rafmacalaba/datause-encoder-data huggingface dataset:
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impact on hours worked or health of deaths of biological children who were not household members of the older adults at baseline.

The rest of the paper is organized as follows. Section 2 describes the data and defines the key dependent and independent variables. Section 3 lays out our empirical strategy and discusses the various types of bias on the measurement of the impact of adult death. Section 4 presents our results, and Section 5 concludes.

# **2. Data**

# **2.1. Geographical context and data summary**

This study uses survey data from the Kagera region of Tanzania, an area west of Lake Victoria, and bordering Rwanda, Burundi and Uganda. Kagera is mostly rural and primarily engaged in producing bananas and coffee in the north, and rain-fed annual crops (maize, sorghum, and cotton) in the south. The region is an area of early and high HIV prevalence, which has led to a significant increase in prime-age mortality rates. Kwesigabo _et al._ (2005) report on three population samples in 1987 in districts of contrasting exposure in Kagera, and find an overall age-adjusted HIV prevalence of 24.2% in urban Bukoba district; 10.0% in Muleba district, a medium-prevalence area; and 4.5% in Karagwe district, a low-prevalence area.

Subsequently, prevalence rates in the region have dropped rapidly, which has been attributed both to mortality of those infected and lower incidence (as measured by repeat testing of the original population-based sample). In urban Bukoba, prevalence went down to 18.2% in 1993 and 13.3% in 1996. In the other areas studied, prevalence also declined considerably, to 4.3% and 2.6% in Muleba and Karagwe, respectively. Kwesigabo _et al._ (2005) note that the decline in these areas of different initial HIVexposure suggests that the epidemic may have been arrested early without necessarily peaking to “saturation levels” (in which all people most at risk are infected). Nevertheless, and of relevance to our study, a rapid decline in prevalence, even without a change in the incidence of HIV, is only possible through a high mortality rate in this period.

The Kagera Health and Development Survey (KHDS) was conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS) and 6
-9-

Table 1.2: Annual Incremental Production from Bank Financed Projects Over the Period 1974-85 (as estimdted **at** appraisal) for selected Crops

|Crop|No. of<br>Projects|Estimate<br>Annual<br>Incremental<br>Output<br>(Thousand<br>Metric Tons)|Volume**as**X<br>of World<br>Production<br>1985|Volume as Z<br>of World<br>Trade<br>1985|
|---|---|---|---|---|
|Rice|304|16,813|3.6|147.0|
|Wheat|156|12,506|2.6|11.1|
|Maize|263|12,815|2.6|18.3|
|Sorghum|121|5,910|8.8|58.5|
|Sugar|118|4,200|4.3|14.1|
|Groundnuts|125|1,969|9.7|198.0|
|Soybeans|70|2,850|2.9|11.6|
|Cotton Lint|<br>147|2,822|15.8|68.8|
|Rubber|42|2,179|50.0|59.7|
|PalmOil|36|1,832|24.3|38.0|
|Coffee|55|220|4.3|5.1|
|Cocoa|44|184|9.8|14.2|
|Tea|5|36|1.6|3.7|
|Tobacco|32|118|1.9|8.4|

Source: AGR, AGR Data Base for the estimated incremental output at full development.

FAO and USDA for Production and Trade Data.
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Pearson, Frederic S., Walker, Scott and Stern, Stephanie. (2006) “Military intervention and prospects for democratization,” _International Journal of Peace Studies_ 11(2): 63-86.

Peksen, Dursun. (2012), Does Foreign Military Intervention Help Human Rights? _Political Research Quarterly_ , 65(3): 558-571.

Pickering, Jeffrey and Kisangani, Emizet F. (2006) “Political, Economic, and Social Consequences of Foreign Military Intervention,” _Political Research Quarterly_ : 59(3): 363-376.

Regan, Patrick. (2000) _Civil Wars and Foreign Powers: Interventions and Intrastate Conflict_ , Ann Arbor: University of Michigan Press.

---. (2002) “Third-Party Interventions and the Duration of Intrastate Conflicts,” _Journal of Conflict Resolution_ .  Vol. 46, No. 1.

---. (2010) “Interventions into Civil Wars: A Retrospective Survey with Prospective Ideas,” _Civil Wars_ 12 (4): 456-476.

Sarkees, Meredith Reid and Frank Wayman (2010). Resort to War: 1816 - 2007. Washington DC: CQ Press.

Stock, James and Yogo, Motohiro (2005). Testing for Weak Instruments in Linear IV Regression. _Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg_ : 80-108.

Sullivan, Patricia L. and Koch, Michael T. (2009) “Military Intervention by Powerful States, 1945–2003,” _Journal of Peace Research_ 46: 707-718.

Tilly, Charles. (1978) _From Mobilization to Revolution_ , Cambridge Studies in Contentious Politics.

Walter, B. (1997) “The Critical Barrier to Civil War Settlement,” _International Organization_ 51(3): 335-364.

---. (2002) _Committing to Peace:  The Successful Settlement of Civil Wars_ . Princeton University Press, Princeton.

World Bank. (2015) _Global Development Goals in an Era of Demographic Chang_ e, Global Monitoring Report 2015/2016, The World Bank and the International Monitory Fund.

42
23

# **S4 Table. Intervention effects on all primary outcomes, by province (pre-specified)**

||||Control|||Intervention|||CI|95%|
|---|---|---|---|---|---|---|---|---|---|---|
|Outcomes|Province|n|Prevalence/Mean|SD|n|Prevalence/Mean|SD|ITT|Lower Bound|Upper Bound|
|Diarrhea prevalence|Kongo Central|146|17%||163|10%||-0.07|-0.15|0.00|
||Kasai|599|40%||679|40%||0.00|-0.09|0.09|
||Kasai Central|781|49%||196|59%||0.10|0.00|0.19|
||South Kivu|784|41%||724|37%||-0.05|-0.10|0.01|
|Length-for-age Z-score|Kongo Central|96|-1.59|1.32|93|-1.75|1.67|-0.18|-0.47|0.11|
||Kasai|319|-2.05|1.60|328|-1.86|1.60|0.20|-0.05|0.44|
||Kasai Central|409|-2.12|1.71|103|-2.58|1.49|-0.42|-0.80|-0.04|
||South Kivu|399|-2.49|1.48|395|-2.50|1.50|0.02|-0.16|0.19|
|WASH institutions index|Kongo Central|18|0.27|0.66|20|0.59|0.57|0.31|-0.06|0.68|
||Kasai|43|-0.48|1.10|48|0.21|0.75|0.70|0.21|1.19|
||Kasai Central|65|-0.18|0.85|16|0.28|0.61|0.35|0.03|0.68|
||South Kivu|59|0.46|0.96|60|0.67|0.78|0.23|-0.25|0.71|

ITT = intention-to-treat effect estimate. Effects are estimated with models that include controls for randomization blocks based on province and number of villages per cluster. There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units.
Table 2: Baseline Balance Child level (Full sample)

||(1|)|(2|)|(3|)|T-test|
|---|---|---|---|---|---|---|---|
||Treat|ment|Con|trol|Tot|al|Diference|
|Variable|N/[Clusters]|Mean/SE|N/[Clusters]|Mean/SE|N/[Clusters]|Mean/SE|(1)-(2)|
|**Child level**||||||||
|Child is female|1436|0.485|1702|0.497|3138|0.492|-0.012|
||[18]|(0.014)|[18]|(0.009)|[36]|(0.008)||
|Child age|1436|2.969|1702|3.069|3138|3.023|-0.100**|
||[18]|(0.043)|[18]|(0.051)|[36]|(0.034)||
|Biological mother is in the household|1432|0.929|1698|0.945|3130|0.938|-0.015*|
||[18]|(0.010)|[18]|(0.007)|[36]|(0.006)||
|Child is the household head son/daughter|1434|0.849|1699|0.868|3133|0.860|-0.019|
||[18]|(0.031)|[18]|(0.009)|[36]|(0.015)||
|Child (age=6) goes to school|131|0.985|155|0.994|286|0.990|-0.009|
||[18]|(0.010)|[18]|(0.006)|[36]|(0.006)||
|Gross motor score|1003|35.952|1132|36.348|2135|36.162|-0.396|
||[18]|(0.563)|[18]|(0.432)|[36]|(0.349)||
|Fine motor score|1003|19.921|1132|19.765|2135|19.838|0.156|
||[18]|(0.463)|[18]|(0.306)|[36]|(0.266)||
|Language score|1003|29.303|1132|28.799|2135|29.036|0.504|
||[18]|(0.806)|[18]|(0.339)|[36]|(0.411)||
|Total child development score|1003|85.176|1132|84.913|2135|85.037|0.264|
||[18]|(1.740)|[18]|(0.933)|[36]|(0.940)||
|The duration in hours where mother keeps the child|1432|3.154|1698|3.336|3130|3.252|-0.182|
||[18]|(0.154)|[18]|(0.174)|[36]|(0.118)||
|The duration in hours where father keeps the child|1432|0.225|1698|0.234|3130|0.230|-0.009|
||[18]|(0.024)|[18]|(0.018)|[36]|(0.014)||
|The duration in hours where sister keeps the child|1432|0.114|1698|0.173|3130|0.146|-0.058***|
||[18]|(0.015)|[18]|(0.024)|[36]|(0.017)||
|The duration in hours where brother keeps the child|1432|0.044|1698|0.053|3130|0.049|-0.009|
||[18]|(0.011)|[18]|(0.014)|[36]|(0.009)||
|The duration in hours where another familly member keeps the child|1432|0.800|1698|0.882|3130|0.844|-0.082|
||[18]|(0.089)|[18]|(0.068)|[36]|(0.054)||
|The duration in hours where non familly member keeps the child|1432|0.214|1698|0.145|3130|0.176|0.069*|
||[18]|(0.044)|[18]|(0.022)|[36]|(0.025)||
|The duration in hours of the child in the caring center|1432|0.048|1698|0.025|3130|0.036|0.023*|
||[18]|(0.014)|[18]|(0.006)|[36]|(0.008)||
|The duration in hours of the child with no care|1432|14.017|1698|13.607|3130|13.795|0.410|
||[18]|(0.480)|[18]|(0.437)|[36]|(0.321)||
|Child used a creche in the past 24 hours|1432|0.018|1698|0.010|3130|0.014|0.008|
||[18]|(0.006)|[18]|(0.003)|[36]|(0.003)||

_Notes:_ The value displayed for t-tests are the differences in the means across the groups. Standard errors are clustered at variable commune. Fixed effects using variable id sample are included in all estimation regressions. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

34
30% of Moroccan, Tunisian and Algerian educated migrants arrived _after_ the age of 22 (Figure 6). Around 60% arrived in France _before_ age 18, indicating they came as children and grew up there. Similarly, 40% of Turkish and Lebanese migrants – the other two large groups – also arrived as children. Thus, a large number of educated migrants in France have not consumed fiscal resources of their native countries for their education.

# **3. Reconciling economic needs with the political reality**

While the economic reality in the EU and its Mediterranean neighbors is creating medium to long-term pressures for increased migration, the political winds across Europe are blowing strongly in favor of stemming the flow of immigrants into the continent. The current stock of Middle Eastern migrants contributed to rising resistance to migration according to public opinion surveys and political debates. The increase in negative views is the outcome of a mix of security, cultural and more recently economic concerns Evidence provided by Mayda (2005) suggests that cultural assimilation and security/crime concerns are relatively more important in shaping attitudes than labor market variables. The opinion surveys<sup>5</sup> reveal seemingly conflicting attitudes toward migration and the economic interests of respondents. For example, older people tend to be more anti-immigration even though they are the ones likely to benefit the most from migrants whether via increased supply of domestic and health service providers or lower dependency ratios. The growing hostility towards immigrants has made the political environment in many EU countries much less welcoming of immigrants. Ironically, the increased restrictions on immigration have not done much to stem the flow of immigrants. Instead it has lead to an increase in illegal entry, with a consequent change in the composition of migration towards less educated people (Boeri and Bruckner 2005).<sup>6</sup> Resistance to further migration amidst the rising demand for workers in the EU suggests that policy makers should consider alternative mechanisms that can partially substitute for permanent movement of workers across borders while at the same time encourage more two-way flows. In this context, a temporary movement scheme might be seen as less “threatening.” In a recent EU consultation on development and in response to

> 5 Among these are the World Values Survey, the International Social Survey Program and others. 6 See also Kessler and Freeman (2005) and Mayda (2004) for analyses of the determinants of European attitudes to migration.

11
**Table 1: Regional variation in Manufacturing Value Added (% of GDP)**

||1960s|1970s|1980s|1990s|2000s|2010s|
|---|---|---|---|---|---|---|
|West Africa|0.08|0.12|0.11|0.11|0.09|0.07|
|Observations|24|63|75|99|121|82|
|Number of countries|3|7|8|10|14|14|
|East Africa|0.07|0.09|0.09|0.09|0.09|0.08|
|Observations|30|62|88|90|90|38|
|Number of countries|4|7|9|9|9|9|
|Central Africa|0.16|0.25|0.17|0.13|0.1|0.09|
|Observations|12|32|50|53|76|61|
|Number of countries|3|4|5|6|9|9|
|Southern Africa|0.11|0.13|0.14|0.14|0.15|0.14|
|Observations|26|69|80|89|90|40|
|Number of countries|5|7|8|9|9|9|
|Sub-Saharan Africa (All SSA)|0.11|0.15|0.13|0.12|0.11|0.10|
|Observations|92|226|293|331|377|221|
|Number of countries|15|25|30|34|41|41|

The table reports SSA regional mean shares of manufacturing output shares in GDP (constant prices) for each south-Saharan African country by decade (e.g., 1960-1969, etc) based on our main sample of 41 SSA countries. Each column represents decade-specific mean shares followed (on the corresponding rows) by the number of countries in each region as well as the number of years for which data are available. See Table A.3 for a list of the SSA countries belonging to each of the four subregional groups in our sample.

9
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# **APPENDICES**

# **Appendix A. Tracking and Survey Instruments**

The sample for this long-term tracking exercise consisted of all individuals between 5 and 15 years old who were part of the 1,807 households sampled for the first LEAPS round in 2003 (5,865 respondents). We had last attempted to survey these respondents in 2011 before we started a tagging and tracking exercise in 2016. The 2011 survey was conducted at the household level, and we collected information about individual respondents through household rosters. In 2016, we were interested in tracking individuals.

The first step in the tagging and tracking process was to go back to the latest addresses we had for these respondents’ households. We could not locate the households of 285 respondents (Table A1). 79% of those we could not find belonged to households that had already attrited by 2011.

For the households we could find, we implemented the following protocol. If the target individual had migrated, we would collect their address and contact information. We attempted to survey all the respondents in person—by visiting them at their new address (for those who had migrated within the country) or by waiting for them to visit their relatives (for those who had migrated abroad). For any individual we were able to meet in person, we would administer five different instruments:

- Questionnaire

- Cognitive skills assessment (on paper)

- Adaptive Cognitive skills assessment (tablet-based)

- Socioemotional skills assessment (on paper)

- Socioemotional skills assessment (tablet-based)

We administered a shorter survey over the phone to individuals we could not meet in person. The phone-based survey was similar to the questionnaire administered in person but shorter. We could not conduct any cognitive or socioemotional skills assessment over the phone.<sup>24</sup> We are therefore missing skills information for all the individuals surveyed over the phone (Table A1).

Finally, we could not survey some respondents in person or over the phone. These respondents mainly fell into two categories: women who got married and were living with their family-in-law and our team was not authorized to visit (30%), and men working in neighboring Arab countries for whom we did not have contact information (30%). Whenever possible, we collected information about these respondents from another person in their original household (parents or siblings, in 80% of the cases). We called this survey mode the “indirect” version of the survey as someone else was _indirectly_ giving information about the respondent of interest. This questionnaire was short and

> 24 Crawfurd et al. (2021) report differential item functioning across in-person and phone surveys for a cognitive assessment in Sierra Leone. Moreover, the adaptive test and part of the socioemotional skills assessment had to be implemented on tablets.

51
**Table 4.4a. Overall bias in HFPS**

|||**Ethiopia**|||**Malawi**|||**Nigeria**|||**Uganda**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**ESS**|**H**<br>**Inter**|**FPS**<br>**viewed**|**IHPS**|**H**<br>**Inter**|**FPS**<br>**viewed**|**GHS-P**|**H**<br>**Inter**|**FPS**<br>**viewed**|**UNPS**|**H**<br>**Inter**|**FPS**<br>**viewed**|
||||_Overall_|||_Overall_|||_Overall_|||_Overall_|
||_Mean_|_Mean_|_bias†_<br> _(2) -(1)_|_Mean_|_Mean_|_bias†_<br> _(5) -(4)_|_Mean_|_Mean_|_bias†_<br> _(8) -(7)_|_Mean_|_Mean_|_bias†_<br> _(11) -(10)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Sample size(number of households)**|_6,788_|_3,247_||_3,181_|_1,729_||_4,976_|_1,950_||_3,308_|_2,259_||
|Rural|0.675|0.535|-0.14***|0.826|0.749|-0.077*|0.680|0.613|-0.067***|0.684|0.662|-0.022|
|Average household size|4.483|4.469|-0.014|4.904|5.026|0.121|5.332|5.520|0.188|4.515|4.647|0.132|
|Dependencyratio|0.955|0.865|-0.091**|1.055|0.939|-0.115***|0.980|0.984|0.004|1.064|1.046|-0.018|
|Female head(%)|0.263|0.244|-0.019|0.296|0.250|-0.046**|0.201|0.191|-0.011|0.342|0.324|-0.019|
|Age of head|42.912|41.049|-1.863**|45.855|45.246|-0.609|49.757|49.402|-0.355|45.311|45.334|0.023|
|**_Religion of head_**|||||||||||||
|Orthodox|0.478|0.528|0.050||||||||||
|Muslim|0.293|0.268|-0.025|0.808|0.838|0.030|0.397|0.385|-0.012|0.000|0.000|0.000|
|Christian|0.211|0.194|-0.017|0.145|0.124|-0.021|0.585|0.599|0.014|0.000|0.000|0.000|
|**_Marital status of head_**|||||||||||||
|Married|0.739|0.741|0.002|0.716|0.751|0.035|0.740|0.756|0.016|0.713|0.743|0.03*|
|Formerlymarried|0.189|0.171|-0.018|0.265|0.228|-0.036|0.209|0.188|-0.021|0.246|0.220|-0.026|
|Never married|0.072|0.088|0.016|0.018|0.020|0.001|0.051|0.056|0.005|0.041|0.037|-0.004|
|**_Education level of head_**|||||||||||||
|Head Literate|0.520|0.643|0.123***|0.921|0.952|0.031***|0.728|0.794|0.066***|0.772|0.821|0.049***|
|None|0.523|0.395|-0.128***|0.675|0.547|-0.129***|0.365|0.275|-0.09***|0.087|0.077|-0.010|
|Primary|0.296|0.324|0.029|0.207|0.262|0.055*|0.246|0.252|0.005|0.584|0.540|-0.044**|
|Secondary|0.102|0.153|0.051***|0.083|0.138|0.054***|0.221|0.256|0.035**|0.290|0.315|0.025|
|Tertiary|0.079|0.128|0.048***|0.034|0.054|0.020|0.167|0.217|0.05***|0.120|0.138|0.018|
|**_Working status of head_**|||||||||||||
|Any|0.735|0.723|-0.012|0.819|0.841|0.022|0.808|0.828|0.019|0.855|0.869|0.014|
|Wage|0.133|0.192|0.059***|0.158|0.225|0.067**|0.179|0.219|0.04***|0.324|0.330|0.006|
|Agriculture|0.551|0.450|-0.101***|0.469|0.439|-0.030|0.447|0.391|-0.055***|0.479|0.470|-0.009|
|Nonfarm enterprise|0.115|0.147|0.032*|0.193|0.217|0.023|0.329|0.366|0.037**|0.266|0.296|0.031|
|Ganyu||||0.248|0.187|-0.061**|||||||

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

28
These findings confirm that the most effective IPAs benefit from visibility and from participation by the private sector through their boards or through institutional relations. These characteristics reinforce the leverage of the agency and its role. They also indicate that including the private sector contributes to broaden the platform and help to achieve a consensus in the agency's effort to market the country abroad. Yet, they should not be used to overestimate the role of the investment agency. The reporting mechanism to the president or the prime minister may reflect the overall commitment of the government toward reforms. Within that context, the positive correlations reported above, while indicative, would capture this global trend in the government's effort rather than the agency's own performance.

Finally, it is worth reporting that some IPA characteristics do not seem to influence the agency performance. The agency's mandate, staff qualification, and the number of overseas agencies have no significant effect on FDI flows. It is possible that these characteristics do not matter. Another possibility is that our survey has too little variations in these factors to identify their eventual impact on the effectiveness of IPAs.

# 'V. Concllsion and Policy Recomximendations

The story that emerges from our investigation is that the IPAs are effective in influencing decision to invest. Although our empirical exercise suffers from obvious limitations, their role seems significant, on top of the influence of key factors such as the quality of the investment climate and the country's market size.

The finding that promotion is positively associated with FDI inflows across countries has to be qualified because it is closely link to the environment in which the agency operates. Investment promotion is more effective in a country with a good investment climate and a relative high level of development. In fact, our results suggest that promotion can be even counterproductive in a country that offers a poor investment climate. It seems more difficult to convince an investor to come back if he was disillusioned during his first visit 18