rafmacalaba/datause-encoder-data huggingface dataset:
defined (e.g., arithmetically or geometrically)? Should we look at the changes in absolute numbers or the changes in relative numbers over time, or both (i.e., the absolute number of the poor in the population or the percentage)?

The importance of employing appropriate metrics to monitor progress cannot be overemphasized.<sup>11</sup> We turn to discussing some of these issues with concrete data. Table 3 provides the latest poverty numbers using the international poverty line of $1.9/day in 2011 PPP from the WDI database for four countries: Brazil, China, Ethiopia, and India (i.e., 2015 for the first three countries and 2011 for India). Judging by the headcount poverty rate (Table 3, Panel A, Row 1)— or the percentage of the population who are poor—China at 1 percent and Brazil at 3 percent have far lower poverty rate than the 21 percent for India, and 27 percent for Ethiopia. Brazil and China are thus strong performers in terms of having a smaller percentage of their population that are poor. Yet, these four countries vary widely in population size, which leads to the interesting result that 1 percent of the population in India (12 million) is even larger than 10 percent of the population in Ethiopia (10 million) (Panel A, Row 5). Consequently, although Ethiopia’s poverty rate is onefourth higher than India’s, its (absolute) number of poor is just around one-tenth that of the latter (Panel A, Row 2). Similarly, Ethiopia has more than 20 times the poverty rate of China, but has only three times as many poor.

As such, while driving down the poverty rate by one additional percentage point may represent similar progress on this metric for each country, when translated into the number of people living

> 11 Indeed, Easterly (2009) famously points out that, depending on whether we target a relative change or an absolute change in poverty rates (or primary school enrolment), Africa’s performance with the MDGs can be viewed as a success or a failure. He also highlights the importance of distinguishing between change targets versus level targets, as well as positive indicators versus negative indicators. Fukuda-Parr, Greenstein, and Stewart (2013) similarly argue that the criterion of success with the MDGs should focus on the pace of progress rather than on achieving the targets. Applying an alternative measurement method based on this criterion, they find that African countries outperformed global averages in their progress toward achieving the MDG targets. We offer more discussion on the technical details in Appendix 1 (Part A).

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30 <!-- Start of picture text --> (a) Rice (b) Sugar (c) Milk (d) Beef (e) Cotton<br>(51 countries, (46 countries, (36 countries, (47 countries, (19 countries,<br>global WRI = 140.9) global WRI = 86.7) global WRI = 72.8) global WRI = 68.1) global WRI = 44.7)<br>US<br>Germany<br>France Japan<br>Colombia Sudan<br>Japan France<br>Lithuania Korea<br>UK Italy<br>Taiwan Italy Japan Germany<br>Japan Indonesia US UK US<br>Vietnam India Switzerland Poland Turkey<br>Korea Spain Canada Turkey Nigeria<br>US Bangladesh India Spain China<br>China Pakistan Germany Mexico Egypt<br>India Turkey France Slovenia Zimbabwe<br>0 20 40 0 5 10 0 50 0 20 40 0 50<br><!-- End of picture text -->

**Figure 3. Country Share of the Global Commodity-Specific WRI for Rice, Sugar, Milk, Beef and Cotton, 2000–04**

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).

Note: The decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100. Focus countries have been omitted from the above charts if their decomposition share has an absolute value of less than 2 percent.
address these issues, this paper presents the results of the analysis of the biofuel sector in Argentina using a Computable General Equilibrium (CGE) model. Up to now, most of the claimed positive results of the development of this industry in the country are conjectural or based in sectoral studies (Cámara Argentina de Energías Renovables, 2009; Chisari, 2009). Our objective is to evaluate the gains and losses of the production of biofuels for Argentina, taking into account opportunity costs of resources and overall impact on economic performance.

We focus in the assessment of costs and benefits in an economy which can be characterized by the following stylized facts. Firstly, biofuels are already being produced, but there are clear differences between biodiesel and bioethanol in terms of development of the industry and competitiveness (with respect to other countries, such as Brazil). Secondly, Argentina has comparative advantages for several agricultural products at the international level, a fact that creates opportunity costs for land use and for direct exports of crops. Also, the country has a developed oilseed industry, with potential complementarities with biofuel production. Additionally, there is a complex tax structure, that has a direct incidence on agricultural exports, and that is subject to changes that accommodate fiscal results and the need of sustaining a positive trade balance. Finally, the cost of capital has been structurally high – basically due to the country risk component – and has discouraged investments in general and biofuel projects in particular.

The plan of the paper is the following. In section 2, we summarize the basic facts of the biofuels industry in Argentina. Then, the third section presents the database required for implementing the CGE model, organized in a Social Accounting Matrix. Most of the sectors in the value chain of bioethanol and biodiesel are disaggregated and introduced explicitly. After that, we discuss the main features of the CGE model (section 4) and we conduct several counterfactual experiments, in section 5, to study the response of the biofuel industry to policy shocks and to changes in international prices, as well as to appraise the reaction of the economy and of industries related to biofuels via substitution or complementarity relations. The final section concludes with main lessons obtained from the analysis.

# **2 THE BIOFUEL INDUSTRY IN ARGENTINA**

Oilseeds production has been growing in Argentina since the late 1980s. This trend corresponds to a long-term path that accelerated in the last five years. Production growth and area expansion were 3
# **<u>Table B3. Distribution of tax deducted pension saving, 2003.</u>**

||**Percent**<br>Men and<br>Women|**of persons ut**<br>**deduction**<br>Men|**ilizing a**<br>Women|**Average**<br>**deduc**<br>Men and<br>Women|**amount of p**<br>**tion, 1000 k**<br>Men|**remium**<br>**ronor**<br>Women|
|---|---|---|---|---|---|---|
|Age|||||||
|-24 år|7.50|7.60<br>|7.40|1.90|2.00|1.70|
|25-34|38.50|36.40|40.80|3.20|3.50|2.90|
|35-44|45.50|41.30|49.80|4.80|5.40|4.30|
|45-54|45.20|38.90|51.70|7.10|8.10|6.40|
|55-64|38.60|33.40|43.80|9.60|10.80|8.60|
|65-|2.40|3.20<br>|1.90|12.60|14.60|9.90|
|20-64|39.40|35.30|43.50|6.00|6.70|5.50|
|Income class,<br>1000kronor|||||||
|1 kr - 99.9|5.90|4.60<br>|6.80|3.60|3.80|3.50|
|100.0 - 119.9|10.50|7.00|11.90|4.10|4.30|4.00|
|120.0 - 139.9|14.10|6.80|18.40|4.10|4.40|4.10|
|140.0 - 159.9|18.50|7.60|26.40|4.20|4.40|4.20|
|160.0 - 179.9|25.10|11.90|34.90|4.30|4.40|4.20|
|180.0 - 199.9|31.00|17.90|40.70|4.30|4.30|4.40|
|200.0 - 219.9|36.20|24.90|46.00|4.40|4.20|4.50|
|220.0 - 239.9|40.90|32.40|49.90|4.60|4.20|4.80|
|240.0 - 259.9|43.50|37.10|52.40|4.90|4.40|5.30|
|260.0 - 279.9|45.90|40.50|54.90|5.30|4.70|5.90|
|280.0 - 299.9|47.50|42.20|57.00|5.90|5.30|6.70|
|300.0 - 339.9|50.00|45.30|59.40|6.90|6.30|7.70|
|340.0 - 399.9|52.20|48.20|61.80|8.00|7.70|8.60|
|400.0 - 499.9|54.00|50.90|63.60|9.90|9.80|10.40|
|500.0 - 999.9|56.80|54.70|66.20|15.00|15.00|14.90|
|1000.0 -|45.90|46.70|40.50|26.40|26.90|22.20|

Source. Statistics Sweden.

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# **Mangroves as Protection from Storm Surges in Bangladesh**

Susmita Dasgupta* Lead Environmental Economist, Development Research Group, World Bank, United States Md. Saiful Islam Specialist, Coast, Port and Estuary Management Division, Institute of Water Modeling, Bangladesh Mainul Huq Consultant, World Bank, Bangladesh Zahirul Huque Khan Director, Coast, Port and Estuary Management Division, Institute of Water Modeling, Bangladesh Md. Raqubul Hasib Specialist, Coast, Port and Estuary Management Division, Institute of Water Modeling, Bangladesh October, 2017

## **This research was conducted under the South Asia Water Initiative - Sundarbans Landscape**

* Corresponding Author: Susmita Dasgupta. Email: sdasgupta@worldbank.org; Tele phone: 1-202-473-2679; Fax: 1-202-522-2714

## **JEL Classification:** Q54; Q57

We are thankful to Polly Means for her help with the graphics. We would also like to extend our special thanks to Md. Istiak Sobhan for his guidance on selection of mangrove species and Sharifuzzaman Choudhury for his help with the literature review. We are also grateful to Sunando Bandyopadhyay, Brian Blankespoor, Abedalrazq F. Khalil and Glenn Marie Lange for their useful comments and suggestions.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Does Food Aid Depress Food Production? The Disincentive Dilemma in tho African Context by Victor Lavy*

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Food Aid and Food Production|3|
|III.|The Empirical Framework|5|
|IV.|Estimation and Yypothesis Testing|10|
||Food Production<br>Food Aid|12<br>15<br>|
||irameter Estimates|17|
|V.|Interpretation and Conclusion|22|
|Refer|ences|27|

* This paper was written under the auspices of the<sup>African Food</sup> Security Unit of the World Bank, as a background paper<sup>to the</sup><sup>_Food Aid_</sup> _in Sub-Saharan Africa_ study. I would like to thank Shlomo Reutlinger for his assistance and useful insight and comments<sup>on the paper.</sup> I would also like to thank the World Bank Research Committee<sup>for financial</sup> support.

Victor Lavy, currently at the Department fo. Population<sup>and Human</sup> Resources of the World Bank, is on leave from the Department of Economics at the Hebrew University of Jerusalem.
Table 1: Importers vs. non-importers of capital goods: Importer of capital goods premia The dependent variable is described in each column

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Sales|Capital|Wage-bill|Profts|Imported inputs share|
|Importer of capital goods|1.356***|1.517***|1.350***|1.558***|0.085***|
||(0.043)|(0.045)|(0.049)|(0.051)|(0.014)|
|Year fxed effects|Yes|Yes|Yes|Yes|Yes|
|Industry fxed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|14,680|14,647|14,680|11,945|14,680|
|R-squared|0.224|0.283|0.202|0.214|0.005|

_Notes_ : The dependent variable is described in the head of each column, all of those variables are expressed in logarithm terms except by the share of imported inputs. The table shows regressions of each firm performance measure on a dummy variable equal to one if the firm imports capital goods in year _t_ and zero otherwise. All regressions include industry and year fixed effects. Heteroskedasticity-robust standards errors clustered at the firm level are reported in parentheses.

_Empirical fact 2: Firms producing with foreign technology embodied in imported capital goods have_ _larger sales, capital stock, are more profitable and have a higher share of imported inputs than nonimporters of capital goods._ We also observed that imports of intermediates and capital goods are positively correlated. Firms that import intermediate inputs have a greater probability to source capital equipments from foreign countries. Table 2 presents a set of simple estimations of the probability of importing capital equipment goods as a function of firms’ imported inputs intensity (the ratio of imported inputs over total inputs). We look at the relationship between the decision to import capital goods and the imported input intensity 8
cultivated, owned and otherwise possessed field (also called plots) of the households. It is these plots which are our unit of analysis from which we generate (weighted) district-level statistics. The dependent variables are defined as a percentage of the rented in acreage or rented in plots at a district level. For each plot we have information on the location (within the village or not), soil type (soil texture), land use, acreage, irrigation facilities/flood frequency, as well as the ownership and rental agreement details.<sup>10</sup> Our analysis builds on plot-level data from the tenant side of the market, as all tenants, even landless tenants, are considered in the sampling process.

# **Migration: Census of India**

We use bilateral district-to-district migration data obtained from the 2001 and 2011 Population Censuses of India. The Census is the only data source with information on both the source and origin district of migrants for all the districts of India.

The Census, which is conducted every decade, asks every individual if the place in which they are enumerated during the census is different from their “last usual place of residence”. As in Kone et al. (2018), we consider a person a migrant if they reply in the affirmative.<sup>11</sup> Additional follow up questions allow us to capture bilateral migration flows. These include the location (district) of the last place of residence, reason for migration (marriage, education, employment etc.), and the duration of stay in the current residence since migration. The census also includes information on the sex, education and age of each individual (but notably not land ownership).

Since the census does not provide unit-level data, under a special administrative agreement we requested them to provide us with aggregated data on the volume of migration between every pair of districts. For a given pair 10We omit plots which are categorized as non-agricultural, and might contain lakes, forest, or other types of lands, in the analysis. We also drop plots which are located outside of the district, when this information is available.

> 11The previous residence does not necessarily imply the residence of the previous census, but can be a residence of 3 years ago, 7 years ago, more than 10 years ago, or even during childhood or shortly after birth - all will be considered migrants.

10
important drivers of infiltration. Much of the literature examining infiltration rates has focused on the United States, where infiltration rates estimates are concentrated between 0.2 and 0.3 (Bi et al., 2021; Liang et al., 2021; Lunderberg et al., 2023).<sup>2</sup> Understanding how infiltration rates may differ in developing countries is critical as these are the parts of the world facing the greatest threats from high levels of ambient air pollution (Shaddick et al., 2020; Fuller et al., 2022). This also contributes to our understanding of how vulnerability to air pollution varies across the income distribution (Behrer and Heft-Neal, 2024). There also remains disagreement about the extent to which infiltration rates vary across SES status. Some studies show notable inequality in infiltration rates during high pollution events (Krebs and Neidell, 2024) and others find no meaningful relationship between infiltration rates and SES (Burke et al., 2021). However, these studies often use crowd-sourced information from publicly available PurpleAir monitor data, a non-random sample of households having bought PurpleAir monitors. In our experiment, we randomly installed monitors in the city and provided them to households that are representative of the broader population.

# **2 Context and Design**

## **2.1 Air pollution in Tbilisi**

Located between Armenia, the Russian Federation, and Azerbaijan, Tbilisi is the capital of the Republic of Georgia and a city of roughly 1.1 million people. Like many cities in the former Soviet Republics, Tbilisi suffers from elevated average levels of ambient air pollution. From 1998 to 2018, average monthly PM2 _._ 5 levels were approximately 20 _μg/m_ 3 (Van Donkelaar et al., 2019) in Tbilisi, 4x the WHO recommended annual average of 5 _μg/m_ 3.<sup>3</sup> While the level of ambient pollution in Tbilisi is low relative the levels seen in the worst cities in the world (e.g. Delhi), it is similar to levels seen in hundreds of medium and large cities around the world and are high enough to lead to significant negative health consequences for residents.

In Tbilisi, levels of pollution are driven by the geography of the city. The center of Tbilisi sits in a topographic bowl, with high mountains to the West, South and Northeast (Figure 1A). This results in population, wealth, and pollution being concentrated in the central and lower elevation

> 2Although higher (0.4-0.6) (Krebs et al., 2021) and lower (0.14) rates (Burke et al., 2021) have also been estimated.

> 3In recent years this level has fallen, with average levels slightly more than 3x the WHO recommendation in 2023.

5
- 22 -

The viability of any credit organization is strongly linked to its success in recovering loans. Strong punitive measures and a proper set of incentives ought to be implemented to induce high repayment rates; those elements seem to be lacking in most credit cooperatives, thus the high failure rates. Most empirical studies of credit group programs in rural areas in developing countries report low recovery rates. Defining default or non-performing loans as those loans that are still on the books, but are past due by 90 days or more, are non-accruing, or have been renegotiated, those studies have indicated default rates ranging from 20 percent to 95 percent for credit programs in Africa, the Middle East, and Latin America. Similar results have been reported in South and Southeast Asia, (see Braverman and Guasch, 1986). To some extent, the reasons for those high levels of default can be attributed to a lack of properly designed incentive schemes, lack of enforcement procedures and quite often to a self-serving confusion on the farmer's part regarding the nature of credit. It is not unusual for them to perceive the loans as grants or welfare. In fact, in some South Asian languages the word used for loans from government institutions (tagai, taccari) means assistance, grant". Thus the reluctance to repay those loans should not be surprising.

# CONCLUSIONS

We have presented an analysis of the motivation, formation and design of credit cooperatives. We have shown that they can provide significant advantages to their members, insofar as their intzinsic informational and moral hazard problems are properly accounted for. Particular care to the design of incentive mechanisms is warranted if credit cooperatives are to prove successful. The incentive schemes, degree of control, enforcement and information gathering by the credit
# **TABLE 3**

||Residential<br>Conn.per<br>Employee|Energy Sold<br>per<br>Employee|Distribut.<br>Losses|Coverage|Energy Sold<br>per Conn.|Duration<br>of<br>interrupt's|Frequency<br>of<br>interrupt's|OPEX per<br>Connection<br>(in dollars)|OPEX per<br>MWH sold<br>(in dollars)|Avg Resid.<br>Tariff<br>(in dollars)|Avg Indust.<br>Tariff<br>(in dollars)|Cost<br>Recovery<br>Ratio|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Dummy Transition of|0.181***|0.231***|0.014|-0.008***|0.047***|-0.124***|-0.063*|-0.484**|-0.458**|0.113***|0.037**|0.024|
|PSP|(0.013)|(0.015)|(0.012)|(0.002)|(0.004)|(0.027)|(0.033)|(0.224)|(0.230)|(0.015)|(0.019)|(0.059)|
|Dummy Post Transition|0.067***|0.024**|-0.141***|0.004***|-0.002|-0.356***|-0.396***|-0.216***|-0.175***|0.013|-0.044**|0.194***|
|of PSP|(0.009)|(0.012)|(0.012)|(0.001)|(0.005)|(0.022)|(0.021)|(0.038)|(0.046)|(0.010)|(0.020)|(0.050)|
|Utility FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Utility Specific<br>Time trend|Yes|Yes|No|Yes|No|No|No|No|No|No|No|No|
|Observations|2000|1981|2073|1323|2515|1056|947|864|873|1728|840|669|
|Number of utilities|199|198|190|144|213|144|132|131|131|175|90|103|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

- 15 -
- Poison Control Center, Vietnam Ministry of Health, 2000.  Statistics reported in _Vietnam Country Report, The 7_<sup>_th_</sup> _GINC Conference, Tokyo, April 18-21, 2001_ . Available at: <u>http://www.nihs.go.jp/GINC/meeting/7th/7profile/vietnam.pdf</u>

- PPD (Plant Protection Department), 2000, Report on Results of Nation-wide Inspection of Pesticide Regulation Enforcement in 2000, Large-scale Inspection Meeting for 2000 in Hanoi, 31 October 2000.

- Rosenstock, L., Keifer, M, Daniell, W. E, McConnell, R., Claypoole, K. 1991. Chronic Central Nervous System Effects of Acute Organophosphate Pesticide Intoxication. _Lancet_ 338: 223-227.

- Xuyen, K., N. C. Hoi and P. Q. Trung. 1998. Occupational Environment and Skin Diseases in Pesticide Exposed Subjects in some Tea Farms in Vietnam. Presentation given at the Third National Scientific Conference on Occupational Health, December 4- 5, 1998. Hanoi.

- World Health Organization (WHO). 1990. _Public Health Impact of Pesticides Used in Agriculture, 1990._

- World Resources Institute, UNEP, UNDP, the World Bank. 1998. Environmental Change and Human Health. _World Resources 1998-99._

- Zahm, S. H., Ward, M. H., Blair, A., 1997. **Pesticides and cancer** . In: Occupational Medicine: State of the Art Reviews. Vol. 12: Pesticides (Keifer M, ed). Philadelphia: Hanley and Belfus, Inc., 269-289.

13