rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Slope = -0.02 [s.d. = 0.11] Slope = 0.19 [s.d. = 0.11]<br>-3 -2 -1 0 1 2 -2 -1 0 1 2<br>Integrity Index - Average Bureaucrats Risk Aversion - Average Bureaucrats<br>(a) Integrity Index (b) Risk Aversion<br>Slope = 0.11 [s.d. = 0.11] Slope = -0.06 [s.d. = 0.11]<br>-4 -2 0 2 4 -1 0 1 2 3<br>Cognitive index - Average Bureaucrats Choose dominated risk option - Average Bureaucrats<br>(c) Cognitive Index (d) Dominated Risk Option<br>Slope = 0.25 [s.d. = 0.11] Slope = 0.06 [s.d. = 0.11]<br>-2 -1 0 1 2 3 -3 -2 -1 0 1 2<br>Altruism - Average Bureaucrats Non-Cognitive Index - Average Bureaucrats<br>(e) Altruism (f) Overall Non-Cognitive Index<br>Slope = -0.01 [s.d. = 0.11] Slope = -0.23 [s.d. = 0.11]<br>-2 -1 0 1 2 -2 -1 0 1 2 3<br>Big Five Index - Average Bureaucrats PSM Index - Average Bureaucrats<br>(g) Big Five Personality index (h) PSM index<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br><!-- End of picture text --> Figure 7: DLT Score vs. Selection Traits - Bureaucrats _Note:_ The graphs present scatter plots, at the District Level, between the residual of a regression of DLT Score on District poverty rates, and selection traits of interviewees.

51
**Figure 14. Impact on child survival inequalities of Bangladesh's Matlab MCH program**

<!-- Start of picture text --> 100% v<br>80%<br>960% -< IU 60% - ~~~~~~~~~~~~~~~~equality<br>-//1982 mch<br>- 1982 no mch<br>E840%-4 1996 mch<br>.- RI ~ ~ ~~~~~~~~~~~~~~~~-1996 no inch<br>75<br>E<br>U<br>20%-<br>40% 2% 4% 6% 8% 10<br>0%<br>0% 20% 40% 60% 80% 100%<br>cumul % of live births<br><!-- End of picture text --> _Source:_ Bhuiya and others (2001).

# _Benefit-incidence studies_

Another type of investigation that can prove useful in assessing the impact of antiequity policies on health inequalities, namely to see how different income groups fare in terms of receipt of publicly financed health services-a type of analysis known as Benefit Incidence Analysis (BIA). Some services may be wholly publicly financed-e.g., immunization. In such cases, the distribution of usage of these services provides an indication of the distribution of government spending on the program in question. Services such as immunization are also easy to deal with insofar as the benefits-in terms of health improvements generated by the program-are likely to be fairly homogenous across users. Trying to assess who benefits from public spending on the health sector more generally is much harder. Not everyone will benefit equally-in terms of expected health gain-from a particular type of visit or episode of treatrnent. In an early study of the British NHS, for example, it was found that general practitioners spent longer per consultation with patients from the higher occupational classes than the lower occupational classes. It is not obvious that this means the health gains per consultation were also higher, but it serves as a warning to consider the possibility that different income groups might benefit differently from what seems on the face of it to be an identical unit of utilization. In practice, this complication is typically sidestepped in BIA studies and each type of service (primary care consultation, outpatient visit, inpatient day, etc.) is valued at its resource cost. There is another problem when moving to the more ambitious task of assessing the distribution of the overall benefits of public expenditure on _32_
networking (World Bank’s Global Antidumping Database, 2012). Indonesia applies safeguards to a larger set of products, for example, lighters, glass, aluminum foil food containers, wire of iron, cotton yarn, woven fabrics of cotton, tarpaulins, articles of iron or steel wire, conveyor belts, articles of finished casing and tubing, polypropylene in granule form, and mackerel. The rest of Eurasia’s trading partners considered in this note do not apply any safeguards against Eurasia. Countervailing measures against the region are absent from China, Japan, and the EU.

Table 14. Antidumping Investigations Source: World Bank’s Global Antidumping Database (2012). Data available up to 2011.

# **Preferential Trade Agreements**

Following the dissolution of the Soviet Union in 1991, the economies of Eurasia concluded a plethora of trade agreements with countries throughout the world but primarily among themselves in an effort to maintain the economic integration of the region. The result was a ‘spaghetti bowl’ of trade agreements. In fact, 31 out of the 39 bilateral free trade agreements (FTAs) that Eurasian economies have concluded are between two countries in the region, and 26 of these entered into force in the 1990s. This is in addition to four plurilateral regional trade agreements (RTAs) concluded solely between Eurasian countries with another in early announcement. (See Appendix for the list of RTAs and Figure 3 for a pictorial representation of the ‘spaghetti bowl’ effect.) In addition, other regional organizations have been used as platforms to build regional integration and trade facilitation initiatives.<sup>9</sup> Bilateral and plurilateral RTAs also exist between Eurasian countries and parties outside the region, although Moldova and Ukraine have been the most successful in negotiating these. Many are in early announcement, suggesting Eurasian countries are just beginning to shift their focus for preferential trade initiatives to outside the region.

Trade agreements can potentially result in trade creation through tariff reductions, trade diversion from third parties through increased preference margins, and export market diversification within and outside the region. In the case of Eurasia, most of the RTAs are not fully implemented or enforced, have narrow coverage, overlapping membership, and complex origin requirements. Trade policies can change frequently and are unpredictable for non-WTO member countries in Eurasia. This creates barriers to

> 9 For example, Armenia, Azerbaijan, Georgia, Moldova, Russia, and Ukraine are members of the Black Sea Economic Cooperation Organization, established in 1992, together with Albania, Bulgaria, Greece, Romania, and Turkey. Other examples include: Collective Security Treaty Organization (1992), Union State of Russia and Belarus (1997), CAREC (1997), Shanghai Cooperative Organization (2001), and The Central Asian Cooperation Organization (2002-5).

17
Cawley, J., Heckman, J. and E. Vytlacyl (1998). Cognitive Ability and the Rising Return to Education. NBER Working Paper 6388, National Bureau of Economic Research, Cambridge, MA.

Devroye, D. and R. Freeman. (2001). Does Inequality in Skills Explain Inequality of Earnings Across Advanced Countries? NBER Working Paper 8140.

- Farber, H., Gibbons, R., 1996. Learning and wage dynamics. _The Quarterly Journal of Economics_ 111 (4), 1007-1047.

Finnie, R. and R. Meng (2001). Cognitive Skills and the Youth Labour Market. _Applied Economics Letters_ 8 (10): 675-679.

Gintis, H. (1971). Education, technology, and the characteristics of worker productivity. _American Economic Review_ 61 (2): 266-279.

Green, D., and C. Riddell (2003). Literacy and Earnings: An Investigation of the Interaction of Cognitive and Unobserved Skills in Earnings Generation. _Labour Economics_ 10 (2): 165-184. Hansen K., Heckman, J. and K. Mullen (2003). The Effect of Schooling and Ability on Achievement Test Scores. _Journal of Econometrics_ 121(1-2): 39-98.

Hanushek, E.A. and L. Zhang (2006). Quality-Consistent Estimates of International Returns to Skill. NBER Working Paper No. W12664.

Ingram, B. and G. Neumann (2005). The Returns to Skill. _Labour Economics_ 13(1): 35-59.

Ishikawa, M, and D. Ryan (2002). Schooling, Basic Skills and Economic Outcomes. _Economics of Education Review_ 21(3): 231-243.

Jolliffe, D. (1998). Skills, Schooling and Household Income in Ghana. _World Bank Economic Review_ 12 (1): 81-104.

21
|published from 1990 to<br>2017 (Bai, et al. 2019)|||conservation tillage<br>(5%).|
|---|---|---|---|
|Crop residue utilization,<br>crop rotation, and<br>minimum tillage in<br>Malawi and Zimbabwe<br>(Makate, et al. 2019)|Increased cereal<br>productivity by 12%-<br>18%|||
|Minimum tillage,<br>mulching with crop<br>residue, and crop<br>rotation in Zimbabwe<br>for a multi-cropping<br>system that includes<br>maize, sorghum, millet,<br>groundnut, and cowpea<br>(Michler, et al. 2019)|No yield gains compared<br>to conventional practices<br>under normal rainfall|Effective in mitigating<br>the negative impacts<br>of low and high<br>rainfall shocks; the<br>returns turn positive<br>after a more than one<br>standard deviation in<br>rainfall||
|No-till management by<br>combining a global<br>dataset of 178<br>experimental sites (Ogle,<br>Alsaker, et al. 2019)|Reduced soil erosion|Helped adapt to<br>climate change and<br>ensure food security|Mixed evidence on<br>soil organic C (SOC)<br>storage: Higher SOC<br>stocks in the surface<br>topsoil at depths less<br>than 20 cm and lower<br>SOC at depths greater<br>than 20 cm; worked<br>better in warmer and<br>wetter climates than<br>drier and cooler<br>climates|
|Rainwater harvesting<br>techniques in 180|Increased the value of<br>agricultural production<br>by 0.12 to 0.15 standard|An average of 0.3<br>hectares of previously||

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face‐to‐face interview time to less than one hour. While these results are encouraging, the rapid consumption methodology has some limitations.

The rapid consumption questionnaire varies comprehensiveness and order of items in the consumption module between households. The effect of a response bias due to this neither can be estimated from the simulations nor from the data collected in Mogadishu. However, an enhanced design with different optional modules varying in their comprehensiveness of items can shed light on this bias. Comparison between responses for the same item in a comprehensive and an incomprehensive list would indicate a lower bound for response bias. Assuming that the context of a comprehensive list is a better estimate, the response bias could be corrected for.

The rapid consumption survey methodology can increase the gap between capacity at the enumerator level and complexity of the survey instrument. Capacity at the enumerator level is often low in developing countries – especially in a fragile context. The rapid consumption survey methodology increases the complexity of the questionnaire, which can further increase the gap between existing and required capacity at the level of enumerators. However, CAPI technology can seal off complexity from the enumerator, as software can automatically create the consumption module based on core and optional modules for each household without showing the partition to the enumerator. In Mogadishu, advanced CAPI technology was used generating the questionnaire automatically based on the assignment of the household to an optional module. While enumerators were made aware that different households will be asked for different items, administering the rapid consumption questionnaire did not require any additional training of enumerators beyond standard consumption questionnaires.

Analysis of rapid consumption survey data requires high capacity. Analysis capacity is usually limited in developing – and especially fragile – countries. While the general idea of assignment of optional consumption modules to households will be digestible by local counterparts, poverty analysis based on a bootstrapped sample of the consumption distribution is likely to overwhelm local capacity. However, even standard poverty analysis is often out of limits for local capacity in fragile countries. Therefore, capacity building usually focuses on data collection skills with a long‐term perspective to increase data analysis capacity. In addition, the rapid consumption survey methodology might be the only possibility to create poverty estimates in certain areas, for example Mogadishu.

The results of the ex‐post simulation and the application in Mogadishu suggest that the rapid consumption methodology can be a promising approach to estimate consumption and poverty in a cost‐efficient and 17
**Figure 6: Ω value assuming 40% as poorest HH**

<!-- Start of picture text --> Poverty line<br>100%<br>A<br>Ω= A / B<br>B<br>0% 40% 100%<br>Households ordered from poorest to richest<br>% of accumulated benefits<br><!-- End of picture text --> Another approach to measure the distributional impact of policies —and complementary to the relative benefit curves— is the estimation of the errors of exclusion and inclusion. The error of exclusion of a subsidy is the percentage of the target population of the policy (poor households) who do not receive any benefit. The error of inclusion is the percentage of benefiting households who should not be receiving this benefit (do not belong to the target population) and represents the leakage of resources of the policy to non deserving households.

Figure 7, taken from Foster (2005), illustrates these concepts graphically. The population is first divided between poor (P) and non-poor households (NP) and beneficiaries (that is households that receive the subsidy) are divided between those who should receive the benefit due to their poverty condition (PB) and those who should not receive it (NPB). The errors of inclusion are the non-poor who receive the benefit (NPB) over all beneficiaries of the policy (PB+NPB). The errors of exclusion are the poor who do not receive any benefit (P – PB) over the total number of poor households (P).

To calculate the errors of inclusion and exclusion households must first be classified as poor and non-poor. Usually this is done by defining households in the first few quintiles of the income distribution as poor. However, this is somewhat arbitrary. An interesting alternative is to present the information graphically so that these errors can be determined for whatever poverty line the analyst wishes to use.

28
## **3.3.2 Debt forbearance: regulator offer**

The second of our debt forbearance treatments, which was assigned to a total of 1,829 contacted borrowers, is identical to the first, with the exception that the offer is framed as being extended as the result of regulatory guidance, rather than the generosity of the lender. In this treatment, the offer was introduced as follows: _We are extending an offer in accordance with guidelines issued by the Reserve Bank of India to all lending institutions to help customers manage their finances in the current economic crisis. As a result of guidance issued by the Reserve Bank of India, we are offering you repayment flexibility on your loan, comparable to programs offered by most other lenders._ The customer service representative then informed borrowers assigned to this treatment about the terms of the offer, which were identical to those in the relationship offer treatment condition, and asked whether they would like to pay the processing fee of Rs 350 and enroll in the program. As in the relationship offer condition, borrowers who accepted the forbearance offer were provided with additional information about the terms and conditions as part of the sign-up process. Similarly, we assigned 146 of these customers to receive the regulator forbearance offer at the slightly higher price of Rs 500 to estimate how sensitive take-up of the offer is to the processing fee.

## **3.3.3 Placebo call: friendly call**

In addition to the forbearance conditions, 2,452 contacted customers were assigned to the placebo call condition, which allows us to explore whether the response to receiving a forbearance offer could be explained by customers being treated in a more friendly way. In this treatment condition, customers received a friendly call in which a customer service representative highlighted the lender’s commitment to its customers using a script similar to the relationship offer condition. However, in the relationship placebo condition no forbearance offer is extended and customers are instead simply invited to access informational resources and offered help with navigating different payment methods.

## **3.3.4 Control group: repayment reminder**

Finally, 3,441 contacted customers were assigned to the control group, which was designed to replicate the lender’s standard repayment reminder calls. Customers assigned to this condition received a call from a customer service representative who reminded them that their payment was past due, and asked the customer to commit to a time frame for making the payment as per the lender’s standard practice. This treatment serves as the main control group in our analysis.

# **3.4 Data and Descriptive Statistics**

To measure the impact of debt forbearance on subsequent repayment behavior and borrower outcomes, we combine data from the experiment with administrative data from the lender, as well as the detailed form of credit than most external sources of credit that customers in our sample can access.

11
the empirical analysis, we group observed community attributes into two broad categories: one relates to availability of infrastructure, services and agro-climatic conditions, and the other includes variables that depict possible sorting of households (human capital, employment composition etc). To emphasize the role of opportunities, we focus on basic infrastructure such as electricity, phone, sanitation etc that most households should have access. Similarly, among household attributes, we focus on human capital that relates directly to (...rm) productivity. We estimate the amount of spatial di¤erence in welfare that can be explained by each set separately and by all di¤erent sets combined together.

We analyze spatial di¤erences in welfare in Bangladesh using two household surveys a decade apart (2000 and 2010). The study of spatial inequality in Bangladesh is interesting at least for two reasons. The country made signi...cant strides in poverty reduction between 2000 and 2010. The poverty headcount rate fell from 48.9 percent in 2000 to 31.5 percent in 2010 (World Bank (2013)). During the same period, the incidence of poverty declined more than proportionately in traditionally poorer regions, pointing to possible narrowing of welfare gaps across regions. There is also no evidence of signi...cant change in overall inequality over the same period. The narrowing of regional disparity is however contrary to the overwhelming evidence of regional divergence found in many developing countries (World Development Report (2009); Kanbur and Venables (2005); Kim (2008)). For policy makers in developing countries, it would be particularly useful to identify the factors that have in‡uenced the change in spatial inequality in Bangladesh. The second interesting feature of Bangladesh is that there is no administrative restriction on either in or out migration from any region. As much of the Bangladesh’s population share the same ethnicity, religion and language, there exist no serious ethnic or cultural barriers to internal migration either. The absence of major formal (o¢ cial) and informal (cultural/ethnic) impediments to labor mobility implies that much of the friction in labor mobility arises from lack of infrastructure (Shilpi (2011)) and lack of human capital to facilitate movement from unskilled to skilled jobs.

4
When SONAPRA was privatized in 2008, after several postponements, three new companies emerged: SODECO ( _Société de Développement du Coton_ ), with ten ginneries; ICA‐GIE ( _Industries Cotonnières Associées‐Groupement d’Intérêt Économique_ ) an industrial group comprising five societies with five ginneries; and LCB ( _Le Label Coton du Bénin_ ), with one ginnery. One third of SODECO’s shares are held by the government, one third by producers, and one third by a private company. Cotton prices are still determined in a pan‐seasonal and pan‐territorial manner.

As in most cotton producing countries in WCA, cottonseed in Benin was left largely unprocessed before 1980. Some seed was exported to Europe where there was strong feed demand from the dairy industry. During the mid‐1980s SONICOG ( _Société Nationale des Industries des Corps Gras_ ), a state‐owned oilseed processing company, began crushing limited quantities of cottonseed obtained under favorable terms from SONAPRA.

The cotton reform efforts of the 1990s affected the cotton by‐products in‐ dustry as well. In 1996, a new seed processing company, _Fludor‐Bénin_ , entered the market, building a new oil crushing facility with an investment of CFAf 3.4 billion and annual capacity of 90,000 tons. In 1997, SONICOG was privatized and two new companies were created: SHB ( _Société des Huileries du Bénin_ ), with an initial investment of CFAf 4.2 billion and annual capacity of 120,000 tons, capable of producing 19,200 tons of oil and 45,600 tons of meal; and IBCG ( _Industrie Beni‐ noise des Corps Gras_ ), with an investment of CFAf 3.5 billion with annual capacity of 40,000 tons (Table C1). However, IBCG has been idle since 2006 (also, there have been unconfirmed reports that SHB did not operate during the 2009/10 sea‐ son).

**Table C1**

**<u>Quantities of Cottonseed Crushed in Benin (tons)</u>**

||**_Labor force_**<br>|**_Capacity_**<br>||**_Seeds c_**|**_rushed (ton_**|**_s/year)_**||
|---|---|---|---|---|---|---|---|
||**_(total)_**|**_(tons/year)_**|**_2003/04_**|**_2005/06_**|**_2006/07_**|**_2007/08_**|**_2008/09_**|
|**FLUDOR**|420|90,000|72,186|58,573|65,710|47,582|70,000|
|**SHB**|310|120,000|105,000|33,000|57,000|78,000|57,000|
|**IBCG**|100|40,000|0|0|0|0|0|
|**Total**|830|250,000|177,186|91,573|122,710|125,582|127,000|

**Source** : Ahohounkpanzon and Zakariallou (2010) and Zanfongnon (2009).

In effect, Benin’s cotton by‐products industry now consists of two large companies: SHB and Fludor‐Bénin. SHB has 110 permanent and 200 temporary employees while Fludor has 250 permanent and 170 temporary employees. Both

**_Page 40_**
# **Is Uruguay More Resilient This Time? Distributional Impacts of a Crisis Similar to the 2001/02 Argentine Crisis**<sup>*****</sup>

Oscar Barriga Cabanillas<sup>†</sup> María Ana Lugo<sup>‡</sup> Hannah Nielsen<sup>§</sup> Carlos Rodríguez-Castelán<sup>**</sup> María Pía Zanetti<sup>††</sup>

**Keywords:** Business cycles, microsimulations, distributional analysis, Uruguay **JEL codes:** E32, E37, F6, I3, O54

**Sector Board(s):** Economic Policy (EPOL), Poverty Reduction (POV)

The authors would like to thank Sergio Olivieri for his indispensable advice and contributions and Louise Cord, Marina Gindelsky, Zafer Mustafaoglu, Mary Alexander Sharman and Augusto Tano Kouame for their thoughtful comments and suggested edits. The authors would also like to thank participants of a workshop on microsimulation techniques in Montevideo in April 2013 for helpful comments and suggestions. The views, findings and conclusions expressed in this paper are entirely those of the authors and do not necessarily reflect those of the World Bank, its Executive Board, or member country governments.

† Junior Professional Associate, Latin America’s Poverty, Gender and Equity Unit, World Bank. Email: <u>obarrigacabanill@worldbank.org</u> ‡ Economist, Latin America’s Poverty, Gender and Equity Unit, World Bank. Email: mlugo1@worldbank.org § Senior Economist, Latin America’s Economic Policy Unit, World Bank. Email: hnielsen@worldbank.org ** Economist, Latin America’s Poverty, Gender and Equity Unit, World Bank. Email: crodriguezc@worldbank.org †† Junior Professional Associate, Latin America’s Economic Policy Unit, World Bank. Email: mzanetti@worldbank.org