rafmacalaba/datause-encoder-data huggingface dataset:
# **Beneficiary Views on Cash and In‐Kind Payments: Evidence From Ethiopia’s Productive Safety**

**Net Programme**

Kalle Hirvonen John Hoddinott

**_Key words_** : social protection, food transfers, cash transfers, Ethiopia, PSNP

# **_JEL classification_ :** D40, I38, O12

# **_Author details_**

Kalle Hirvonen is a Senior Research Fellow at the International Food Policy Research Institute, based in Addis Ababa, Ethiopia; his email address is <u>k.hirvonen@cgiar.org. John Hoddinott</u> (corresponding author) is the H.E. Babcock Professor of Food & Nutrition Economics and Policy at Cornell University, Ithaca NY, USA; his email address is <u>jfh246@cornell.edu. Funding</u> supporting this work was received from the European Union, the United States Agency for International Development and the CGIAR Research Program on Policies, Institutions, and Markets (PIM). Data collection was conducted by the Central Statistical Agency (CSA) of Ethiopia and funded by the PSNP Donor Working Group. The authors thank Katrina Kosec and the conference participants at the Ethiopian Economic Association Conference for useful comments. The authors are responsible for all opinions and errors.
Achieving these enabling conditions can require policy interventions. Improving the shares of skilled workers and productivity requires improvements in both the quantity and quality of education services. Absorbing the new labor market entrants requires policies that facilitate job creation, like maintenance of a stable macro-economy and a business environment that empowers the private sector to hire workers (World Bank 2013). Active labor market policies may also be necessary to remove market failures, like gender discrimination. Policies to mobilize savings, such as improving financial inclusion, may also be necessary.

For the different SACU economies, the “bang for the buck” for these policies may differ, given their different economic attributes. The labor market differences between these economies can illustrate. From a labor supply perspective, labor force participation rates vary tremendously across the region, but high unemployment rates are a significant challenge for all economies (Figure 3). However, these unemployment rates may be more urgent and of greater concern for countries that are closer to their peak working-age population share, like Botswana and South Africa. From a labor quality perspective, Botswana, South Africa and Namibia have some of the best educated populations in the region, with 61-67 percent of their populations having at least 9 years of schooling (Figure 4). Lesotho and Swaziland, on the other hand, lag far behind in terms of educational attainment, with only 20 to 37 percent of their populations having at least 9 years of education. If current educational attainment rates remain unchanged, the average skill shares in the least-educated economies will remain lagging far behind that of the region and more developed economies. So, improvements in educational attainment may lead to greater demographic dividends in an economy like Lesotho, while the growth contributions to improvements in employment ratios may be greatest for countries like South Africa.

**Figure 3: Unemployment rates in SACU are more than triple that of middle-income countries on average**

Labor force participation rate (share of population 15+, percent) and unemployment rate (share of labor force, percent) <!-- Start of picture text --> 80 Labor Force Participation Rate<br>Unemployment Rate<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Botswana Lesotho Namibia Swaziland South Africa High Upper Lower<br>income middle middle<br>OECD income income<br><!-- End of picture text --> Source: Authors’ estimates Note: Data are from World Bank (2016). High income OECD, upper-middle income, and lower-middle income country groupings are based on the World Bank Group’s income based country classification.

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relatively small number of samples, and mostly in developed countries. In Tunisia, an innovation survey similar to CIS was conducted in 2005, however, it targeted a specific group of firms that were most likely to innovate, which cause a sample selection bias.<sup>8</sup> Also, the survey was conducted only once in Tunisia, which makes it harder to understand the time trend of innovation effects or to apply the panel technique to remove potential effects related to time trend.

With general firm-level surveys, researchers have used different proxies for innovation. Most studies have used capitalized R&D as a proxy for innovation input. Fewer studies have used patents as a proxy for intermediate innovation output. The INS data provide detailed information about expenses on innovation activities, such as conducting R&D, receiving consulting services, and purchasing royalties. Hence, I will use the expenses related to innovation activities as proxies for innovation inputs.

While the concept of innovation could include a wide range of activities, I follow a more conservative approach and construct an innovation variable using cost items that are directly related to technical innovation, such as the cost of conducting R&D, cost of receiving technical consulting services, and cost of purchasing royalties. Specifically, I consider cost of conducting R&D as internal innovation (create), and purchasing royalties and receiving technical consulting services as external innovation (buy). Then, I create a total innovation (all) variable by calculating the total cost of these three activities. Therefore, my innovation variable is limited to innovation inputs on technical innovation. This narrow definition of

> 8 . The first innovation survey of Tunisia was carried out in 2005 by the Ministry of Scientific Research and Technology. The survey asked firms about various aspects of innovation for the period 2002 to 2004, modeling the CIS survey. The survey was given to 739 firms likely to conduct innovative and/or R&D activity. Their sample criteria include manufacturing firms with high technology intensity and/or strong value addition, with firms with over 10 employees registered in the Agency for Investment Promotion (API) and the Institutes of National Statistics (INS). Among these, 586 firms took the survey and 322 firms (around 55 percent) responded in the affirmative to innovation. The survey included questions about product and process innovation. Firms were asked if they have introduced a new product or process during the three years preceding the survey. The firms taking the survey were predominantly in textiles (19 percent of respondents), food and agro-processing (17 percent), electrical and equipment (17 percent), and IT (4 percent). Like the CIS, this survey included subjective questions and qualitative variables that are difficult to codify and interpret. Also, the sample was restricted to certain types of firms.

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# _6.2.4. Workload_

High vacancy rates and increased absenteeism across organizations has meant more work for ‘survivors’. For example, assuming that a proper job analysis was done when establishing posts at Dis.Mgment, the high vacancy rates mean that each officer is covering the tasks that should be done by more than one person. The result is the overload of existing staff. According to staff interviews, the extra work does not in any way turn into more pay or overtime allowances and is considered a major source of frustration.

Similarly, all staff interviewed at M.Agr stated that their workload had increased as a result of the number of vacancies in their sections or departments. It was noted that increased workload particularly affects field staff, because they are at the helm of service delivery. The analysis of the workload of frontline extension staff, which compares the ratio of Field Assistants to farmers, confirmed that shortage of staff had resulted in excess workload. According to the Director of the Agricultural Extension Service, the standard ratio is one Field Assistant to 500 farmers. Yet, as shown in Table 5 below, the average ratio is about 1: 1600, more than tripling the number of farmers that an average field assistant must serve.

**Table 5.** Ratio of Field Extension Assistants to Farmers, 2003

|**District**|**Estab.**<br>**Posts**|**Staff**<br>**In Post**|**Farmers**|**Staff: Farmer**<br>**Ratio**|
|---|---|---|---|---|
|Karonga|104|133|66,757|1: 502|
|Mzuzu|268|342|185,901|1: 543|
|Kasungu|232|151|270,000|1: 1,788|
|Machinga|596|278|484,911|1: 1,744|
|Salima|560|240|182,734|1: 761|
|Lilongwe|394|209|452,244|1: 2,164|
|Blantyre|544|231|500,000|1: 2,164|
|Shire Valley|184|171|143,987|1: 842|
|_TOTAL_|_2,880_|_1,426_|_2,286,534_|_1: 1,603_|

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<!-- Start of picture text -->
#00 0.93 0.98<br>0.90 0:86 , 0:88 0-88 pags 6 9°? ; oe og<br>. 0.7 0.8 0.7 . . ;<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>2008 2009 2010 2011 2012 2013 2014<br>Burban mrural<br><!-- End of picture text -->

<!-- Start of picture text --> 7<br>.<br>g o<br>z<br>2 Lo<br>: : os<br>; g *<br>zs<br>a<br>o 8<br>i o><br>: bo<br>s Ss<br>expenditure per capita deciles<br><!-- End of picture text -->
wage areas, and the influx would equalize wages across affected and lesser affected areas. However, the premium may remain intact if people are hesitant to migrate into areas that were affected by conflict because they are afraid of the recurrence of violence. Female wages in the conflict-affected areas may decrease due to an increased supply of female labor. However, if women tend to take on jobs previously filled by men, we should expect to see higher female wages in the conflict affected areas, and lower wages for males in these occupations (Acemoglu et. al. 2004).

Therefore, we may observe a higher number of women and men in the workforce in the conflict affected areas. The entry into the workforce is likely to be higher for younger women with no children at the time the conflict started and who thus were available to take on the jobs vacated by men, however for reasons described above the theoretical effect of the conflict on wages is ambiguous.

The analysis in this paper focuses on the supply side of labor market. However, the conflict could have also affected the demand side of the labor market by destroying labor market opportunities.

# **3. The 1992-1998 Armed Conflict in Tajikistan**<sup>**1**</sup>

Soon after its independence in 1991 Tajikistan was afflicted by a violent civil war that started in early 1992 and was followed by a prolonged armed conflict ending in 1998.<sup>2</sup> The cause of the war was a combination of long-standing grievances and perceived opportunities to gain a larger share of the pie that became available once the country became independent. The war led to significant destruction of state and private property. The capital, Dushanbe, and southern region Khatlon and the Rayons of Republican Subordination (RRS) were severely affected by the war and the accompanying terror, including assassinations, hostage-taking, rapes, murders and robberies.<sup>3</sup> The government was unable to contain the conflict independently and negotiated for outside political and military assistance, provided by Russia and Uzbekistan from 1992 to 1999. Some regions in Tajikistan, such as Khatlon, the Regions of Republican Subordination (RRS) and the country capital Dushanbe, were greatly affected by the conflict, while other

> 1 This section heavily relies on the description of the Tajik armed conflict provided in Shemyakina (2007). 2 University of Uppsala Conflict Database. (Accessed: April 2010.) 3

> Based on the “Vechernii Dushanbe” and “Narodnaya Gazeta” news material for 1991-1999.

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MONGOLIA'S INFORMAL SECTOR Appendix B: Methodologies and Descriptive Statistics 45

# Informal Sector Surveys of Outdoor Markets and Street Informals

121. Two hundred and seventeen additional informals were surveyed in June of 1997. The purpose of the Generalized Informal Survey was not to provide estimates of overall value added, as little was known _a priori_ about the population of the informals that pack the various outdoor markets and offer services on the streets. Rather, we sought to illuminate the ,great diversity of the informal sector, by providing some contrast to taxicabs and kiosks. It was the quest for contrast that drove the sampling procedures.

122. At least 40 informals were interviewed from each of Ulaanbaatar's three largest outdoor markets, the "Black" Market, the Technical Market, and the _Harhorin Market._ Also interviewed were 80 informals who made their livings on the streets of Ulaanbaatar.<sup>9 5</sup> The parameters of sample selection were simple: (i) We only interviewed informals who occupy their space on a daily basis; if they work from any sort of structure they were excluded from the sample. (ii) We only interviewed informals whose operations appeared to be "small." At the "black" market, for example, containers clearly failed this test and were excluded from the sample. (iii) We only interviewed people that appeared to be engaging in the activity regularly. This criterion excluded people who attend the markets occasionally to sell unneeded household items. (iv) No one under age 16 was interviewed. A large number of informals were thus excluded, but we felt that children were not likely to understand many of the questions. The sample for the Generalized Informal Survey is, then, not a random sample. It is probably biased toward low income - low productivity informals. The bias was intentional, designed to provide contrast to the high income - high productivity taxicabs and kiosks. The composition of the sample is presented in Table B4.

> Table **B4.** Generalized Informal Sector Survey - Activities Represented in the Sample Number of observations by location, type of activity, and category of product or service.

|Location||Categories of Products or Services||
|---|---|---|---|
|"Black" Market|57|Prepared foods_(buuz, hushuur),_baked goods, etc.|17|
|Harhorin Market|40|Dairy, meat, pine nuts, etc.|7|
|Technical Market|40|Art|3|
|Street|80|Individual cigarettes, soft drinks, candy, bread, etc.|37|
||217|Newspapers|14|
|||Retailing food and carton cigarettes|20|
|||Ice cream|9|
|||Clothing|3|
|||Household goods|15|
|**Type of Activity**||Religious souvenirs|I|
|Selling own-produced goods|27|Oil and fuel (breaking bulk)|10|
|Selling new goods|109|Automobile parts and tools|17|
|Selling used goods|32|Books|15|
|Providing services|49|Boot and shoe repair, painting leather|29|
||217|Shoe shine|9|
|||Measuring weight|3|
|||Photography|3|
|||Billiards (outdoor)|I|
|||Porter|4|
||||217|

_Source:_ Informal sector surveys conducted by the author.
# **_Food Situation in South Asia_**<sup>**_2_**</sup>

**_Supply and Demand Factors:_** Tables 3 and 4 show the trends in rice and wheat production and consumption in South Asia over 2000-2008.  Total food production (rice

**Table 3: South Asia Food Grain Production** (1000 MT)

|Afghanistan|**2000/2001**<br>169|**2001/2002**<br>157|**2002/2003**<br>260|**2003/2004**<br>174|**2004/2005**<br>312|**2005/2006**<br>315|**2006/2007**<br>362|**2007/2008**<br>335|**2008/2009**<br>168|
|---|---|---|---|---|---|---|---|---|---|
|Bangladesh|25,086|24,310|25,187|26,152|25.600|28,758|29,000|28,600|29,400|
|India|84,980|93,340|71,820|88,530|83,130|91,790|93,350|96,430|96,000|
|Nepal<br>ice|2,808|2,774|2,752|2,752|2,968|2,857|2,804|2,810|2,850|
|Pakistan<br>R|4,802|3,882|4,479|4,848|5,025|5,547|5,200|5,500|5,600|
|Sri Lanka|1,940|1,820|2,058|1,900|1,974|2,100|2,145|2,200|2,300|
|_South Asia_|_119,785_|_126,283_|_106,556_|_124,356_|_119,009_|_131,367_|_132,861_|_135,875_|_136,318_|
|World|398,902|399,700|378,318|391,861|401,298|418,313|420,164|428,989|430,751|
|Afghanistan|1,469|1,597|2,686|3,480|2,293|4,265|3,200|3,800|1,500|
|Bangladesh|1672|1610|1510|1253|976|820|740|1200|800|
|India<br>at|76,369|69,680|71,810|65,100|72,150|68,640|69,350|75,810|78,400|
|Nepal<br>he|1,184|1,157|1,258|1,344|1,387|1,442|1,394|1,395|1,400|
|Pakistan<br>W|21,079|19,024|18,227|19,183|19,500|21,612|21,277|23,300|21,500|
|South Asia|101794|93088|95511|90380|96326|96,799|95981|105325|103620|
|_World_|_582899_|_583078_|_568708_|_553838_|_625738_|_620851_|_596273_|_610537_|_670751_|
|<br>_Source: USDA Data_<br>**T**|_,_<br>_base_<br>**able 4: S**<br>**2000/2001**|_,_<br>**outh Asia**<br>**2001/2002**|_,_<br>**Food Gr**<br>**2002/2003**|_,_<br>**ain Con**<br>**2003/2004**|_,_<br>**sumption**<br>**2004/2005**|_,_<br> (1000 M<br>**2005/2006**|_,_<br>T)<br>**2006/2007**|_,_<br>**2007/2008**|_,_<br>**2008/2009**|
|Afghanistan|385|427|440|369|455|516|550|485|343|
|Bangladesh|24,958|25,553|26,100|26,700|26,900|29,000|29,764|30.600|30,600|
|India|75,960|87,611|79,860|85,630|80,861|85,088|86,940|90,760|93,000|
|Nepal<br>ce|2,828|2,793|2,762|2,756|3,029|2,859|2,863|2,870|2,910|
|Pakistan<br>Ri|2,615|2,540|2,545|2,595|2,550|1,896|2,257|2,450|2,420|
|Sri Lanka|2,020|2,075|2,100|2,075|2,139|2,150|2,152|2,287|2,376|
|_South Asia_|_108,766_|_120,999_|_113,807_|_120,125_|_115,934_|_121,509_|_124,526_|_129,052_|_131,349_|
|World|393,291|412,455|405,681|411,615|406,545|411,629|417,017|421,962|425,519|
|Afghanistan|2,043|2,597|3,186|3,930|3,293|4,765|4,250|4,700|3,800|
|Bangladesh|2866|2950|3000|3050|3000|2950|2800|2900|2850|
|India<br>|66,821|65,125|74,294|68,258|72,838|69,971|73,358|75,850|77,600|
|Nepal<br>eat|1,194|1,158|1,266|1,361|1,407|1,462|1,408|1,415|1,420|
|Pakistan<br>Wh|20,500|19,800|18,380|19,100|19,600|20,900|21,900|22,400|22,600|
|Sri Lanka|850|801|875|931|950|1,000|817|825|850|
|_South Asia_|_94,294_|_92,451_|_101,028_|_96,655_|_101,118_|_101,078_|_104,563_|_108,120_|_109,150_|
|World|583,564|587,816|603,659|581,173|605,943|618,197|618,703|619,007|647,079|

_Source: USDA_ 2 Due to data limitations, the paper is not able to cover Bhutan and Maldives 7
new investors then select similar strategies to their experienced peers. In turn, social interactions lead to high volatility portfolios even when new investors have no inherent demand for volatility.

# **6.2. SELECTIVE INFORMATION NEGLECT**

A potential explanation for our results is that experienced individuals accurately share their performance, but signal receivers ignore negative outcomes. Specifically, if inexperienced students believe they can replicate peers’ successful trades and avoid peers’ losses, only positive outcomes would impact market entry. In psychology, there is evidence of self-enhancing thought processes, such as the tendency of people to attribute wins to their own virtues but losses to external circumstances (Bem, 1972; Langer & Roth, 1975). Similarly, people might disregard negative peer outcomes, assuming that these result from lack of skill or from other unique circumstances that do not apply to them. At the same time, people might be encouraged to trade actively if they overestimate their ability to reproduce good peer outcomes.

To shed more light on this issue, we use the answers from the social interaction section of the electronic survey. We ask former students if they engaged in informal discussions about stock trading with classmates.<sup>29</sup> Among the respondents of our electronic survey, 760 students had no trading history prior to the course start date. Within this group, 80% of people reported engaging in investment conversations with others in the class. We classify our respondents in three groups based on their classmates’ experience: (i) those who attended courses with no experienced classmates (330 students), (ii) those in courses where peer returns were positive (252 students), and (iii) those registered in courses with negative peer returns (178 students).

Among students in courses where the _Experience Rate_ was zero, 72% indicated having investment conversations with peers. This number was significantly higher at 93% among students in courses with positive peer returns. Even in courses where peer returns were negative, students report more investment

> 29 A question to directly test information transmission bias would inquire about the type of the personal experience that was shared among classmates (e.g., positive vs. negative outcomes). Unfortunately, at the time of the survey design, the objective was to test direct spillover effects.

32
|**Norm Reference Assessments**|**Cognitiv**<br>**Grades**<br>**Reading Comprehension**<br>**Language Comprehension**<br>**Background Knowledge**|**e Elements Evaluated by this As**<br>**Linguistic Knowledge**<br>**Phonology**<br>**Syntax**<br>**Semantics**<br>**Decoding**<br>**Cipher Knowledge**<br>**Lexical Knowledge**<br>**Phoneme awareness**|**sessment**<br>**Alphabetic Principle**<br>**Letter Knowledge**|<br>**Concepts about print**|
|---|---|---|---|---|
|Qualitative Reading Inventory --4th Edition (QRI-4)|K, 1, 2, 3, and higher<br>�<br>�|�|||
|Rapid Automatized Naming and Rapid Alternating<br>Stimulus Tests (RAN/RAS)|<br>K, 1, 2, 3, and higher||�||
|Ready to Learn: A Dyslexia Screener|Pre-K, K, 1|�<br>�|�||
|Receptive<br>One-Word<br>Picture<br>Vocabulary<br>Tes<br>(ROWPVT)|t<br>Pre-K, K, 1, 2, 3, and<br>higher|�|||
|School Readiness Test (SRT)|K and 1 (Early 1st grade<br>only)<br>�|�|�||
|Standardized Reading Inventory - 2nd Edition (SRI-2)|Pre-K, K, 1, 2, 3, and<br>higher<br>�|�<br>�|||
|Stanford Achievement Test -- 10th Edition (SAT-10)|K, 1, 2, 3, and higher<br>�<br>�|�<br>�<br>�|||
|Stanford Diagnostic Reading Test -- 4th Edition (SDRT-4)|K, 1, 2, 3, and higher<br>�|�|||
|Stanford English / Spanish Language Proficiency Tes<br>(Stanford ELP / SLP)|t<br>K, 1, 2, 3, and higher<br>�<br>�||||
|Stanford Reading First|K, 1, 2, 3, and higher<br>�<br>�|�<br>�|||
|STAR Early Literacy Computer-Adaptive Diagnostic<br>Assessment|<br>Pre-K, K, 1, 2, and 3<br>�<br>�|�<br>�<br>�|�<br>�|�|
|STAR Reading Computer-Adaptive Reading Test|1, 2, 3 and higher<br>�||||
|Test of Early Language Development - Third Edition<br>(TELD-3)|<br>Pre-K, K, 1, and 2<br>�||||
|Test of Early Reading Ability-3rd Edition (TERA-3)|Pre-K, K, 1, 2, 3||�|�|
|Test of Kindergarten/First Grade Readiness Skills|K and 1<br>�|�|�||
|Test of Kindergarten/First Grade Readiness Skill<br>(TKFGRS)|s<br>K and 1||||
|Test of Oral Reading and Comprehension Skills (TORCS)|K, 1, 2, 3, and higher<br>�|�|||
|Test of Phonological Awareness Skills (TOPAS)|K, 1, 2, 3, and higher|�|||
|Test of Phonological Skills in Spanish (TPAS)|K, 1, 2, 3, and higher|�|||
|Test of Reading Comprehension -- 3rd Edition (TORC-3)|2 and 3<br>�|�<br>�|||
|Test of Silent Word Reading Fluency (TOSWRF)|1, 2, 3, and higher|�|||
|Test of Word Reading Efficiency (TOWRE)|1, 2, 3, and higher|�<br>�|||
|Wechsler Individual Achievement Test - Second Edition<br>(WIAT-II)|<br>Pre-K, K, 1, 2, 3, and<br>higher<br>�<br>�|�<br>�|||

# **7.2. Summary of fluency-comprehension correlation studies**

|**Authors**|**Year**|**Subjects**|**Criterion measure**|**Correlation**|
|---|---|---|---|---|
|Fuchs, 1981; see|1981|91|Woodcock Reading Mastery Test|<br>.91 (Ginn)|
|also Fuchs & Deno,||1st to 6th grade with|(WRMT)|.91 (S-F)|
|1992||15 specialed. &23Title1|||

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