rafmacalaba/datause-encoder-data huggingface dataset:
cases. Multi-drug-resistant TB (MDR-TB) in particular poses a significant challenge (Kazionny, Wells et al. 2001); TB patients in Eastern Europe and Central Asia are 10 times more likely to have MDR-TB than in other regions of the world, and 5.5 percent of new cases are multi-drug resistant (Dye, Espinal et al. 2002).

# **Evidence on the effect of treatment (DOTS programs) on TB incidence, prevalence, and mortality**

Before drawing a link between DOTS and declines in TB mortality, we review the evidence on the effectiveness of DOTS in controlling TB. Although TB declined before the introduction of drug treatment in many parts of the world, the decline has been accelerated since the 1950s by good chemotherapy programs, as seen in Western Europe (Styblo 1991), part of Northern Africa, and Latin America (e.g., Chile, Cuba, Uruguay). Data from Morocco and Peru provide two recent examples of the effect of treatment on transmission and incidence. Between 1994 and 2000, the incidence of pulmonary TB among Moroccan children 0–4 years of age fell at more than 10 percent per year, suggesting that the risk of infection was falling at least as quickly (Ministry of Health Morocco, unpublished data). The average age of TB cases also has been increasing for more than 20 years in Morocco as a consequence of falling transmission rates. The overall reduction in the incidence rate of pulmonary TB over the past decade was 4 percent per year. In Peru, DOTS was launched in 1991, and high rates of case detection and cure have pushed down the incidence rate of pulmonary TB by 6 percent per year (Suarez, Watt et al. 2001).

Some countries have measured the reduction in TB prevalence over time in the presence of good chemotherapy programs, though the reduction cannot always be attributed entirely to drug treatment. The Republic of Korea carried out seven surveys at five-year intervals between 1965 and 1995, during which time the prevalence of bacteriologically positive cases (smear- and/or culture-positive) of disease fell from 940 per 100,000 to 219 per 100,000 (Hong, Kim et al. 1998). Two prevalence surveys done in China in 1990 and 2000 showed a 32 percent (95% CI, 5%–68%) reduction in the prevalence per capita of smear-positive TB in DOTS areas, as compared with a negligible 5
# 3. Results

## 3.1. Labor market impacts of COVID-19

Chad has a predominantly informal economy where 92 percent of the labor force work as family farm workers, wage laborers in other farms, or non-salaried off-farm workers. The agriculture sector is the primary employer for both men (70 percent), and women (60 percent), where women work primarily as labor in their family farms (66 percent of the total female agricultural labor force) and men work mostly as own-account workers and as labor in other farms. It is critical to note that formal employment among women in Chad’s mostly informal economy is much lower than among men—only 8.6 percent receive wages from an employer, and women are 9 percent less likely than men to be in formal employment. Even in the manufacturing sector, where the proportion of female employment is slightly higher than male employment, 98 percent of the women work as non-salaried workers.

Women constitute 45.8 percent of Chad’s working population. The female labor force participation rate<sup>9</sup> is 49 percent, which is 13 percentage points lower than the Sub-Saharan Africa regional average and 24 percentage points lower than the male labor force participation rate of 73 percent.

Table 2: Distribution of male and female labor across main sectors (percent share of workers in total employed in primary and secondary employment)

||Agriculture<br>Manu|facturing|Services|
|---|---|---|---|
|Female|32.7|5.2|8.6|
|Salaried Non-farm|0.0|0.1|1.2|
|Non-Salaried Non-farm|0.0|5.1|7.4|
|Family Farm|21.5|0.0|0.0|
|Other Farm|11.2|0.0|0.0|
|Male|35.0|3.6|15.0|
|Salaried Non-farm|0.0|1.1|5.9|
|Non-Salaried Non-farm|0.0|2.4|9.0|
|Family Farm|12.7|0.0|0.0|
|Other Farm|22.3|0.0|0.0|
|% of the total female labor force|70.3|11.2|18.5|
|% of the total male labor force|65.3|6.7|28.0|

Source: Authors’ calculations using Harmonized Household Living Standards Survey 2018/19/ECOSIT 4 The impact of the COVID-19 pandemic on labor markets is potentially disastrous. Results from the phone survey suggest that less than three months into the pandemic, 20 percent of the respondents who were employed before the outbreak stopped working due to potentially COVID-19 related reasons, and 6 percent could not work because of business or office closure, which could also partially be due to COVID19. More than nine out of ten employed respondents did not receive payment during the outbreak period.

The CGE simulation results suggest that wage rates would decline by about 60 percent on average, with a 50 to 60 percent decline for women and a 45 to 65 percent decline for men (Figure 1 and Table 3). Female labor force participation reduces by more than 3 percent in 2020 due to the pandemic and bounces back

> 9 Labor force participation rate (LFP) is defined as the percentage of the female population ages 16+ that is working or actively looking for employment.

7
geographic reach of migration to Gulf-Malaysia has also been quite impressive in these periods. In 2001, about half of the villages (lowest level of administrative units) had no migrants in countries outside India, whereas by 2011, only about 5 percent of the villages had no migrants outside India (Figure 3). This paper exploits the variation in different migration rates over time and across different villages to identify the impact of migration.

# **3 Data and empirical strategy**

## **3.1 Data**

The migration and outcomes data used in this analysis come from two types of sources which I describe in detail below.

### **Housing and Population Census 2001 and 2011**

The Housing and Population Censuses are conducted by the Central Bureau of Statistics (CBS) of Nepal every 10 years. I use the public-use microdata for tenth and eleventh rounds of the national census, conducted in June of 2001, and 2011 for this analysis. The public-use micro-data of 2001 covers over 95% of all the villages (rural villages (VDCs) and urban municipalities) in the country and has data on the 11% random sample of all households. The 2011 data covers all the villages and has data on 8-100% of the households.<sup>7</sup> In both the datasets, I use sampling weights to adjust for the sampling procedure used to develop the micro-data.

The census collects information on the basic demographics, education, and labor supply characteristics of household members, as well as basic information on the household ‘members’ that are currently abroad. I use this measure to capture international migration. Though households cannot be tracked across the two censuses, villages can be identified and tracked across these rounds. The large sample size within the village allow for precise measurement of outcomes as well as the aggregate migration rate in the village. The large sample size of the census comes at a cost of not having detailed information on the individuals and households such as earnings, consumption, and expenditure. Furthermore, the information on labor supply is limited to information about the ‘usual’ status in the past 12 months, and misses the richness typical of labor force and living standards surveys.

However, following the estimation strategy developed by Elbers, Lanjouw, and Lanjouw (2003), statistical offices worldwide, with the support of the World Bank, have begun to estimate poverty and consumption measures at granular geographic level by combining census data with the richness of the living standards surveys. In case of Nepal as well, the 2003/2004 Nepal Living Standards Survey (NLSS-II) was used to prepare small area estimates of poverty and consumption for village units ( _illakas_ ) in 2001, and the 2010 round of the Nepal Living Standards Survey (NLSS-III) was used to prepare estimates for the village units in 2011. Due to the smaller sample size of the 2001

> 7For an average rural village, the microdata cover 13 percent of the households, and for the average urban municipality, the data cover about 20% of the households.

6
# **1 Introduction**

Each year, hundreds of billions of dollars are spent on targeted social protection programs. The importance of these programs increased dramatically in the past 18 months: In 2020, global extreme poverty increased for the first time in two decades, and most countries expanded their social protection programs, with more than 1.1 billion new recipients receiving government-led social assistance payments (Gentilini et al., 2020).

— — Determining who should be eligible for program benefits _targeting_ is a central challenge in the design of these programs (Hanna & Olken, 2018; Lindert et al., 2020). In high-income countries, targeting frequently relies on tax records or other administrative data on income. In low- and middle-income countries (LMICs), where a large fraction of the workforce is informal, programs often require primary data collection. The difficulty and cost of collecting data, and the variable quality of what gets collected, can introduce significant errors in the targeting process (Deaton, 2016; Jerven, 2013; Grosh et al., in press). These issues are exacerbated in fragile and conflict-affected countries, where two-thirds of the world’s poor are expected to reside by 2030 (Corral et al., 2020).

This paper evaluates the extent to which non-traditional administrative data, processed with machine learning, can be used for program targeting. Specifically, we match call detail records (CDR) from a large mobile phone operator in Afghanistan to household survey data from the Afghan government’s Targeting the Ultra-Poor (TUP) anti-poverty program. Eligibility for the TUP program was determined through a _hybrid targeting method_ , combining a community wealth ranking (CWR) and a short follow-up survey. Our analysis assesses the accuracy of three counterfactual targeting approaches at identifying the actual beneficiaries of the TUP program: (i) our _CDR-based method_ , which applies machine learning to data from the mobile phone company; (ii) an _asset-based wealth index_ , which uses asset ownership to approximate poverty; and (iii) _consumption_ , a common benchmark for measuring poverty in LMICs.

Our analysis produces three main results. First, by comparing errors of inclusion and exclusion using the program’s hybrid method as a benchmark, we find that the CDR-based method is nearly as accurate as the commonly employed asset and consumption-based methods for identifying the phone-owning ultra-poor households. Second, we find that methods combining CDR data with measures of assets and consumption are more accurate than methods using any single data source. Third, we find that when non-phone-owning households are included in the analysis, the CDR-based method remains accurate if non-phone-owning households are classified as ultra-poor; however, targeting performance is quite poor if house2
_Annex_ C _Expet Assessments_ _79_ For the world as a whole, the revised mortality rate rises from the original estimate of 3 to 11 and the number of deaths from 146,000 to 537,000, with 75 percent of them in Africa.

# _Schistosomiasis and filariasis_

Two sets of age- and sex-specific morbidity estimates were provided for each developing country region, one based on Pike (1987:204-205) and another on Walsh (1988:15), and various case-fatality rates ranging from 2.5 to 5 percent, and averaging 3.5 percent, were recommended.

First we observed that multiplying the average of the morbidity estimates by a constant case-fatality rate would produce an age distribution of deaths similar to that of the regression estimates (though very different in total) only if one assumed a tenyear lag, i.e., that average duration from initial infection to death is 10 years plus the average current duration of infection. Making the bold inference from this that those who die of schistosomiasis do so on average ten years after infection, we adopted ratios of deaths in the current year to current cases that are a tenth of the recommended casefatality rates. (Note that these ratios are not strictly case-fatality rates, and depend on complicated assumptions about whether numbers of cases have been rising or falling and how long a case stays active if death does not occur.) We did allow these ratios to vary by region, assumning a simplified distribution of the three principal species: we chose a high ratio of .0050 for Asia, assuming _Schistosomiasis japonicum is_ more lethal than other spedes; .0035 for Latin America and the Middle East, where S. _mansoni_ may have intermediate lethality; and .0025 for Sub-Saharan Africa where S. _hamatobium is_ the least lethal. The last parameter is consistent with a reported estimate of a causespecific mortality rate of 2 per thousand per year among heavily infected men in Zanzibar (Morrow n.d.:Table 4). The parameter choices were made to minimize deaths from this cause. Similarly, we took the lower set of morbidity estimates, based on Walsh (1988:15), with the additional argument that the other set used mid-1970s data. Total deaths by region calculated using these assumptions were distributed among agesex groups, using the distribution in the regression estimates.

Even with the choice of mdnimizing parameters, the resultng revision raises the world mortality rate from 0.25 to 0.70 per 100,000 and increases deaths from 12,000 to 34,400. The revision gives rates for Latin American males 45-64 of 7.8 per 100,000, and for males 65 and over of 10.3. For comparison, the highest reported rates for males in Latin America and the Caribbean in 1985 are these: Age 45-64 Age 65 + Suriname 4.4 15.9 Guyana 2.2 8.6 No other reporting country in the region has rates dose to these. Even with minimizing assumptions, therefore, the revised mortality rates are still high in comparison, with reported rates.
# **Table 6: Regressions for log cell expenditure per capita**

|||(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||(1)Full sample|Rural|Urban|Men|Women|
|Male|0.0137|0.000143|0.0711|--|--|
||(0.0562)|(0.0770)|(0.0850)|||
|Age|0.00472|0.0107|0.00586|0.0264|-0.0274**|
||(0.00963)|(0.0127)|(0.0140)|(0.0161)|(0.0116)|
|Age squared|-0.000111|-0.000217|-6.26e-05|-0.000352**|0.000289**|
||(9.89e-05)|(0.000132)|(0.000139)|(0.000155)|(0.000121)|
|Muslim|0.223*|-0.154|0.343**|0.304*|0.165|
||(0.124)|(0.224)|(0.137)|(0.184)|(0.127)|
|Serere ethnicity|-0.350***|-0.610***|-0.156*|-0.461***|-0.243**|
||(0.0827)|(0.147)|(0.0914)|(0.109)|(0.0968)|
|Poular ethnicity|<br>-0.114|<br>-0.222**|<br>-0.0258|<br>-0.213**|<br>-0.0288|
||(0.0699)|(0.113)|(0.0860)|(0.0957)|(0.0786)|
|Diola ethnicity|-0.265*|-0.567|-0.0682|-0.118|-0.366**|
||(0.156)|(0.534)|(0.152)|(0.221)|(0.151)|
|Mandingue ethnicity|<br>-0.125|<br>-0.0702|<br>-0.164|<br>-0.156|<br>-0.0884|
||(0.0966)|(0.159)|(0.117)|(0.134)|(0.105)|
|Sarakole ethnicity|-0.00777|0.0638|0.0186|-0.0864|0.0374|
||(0.167)|(0.299)|(0.228)|(0.230)|(0.187)|
|Mandiaque ethnicity|-0.362*|-0.551***|-0.190|-0.0677|-0.459**|
||(0.190)|(0.163)|(0.250)|(0.285)|(0.225)|
|Other ethnicity|0.0434|0.0361|-0.0897|0.220|-0.0854|
||(0.142)|(0.200)|(0.145)|(0.189)|(0.143)|
|Brothers same father|<br>0.00307|<br>-0.00771|<br>0.00710|<br>0.00506|<br>-0.000408|
||(0.0109)|(0.0148)|(0.0160)|(0.0182)|(0.0135)|
|Brothers same parents|0.0157|0.0176|0.00880|0.0312|0.00431|
||(0.0134)|(0.0190)|(0.0190)|(0.0207)|(0.0171)|
|Sisters same father|-0.00188|-0.00907|0.000743|0.00145|0.00182|
||(0.0111)|(0.0163)|(0.0153)|(0.0195)|(0.0132)|
|Sisters same parents|<br>0.0286**|<br>0.0365*|<br>0.0276*|<br>0.0183|<br>0.0379**|
||(0.0126)|(0.0209)|(0.0164)|(0.0200)|(0.0167)|
|Brothers same mother|0.0215|-0.0416|0.0573*|0.0181|0.0270|
||(0.0244)|(0.0428)|(0.0297)|(0.0380)|(0.0312)|
|Sisters same mother|0.00623|0.0337|-0.00222|-0.0210|0.0117|
||(0.0292)|(0.0424)|(0.0404)|(0.0451)|(0.0361)|
|First same gender|0.00601|0.0437|-0.0336|0.0467|-0.0108|
||(0.0468)|(0.0667)|(0.0660)|(0.0882)|(0.0657)|
|First of siblings|-0.0228|-0.117*|0.0842|-0.0870|0.0397|
||(0.0520)|(0.0704)|(0.0788)|(0.0964)|(0.0809)|
|First born is male|0.0545|0.0895|0.0153|0.0723|0.0898|
||(0.0417)|(0.0570)|(0.0583)|(0.0865)|(0.0560)|
|Father died recently|<br>-0.0578|<br>-0.0145|<br>-0.0761|<br>-0.111|<br>-0.0853|
||(0.0850)|(0.128)|(0.112)|(0.165)|(0.0920)|
|Father in farming|<br>0.170***|<br>0.153**|<br>0.192**|<br>0.215***|<br>0.108|
||(0.0574)|(0.0722)|(0.0952)|(0.0822)|(0.0692)|
|Mother in farm.|-0.0145|0.0552|-0.109|-0.168*|0.108|
||(0.0600)|(0.0769)|(0.0952)|(0.0875)|(0.0731)|
|Father in non-farm|0.295***|0.315***|0.259***|0.386***|0.217***|
||(00593)|(00962)|(00776)|(00862)|(00728)|
|Mother in non-farm|.<br>-0.0823|.<br>0.131|.<br>-0.171**|.<br>-0.119|.<br>-0.0608|

30
**<u>Table 7: Estimated temperature trend (oC/decade) for seven stations in Chile</u>**

|**Station**|**Trend**|**t-value**|**P-value**|**# of obs.**|
|---|---|---|---|---|
|Arica|0.11|4.40|0.000|648|
|Antofagasta|0.01|0.49|0.628|599|
|La Serena|0.02|0.73|0.465|624|
|Isla Juan Fernandez|-0.12|-6.14|0.000|531|
|Valdivia|-0.18|-5.66|0.000|547|
|Puerto Montt|-0.24|-9.98|0.000|605|
|PuntaArenas|-0.06|-2.54|0.011|549|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

There seems to be a clear tendency for northern stations to be warming, southern stations to be cooling, and intermediate stations to experience no change (see Figure 3). This general pattern is confirmed by the latest IPCC report (see Trenberth et al. 2007, Figure 3.9 based on Smith and Reynolds, 2005)<sup>3</sup> .

**Figure 3: Estimated temperature trend (oC/decade) versus latitude in Chile**

<!-- Start of picture text --> 0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>10 20 30 40 50 60<br>Latitude<br>Temperature trend (oC/decade)<br><!-- End of picture text --> _Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

We will use this simple relationship to estimate the average temperature change experienced in each of the municipalities in Chile over the last 50 years. According to a simple regression, the trend is reduced by 0.067 oC/decade for each 10 degrees we move south. This means that at the latitude of Antofagasta (23°26'S), there is no trend in temperatures, but for each 10 degrees further south, the 50-year change is reduced by

> 3 Falvey and Garreaud (2009) partly confirm this, but their interpretation of the data is different. They suggest cooling along the coast and warming in the mountains. However, they only use data from 1979 to 2006, and such a short period is vulnerable to spurious trends produced by natural multi-decadal cycles.

15
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as determinantst the stock of workers (or population) abroad, the level of income in the host country, a proxy for the length of stay, domestic inflation in the sending country, the exchange rate premium in the parallel market (or the interest rate differential between the host and the home country), in addition to indicators of special incentive schemes designed to attract IWR.

Subscribing to the above framework, we conducted in the third section a preliminary assessment of demographic, income and other macroeconomic factors as potential determinants of official remittances to six labor-exporting countries in North Africa and Europe: Algeria, Morocco, Portugal, Tunisia, Turkey and Yugoslavia. Our analysis shows that remittances are closely correlated with income cycles in the host countries and, to a lesser extent, with the stock of migrant workers. Although the association between remittances and the number of nationals working abroad seems weak on an individual basis, it is substantially strong across countries. The relationship between the three variables, however, is not strictly proportional. Furthermore, the significant variations in remittances per worker across the six countries suggests an important potential influence of the length of stay and of macroeconomic factors.

A more rigorous analysis was conducted in the fourth section of the paper, based on variants of the synthesis outlined in section two. We estimated a fixed-effect model using panel data from the above labor-exporting countries (excluding Algeria) over the 1977-1989 period. The analysis included formal diagnostic testing of heteroakedasticity and the possible endogeneity of the premium and inflation. Our results broadly corroborates the predictions of the model and provide interesting policy implications.

The stock of migrant labor has a positive and significant effect on real remittances, and appears with a unitary elasticity. This result is
progress. The cellular operator COMCEL started selling VoP services without a long distance license. All the existing long distance carriers who had paid the required $150 million for that license sued immediately revealing the difficulties of building a dynamic vision of competition in this environment with changing technologies.

Second, high prices continue to be perceived as an issue in the region. To a large extent this is a result of the limited competition in the sector. For most countries, the exclusivity periods granted to get the privatization deals done resulted in lasting high connection and usage tariffs. Table 2 shows that indeed residential connection rates continue to be high when compared to the US even if they have gone down significantly. These exclusivity periods are now coming to an end as in Argentina or Venezuela and this should lead to a reduction in tariffs driven by the market. However, most countries have not yet defined the rules of the game to ensure competition in a sector where costing the access to bottleneck facilities continues to be at the core of the regulatory debate. Section 4 is devoted to this key aspect.

Finally, there are concerns that tariffs may not have dropped enough and that demand from the poorest is likely to be rationed, a consequence of the continued Latin American income distribution problem. As of mid-1999 for instance, only 10-15% of the population had the resources to get on line.<sup>6</sup> This is not a random fact. A study of the distribution of the gains from utilities privatization in Argentina conducted in 1999 already suggested that unless regulation was effective in redistributing the gains to all users, reform mostly benefited the local co-owners of privatized assets (Chisari _et al.,_ 1999). In terms of the access to new technologies, this implies that unless service obligations and ability to pay constraints are built in regulatory decisions, there is a reasonable risk that the distribution of the benefits of reforms will reflect the current distribution of wealth.<sup>7</sup> Section 4 discusses the instruments that could be used to achieve redistribution, e.g. via general taxation or sector specific service obligations.

> 6 Latincom (1999), "Mass market internet still some way off in Latin America", June **16'h,** p. 5.

> 7 See the related WIDER research program and also Estache, Foster and Wodon (2001), and Estache, GomezLobo and Leipziger (2001).

_5_