rafmacalaba/datause-encoder-data huggingface dataset:
27 Notes

> lSee Frank Ellis, "Household Strategies and Rural Livelihood Diversification," Journal of Development Studies 35 (October 1998): 1-38.

2 John Mellor and Uma Lele, "Growth Linkages of the New Food Grain Technologies," Indian Journal of Agricultural Economics 18 (January 1972): 10-15; John Mellor, The New Economics of Growth (Ithaca, NY: Cornell University Press, 1976); and Bruce Johnston and Peter Kilby, Agriculture and Structural Transformation (New York: Oxford University Press, 1975).

3 Alain de Janvry and Elizabeth Sadoulet, "Rural Development in Latin America: Relinking Poverty Reduction to Growth," in Including the Poor. eds. M. Lipton and J. van der Gaag (Washington, DC: World Bank, 1993).

4Peter Lanjouw, Ecuador's Rural Nonfarm Sector as a Route Out of Poverty, World Bank Policy Research Working Paper 1904 (Washington, DC: World Bank, 1998); Richard H. Adams, Jr., Sources of Income Inequality and Poverty in Rural Pakistan, Research Report 102 (Washington, DC: International Food Policy Research Institute, 1995); and D. L. Chinn, "Rural Poverty and the Structure of Farm Household Income in Developing Countries: Evidence from Taiwan," Economic Development and Cultural Change 27 (January 1979): 283-301.

> 5 Adams, Jr..

6Thomas Reardon and J. E. Taylor, "Agroclimatic Shock, Income Inequality and Poverty: Evidence from Burkina Faso," World Development 24 (May 1996): 901-914; Paul Collier, Samir Radwan and Samuel Wangwe, Labour and Poverty in Rural Tanzania (Oxford: Clarendon Press, 1986); and Peter Matlon, "Income Distribution Among Farmers in Northern Nigeria: Empirical Results and Policy Implications," African Rural Economy Paper No. 18 (Lansing, MI: Michigan State University, 1979) 7Among the factors affecting the distribution of land in countries are: wealth accumulation (richer people will own land if land is an asset); and returns to scale (farm size will be larger if there are returns to scale). 8 Oded Stark, J. E. Taylor and Shlomo Yitzhaki, "Remittances and Inequality," Economic Journal 96 (September 1986): 722-740, esp. p. 725.

9 As noted by Leibbrandt et al, Rk is a form of rank correlation coefficient, because it measures the extent to which the relationship between Yk and the rank distribution of total income coincides with the relationship between Yk and its own rank distribution. See Murray Leibbrandt, Christopher Woolard and Ingrid
current stochastic distribution of climate events is expected to change during the coming century.

# **2.7  Selection of representative future monthly forecasts**

The procedure summarized in 2.6 provides a view of expected change in annual temperature and rainfall conditions. However, investment planning in agriculture and other sectors will also be sensitive to expected changes in the monthly pattern of temperature and rainfall. So the final challenge is to distill our best guess about future monthly patterns from the huge set of future monthly predictions at our disposal. And we have to do this while preserving the jointness of temperature and rainfall data – they cannot be separated for summary purposes.

We begin by expanding the previously-computed annual totals into monthly distributions, in two steps. First, for annual temperature and rainfall data for each GCM, we compute the ratio of each monthly observation to the corresponding annual mean value. Then we apply these ratios to the forecast annual means produced in Step 2.4. The result is a dataset with 3,571,200 observations (372 grid squares; 8 GCMs; 100 years; 12 months/year). In each grid square, we have nine probabilities (which sum to 100%) assigned to Low-Medium-High combinations of annual temperature and rainfall. For cells with non-zero probabilities, we select representative monthly distributions using a weighted variant of our previously-described approach to benchmarking: Within each of the nine cells in each future period (and grid square), we compute squared monthly deviations from medians for temperature and rainfall for each year and GCM model. We weight the squared deviations by the inverse of the GCMs “goodness-of-fit” score (this assigns lower deviation values to better-fitting GCMs, ceteris paribus), which has the 8
To estimate the impact on labor force participation, we use the data as our baseline round and three post-program rounds of November 1998, June 1999, and November 1999.

> The dependent variable _Y_ ( _i_ , _t_ ) in equation (1) is specified by a binary variable indicating whether an individual _i_ works in the labor market in period _t_ . Specifically, a person is classified

> as working in the labor market ( _Y_ ( _i_ , _t_ ) = 1) if he/she reported that having worked over the previous week (whether paid or unpaid).  In addition, we take into account a follow-up question to capture individuals who may engage in informal activities, which the respondent may not have initially considered as work.  This question collects information about

participation in a) selling a product; b) helping in family business; c) making products to sell; d) washing, cooking or ironing; and e) working in agriculture activities or caring for animals. We also include as working those individuals who respond that they engage in any of these activities. It is important to keep in mind that domestic activities are not included in this definition of work.

We also consider two other outcomes variables, salaried work and non-salaried work and estimate the impact of PROGRESA on each category. The distinction between salaried and non-salaried<sup>10</sup> work is made through what a worker reports as his/her occupational position. Workers who report that they were daily agricultural workers or non-agricultural employees are considered as salaried workers. All other workers are classified separately and include selfemployed workers, business owners, unpaid workers and _ejidatarios_ .

Equation (1) is estimated separately for males and females. In addition, we conduct the empirical analysis separately for 5 age group (ages 18 to 24, 25 to 34, 35 to 44, 45 to 54 and 55

> 10 In preliminary analysis, we considered separating non-salaried workers between self-employed workers and unpaid family workers. Nevertheless, the proportions of individuals participating in each of these activities are quite small for all age groups, and the distinction between these activities is often blurred so that we prefer to aggregate these groups in the impact analysis.

14
n <!-- Start of picture text -->
- 13 -<br><!-- End of picture text -->

<!-- Start of picture text --> GRAPH 3 LOGS: NIAGON<br>COTE D'IVVIRB<br>190<br>180<br>170<br>160<br>150<br>140<br>U 130<br>AOD<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>1956 196D 1965 1970 1975 1980 1985 1989<br>YBAR<br><!-- End of picture text -->

> GRAPH 4 _LOGS: DARK RED MERANTI_ MAL&YSIA

<!-- Start of picture text --> a40 --<br>930<br>210<br>ROD<br>190<br>180<br>17D<br>160<br>150<br>140<br>130<br>110<br>100<br>90<br>O0<br>70-<br>60-<br>50<br>40<br>30<br>1955 1960 1965 1970 1975 1980 19B5 1989<br>YBAR<br><!-- End of picture text -->
relationship between permanent income and remittances. We believe this is a fruitful area for further research which collects detailed information on shocks.

# **5. Conclusions**

Using a new database of over 12,000 African migrants we have provided a detailed analysis of which migrants remit and which don’t, and how remitting patterns vary with key variables such as gender, education, income, and time abroad. In doing so, we have refuted several common perceptions about remitting patterns. We find male migrants to remit more than female migrants, contrary to the perception that women are better remitters. We find the more educated and those earning higher incomes remit more, contrary to the belief that the high-skilled are less likely to remit than the lower skilled. And we find that the likelihood of remitting and the amount remitted increase over the first ten years of time abroad, contrary to fears of remittance decay, and that even with more time abroad, the drop in the likelihood of remitting with time abroad is offset by individuals remitting more when they do remit.

We consider this a reasonable start in overcoming the data limitations and information gaps that currently restrict understanding of remittance behavior of Africans. Nevertheless, it is but a start. The data we have is the most comprehensive currently available. Yet ideally one would like to extend the analysis to also include representative datasets on African migrants in major migrant destinations within Africa; to provide more detail on the circumstances of the household members remaining in the origin country; and to incorporate a panel data element that would enable study of how the behavior of the same particular migrants changes with time spent abroad and with shocks experienced. The household surveys which we use have the advantage of capturing informal transfers that are not recorded in official remittance flows. However, there are likely to be lingering concerns about the quality of the remittance figures being reported. We believe that migrants are less likely to underreport amounts remitted than might be the case in surveys in receiving countries, where household members might be concerned about tax and theft.<sup>9</sup> In future work it would be useful to extend the matched sample

> 9 Conversely, there is the possibility that migrants might be overreporting the amounts they remit, in order to make themselves look better in the eyes of the interviewer.

26
_Table 3: Number of jobs_

||(1)<br>|(2)<br>ln(Jobs)<br>|(3)<br>|
|---|---|---|---|
||Trade War|Covid-19|Invasion of Ukraine|
|**I(t>Event) x I(US) x ...**||||
|... x I(Same country)|0.469|0.697**|0.723**|
||(0.439)|(0.353)|(0.322)|
|... x I(Border)|0.424|0.917**|0.723**|
||(0.454)|(0.352)|(0.317)|
|... x ln(distance)|0.374**|0.151|0.193*|
||(0.151)|(0.113)|(0.098)|
|... x I(NATO)|0.147|-0.229|-0.367*|
||(0.306)|(0.251)|(0.217)|
|... x I(Common language)|-0.180|-0.267***|-0.262***|
||(0.130)|(0.100)|(0.091)|
|**I(t>Event) x ...**||||
|... x I(Same country)|-0.439|-0.421|-0.589**|
||(0.364)|(0.303)|(0.282)|
|... x I(Border)|-0.438|-0.367*|-0.374*|
||(0.276)|(0.214)|(0.198)|
|... x ln(distance)|-0.457***|-0.187**|-0.193**|
||(0.107)|(0.086)|(0.075)|
|... x I(Common language)|0.195|0.186*|0.124|
||(0.135)|(0.101)|(0.090)|
|Observations|20,088|27,540|34,992|
|R-squared|0.863|0.863|0.862|

Note: The regression includes source country-time, host country-time, and source country-host country fixed effects. The sample covers bilateral US and EU investment. The sample covers bilateral US and EU investment. In column (1), the sample covers the period from January 2015 to February 2020 (Trade War); in column (2), from January 2015 to January 2022 (COVID-19); and in column (3), from January 2015 to December 2023 (Invasion of Ukraine). Robust standard errors, clustered at the source-time level, are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1 18
> Finally, the full set of simulated per capita expenditures, _y_ ˆ _chr_ , are used to calculate estimates of the welfare measures for each target population.<sup>9</sup>

> This procedure is repeated _R_ times drawing a new **_α_**<sup>~</sup> _r_ , **_β_**<sup>~</sup> _r_ , ( σ ~ η 2 ) _r_ and disturbance terms for each simulation.  For each subgroup, the mean and standard

> deviation of each welfare measure are calculated over all _r_ =1,..., _R_ simulations.  For any given location, these means constitute our point estimates of the welfare measure, while the standard deviations are the standard errors of these estimates.

There are two principal sources of error in the welfare measure estimates produced by this method.<sup>10</sup> The first component, referred to as _model error_ in ELL (2002) _,_ is due to the fact that the parameters from the first-stage model in equation (2) are estimated. The second component, termed _idiosyncratic error_ , is associated with the disturbance term in the same model, which implies that households’ actual expenditures deviate from their expected values. While population size in a location does not affect the model error, the idiosyncratic error increases as the number of households in a target subgroup decreases.

# **3. Data**

The analysis in this paper uses data collected as part of the targeting and evaluation program of PROGRESA, a health, education, and nutrition program of the Mexican government. Assignment to PROGRESA for households in these communities was randomized by community; a census of all households in 506 communities was conducted in November 1997, 320 were integrated into PROGRESA in late spring of 1998, and three follow up surveys (complete censuses) of households in all 506 communities were conducted in 1998 and 1999. Additionally, a survey was conducted in

> 9 These calculations are performed using household size as weights, implicitly assuming that expenditure is distributed uniformly within households. The same methodology could be applied using equivalence scales to capture alternative intrahousehold distributional assumptions.

7
micro-enterprises where wages are notoriously low, large African employers pay<sup>higher wages (Mazumdar</sup> and Mazaheri 1998, Velenchik 1997).

Trading enterprises are not very centralized. In addition to the owner, 1.1 persons on average<sup>are</sup> authorized to buy for the firm in Benin, and 0.6 persons in Malawi. Similar<sup>though slightly lower numbers</sup> are reported for those authorized to sell for the firm. Surveyed trading firns<sup>operate an average of 4.7 days a</sup> week in Benin, vs. 6.1 days a week in Malawi.

On average, the owner is absent for 12 days a year in Benin, and 46 days in Malawi.<sup>Absences are</sup> mostly motivated by the need for the trader to visit distant purchase and sales<sup>market. In most cases, the firm</sup> continues to operate in the trader's absence. In the absence of the trader,<sup>the business is normally run by an</sup> employee of the firm, preferably a relative, in 45% of the cases in Benin and 17% in Malawi. It is also<sup>very</sup> common for the trader to rely on someone external to the firm-either a friend or a relative-to look<sup>after</sup> the business in his or her absence. This occurs in 48% of the cases in Benin and 64% of the cases in Malawi. If such a person cannot be found, the business closes.

15
his/her gender and associated homecare duties. On the other hand, if the individual is not the primary breadwinner, during brief periods of economic need, such as business cycle downturns, she/he may act as secondary labor and enter the labor force. However, the need to also perform as caregiver will lead to entry in the more time- and locationflexible informal sector.

Answers to these two questions will also add to the debate about whether or not the labor market in Mexico is highly segmented. The informal sector is traditionally interpreted as an employer of last resort in highly regulated labor markets (Thomas 1992). A competing view suggests that the labor market is well integrated, so if people enter the informal sector, they may do so in search of flexibility (Maloney 1997, Cunningham and Maloney 1998). The sectoral allocation patterns detected in the study will reveal whether individuals always prefer the higher paying formal sector (or selfemployment) jobs or if they enter the flexible, lower paying informal wage jobs to simultaneously fulfill market and household roles.

A Mexican household survey that follows individuals over five consecutive quarters for 1987-1993 is used for the analysis. It offers three advantages over most other developing country. First, each observation's labor force behavior over time can be identified, thereby omitting the problem of sample heterogeneity faced in cross-sectional time series studies. Second, the household survey permits the incorporation of information about the presence and labor market activity of other household members over the same period. Finally, the Mexican economy went through a full business cycle during this period, so labor supply during both peaks and troughs can be considered.

household income, although it may be received through non-labor instruments or through the labor of other household members who are not the head of household.

4
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|Descriptor||2006|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|
|Externaldebtmanagementassumptions|4|Chose the strategynumber||||||
|same as the baseline|1|199,7e2,037.3|206,046,587.2|2096068,708.8|Z17,8205709|224640,375.1|231,023,019.4|
|Constantindollarterms|2|199,782,037.3|202624,757.38|<br>203,613,1713)|204037,202.1|<br>204463,518.5|<br>204,463,318.5|
|<br>Constantinpercent ofGDP|3|<br>1997820373|<br>21911E9805|BSR<br>12SSTEA|<br>ARTRATI 2|<br>2R12321592)|<br>305,629,0490|
|Sudden stop in2006 (no disbursements)|4|155,4%6,5404|<br>1635814445|<br> <br>1689662001)|<br>1826480281|<br>194083219.3)|<br>205,137,490.0|
|Dechningpath|3|199,782,037.53|279,208,680.0|3048291875|304968,009.9|291240233.9|2%4.156,243.1|
|<br>Inpercent of GDP||<br>37.3|<br>323|<br>29.0|<br>27|<br>22.6|<br>20.0|
|Constantdecline|||12%|-12%|-12%|-12%5|12%|
|<br>Userdefined|||||||-12%6|
<!-- Start of picture text --> Manufacturing Construction<br>20 14 11.5<br>12<br>16<br>10 6.6<br>9.1 8<br>12<br>7.1 6<br>8 4<br>3.1<br>8.5 2 3.6<br>4 8.6 0<br>-2<br>0<br>-4<br>-4 -6<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018 Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text --> 28
mathematics as an outcome variable. Second, we use data on subjective-well being<sup>11</sup> to form an additional dependent variable and examine the effect of arsenic exposure in drinking water. Next section reports the regression estimates of equation (1)-(3) using variety of outcome measures.

# **4 Results: Arsenic poisoning and student achievement**

- **Key findings**

- Controlling for past school inputs, school characteristics (including arsenic poisoning of tube well at school) personal attributes and family background, we unambiguously find a negative and statistically significant correlation between mathematics score and arsenic contamination of water well at home (Table 1).

- Net of household-level exposure to arsenic poisoning, arsenic contamination of tube well at school has negative but statistically insignificant impact (Table 1).

- Weak effect of arsenic poisoning at school is explained by the fact that children in rural Bangladesh switch from one school to another at the time of their transition from primary to secondary education. As a matter of fact, only 3% of the sample children attended the same school for primary education.

- Interaction of arsenic poisoning at home with gender variable indicates that boys from exposed households are significantly worse-off than girls. This finding seems consistent with the hypothesis that boys in rural areas consume more fluid than girls (as they’re engaged in manual labor) and hence more likely to have greater intake of poisoned water (Table 1).

> 11 There is a large number of empirical studies that have used subjective response data to model economic behaviour. For a review, see Tella and MacCulloch (2006).

12
agencies responsible for compliance, empowered local village councils to request information regarding the schemes, required states to fully utilize their grain reserves, asked the state and central governments to publicize the court‘s orders through state-run media, and proscribed governments from eliminating or restricting the schemes without the consent of the Court ("People's Union of Civil Liberties versus Union of India and Others, Writ Petition (Civil) No. 196 of 2001" Court Order 8 May 2002; Banik 2010).

The mode of intervention placed significant emphasis on structuring the means of communication. For instance, the Second, Third, Fourth, Fifth, and Sixth Reports of the Courtappointed Commissioners in the case included long sections on access to information, which the Commissioners believed were crucial for enforcement.<sup>7</sup> These typically called for opening records for food and employment schemes, dissemination of information on food prices, dissemination of the courts orders, and the implementation of the Right to Information Act. In the Second Report, the Commissioners noted: ―We wish to emphasize that the approach of the commissioners is not fault finding. The States and Union Territories must take us as a friend and cooperate in a common endeavor to tackle the issue of food insecurity‖ (Saxena 2002). The Commissioners‘ reports also include letters sent from the Chief Ministers to central government officials, and provide substantial information about grain prices, the extent of poverty, and wages in various states.

A number of the Court‘s orders were extremely detailed and set time limits by which various officials were to undertake a variety of specific actions. But the Court did not, for the most part, understand these orders to be determinate duties to be enforced by the threat of contempt orders or other sanctions. Rather, the Court used them to assess the seriousness of the official response. For instance, although the first Court order in 2001 required states to establish the midday meals scheme in the 25% of the poorest districts, by 2002, it was clear that the states of Uttar Pradesh, Jharkhand, and 7 These reports are available on the website of the Right to Food Campaign: http://www.righttofoodindia.org/index.html. 26
Table 6: Cost‐effective analysis of selected interventions

|Teacher incentives (ear|Effect<br>size|Added<br>years of<br>equivalent<br>schooling|Return<br>to<br>impact<br>(%)|Program<br>cost per<br>individual,<br>2011 PPP<br>|EYOS per<br>$100<br>Sub‐Saharan|Net Present<br>Value of<br>increased<br>income, PPP<br>current<br>international $ Africa|Benefit‐<br>cost ratio|References<br>Glewwe Ilias and|
|---|---|---|---|---|---|---|---|---|
|y<br>2), Kenya|0.14|0.88|8.0|4.2|21.3|$652|155.9|, ,<br>Kremer(2010)|
|Streaming by<br>achievement, Kenya|0.18|1.14|10.3|1.0|117.6|$707|725.4|Duflo, Dupas, and<br>Kremer (2015,<br>2011)|
|Providing earnings<br>information,<br>Madagascar|0.20|1.31|11.9|0.4|370.4|$694|1957.3|Nguyen (2008a)|
|Minimum conditional<br>cash transfers,Malawi|0.20|1.31|11.9|748.5|0.2|$675|‐0.1|Baird, McIntosh,<br>and Özler(2011)|
|Textbooks for top<br>quintile, Kenya (grades<br>3‐8)|0.22|1.42|12.8|2.6|54.7|$1,046|402.9|Glewwe, Kremer,<br>and Moulin (2009)|
|Extra contract teacher +<br>streaming, Kenya|0.25|1.61|14.6|24.2|6.7|$996|40.2|Duflo, Dupas, and<br>Kremer (2011,<br>2015)|
|Girls Scholarships,<br>Kena|0.27|1.76|15.9|41.3|4.3|$1,295|30.4|Kremer, Miguel,<br>and Thornton|
|y||||||||(2009)|
|Villae‐based schools||||S|outh and Eas|t Asia||Burde and Linden|
|g ,<br>Afghanistan|0.50|3.22|18.s1|111.2|2.9|$2,262|19.3|<br>(2013)|
|Read‐a‐thon ,Philippines<br>(after 3 months)|0.06|0.39|1.5|19.2|2.0|$640|32.3|Abeberese, Kumler,<br>and Linden(2014)|

26