rafmacalaba/datause-encoder-data huggingface dataset:
The resulting labor shedding has led to improved productivity and lower<sup>costs,</sup> but also - given limited job creation - has contributed to unemployment.<sup>Indeed,over</sup> 50 percent of the unemployed are persons who lost<sup>their job,of which20 percentin</sup> the public sector, and 30 percent in the private sector (Table<sup>4).Nonetheless,itis</sup> worth noting that this proportion of job losers in total<sup>unemployment issignificantly</sup> lower than in other intensively restructuring transition<sup>economies, (suchas Hungary</sup> or Poland) and OECD countries, where it varies around<sup>75percent.Thismeansthat</sup> new entrants account for a disproportionately large fraction of<sup>unemployment,which</sup> again points to labor market rigidities, preventing persons without<sup>previousjob</sup> experience to enter the labor market.

**Table 4 Job seekers by previous labor market status, 2001**

|Labor forcestatus beforethe startofjobsearch|Percentof<br>alljob<br>seekers<br>|
|---|---|
|**Employed**|**55.9**<br>|
|Workersinthepublicsector<br>|19.9<br>|
|Workersintheprivatesector<br>|25.9<br>|
|Self-employedandemployers(privatesector)|3.9<br>|
|Unpaidfamilyworkers<br>|1.4<br>|
|Temporaryworkersforpaymentinkind|4.9<br>|
|**Outofthelabor force**|**44.1**<br>|
|Students|26.1<br>|
|Housekeepers|7.7<br>|
|Pensioners|1.0<br>|
|Militaryservice|8.2<br>|
|Others|1.1|

Source: CBS, LFS second half-year 2001, Author's calculations.

Enterprise restructuring has given rise to accelerated inflows into unemployment, a process which begun in the mid 1990s in Croatia<sup>(Table5).This</sup> has been reflected in an increasing proportion of workers who<sup>werelaid-off among</sup> the unemployed and, correspondingly, in a decreasing proportion of<sup>workerswithout</sup> previous labor market experience (whose share in unemployment was disproportionately large at an early stage of the transition in Croatia).

At the same time oufflows from unemployment, including outflows to jobs, have slowed down, and presently fall short of inflows into<sup>unemployment.</sup> For example, in 2001 as much as 74 thousand persons newly registered<sup>atEmployment</sup>

-6-
33 Table 3: Trade Reduction Index, all covered products<sup>a</sup> , 19 African focus countries, 1961–64 to 2000–04 (percent)

||1961–64|1965–69|1970–74|1975–79|1980–84|1985–89|1990–94|1995–99|2000–04|
|---|---|---|---|---|---|---|---|---|---|
|**Africa**|**24**|**22**|**20**|**21**|**15**|**24**|**14**|**9**|**10**|
|Benin|na|na|2|1|1|0|2|3|1|
|Burkina Faso|na|na|2|3|4|1|3|3|0|
|Cameroon|2|5|6|14|12|3|2|2|1|
|Chad|na|na|12|11|8|1|3|3|1|
|Côte d'Ivoire|13|13|24|27|19|17|12|15|22|
|Ethiopia|na|na|na|na|14|16|19|11|9|
|Ghana|6|11|10|22|20|15|7|3|7|
|Kenya|-16|-19|-4|12|21|19|-7|9|11|
|Madagascar|20|15|-13|6|-1|17|3|3|8|
|Mali|na|na|4|7|6|3|5|7|0|
|Mozambique|na|na|na|27|-6|-14|1|6|24|
|Nigeria|39|38|31|18|11|19|7|8|1|
|Senegal|14|10|30|36|28|25|26|7|12|
|Sudan|29|28|29|29|22|56|40|17|31|
|Tanzania|na|na|na|16|18|34|30|16|17|
|Togo|na|na|0|1|2|1|4|3|1|
|Uganda|2|4|8|14|9|10|2|2|1|
|Zambia|21|1|1|36|-11|-45|-27|-7|29|
|Zimbabwe|33|38|43|51|29|37|19|10|12|

Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008).

a. Includes all import-competing, exportable and nontradable products; with nontradable sectors assumed to have a zero level of distortion on the volume of trade.
# **2.3.The Challenges of Protracted Refugee Situations in Sub‐Saharan Africa**

At the global level, about 54% (i.e. about 6.3 million) refugees were in protracted refugee situation by the end of 2013 (UNHCR 2014).<sup>2</sup> As reported by Kreibaum (2016), the number of protracted refugee situations has increased from 22 in 1990 to 30 in 2008. These protracted situations in Africa have been characterized by Crisp (2003) as in most of the cases: i) peripherally located with poor security, unfavorable climatic conditions, and economical and political marginalized; ii) concentrating people with special needs like e.g. children and women (see Section 3); and iii) lacking basic human rights, including those covered by the provision of the 1951 refugee convention.

Another distinct feature of refugees in SSA is that they are mostly hosted in organized camps. While in developing countries around one third of refugees are hosted in camps, the share raises to about 40 percent in Sub‐Saharan Africa (Figure 5).<sup>3</sup> The percentage of 76 percent in Eastern Africa and the Horn of Africa stresses again the pressing situation in this part of the world. While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned/managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA.

> 2 UNHCR defines a protracted refugee situation as “one in which 25,000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country” (2012: 23).

> 3 The figures are based on refugees (including those in refugee‐like situation). Persons in refugee‐like situations include “groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained” (UNHCR 2014: 39). The number of refugees and people in refugee‐like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19% in the world and 8% for SSA).

12
# **Figure 4: Dropout rate from public schools**

<!-- Start of picture text --> Unadjusted differences between treatment and comparison districts<br>Public schools<br>2 3 4<br>Survey Round<br>Lower bound Comparison Group<br>Treatment Group Upper Bound<br>.08<br>.06<br>.04<br>.02<br>0<br>Percentage of students dropping out<br><!-- End of picture text --> Note: Thick solid line represents sample average for the treatment district. Thick dashed line represents sample average for the comparison districts. Confidence intervals are marked using small dashes (lower bound) and lines that alternate dashes and dots (upper bound). Data exist for all four rounds. The y-axis is the percentage of children dropping out. The mean at baseline for the treatment district is 0.061 and the mean at baseline for the comparison districts is 0.049.

29
**Figure 2e. Workers' remittances, compensations of employees, and migrant transfers, credit: By Regions** _(Data in the table are in US $ millions, the  Right Scale  is  only  All Developing Countries' Group)_

<!-- Start of picture text --> 100,000 400,000<br>90,000<br>350,000<br>80,000<br>300,000<br>70,000<br>250,000<br>60,000<br>50,000 200,000<br>40,000<br>150,000<br>30,000<br>100,000<br>20,000<br>50,000<br>10,000<br>‐ ‐<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>East Asia and Pacific 3,228 3,792 4,333 5,453 6,633 9,526 10,142 14,489 12,244 14,912 15,675 18,757 27,468 32,695 40,336 50,460 57,598 71,309 86,115 84,785<br>Europe and Central Asia 3,246 2,819 3,160 3,253 8,091 7,206 9,728 9,373 13,170 11,072 12,143 11,647 12,844 14,418 20,955 30,089 37,341 50,777 57,801 49,279<br>Latin America and Caribbean 5,722 6,765 8,372 8,706 11,048 13,335 13,505 14,389 15,825 17,603 19,987 24,229 27,918 36,609 43,330 50,122 59,199 63,239 64,717 58,481<br>Middle‐East and North Africa 11,393 12,750 15,832 15,209 14,076 13,319 12,517 12,809 13,052 12,800 12,898 14,653 15,211 20,361 23,034 24,958 26,112 31,364 34,696 32,212<br>South Asia 5,572 6,051 5,933 6,664 9,524 10,005 12,294 14,557 13,350 15,084 17,212 19,173 24,137 30,366 28,694 33,924 42,523 54,041 73,293 71,955<br>Sub‐Saharan Africa 1,862 1,859 2,102 2,611 2,304 3,193 3,190 4,397 4,327 4,433 4,623 4,663 5,030 5,970 8,021 9,379 12,629 18,646 21,139 20,525<br>All developing countries 31,023 34,036 39,732 41,896 51,677 56,584 61,376 70,014 71,967 75,904 82,537 93,122 112,60 140,42 164,37 198,93 235,40 289,37 337,76 317,23<br>Source: Authors and World Bank staff estimates based on the International Monetary Fund's Balance of Payments Statistics Yearbook 2008.<br>Notes: (1) See Notes sheet for the country group classifications. (2) For a discussion of the definition of remittances, see Dilip Ratha, 2003, "Workers' Remittances: An<br>Important and Stable Source of External Development Finance", Global Development Finance 2003, World Bank.<br><!-- End of picture text --> 43
The remainder of the paper is organized as follows. Section 2 presents the methodology and data used for estimating the district-level prevalence in the absence of nationally-representative population surveys. Section 3 presents the regression results and discusses how the district-level estimates of HIV prevalence obtained from the application of the SAE methodology to the DHS 2000 survey compare with the sentinel data estimates for 2001 and the “gold-standard” prevalence rates derived directly from the 2004 DHS Survey. Section 4 concludes by reviewing the main findings.

# **2. Methodology and Data**

## **2a. The basic idea**

The methodology used in this paper draws on the recently developed small area estimation techniques applied to welfare measurement (Elbers et al., 2003).<sup>12</sup> The idea is straightforward. Let p _i_ be an indicator of individual HIV infection found in the sentinel data. We first model p _i_ as a function of individual level variables (such as age and education) found in the 2001 sentinel (ANC) survey, and of selected commune/district means derived from the 1998 Malawi National Census data (Benson, 2002), the 2001 Health Facilities data, 2001 GIS data, and the 2000 Demographic and Health Survey (DHS) data.<sup>13</sup> Such commune/district means are useful because individual infection status is clearly seen as dependent not only on individual characteristics, but also on regional factors. The construction of regional means is driven by a priori expectations on which regional factors are likely to be correlated with the probability of HIV infection, as well as by data availability. Importantly, the set of explanatory variables is restricted to those that can be linked to individuals in the DHS 2000 sample. Second, the set of parameter estimates resulting from the estimation of the model is applied to the identically defined variables in the nationally representative DHS 2000 sample (again,

> 12 The important distinction is that while in the welfare measurement the household survey is representative of the total population of households, in the prevalence measurement ANC data are representative of only the respective sub-population of all population of women. The paper discusses in a greater detail below the possible ways to deal with this caveat.

> 13 We potentially could use the 2004 DHS data instead of the 2000 DHS data, but since the reliability of estimates depends on the various data sources been closer to each other in time, we use the 2000 DHS which is more consistent with the 2001 ANC survey, 2001 GIS data, and the 1998 Census.

6
uniformly below even the American South in density of engineers. As we discuss next, despite four centuries of mining, it had not acquired a corpus of trained professionals in the field compared to relative newcomer, the American West. The other countries show similar patterns.

Table 6 employs the sub-national data and comes to similar conclusions to those previous. We estimate: Engineering density is persistently significant and broadly of the magnitude of the OLS estimates from the US. The finding of continued significance even after controlling for a measure of population density (column 2) is consistent with Comin et al. (2010a). In column 3, Literacy again enters positively and significantly and reduces the coefficient on engineering by 40%, again, broadly consistent with the US results. Though we cannot control for a more complete set of human capital measures, the county level exercises above suggest that, in fact, engineering is capturing innovation related human capital and not human capital accumulation more generally. We also run the regressions with a complete set of geographical controls but these absorb substantial degrees of freedom and do not enter significantly so we omit them.

In sum, the various estimations suggest that our county level US findings resonate internationally: both literacy and higher order human capital related to engineering and science and technology are important determinants of long run income.

# **5.1 Mechanisms of influence today**

Table 7 offers some suggestive mechanisms through which these engineering densities in 1900 could continue to affect output today. We collect five indicators at the country level that 22
Policy Research Working Paper 8454

# **Abstract**

The time teachers spend teaching is low in several developing countries. However, improving teacher effort has proven difficult. Why is it so difficult to increase teacher effort? One possibility is that teachers are resistant to increasing effort because they do not believe their effort is suboptimal. Such beliefs may be based on their mental models on absenteeism, accountability, and student learning. This paper explores this idea using data from 16,000 teachers across eight developing countries, spanning five regions. It finds that, on average, teachers support test-based accountability and believe that they are in fact held accountable for student learning. In several countries, many teachers tend to normalize two types of suboptimal behaviors. These are (i) certain types of absenteeism, and (ii) paying extra attention to well-performing and well-resourced students. Finally, the paper shows that ideas of accountability and absenteeism are strongly framed by context in two direct ways. The first is whether teachers favor exclusively reward-based forms of accountability. The second is the degree to which they support absenteeism linked to community tasks. These results provide actionable insights on how changing teacher behavior sustainably might require reshaping underlying mental models.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ssabarwal@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
percent, hence a "comparable profit tax" of 43 percent and an unchanged effective net payroll tax rate of 59 percent. He then estimated that dR=d(wL)*0. I 1, thus a net revenue gain from wage increases.<sup>2</sup> Table 2: Gross Profit Tax Rates and Net Total Payroll Tax Rates, 1988-93 (percent)

||1988|1989|1990|1991|1992|1993|
|---|---|---|---|---|---|---|
|Gross profit tax rate (tl)|65|40|40|40|40|40|
|**Comparable profit tax rate**|**186**|67|67|67|67|67|
|**_(ttl/(I - td))_**|||||||
|Overall net payroll tax rate (t2)|58|58|67|67|76|78|
|Payroll tax|20|20|20|20|0|0|
|Average personal tax|0|0|0|0|20|21|
|Contribution to ZUS|38|38|43|43|54|54|
|Contribution to FP|0|2|2|2|2|3|

> Note ZUS = Zaklad Ubezpiecien Socialnych; FP Fundusz Pracy. Statutory rates in percent, converted into rates on net wages after 1991. De Crombrugghe (1994a) grossed up the payroll tax rates before 1992 and came to the same conclusions.

The use of an effective profit tax rate can be debated, especially in 199 1. It transforms a neutral tax revenue effect into a positive one. Shaffer's low effective profit tax rate is not consequence of a political decision to increase the number of deductibles or exemptions. Rather, it is a consequence of the fact that profit taxes are earned on only some of the enterprises. These are not exogenous, though. Some enterprises may be loss-making precisely because they raised wages.

Other enterprises may have no incentive to earn profits because they have no internal claimant for these profits. In that case their profits go to zero, and the effective profit tax rate becomes zero as well.

**2**

2Schaffer observes that gross losses were between one-fourth and one-third of gross profits in 1991. So he takes as the marginal tax rate 0.5*37 percent + 0.5(1/3*0 percent+2/3*37 percent)=30 percent. Hence for 1991 the wage revenue effect (t2-tlt2-tl)=(0.59-(0.3*0.59)-0.3) =0.11.

8
Steve, J., A. Severn and B. Raum. 2008. “Wind Energy Production Tax Credit (PTC).” AWEA, Washington, DC. Retrieved from <u>http://www.awea.org/pubs/factsheets/ PTC_Fact_Sheet.pdf, May 27, 2008.</u> UNEP (United Nations Environment Programme). 2001. “Proceedings of the African HighLevel Regional Meeting on Energy and Sustainable Development.” Prepared for the 9th Session of the Commission on Sustainable Development. 10-13 January 2001, Nairobi, Kenya. Retrieved from <u>http://uneprisoe.org/CSD9/AfricanMeetingCSD9.pdf,</u> June 13, 2008.

URC (UNEP Risoe Centre on Energy, Climate and Sustainable Development). 2009a. “CDM Pipeline Overview.” Retrieved from <u>http://cdmpipeline.org/publications/ CDMpipeline.xls, January 23, 2009.</u> _____. 2009b. “JI Pipeline Overview.” Retrieved from <u>http://cdmpipeline.org/publications/ JIpipeline.xls, January 23, 2009.</u> USDOE (U.S. Department of Energy). 2008. “20% Wind Energy by 2030 Increasing Wind Energy’s Contribution to U.S. Electricity Supply.” USDOE, Washington, DC.

White, D.J. 2004. “Danish Wind: Too Good to be True?” _The UTILITIES Journal,_ 37-39. World Energy Council. 2007. “Country Notes,” in _2007 Survey of Energy Resources_ (pp. 490524). World Energy Council, London, UK. Retrieved from http://www.worldenergy. org/documents/ser2007_final_online_version_1.pdf, June 13, 2008.

32
- 63 -

## **Table** A.7: Rwa$4h%n **Differene** In Nonfum **Actitvity** In States **with DiffeNg AgicWtural** khoo_e,

# **rdla** 1981

||Tota<br>Nonfrm|Mhhg|Hou-<br>Hold<br>Mf.|Non-<br>House<br>Hold<br>Mfr.|.<br>Const.|Comm|Other<br>. Trars.|Services|
|---|---|---|---|---|---|---|---|---|
|||<br>(Full-|Tkne W|orkers|per 1,00|0 Popu|lation)||
|High Agricultural Income<br>|||||||||
|Punjab|||||||||
|Urban/rural|3.9|-|1.4|4.9|2.0|5.8|3.4|3.4|
|Urban<br>|263|0|10|73|10|70|24|75|
|Rural|67|0|7|15|5|12|7|22|
|Haryana|||||||||
|Urban/rural|3.9|-|1.4|5.1|2.8|6.6|4.2|3.|
|Urban|258|0|10|76|14|68|21|71|
|Rural|67|0|7|15|5|10|5|23|
|Medium Agricultural Income|||||||||
|Karnataka|||||||||
|Urban/rural|4.0|1.5|1.3|5.4|3.8|5.2|8.3|4.L|
|Urban|247|3|18|70|15|57|25|60|
|Rural|61|2|14|13|4|11|3|13|
|Gujarat<br>|||||||||
|Urban/rural<br>|4.9<br>|1.0|4.0|5.6|3.0|5.7|5.6|3.9|
|Urban|258|1|8|95|9|57|28|59|
|Rural|53|1|2|17|3|10|5|15|
|Low Agricultural Income|||||||||
|Madhya Pradesh|||||||||
|Urban/rural|5.5|2.3|1.5|9.8|3.3|8.5|12.5|5.7|
|Urban|241|7|18|59|13|51|25|68|
|Rural|44|3|12|6|4|6|2|12|
|Bihar|||||||||
|Urban/rural|5.6|6.3|1.1|6.9|10|7.1|7.7|4.9|
|Urban|212|19|8|48|10|50|23|54|
|Rural|38|3|7|7|1|7|3|11|

**V/** Urbtt i Al - aN _k2m__ aWt_ 5,000 in **popUWlbo ft _wnpin.** both ruie and 1 **.** _e_ Source: India (1371a-b. 1.1C)
both cohorts, but access to nutrition and early childhood services for the youngest cohort is clearly unsatisfactory, because access has remained very low and very unequal during the whole decade.<sup>19</sup>

|**Table 2. Human Opportunity Index for E**<br>**_Opportunity_**|**gypt, 2000**<br>**_2000_**|**and 200**<br>**_Circa_**<br>**_2009_**|**9**<br>**_Annual_**<br>**_rate of_**|**_Deco_**|**_mposition *_**<br>|
|---|---|---|---|---|---|
||||<br>**_change_**|**_Access_**|**_Equality of_**<br>**_opportunity_**|
|Completeprimaryeducation on time|84|86|0.4%|51%|49%|
|Complete secondaryeducation on time|62|63|0.3%|37%|63%|
|School attendance, 9-15|n.a.|89|---|---|---|
|Water|77|88|1.3%|67%|33%|
|Sanitation|26|30|0.5%|64%|36%|
|Lightingenergysource|98|99|0.1%|---|---|
|Cookingenergysource|73|98|2.8%|64%|36%|
|Non-overcrowding,0-5|48|59|1.2%|62%|38%|
|Telephone|14|71|6.3%|78%|22%|
|Assisted birth delivery|64|84|2.5%|74%|26%|
|Post-natal care,0-5|19|28|1.1%|91%|9%|
|Prenatal care,0-4|58|78|2.6%|76%|24%|
|Immunization vaccines,0-4|87|85|-0.2%|12%|88%|
|Non-wasting,0-4|88|75|-1.6%|79%|21%|
|Non-stunting,2-17|69|69|0.0%|---|---|
|Non-underweight,0-17|80|85|0.6%|60%|40%|
|_Aggregate HOIs_||||||
|IOHa9-15-HousingServices|35|72|4.1%|66%|34%|
|IOHa0-4-HousingServices|49|83|3.8%|64%|36%|
|IOHa0-4-Nutrition & ECD|14|14|0.0%|--|--|
|<br>Note: (*) Decomposition is omitted for trivi<br>_Source_: HIECS 2000 and 2009 surveys, ED|<br>al cases in<br>HS 2000 an|<br>which the<br>d 2008,|<br>HOI chang<br>And authors|<br>e is negli<br>’ calculat|<br>gible.<br>ions.|

_Uneven Improvement across Sectors._ Figure 2 shows that improvements in the opportunity indices of basic housing services (BHS) and early childhood development (ECD) were impressive (18 and 12 points respectively). The rates of improvement of the opportunities in education and nutrition and hunger were modest or suffered a setback (5 points and -3 points, respectively). Table 2 (third column) shows the rates of annual progress for each opportunity separately. The telephone HOI recorded the largest annual rate of improvement at 6.3 points, followed by three opportunities: cooking energy, prenatal care, and assisted birth delivery (with similar annual rates, 2.8-2.5 points). Three opportunities – access to water, non-overcrowding, and post-natal care – showed rates of progress close to the average rate (between 1.1 and 1.3 points). These were followed by five opportunities with _modest_ improvements at rates close to half the average rate, namely the two education opportunities for graduation on time, access to sanitation, and nutrition weight-for-age. By contrast, the HOI index for nutrition non-stunting did not show any progress, and two other

> 19 In circa 2009 the percentage of children with access to four of six opportunities in ECD and nutrition was just 25% and inequality of access was the highest across all opportunities  (see Table A.1).

11