsky, D., Gradstein, M.,and Spiegel, U.,"Optimal Consumer Subsidies and Income Transfers for Minimum Nutritional Requirements: Basic Concepts." PHN Technical Note87-5. Washington D.C. The World Bank , March 1987.

Ben Sira, Z. "Positions and Knowlege in Nutrition." Institute forSocial Research. Jerusalem. July 1976. (Hebrew).

Bender, D., "A Refinement of the Concept of Household: Families, Co-Residence, and Domestic Functions". American AnthropologIst. 69, 1967.

- Berg, A., Malnourished People a policy view, Poverty and Basic Needs Series, Washington D.C. World Bank. June, 1981.

Berg, A. and Brems, S., "Micronutrient Deficiencies: Present knowledge on Effects and Control." PHN Technical Note 86-32 Washington D.C. The World Bank. 1986.

Berg, A., The Nutrition Factor; its Role in National De.'elopment. Washington D.C. The Brookings Institution. 1973.

Chernichovsky, D., and Coate, D. "The Choice of Diet for Young Children and its Relation to Children's Growth." Journal of Human Resources. 15:255-263, 1980.
total portfolio is measured by the investments made between the year of their implementation and the end of 2020.<sup>13</sup> A somewhat different picture emerges once the portfolio size is expressed as a percentage of GDP (figure 6). Brazil, with its large PPP portfolio, and Albania rank at the top, with active PPP portfolios representing 22.6 percent and 18.6 percent of their GDPs, respectively. The Solomon Islands’ single active PPP represents about 15.2 percent of its GDP. Peru, Ghana, Bhutan, South Africa, Argentina, Kosovo, Georgia, and Indonesia follow, with active portfolio sizes reaching at least 5 percent of their GDPs. The PPP portfolios of Ethiopia and Burundi are below 1 percent of their GDPs.

**Figure 6 Size of public-private partnership portfolios as percentage of GDP**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>AlbaniaArgentina Bhutan BrazilBulgariaBurundiEthiopiaGeorgia GhanaIndonesia Kenya Kosovo PeruRomaniaSolomon IslandsSouth AfricaUkraine<br>PPP Portfolio as percent of GDP<br><!-- End of picture text --> _Source_ : Authors’ calculations based on data from PPI and WDI databases.

# **_5.2 Predicted Probabilities of Early Termination in Sample of Developing Countries_**

We predict the probabilities of early termination using equation (2), assuming all active projects survived until the end of 2020. We tried to fill in missing values for project-specific variables essential for prediction; when such efforts proved fruitless, we imputed these values using the

> 13 To remain true to the spirit of the PPI database, we collected each year’s investment amount using the financial statements of the special purpose vehicles whenever possible. The financing composition was determined using the last five years of statements, considering that each new debt contributes to financing of seven years of investment. The investment commitments of these projects do not reflect potential future investments.

19
<!-- Start of picture text --> EMU<br>1<br>Greece revised<br>budget deficit<br>(05/11/2009)<br>0.8<br>PHASE 2<br>PHASE 1<br>0.6<br>Lehman<br>collapse<br>(15/09/2008)<br>0.4<br>0.2<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>Average cDCC<br><!-- End of picture text --> Figure 2: Average dynamic conditional correlation between EMU sovereign bond yields. Vertical lines denote two specific critical dates for the region.

Phase 2.<sup>7</sup> In Phase 1, a high degree of integration is observed between EMU sovereign debt markets, i.e. almost every member is connected to another. However, strict segmentation is clearly visualized in Phase 2. There are two main clusters where in one of them, the members struggling with debt problems (denoted in red) are present. In the other cluster, we observe the countries with strong fiscal positions during the sovereign debt crisis. Moreover, Belgium, which is one of the two members having relative weak fiscal performances within this cluster (denoted in yellow), serves as a hub in the new segmented structure. Interestingly, Greece is not connected to any other member in Phase 2 which reveals that its debt market no longer belongs to the same asset class with others and currently stands as an outlier in the EMU. Interestingly, simplystable networks show that the decrease in the overall correlation level in Phase 2 is not due to a general sparsity of EMU members. It is more likely to be

> 7The size of the circles in the figures do not have any economic interpretation and are chosen for optimal allocation purposes when constructing networks.

10
# Figure 4. Occupational wage inequality, average level of protection and mean income

(a) Occupational wage inequality and level of income (b) Average tariff rate and level of income <!-- Start of picture text --> 6 7 8 9 10 11<br>log(gdppp)<br>gini of wages, x1wu F itted values<br>.8<br>.6<br>.4<br>gini of wages, x1wu/Fitted values<br>.2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 6 7 8 9 10 11<br>log(gdppp)<br>unweighted average tariff rate f rom W B sourcesF itted values<br>100<br>80<br>60<br>40<br>20<br>unweighted average tariff rate from WB sources/Fitted values<br>0<br><!-- End of picture text --> (c ) Occupational wage inequality and average tariff rate <!-- Start of picture text --> 0 20 40 60 80 100<br>unweighted average tariff rate from WB sources<br>gini of wages, x1wu Fitted values<br>.8<br>.6<br>.4<br>gini of wages, x1wu/Fitted values<br>.2<br>0<br><!-- End of picture text --> (d) Occupational wage inequality (controlled for income) and average tariff rate <!-- Start of picture text --> 0 20 40 60 80 100<br>unweighted average tariff rate from WB sources<br>res Fitted values<br>.3<br>.2<br>.1<br>0<br>res/Fitted values<br>-.1<br>-.2<br><!-- End of picture text --> 34
**Table 2. The Product Composition of Mercosur Countries' Intra-Trade: 1979-81**<sup>to</sup><sup>**1994.**</sup>

|||All<br>Items<br>($ million)|||Share in tot|al exports(%)|||
|---|---|---|---|---|---|---|---|---|
||||Food and<br>Feeds|Agricultural<br>Materials|Ores and<br>Metals|Mineral<br>Fuels|All<br>Manufactures|of which:|
|||||||||Transport &|
|Exporter|Years|||||||Machinery|
|Argentina|1994|4,803.2|36.0|2.6|0.7|16.3|44.3|23.6|
||1993|<br>3,684.0|34.3|0.8|0.7|18.4|45.7|24.1|
||1990-92|<br>2,045.5|44.1|_1.5_|1.2|9.0|44.2|16.4|
||1984-86|739.3|48.7|1.8|_1.5_|11.8|36.2|10.5|
||1979-81|1,112.2|51.3|0.8|1.1|9.3|37.5|14.8|
|Brazil|1994|5,920.0|11.2|0.9|2.8|3.6|81.3|39.6|
||1993|5,393.7|10.6|1.1|3.2|3.1|81.7|40.8|
||1990-92|2,564.2|9.7|1.2|_5.5_|1.4|82.1|38.6|
||1984-86|<br>1,175.4|13.1|3.3|9.1|4.9|69.5|24.2|
||1979-81|1,588.1|14.4|3.9|5.0|5.9|70.8|34.6|
|Paraguay|1994|424.8|41.2|38.8|0.5|_0.5_|19.1|1.7|
||1993|287.3|23.2|58.9|_0.5_|0.6|16.9|1.0|
||1990-92|295.0|32.8|56.2|0.2|0.6|10.5|0.3|
||1984-86|105.4|48.8|46.1|-|--|5.1|-|
||1979-81|116.7|36.2|45.7|0.2|0.3|17.6|-|
|Uruguay|1994|895.7|39.1|2.0|0.8|0.0|57.7|23.9|
||1993|661.3|39.7|2.4|0.7|0.1|56.8|18.4|
||1990-92|565.6|45.0|3.3|0.9|0.1|50.4|6.9|
||1984-86|270.8|51.0|4.4|0.4|1.0|41.8|5.3|
||1979-81|312.2|49.5|1.7|1.8|--|46.4|9.1|
|MERCOSUR|1994|12,043.6|24.2|3.0|1.7|8.3|62.6|30.7|
||1993|<br>10,026.3|21.6|2.7|2.0|8.5|65.0|32.1|
||1990-92|<br>5,482.0|27.4|4.5|3.1|4.1|60.8|25.0|
||1984-86|2,290.9|30.7|4.9|5.2|6.4|52.5|16.4|
||1979-81|3,129.2|31.8|4.1|3.1|6.3|54.6|23.7|

Source: Computed from UN Comtrade records.
## **2.2 Cost benefit analysis**

The USDA subsidizes the uptake of no-till agriculture through the Environmental Quality Incentives Program (EQIP). This program is designed to encourage farmers to take up conservation farming practices through technical and financial assistance. No-till agriculture is one practice that it encourages but reductions in air pollution are not given as a reason supporting the subsidization of no-till specifically.<sup>33</sup> Despite disagreement about no-till’s carbon benefits, sequestration is offered as a benefit of the subsidies. Comparisons of the costs of subsidizing no-till and the potential carbon benefits have suggested that EQIP payments may be a cost-effective way of encouraging carbon sequestration relative to other options.<sup>19</sup> We use our estimates of the potential mortality benefits of reduced air pollution associated with the uptake of no-till to estimate whether increases in EQIP payments could be justified due to air pollution benefits. The USDA NASS reports that in 2017 there were just over 28 million hectares of land in corn and soybean production in the states we study. We focus on these crops as they are the largest crops by area planted in the region. If we assume that every hectare planted in these crops utilized no-till approaches and received an equal subsidy payment our estimated benefits of 1.25 billion USD suggest that the air pollution benefits alone would justify an EQIP payment of $43 per hectare. That represents somewhere between 50% and 120% of the current EQIP payments for using no-till in these states.<sup>19</sup> These results suggest that the air pollution benefits of adopting no-till may represent a substantial portion of the monetary benefits of adopting the practice. Our estimated benefits implicitly assume that all farmers would take-up no-till agriculture at these higher subsidy rates. This may not be true, as the share of farmers using no-till increases, the cost of encouraging the marginal farmer to adopt it is likely to increase and may surpass these subsidy rates. We leave estimation of the total cost of achieving 100% adoption of no-till to future research. Even if 100% adoption cannot be achieved our results suggest that inclusion of air pollution benefits would support higher EQIP payment rates.

# **3 Discussion**

Adoption of no-till agricultural practices has benefits for erosion and soil health, and it may have carbon benefits as well. Less well understood are the potential benefits from reducing local air pollutants. We show here that adoption of no-till throughout the U.S. Corn Belt is associated with declines in local PM2 _._ 5 levels. The implied reductions in mortality due to these reductions in pollution suggest that the monetary benefits of adopting no-till may be substantially higher than previously estimated, which have focused primarily on the environmental and carbon reduction benefits. Including benefits from reduced mortality due to lower air pollution could raise these 10
presidential elections since the independence of Guinea. Following the presidential elections, the new regime has undertaken several reforms, including the adoption of a new mining code in 2011, the construction of Kaleta dam, as well as the implementation of an Extended Credit Facility with the International Monetary Fund, launched in January 2012. Moreover, the country reached the completion point under the Heavily Indebted Poor Countries program in September 2012. Thus, for a deeper analysis, we split the period 2006-15 into two periods of five years each: 2006-10 and 2011-15.

The main findings show that structural change has made a positive contribution to productivity growth, during each of the two periods under consideration. However, the contribution of structural change to productivity growth has declined over time. While structural change has contributed to productivity growth to 0.5 percentage point per year on average during the period 2006-10, that contribution was reduced by 0.2 percentage point during 2011-15. Such finding raises a question about the driving factors of the dynamics of structural change in Guinea.

# 4.2 What are the driving factors of the dynamics of structural change in Guinea?

# _4.2.1 Strategy of investigation_

For the analysis of the drivers of the dynamics of structural change, we rely on the findings of McMillan _et al_ . (2014), and Martins (2019). Using cross-section data over the period 1990-2005 and covering 38 countries (including 9 SSA countries), McMillan _et al_ . (2014) find that three factors significantly affect structural change. These include: (i) natural resources abundance, (ii) currency undervaluation, and (iii) labor market rigidity. Natural resources abundance negatively affects structural change because the mining sector does not generate much employment, unlike manufacturing industries. Likewise, labor market rigidity does not facilitate structural change. Structural change is facilitated when the existing regulatory framework allows the easing flow of labor across sectors. Finally, countries that maintain competitive or undervalued currencies tend to experience more growth enhancing structural change, because undervaluation acts as a subsidy for tradable and modern industries and facilitates their expansion (Rodrik, 2008). As for Martins (2019), he runs econometric regressions with a sample of 169 countries (including 47 countries in Africa) to highlight the importance of human and physical capital for structural change. Skilled and healthy workers are more likely to develop the right capabilities to move to higher-productivity 16