rafmacalaba/datause-encoder-data huggingface dataset:
an estimated 85 percent of farmers cultivating the crop on plots of less than one hectare.<sup>4</sup> It is a rainfed with limited use of modern cultivated crop produced inputs, mostly by hand hoe, and vulnerable to weather shocks; productivity is low, at less than one ton per hectare compared to a world average of more than 5 tons per hectare.<sup>5</sup> During 2010-14, Tanzania’s annual maize production averaged 4.5 million tons, up from 2.5 million a decade earlier (Figure 2). Global maize production averaged about 800 million tons during this period, rendering Tanzania a small country in terms of its relevance in the global maize market.

Maize is grown in all five of Tanzania’s agroecological zones and sold in 18 markets (Figure 1). Nearly one third of national output is produced in the Southern zone (Table 1), whose four surplus markets — Iringa, Mbeya, Songea, and Sumbawanga — are relatively isolated, being more than 500 kilometers away from Dar es Salaam (a major consumption market) and Nairobi (an export destination) and having no convenient access to a port. Mbeya and Songea export maize to Malawi and Mozambique, most notably to Nampula in northern Mozambique; this trade often goes unrecorded (FEWSNET (2014); Burke and Myers (2014)). Tanzania’s Northern zone markets — Arusha, Moshi, and Tanga — account for almost 15 percent of maize output and are well connected to other major food-deficit markets: Arusha and Moshi are close to Nairobi while Tanga is close to the Kenyan port of Mombasa, in addition to having Tanzania’s second largest port.<sup>6</sup> The Central zone markets of Dodoma, Singida, and Tabora account for about 20 percent of maize production. They are food-deficit markets but have good access to transport infrastructure. The four Lake zone markets — Bukoba, Musoma, Mwanza, and Shinyanga — are all food-deficit markets as well, with convenient access to the markets of Kenya and other neighboring countries. Last, in the Coastal zone, which includes the large market of Dar es Salaam along with Lindi, Mtwara, and Morogoro, only the latter is an important maize producer. The relative isolation of the Southern zone surplus markets, combined with the long distances from markets to consumption centers and ports, and poor transport and storage infrastructure, subjects Tanzania’s maize sector to seasonal influences.

# **The Policy Environment**

The increased importance of maize in the local diet after World War II attracted government intervention. To secure food grain supplies during WWII, Kenya and Tanganyika (now mainland Tanzania) established a Cereals Board to control food grain trade. Gov4Government of Tanzania.

> 5 USDA, psdonline : https://apps.fas.usda.gov/psdonline/.

> 6Arusha has historically been one of Tanzania’s most important transport hubs (Iliffe (1979)).

3
Figure 5. Evolution of speech content in Costa Rica, Ecuador, Mexico, Paraguay, Peru, and Spain, 1940-2016

# (b) Ecuador

<!-- Start of picture text --> (a) Costa Rica (b) Ecuador<br>100% 100%<br>75% 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>1940 1956 1971 1986 2001 2016 1940 1949 1960 1980 2006 2015<br>(c) Mexico (d) Paraguay<br>100% 100%<br>75% 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>1940 1954 1968 1983 2000 2014 1955 1980 1996 2006 2016<br>(c) Peru (d) Spain<br>100% 100%<br>75% 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>1940 1955 1971 1986 2001 2016 1940 1949 1960 1980 2006 2015<br><!-- End of picture text --> Note: This Figure shows the topic probabilities estimated with LDA. The topic probabilities can be thought of as representing the proportion of each document that belongs to each topic.

15
# **Attitudes to equality: The “socialist legacy” revisited**

Mamta Murthi and Erwin R. Tiongson<sup>1</sup> The World Bank, 1818 H Street, NW, Washington, DC 20433 JEL Classification Numbers: I38, P29 Keywords: income equality, attitudes, preferences, transition

> 1 Mamta Murthi (mmurthi@worldbank.org) and Erwin R. Tiongson (etiongson@worldbank.org) work in the ECA Region of the World Bank.
supply, is obtained by solving the following problem: In this framework, pollution affects household’s decisions by affecting dependents’ health and increasing minimum caregiving needs, _s_ . Using standard methods, we obtain that _h_ 1 = _sk_<sup>_ρ−_1</sup> and _h_ 2 = _sk_<sup>_ρ_</sup> where _k ≡ w_<sup>_<u>w</u>_</sup> 2<sup><u>1</u></sup> 1 _−ρ_ _<u>ρ</u>_<sup>.Thus,anincreaseinpollutionwouldincreasedomesticwork</sup> ( _h_ 1 _, h_ 2) and decrease labor supply of both workers.

It is easy to show that if _k_ = 1 the effects of pollution on domestic work and labor supply,<sup>_<u>dh</u>_</sup> _ds_<sup>_<u>i</u>_</sup> and<sup>_<u>dL</u>_</sup> _ds_<sup>_<u>i</u>_respectively,arethesameforbothindividuals.However,if</sup><sup>_k>_1,then</sup><sup>_<u>dh</u>_</sup> _ds_<sup><u>2</u></sup><sup>_>_</sup><sup>_<u>dh</u>_</sup> _ds_<sup><u>1</u></sup><sup>_>_0.</sup> This implies that an increase of air pollution would reduce labor supply of worker 2 _more_ than for individual 1.<sup>44</sup> Condition _k >_ 1 could happen if individual 1 earns a higher wage, _w_ 1 _> w_ 2, or if individual 2 has an advantage in providing caregiving, _ρ <_ 1 _/_ 2.

The model suggests that, in the presence of heterogeneous workers, households could minimize the negative shock of pollution by re-allocating domestic duties, and labor, among its members. This implies that the negative effect of air pollution on hours worked would be _larger_ for individuals with relatively lower wages.

To examine this possible attenuation behavior, we estimate the baseline model (1) adding interactions of _PM_ 2 _._ 5 with several factors associated with wage differentials such as age, gender, education, role as head of household, or being an independent worker.<sup>45</sup> Similarly to the baseline results, we split the sample between individuals in households with and without susceptible dependents.

We find, however, no evidence of significant heterogeneous effects of pollution driven by these characteristics (see Table 5).<sup>46</sup> One possible interpretation is that other constraints preclude using within-household reallocation of caregiving as a way to attenuate this shock. For example,

> 44 This results depends on the substitutability of _h_ 1 and _h_ 2. If both are perfect substitutes the effect is even more evident. In that case, pollution would only reduce _L_ 2 and have no effect on _L_ 1.

> 45We check that these variables are indeed significantly correlated with differences in earnings. These results are available upon request.

> 46See Table B.6 in the Appendix for results using the sample of households without susceptible individuals.

23
> where _Comp"'_ is the _actual_ proportion of students entering and completing Grade I _only_

between times t -1 and t; _Comp,_ is the _actual_ proportion of students entering and completing Grade 2 _only_ between times t -1 and t; and so on. These one-year actual completion rates for each grade individually can be computed from data on enrolment and repetition rates for Grades 1 through 5, at times t -1 and t. Using the "Reconstructed Cohort Method" to compute expected five-year completion rates in this way is far less data-intensive than calculating actual five-year completion rates, which would require tracking of new students over five consecutive years (and more if there are repeaters). Data on the latter are available for only a few countries. Educational attainment data are also available, over five-year periods until 2000 for a wide range of countries, in Barro and Lee (1996, 2000). However, unlike the data from UNESCO, these are stock rather than flow measures; they provide information on the proportion of _all_ adults that have completed primary education. These do not have a clear relationship between flow measures of education spending at different points in time.

# **II. Data on governance indicators**

The indicators of corruption and quality of bureaucracy are taken from ratings made by experts at Political Risk Services (2001). This is not the only source of such ratings; however, it is the only one with wide country coverage, providing ratings for 140 countries. Researchers quite commonly use it.

# **III. Other variables**

The World Bank (various years) is the source of our data on GDP per capita, safe water access, total population and the percentage of the latter living in urban areas. The data on the Gini coefficient come from the database compiled for the Dollar and Kraay (2001) paper; these were obtained directly from the authors. Our data on ethno-linguistic fractionalization (a measure of ethnic diversity), the proportion of Muslims and the distance of each country from the Equator are from the well-cited paper by La Porta and others (1999). The distance from the Equator is computed by taking the absolute value of the latitude of each country from CIA (1996), and then scaling this to take values between 0 and 1.

_25_
# **The Mauritanian Labor Market through the Lens of the 2004 National Household Survey**<sup>**1**</sup>

Tania Rajadel _World Bank, Washington DC, USA_ Nicola Pontara _World Bank, Washington DC, USA_ Maria Laura Sanchez Puerta _World Bank, Washington DC, USA and IZA, Bonn, Germany_

**JEL Classification:** O-12, J-7, J-21, J-31

**Key words:** Mauritania, Labor Market, Wage Determination, Gender, Youth, Poverty.

> 1 We are grateful to the Mauritanian authorities for their support, notably the National Institute of Statistics, (ONS) for the availability of the 2004 National Household Survey data, and the Ministry of Economic and Finance (MEF). We also thank for insight and comments Alexandre Kolev and Paul Cichello. The findings, interpretations, and conclusions are our own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries. The usual disclaimers apply.
_Fandy Planning Programs in Sub-Saharan Afiica_ _17_ a time lag in the Original area. Both areas reached 20 percent prevalence of current use of modem contraceptives by 1987; the Original area starting from a base of **8.5** percent in 1980 and the Extension area from a base of 6.8 percent in 1984. Between 1981 and 1986 there was a gain of just 2.7 percentage points in the Original Area. Following revitalization of the village program of education and supply by the Ministry of Health in 1986, there was a gain of 6.4 percentage points in about one year. Stabilization of the revised program and continuation by the Ministry was essential to recapturing the momentum of community acceptance.

# _Fadors Contributing to Family Health Project Success_

The intensive series of meetings of Department of Community Medicine staff and village leaders held at the outset of program planning brought to the surface community views on health conditions and the services they needed. With the exception of family planning, the problems expressed by the villagers coincided with the interventions proposed. It is likely that the dialogue and acceptance of the program was influenced by the identity of the Department head-a persuasive leader from a family of village chiefs who is known and esteemed for his accomplishments as well as for his lineage.

A belief that the teachings of Islam forbid the use of contraceptives was a potential obstacle the program planners met head on by convening a workshop for local religious leaders. Religious beliefs, however, did not appear to be a problem for the villagers. According to the survey data, few women do not use or discontinue use of contraceptives for religious reasons.

|The midwives who made household visi<br>families. They were charged not merely with supp<br>for activities necessary for successful promotio<br>**Table 2. Current use of cont**<br>**women aged 15-49, Sudan E**<br>**198**|ts were of t<br>lying contra<br>n of family<br>**raceptives a**<br>**xtension Pr**<br>**7**|he village<br>ceptives,<br>planning;<br>**mong ma**<br>**oject, 198**|s and were known to the<br>but also were responsible<br>that is, reducing fear of<br>**rried**<br>**4 and**|
|---|---|---|---|
|_19_|_84_|_19_|_87_|
|_Charaaeristic_<br>_N_|_Percent_|_N_|_Percent_|
|_Age_||||
|15-24<br>325|4.3|353|13.9|
|25-34<br>557|7.0|651|27.5|
|35+<br>537|8.2|487|13.8|
|_Education_||||
|None<br>955|5.7|738|19.2|
|1-4 years<br>263|9.1|352|22.4|
|_5+_years<br>201|9.5|401|18.5|
|_Total current use_<br>1,419|6.8|1,491|19.8|
|_Total ever use_<br>1,419|22.6|1,491|37.1|

Souwe: Center for Population **and** Family **Health,** Columbia University. Operations Research **Progrm,** Sudan Community-Based **Family** Health **Project Data** Files.
Panel C. Domestic pull factors around time of issuance

|Year|Avg. GDPPC<br>(realUS$)|Avg.<br>GROWTH(%)|Avg. CA (%)|Avg. EXT (%)|Avg. PCRED<br>(%)|
|---|---|---|---|---|---|
|2000|4706|3.64|-3.14|45.39|31.89|
|2001|4077|0.88|-3.03|52.08|34.40|
|2002|2980|3.50|-0.87|54.66|36.99|
|2003|3282|3.00|0.53|48.83|31.53|
|2004|3983|6.50|-0.01|51.60|36.24|
|2005|4662|5.36|-0.71|42.64|35.98|
|2006|4946|6.43|-0.88|47.95|39.65|
|2007|5799|6.63|-2.62|42.53|42.72|
|2008|7848|4.28|-1.85|41.81|46.30|
|2009|6401|0.00|-1.11|41.28|40.34|
|2010|7507|6.87|-1.62|33.97|41.86|
|2011|8293|4.64|-2.45|40.36|50.92|
|2012|7856|3.51|-3.15|38.16|54.65|
|2013|7611|3.66|-3.40|39.96|53.76|
|2014|7669|3.43|-3.22|40.77|53.34|

Panel D. Fraction of country-industries with monthly issuance volume above historical average

|Year|% of country-<br>industries that<br>issue above their<br>historical average|
|---|---|
|2000|1.98%|
|2001|1.83%|
|2002|1.59%|
|2003|1.91%|
|2004|2.53%|
|2005|3.02%|
|2006|3.35%|
|2007|3.10%|
|2008|1.14%|
|2009|1.96%|
|2010|3.67%|
|2011|3.79%|
|2012|4.63%|
|2013|5.30%|
|2014|5.26%|

39
Formal education in Burkina Faso consists of six years of primary school, four years of lower secondary school, and three years of upper secondary school, followed by tertiary education. A national law officially makes school mandatory until age 16. However, this law is not enforced because the government lacks the resources to provide education to all school age children. In principle, children can attend public primary schools free of charge. However, parents still have to pay for various fees and school supplies. These expenses must be paid in cash and in a timely manner, with most being paid at the start of the academic year, thus representing a significant constraint for cash-strapped households. Cash transfers reduce this cash-constraint in addition to compensating for the opportunity cost of child time.

The pilot cash transfer program was conducted in Nahouri province in southern Burkina Faso, located approximately 100 miles from the capital, Ouagadougou. Households in the province are predominantly subsistence farmers growing millet, sorghum, and peanuts. On average, there are 6.5 members in each household (Table 1) of whom 3.4 are children ages 0-15 (1.5 between 0 and 6 and 1.8 between 7 and 15).

Of the children aged 7 to 15, 61.3 percent are reported by their parents to be enrolled in school, but when enrollment is measured using the information available in school rosters, the enrollment rate is lower at 49.1 percent. School attendance conditional on enrollment as measured from school rosters is higher at 98.3 percent, suggesting that, once enrolled children are very likely to attend classes. The attendance rates are consistent with other research in Africa using administrative school data (Miguel and Kremer, 2004 in Kenya; Benhassine et al., 2015 in Morocco; Kazianga, de Walque, and Alderman, 2012 in another region of Burkina Faso). Furthermore, at least for Burkina Faso where enrollment is low, they are suggestive of an environment where parents strategically choose which children they enroll and then make sure the children attend regularly. Focusing on attendance unconditional on enrollment, a broad measure of school participation that accounts for enrollment and attendance effects, we find that on an average school day, 47.1% of the age-eligible children are in class. Education expenses per child are on average $9.54 per year and discretionary education expenditures (school supplies and parent association fees) are $4.71.

Among children younger than 5, 26.7 percent were stunted (2 standard deviations below the reference population for height-for-age), and 35.4 percent were underweight (2 standard deviations below the reference population for weight-for-age).

5
official data by the end of that year, per rural person in the 2001 census.

– – The years in which the studied children were born 2001 to 2003 were the earliest years in which any TSC latrines were built. By 2003, the program had built about one latrine, on average, per 20 households in the districts where it was active. As the two last columns of table 1 show, over half of Indian districts had not received any TSC latrines at all by the end of 2003. The distribution of TSC intensity was not, of course, randomly assigned, and therefore it is unsurprising that there are differences between the districts that received TSC latrines in this early period and those that did not. However, there is no monotonic difference: on average the districts that received TSC latrines appear richer (with, for example, better houses), but they also show lower overall educational performance. These differences indicate that the use of district fixed effects will be a necessary part of the identification strategy.

We interpret TSC latrines reported built per capita as a noisy measure of overall TSC intensity. It is imperfect in at least two ways. First, it measures only latrine _construction_ , an easily quantifiable but secondary part of a program that would succeed only if it encouraged people to use latrines, including latrines they, in some cases, already owned (Barnard et al., 2013). Second, bureaucratic agents of distant government principals clearly had incentives to inflate latrine counts. Our strategy requires only that reported latrine construction be _correlated_ with true TSC program intensity. Insofar as our independent variable is a noisy measure of overall TSC activity, our estimates of the effect will be attenuated towards zero. If administrative capacity or corruption caused administrative records to be inflated upwards – presumably in the districts where other dimensions of welfare that would be correlated – with test scores are _worse_ then this would introduce _negative_ omitted variable bias into our estimates.<sup>7</sup> Therefore, we use these administrative records cautiously as an imperfect,

> 7To see this omitted variable bias from inflation of administrative records, assume that test scores are shaped by the TSC and other uncorrelated factors, so _test_ = _βTSC_ + _ε_ . We observe only _TSC_<sup>�</sup> , which is true program intensity plus an inflation factor _ι_ : _TSC_<sup>�</sup> = _TSC_ + _ι._ Then the numerator of the OLS estimator of _β_<sup>ˆ</sup> will be _β_ var( _TSC_ ) + _β_ cov( _TSC, ι_ ) + cov( _ι, ε_ ). Under the assumptions, which we find reasonable, that inflation will not be positively correlated with actual performance and service delivery, and that inflation

11
in the ‘disadvantage’ of being located in any region other than Dhaka.<sup>12</sup> This trend is seen more clearly for the period 2000-2005 where decompositions show a reduction in the size of the average (negative) effect of being located in any region other than Dhaka (Table A-3).

Second, a more disaggregated analysis reveals a nuanced story suggesting that the earlier divide between Dhaka and the rest of Bangladesh is being replaced by an ‘EastWest’ divide. We run the following regression specification: _Y_ = α + _X_ β 1 + β 2 _D_ ( _year_ = 2005) + _Zk_ β 3 + _D_ ( _year_ = 2005) _Zk_ β 4 + ε<sup>,</sup> where Y is the vector of log of real per capita consumption, X represents other control variables and Z the district dummies. Chow test results (for changes in location effects between 2000 and 2005) represent coefficients of the interaction terms between district dummies and dummy for 2005 in the model where both years are pooled.

Results presented in table A-4 show that the narrowing of the gaps with Dhaka region occurred mostly for the eastern regions while the gap with Dhaka region has widened for most of the regions to the west and southwest. These are evident from the results of Chow tests indicating whether the location effects in 2005 are significantly different from those in 2000. For the rural sample, of the nine western regions, the gap with Dhaka region has increased for four and remained unchanged for two. In contrast, the gap with Dhaka region has not increased for _any_ of the eastern regions, and shrunk for five out of the seven. For the urban sample, the gap with Dhaka region has increased

> 12 There are difficulties in making exact comparisons between the results of Ravallion and Wodon (1999) and results for 2000 and 2005 – because of some differences in the specifications, which are necessary due to changes in the household survey over time.

19
coverage, detail of data collected, frequency and comparability across time and countries. Changes in the questionnaires and data collection methods are two key determinants of comparability over time or within a country. Some issues are more relevant for data collected in the country of destination; others present more of a challenge when collecting data on migrants at the origin. Below we discuss some of the more systematic challenges faced in data collection and analysis.

# **_Definition of a migrant_**

How a migrant is defined has been a challenge, and Ravenstein (1885) devoted a long discussion to the issue. According to the United Nations Statistics Division (1998, p. 6), a migrant is ‘any person that changes his or her country of usual residence’. The essence is movement from one geographic location to another, which is the most relevant criterion for economic analysis. In practice, however, migration in official statistics manifests itself in myriad guises and is anchored to different concepts and definitions, including the individual’s country of birth, country of citizenship, purpose of visit or visa type, place of last permanent residence, duration of stay, and even ethnicity.

Place of birth and nationality are the two most commonly used definitions. Foreign-born applies to the first generation that crosses the national boundary.

Nationality, on the other hand, might apply to dependents and offspring if the destination country’s laws are relatively restrictive in granting citizenship to migrants and/or their children born there. Individuals may be classified as migrants or non-migrants, depending on the definition, even in different sources within a single country. Variation in countries’ 10
CHARACTERISTICS kND PERFORMANCE OF SEITLEMENT PROGRAMS:

# A REVIEW

# _Table of Conter.r_

|Sufmm|ary .|||Page<br>i|
|---|---|---|---|---|
|1*|INTR|ODUCTION.|**P**|**ago**1|
|2.|CHAR|ACTERISTICS AND PERFORMANCE|||
||(a)|Land tenure, allocationrules<br>and practice.|Page|5|
||(b)|<br>Scale of program.|<br>Page|9|
||(c)|Size ofholding......<br>.......<br>.|Page|9|
||(d)|Typeof<br>scheme organization and administration|Page|11|
||(e)|Combinations of settlement|||
|||withcontract farming<br>....<br>.Page||17|
||(f)|<br> <br>Modalities of land acquicition<br>.Page||17|
||(g)|Settler selection rulesandpractice .<br>.Page||18|
||(h)|Restrictions imposed on settlers .Page||20|
||(i)|<br>Provision<br>of<br>infrastructure<br>and|||
|||speed of construction.Page||21|
||(j)|<br>Access to and modality of supply of services|Page|22|
||(k)|Social services.|<br>**Page**|26|
||(1)|Resource base<br>..|Page|2'|
||(M)|Cost per beneficiary family<br>.<br>.Page||27|
||(n)|Grant elements in settlemert programs.Page||28|
||(0)|Cost recovery.|Page|28|
||(p)|Settler groups<br>.Page||29|
|3.|CONC|LUSIONS<br>.Page||30|
|Notes||..||Page 32|
|Refer|ences|...||Page 34|

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