rafmacalaba/datause-encoder-data huggingface dataset:
(the most recent year available at the time of this study) provide a baseline indicating the proportion of TB cases, TB cases co-infected with HIV, and deaths allocated to each country within a region. Since Global Plan projections are only available by region and not by country, for each subsequent year (2005–2015) we allocate region-specific TB deaths to each country in the same proportion as in the baseline year of 2004. This overestimates TB deaths in countries where deaths are expected to decline at a faster rate than the rest of the region and underestimates deaths in countries where TB deaths in coming years are likely to grow more rapidly than the rest of the region.

Since prevalence surveys are lacking in most countries, estimates of the number of TB cases in most countries are based on case-notification data. Estimates based on casenotification data can be unreliable (Murray, Lopez et al. 2004), since case-notification rates can be poorly correlated with actual prevalence, as shown by one study from India where the majority of TB cases are treated in the private sector (Borgdorff, Nagelkerke et al. 2000). However, WHO projections do make use of data from prevalence surveys wherever they are available. Given the difficulties in determining prevalence in individual countries, WHO’s TB department has tended to rely more on forecasts of relative changes rather than on absolute numbers of cases and deaths. Typically, these forecasts are accompanied by multivariate uncertainty and sensitivity analyses (see Dye, Garnett et al. 1998 for instance) to provide a range on the estimates as discussed later in this section.

# **Value of Statistical Life Year Calculations**

We estimated the economic burden of TB deaths under the three scenarios outlined above for the 22 countries with the highest burden of TB. Region-specific life expectancies were derived from the Disease Control Priorities Project (www.dcp2.org). We followed WHO methods in assuming that a person faces the same probability of death at each subsequent age as the existing population. This is equivalent to supposing 13
# **Introduction and Program Description**

Afghan refugees form one of the largest protracted refugee populations in the world, with many Afghans having lived in exile for decades. Despite the ongoing conflict, roughly 2.4 million displaced Afghans have returned to Afghanistan since 2014, including over half a million registered refugees who returned under the voluntary return program of the United Nations High Commissioner for Refugees (UNHCR), which provided them with immediate humanitarian assistance. More than 370,000 Afghan refugees returned to Afghanistan in 2016 alone. Between 2016 and 2018, UNHCR assisted more than 458,000 documented Afghan refugees who returned from Pakistan.<sup>1</sup> The majority of refugees arrived at the Afghan border without any assets or income-generating means. A significant share of returnees were women accompanied by young children, which complicated reintegration.

The majority of Afghan refugees moved to Pakistan during the Soviet invasion in 1979–80, according to the Population Profiling Verification and Response (PPVR) survey conducted in 2011. The protracted nature of the Afghan refugee situation, the large return wave since 2016, ongoing conflict, low economic growth, and weak job creation raise questions about how best to absorb and reintegrate the large number of returnees.

As a response to these challenges, UNHCR offers returning refugees under their mandate cash assistance for reintegration and transportation. Returnees receive two types of cash assistance. First, UNHCR pays transportation assistance, based on the distance traveled and the province of origin. The median transportation assistance received by Afghan returnees in our sample was $45 per returnee. Second, returnees receive reintegration assistance.

Between July 2016 and March 2017, UNHCR increased its reintegration assistance amount from $150 per returnee to $350 per returnee because of the increased needs of returning Afghan refugees and the need to ensure dignified reintegration. The amount of assistance was returned to $150 per returnee in April 2017. The amount of reintegration assistance is thus determined purely by the timing of return, with returnees returning between July 2016 and March 2017 receiving $350 per returnee and those returning before July 2016 or after March 2017 receiving $150 per returnee. We therefore estimate the total assistance received as either $200 or $400 per returnee.

The amount of assistance provided to refugees returning to Afghanistan is large compared with their income (figure 1). The one-time $350 per capita reintegration assistance was equivalent to 60 percent of the annual GDP per capita of Afghans living in Afghanistan ($591) (ALCS 2017). Households that received $350 per returning household member received approximately 2.5 times their annual income upon return; households that received $150 per household member received 1.2 times their annual income.

> 1 More than 380,000 undocumented refugees also returned from Pakistan to Afghanistan in 2016–18, according to annual reports of the International Organization for Migration and UNHCR.

2
<!-- Start of picture text --> 2<br>°<br><a<br>Oo<br>o<br>a<br>ite)<br>Oo<br>-3 -2 -1 0 1 2 3 4 5 6<br>log(Earnings abroad / Migration costs)<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>rad<br>Oo<br>o<br>a<br>wo<br>Oo<br>-4 -2 0 2 4 6<br>log(Excess earnings abroad / Migration costs)<br><!-- End of picture text -->
Some specifications replace the location-specific characteristics ( _Xjd_<sup>_l_) with fixed effects for</sup> household primary sampling unit (PSU) that corresponds to the household’s village, town, or neighborhood. These fixed effects capture unobservable local factors that may influence sanitation practices in a geographic area. PSU fixed effects absorb the control variables for household location, as the household location characteristics are homogeneous within a PSU.

All specifications limit the sample to households that are headed by a Hindu or Muslim. Standard errors are clustered at the PSU level (village, town, or neighborhood), which adjusts for the spatial correlation in sanitation practices within nearby areas.

# **IV Results**

# **IV.A Main Estimates**

Table 2 reports results from estimating equation (1), using the IHDS data set which observes three sanitation practices. Panel A reports estimated differences in latrine ownership for Hindus, relative to Muslims. Panel B reports estimated differences in handwashing. Panel C reports estimated differences in whether no fecal material was observed around the house.

Column 1, panel A, reports that Hindus are 17.9 percentage points less likely to own a latrine than Muslims, without adjustment for other covariates (95% confidence interval: – 13.0pp less likely 22.9pp less likely). Column 2 reports similar differences between Hindus and Muslims when controlling for household characteristics, such as household demographics and proxies for household wealth, though these controls do contribute substantial explanatory power (raising the adjusted R-squared from 0.014 to 0.341).

Column 3 reports that Hindus are 5.6 percentage points less likely to own a latrine than Muslims, after controlling for differences in household location (95% confidence interval: 1.2pp less likely – 10.0pp less likely). This estimate is statistically smaller than the unconditional difference between Hindus and Muslims, and represents a 68.7 percent decline in the unconditional difference (reported in column 1). Similarly, column 4 reports a difference of 6.8 percentage points when replacing these location controls with fixed effects for household across India along with differences in the local economy.

10
# **Table 5 continued from previous page**

||Ch|ose the IC|T or the e|nergy voca|tional train|ing|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||||(0.022)|(0.022)|
|Female role model|||||0.060|0.059|
||||||(0.040)|(0.037)|
|Can seek professional advice from individuals outside<br>the family|||||0.042|0.041|
||||||(0.029)|(0.028)|
|**Gender attitudes and agency**|||||||
|Gender attitudes (z-score)||||||-0.005|
|||||||(0.010)|
|Agency: input in productive decisions (z-score)||||||-0.009|
|||||||(0.014)|
|Observations|1400|1400|1400|1400|1400|1400|
|Cohort FE|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|

_N_ otes: Robust standard errors in parentheses.

*** ** * p _<_ 0.01, p _<_ 0.05, p _<_ 0.1 .

Standard errors are clustered by zone. Due to a low number of clusters (6), we rely on wild bootstrap to compute p-values. For each column, we include cohort and city fixed-effects. The acronym EICT stands for Energy or Information Communication and Technology.
9

**Table** 2: Skewness and kurtosis statistics for annual percentage changes in RER for 53 developing countries with managed excIange rates, 19661988 (continued).

||b3<br>Skewness|SI<br>max-<br>min<br>**_0_**|S2<br>min|/avg|b4<br>kurtosis|SR<br>std range|
|---|---|---|---|---|---|
|Sub-Saharan Afr|ica|||||
|Burundi|0.62|0.90|1.88|-0.07|3.90|
|Ethiopia|-0.16|-0.25|3.61|0.95|4.77**|
|Ghana|-1.88**|-2.16**|6.25|6.48**|5.30**|
|Kenya|0.52|0.45|1.97|-0.29|3.80|
|Madagascar|-3.13**|-3.91**|9.41|11.27**|4.98**|
|Malawi|-0.59|-1.11|3.34|-0.23|3.82|
|Mauritania|0.96**|1.56**|1.73|1.00|4.25|
|Mauritius|0.02|-1.00|2.58|-0.79|3.70|
|Nigeria|-2.57**|-2.82**|7.20|7.90**|4.98**|
|Rwanda|-1.97**|-2.13**|6.35|5.47**|4.99**|
|Sierra Leone|0.05|-0.58|3.31|0.27|4.09|
|Somalia|0.50|0.40|2.75|0.54|4.08|
|S. Africa|-0.78*|-1.50**|4.39|1.83**|4.78**|
|Sudan|0.02|0.17|3.33|1.13*|4.78**|
|Tanzania|-1.89**|-2.24**|5.50|4.84**|4.83**|
|Zaire|-2.03**|-2.69**|6.10|6.62**|4.89**|
|Zambia|-1.42**|-1.37*|6.82|6.77**|6.03**|
|Zimbabwe|-0.54|-0.94|3.31|0.12|4.03|
|Region median|-0.57|-1.05|3.47|1.07|4.81|

regions except SSA the median is near four<sup>8</sup> . The effect on the RER of the delayed depreciations, when they do occur, are larger than a typical "random shock" by a factor of four.

# B) Kurtotic distributions

Some phenomena of economic interest have distributions that are more peaked and fatter tailed than the normal, implying a larger relative probability of observations far into the tails. The last two columns of table 2 present two measures of fat tails for our RER innovations series, the excess kurtosis coefficient which is the scaled fourth moment minus three: _8_ In a sample of size 23 from a standard normal sample the average value of this statistic would be 2.5.
As can be seen in Table 2, the average levels of productivity, bribe tax and CPI are different between low CPI and high CPI countries<sup>15</sup> and between EU and non-EU countries. These variations could imply that the impact of bribe tax on productivity could be different for high and low CPI countries as suggested by equation (5), as well as for EU and non-EU countries. No major differences can be depicted in the level of time tax, however.<sup>16</sup>

**Table 2 Summary statistics by group of countries**

||**Tot**|**al**|**Non**|**EU**|**E**|**U**|**_CPI_**|**_CPI_**<br>|**_CPI_**|**_CPI_**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|
|**TFP**|0.148|0.916|0.150|0.941|0.144|0.874|0.239|0.983|0.090|0.867|
|**Bribe Tax**|0.099|0.298|0.139|0.346|0.030|0.171|0.195|0.396|0.030|0.169|
|**Time Tax**|14.528|18.223|14.313|19.819|14.884|15.220|14.669|21.172|14.430|15.851|
|**CPI**|3.393|1.150|2.863|0.904|4.288|0.949|2.287|0.362|4.191|0.814|

_Note: TFP is calculated as a residual from a simple production function, see appendix_ As a robustness check, we also use an alternative measure of institutional quality, namely the World Economic Forum’s index of the _effectiveness of the legal framework_ is solving legal disputes, which can be interpreted as a proxy for the ability of formal institutions to enforce contracts and prevent or sanction the occurrence of corrupt practices. This is also a country level index for the year 2008, where increases in the index imply better legal quality.

Including either of the indices in the model gives the following equation (4) as

> 15 Low and high CPI countries are defined as countries with CPI below and above the mean level, respectively. 16 The use of perceived corruption (the CPI index by Transparency International) may be problematic if there are discrepancies between perceived and actual corruption. Olken (2009) shows that this may be the case by examining a specific case of a road building project in rural Indonesia.  He compares corruption perceived by villagers with a more objective measure of corruption based on missing expenditure.  While a difference between actual and perceived corruption may potentially be a problem, one should keep in mind that Transparency International reports broad country level indices which are based on expert and business surveys.

19
- Joubert C, Todd P. 2011. The impact of chile’s 2008 pension reform on labor force participation, pension savings, and gender equity. _Univ. of Pennsylvania, Working Paper_ .

- Landa F, Yanez-Pagans P. 2008. Informe especial: La informalidad en el mercado laboral urbano: 1996-2006. _Manuscript, World Bank_ .

- Lee DS, Lemieux T. 2010. Regression discontinuity designs in economics. _Journal of economic literature_ **48** : 281–355.

- Martinez S. 2004. Pensions, poverty and household investments in bolivia. _Department of Economics, University of California at Berkeley_ .

- Molina R, Albó X. 2006. Gama étnica y linguística de la población boliviana. Technical report, Sistema de las Naciones Unidas en Bolivia.

- The World Bank. 2016. World Development Indicators. www.worldbank.org.

- Valencia H. 2011. Efecto del ingreso en las decisiones de co-residencia de los adultos mayores en bolivia. _Tesis, Magíster en Economía, Instituto de Economía - Pontificia Universidad Católica de Chile_

- .

17
workers who were relatively lower paid in 2001 experienced faster increases in wages while those who were better paid experienced slower increases. The highest decile in fact experienced, first, a decline in real earnings between 2001 and 2002 and then a steady increase thereafter; the lowest decile experienced a particularly sharp increase in 2002, then increased more moderately after. Simple averages by decile, however, may be subject to measurement errors.

<!-- Start of picture text --> Figure 3. Index of Net Real Wages and Real GDP Per<br>Capita<br>(2001=100)<br>145<br>Formal sector wage<br>130 Average wage<br>Informal sector<br>115<br>GDP per capita<br>Index<br>100<br>2001 2002 2003 2004<br>Source: LSMS Panel Data; IMF World Economic Outlook database; and World Bank staff estimates.<br><!-- End of picture text --> Wages have increased rapidly in the formal sector but not in the informal sector (Figure 3). The labor market as a whole experienced rapid real wage growth since 2001, outpacing productivity growth (GDP per capita growth). Estimates of the evolution of output prices and unit labor costs among enterprises in BH over the period 1998-2003 signify that the evidence is consistent with a profit squeeze in FBH (IMF 2004a), potentially affecting international competitiveness adversely, and depressing employment growth in the process. The data also suggest that corporate profits have been falling steadily over the last five years. The CEM (World Bank 2005b) reports that wages are high by regional standards and are not underpinned by proportionately higher labor productivity.

Across sectors, there are wide disparities in wage increases. The increase in formal sector wages, for example, outpaced the average wage increase while informal sector wages have been relatively flat. The increases in formal sector wages seem to have been led by relatively high paying firms and the government sector. In addition, indexation mechanisms, such as wage floors in collective arrangements being indexed monthly to average wages or the experience premium, have facilitated the spillovers of the wage increases to the rest of the economy. These wage settings systems are comparable to those of other countries in the region.<sup>8</sup> In South Eastern Europe and in the Commonwealth of Independent States (CIS), wage determination is still heavily influenced by the government, underpinned by a high share of the public sector in employment and privatized enterprises that reflect the behavior of state enterprises (World Bank 2005b). In BH as in some other economies, centralized wage setting systems have led to rapid formal sector wage increases, which in turn, have contributed to depressed employment generation in the formal sector, as evidenced by the growing informality of the labor market.

> 8 The system is centralized on paper but appears much less so for the economy at large.

7
<!-- Start of picture text --> Formal and Informal Employees by Occupation<br>In European Countries, by Region. 2004-2009<br>PL EELVSKBGRUROUAHUCZ SI DKSE FI NO ESPT IT GRCY IL FRBECHDEAT NLUK IE<br>East North South West<br>PL EELVSKBGRUROUAHUCZ SI DKSE FI NO ESPT IT GRCY IL FRBECHDEAT NLUK IE<br>East North South West<br>Manual, low-skilled Manual, high-skilled<br>Non-manual, low-skilled Non-manual, high-skilled<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25<br>0<br><!-- End of picture text --> Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Source: Calculation with ESS data

# **Figure 9 Occupational composition of formal and informal employees, by country**

This is true for all countries examined except for the Czech Republic and Slovenia. These three countries aside, the share of low-skilled manual (elementary) occupations among no-contract employees in Central and Eastern European countries except Poland, as well as in Denmark, Italy, Greece, Cyprus, and Western European countries except Ireland, varies between 14% and 24%, whilst in the formal sector it varies between 6% and 14%. The contrast is even sharper in Poland, Spain, Portugal and France, where the share of elementary occupations is less than 14% among formal employees but among their informal counterparts varies from 31% in France and Poland to 36% in Portugal to 56% in Spain. For the latter two countries this, plausibly, has to do with infamously strict EPL which makes it prohibitively expensive for employers to hire formally anyone but the most productive.

On the opposite end of the skill ladder, one finds that in every European country but Greece, Spain, and Portugal, 34% to 55% of formal employees hold high-skilled non-manual jobs, whilst among informal employees this proportion is less than 20% in 13 of the countries examined and falls between 20% and 30% in 12 countries; only in the Czech R., Israel, Belgium and France does it exceed 30%. Figure 10 summarizes these and related findings by comparing distribution of shares of manual and non-manual occupations in countries‘ formal and shadow sector across European regions.

29
regulatory and contract characteristic affects each performance variable differently. In other words, a certain contract characteristic could have a positive influence on one performance variable while having a negative or insignificant impact on another. Third, some regulatory and contract variables have bigger impacts than others. For instance, in some cases, the changes attributed to a fully autonomous regulatory body are much larger than changes attributed to other regulatory variables.

The third econometric study is global in scope, covering 22 countries around the world over a decade (Vagliasindi 2013), which are clustered by income level and system size. The study finds for the overall sample that the introduction of an autonomous regulatory agency is significantly positively associated with higher access, higher labor productivity, higher tariffs, and higher carbon emissions. However, results differ dramatically across country clusters. For countries with higher incomes and larger power systems, the introduction of an autonomous regulator is significantly associated with enhanced access, higher operational efficiency, lower tariffs and lower carbon intensity. By contrast, for countries with lower incomes and smaller power systems, the introduction of an autonomous regulator is significantly associated with reduced access, lower operational efficiency, higher tariffs, and lower carbon intensity. This result underscores the challenges in implementing regulatory reforms in less developed countries.

A brief overview of the main empirical results on regulatory governance reported in this section is provided in Table 7.

20
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valid and reliable cross-national data regarding economies’ education outcomes (Chabbot & Elliott, 2003; Hanushek & Woessmann 2012; Lockheed 2013).

The spread of PISA, in particular, provides an opportunity to investigate these explanations. First, we can examine the “globalness” of international large-scale assessments, using PISA as an example. If ILSAs are “global”, then countries in all regions and at all levels of income should have an equal likelihood of participating in PISA, controlling for a country’s economic status; if ILSAs are not “global”, then differences in participation rates should be observed for countries in different regions and at different levels of economic development. Moreover, if ILSAs are “globalizing”, then the rate at which countries join PISA should be comparable across regions and levels of income.

Second, we can examine the assessment capacity-building effects of participating in large-scale assessments. If participating in a large-scale assessment builds assessment capacity, then countries that have participated in another large-scale assessment (such as TIMSS, a regional assessment or a national assessment) should have greater assessment capacity and therefore should be more likely to participate in PISA than countries without this experience, controlling for the country’s economic status. The assessment capacity that is built through participation could be administrative, technical or both.

Third, we can explore the role of economists in creating a demand for information by looking at the role the Organization for Economic Cooperation and Development (OECD) plays in PISA. If an economists’ organization such as OECD creates a demand for information, then OECD member countries should have a higher level of participation in PISA than other countries, controlling for the country’s economic status.

# **METHOD**

This paper tests some of these notions empirically, using a purpose-built data set covering over 200 countries and economies<sup>3</sup> as of 2015.

# **A. Data sources**

The data used in this paper come from four main sources:

- Economic status and geographic location, from the World Bank.

- Participation in international and regional large-scale assessments, from published reports and websites of sponsoring agencies.

> 3 Hereafter “countries”.

4