rafmacalaba/datause-encoder-data huggingface dataset:
from late 2010 to mid-2012 for the second wave, constituting an individual panel. The data provide, in particular, detailed information on marital trajectories, savings, and labor market participation at the individual level. The overall sample in the first wave is made of 1,750 households and 14,450 individuals, in 150 randomly drawn census districts. In the whole sample, 57.1 percent of the individuals are living in a rural area, 48 percent are males, and 95 percent are Muslim. The average household size is between eight and nine members.

The PSF survey data are rich and unique in that they intend to account for the complexity of household structures in Senegalese society. The questionnaire relies on the preliminary identification of household substructures, referred to as cells. Cells are defined as units that are semi-autonomous as regards resource allocation decisions, composed of a cell head and his or her direct dependents—in particular, children, foster children, or widowed mother or father. The average household is made of 2.4 cells of around three members each. Notably, expenditures data were collected at the cell level, with a distinction between expenditures specific to each cell or common to the whole household, and the identification of all contributors to each type of expenditures. This allows us to identify intrahousehold variations in consumption patterns and resources reallocation between spouses.

The PSF data are also particularly suitable for this analysis since savings stock, as well as the flows of transfers sent out and received from individuals out of the household in the past 12 months, were collected at the individual level, meaning in particular that each spouse reports his or her own savings and transfers. We have detailed information about savings; notably, we can distinguish between savings in formal institutions, informal associations, or Rotating Saving and Credit Associations (ROSCA, or _tontine_ in Senegal) and savings held at home.

# _Descriptive Statistics_

Our sample of interest in the first step of our empirical analysis is composed of all coresident monogamous and polygamous unions made of a husband and his only or first-rank wife. We restrict our sample to unions in which women are between 15 and 60 years old and drop the 60 unions with the husbands who are Christian. Our final sample for the first step of the analysis is thus made of 1,388 unions surveyed in the first wave of the PSF survey.

On the subsample of 457 men living in a polygamous union (434 observations, once missing values on years of marriage are taken into account), we observe that the majority of polygamous men first married around 25 years old and took a second wife just before 40. The median interval between first and second marriages is 10 years, and 75 percent of second marriages occurred in the first 16 years of the first union.

Table S.1 in supplemental appendix provides descriptive statistics of socioeconomic characteristics of our first step sample made of monogamous and polygamous first-rank unions.

14
- Sutter, Christophe and Juan Carlos Parreno (2007). “Does the Current Clen Energy Development Mechanism (CDM) Deliver its Sustainable Development Claim? An Analysis of Officially Registered CDM Projects.” _Climatic Change_ 84:75-90.

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- UNFCCC (2008). Clean Development Mechanism (CDM) <u>http://cdm.unfccc.int/index.html,</u> (accessed October 18, 2008).

- United Nations Environment Programme (UNEP) Risoe Center (2007). CDM/JI Pipeline Analysis and Database, <u>http://cdmpipeline.org</u> (accessed November 6, 2008).

- United Nations Environmental Program UNEP/GRID-Arendal, (2000). Methodological and Technological issues in Technology Transfer. Accessed on the web at <u>http://www.grida.no/climate/ipcc/tectran/index.htm</u> (accessed on July 29, 2007).

- Velasco, Ana Pueyo (2007).  Variables Underpinning Technology Transfers Through the CDM. Joint Implementation Quarterly, 13(3):5-6.

- Waltz, Kenneth (1979). Theory of International Politics. New York, NY: McGraw-Hill.Waltz, 1979

WDI, The World Bank (2007). World Development Indicators, <u>http://publications.worldbank.org/WDI.</u>

- World Business Council for Sustainable Development (1998). Technology Cooperation for Sustainable Development. Position Brief, Geneva (accessed on June, 2007).

25
-  Trada Agreements Data. To account for the presence and depth of trade agreements, we use the World Bank's database on deep trade agreements (DTA), cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), (https://datatopics.worldbank.org/ dta/about-the-project.html).<sup>18</sup> Capitalizing on the rich dimensionality of the DTA database, we construct and utilize several variables for our analysis. _PTAij,t_ is an indicator variable for the presence of any trade agreement between _i_ and _j_ at time _t_ . _DTAij,t_ is an indicator denoting the presence of a deep agreement between _i_ and _j_ at time _t_ . _DEPTHij,t_ is a count variable for the number of provisions in the corresponding DTA between _i_ and _j_ . _INVij,t_ is an indicator variable that takes a value of one if the DTA between _i_ and _j_ includes investment provisions. Finally, _INV_  DEPTHij,t_ is a count variable for the number of investment provisions in the corresponding DTA between _i_ and _j_ . For further details on the general features of the DTA database we refer the reader to Hofmann et al. (2019) and Mattoo et al., eds (2020). In addition, for analysis with specic focus the investment provisions in the DTAs, we refer the reader to Crawford and Kotschwar (2020).

-  Production Data. Data on GDP, employment, and capital stocks are from the Penn World Tables 8.0, cf. Feenstra et al. (2013) (http://www.rug.nl/research/ggdc/data/ pwt/). For data on GDP, we employ Output-side real GDP at current PPPs ( _CGDP_<sup>_o_</sup> ), which compares relative productive capacity across countries at a single point in time, as the initial level in our counterfactual experiments, and we use Real GDP using national-accounts growth rates ( _CGDP_<sup>_na_</sup> ) for our income-based cross-country growth regressions. We measure employment in eective units by multiplying the Number of persons engaged in the labor force with the Human Capital Index, which is based on average years of schooling. Finally, capital stocks in the Penn World Tables 8.0

> estimation analysis in this paper, we also utilize the time variation in the FDI data. In the counterfactual experiments, we rely on the methods of Anderson et al. (2019) to calibrate some parameters and vectors. See Section 4.2 for further details.

> 18Specically, we used the DTA 2. Database: Information by Trade agreements. Bilateral observations.

18
pay their bills and deliver quality products on the prospect of future business. Trust is built on a history of successful, repeat transactions. The RPED surveys show, for example, that firms generally deal with a single supplier of a particular input on a regular basis (even when they have a choice among sources of supply) and the average length of relationship exceeds seven years (Bigsten et al. 2000a). But, as many economic activities require dealing with different partners at different times and cooperation is more easily sustained in relationships if sanctions for opportunistic behavior come not just from the business partner who has been cheated but also from other firms in the business community, business and community networks are formed to govern transactions.<sup>5</sup> Selfgovernance in such networks works by sharing information on non-delivery, late payment, and default via a multilateral reputation mechanism, supported by a framework of credible commitment, enforcement, and coordination.

At early stages of industrialization incentives based on repeated interactions work well. The fact that it is difficult to locate alternative business partners in the SSA environment, because there are few firms, because market information is inadequate, and because transportation costs are high, persuades firms to make efforts to maintain their existing relationships. They recognize that they are locked in to some extent with existing business partners because of high search and screening costs. This provides incentives to behave cooperatively – i.e. reduces incentives for opportunism (Kranton 1996; Ramey and Watson 2001). As a consequence, such self-enforcing relational contracts are shown in the RPED surveys to be one of the standard ways for manufacturers, suppliers and clients to do business in the region. A large majority of African manufactures describe their relationships with suppliers and clients as simple long-term business acquaintances (Bigsten et al. 2000a).

But the SSA business environment also has features that work to undermine and weaken repeated-game incentives for cooperative behavior. Self-enforcing transactions, governed through repeated interaction, depend on expectations about the future (Axelrod 1984; McMillan and Woodruff 2003). There are generally short-term gains to be made

> 5 One could, of course, consider the transactions of a repeated game and the relational contracting thereof a network.  Here we define a network to include a broader set of economic functions where members of a business group or “club” share information and informally enforce contracts.

8
**<u>Table 3</u>** <u>: continued</u>

||VUP Ben|eficiary|||DS Benefic|iary|||PW Benefi|ciary|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)||(2)||(3)||(4)||(5)||(6)||
||Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|
|Share of children (0-5 yrs.)|-0.217|-0.044|-0.352|-0.069|0.270|0.021|<br>0.047|0.004|-0.380|-0.046|-0.506|-0.061|
||(0.598)||(0.599)||(0.838)||(0.920)||(0.637)||(0.628)||
|Share of children (6-18 yrs.)|0.078|0.016|0.014|0.003|1.026*|0.080|<br>1.051*|0.092|0.097|0.012|-0.026|-0.003|
||(0.472)||(0.457)||(0.537)||(0.558)||(0.505)||(0.475)||
|_Other characteristics_|||||||||||||
|Landholding >1 ha (=1)|Ref.||Ref.||Ref.||Ref.||Ref.||Ref.||
|Landholding 0-0.25 ha (=1)|0.236|0.048|0.002|0.000|0.009|0.001|-0.264|-0.023|0.126|0.015|-0.079|-0.010|
||(0.202)||(0.215)||(0.371)||(0.434)||(0.227)||(0.242)||
|Landholding 0.25-1 ha (=1)|-0.159|-0.032|-0.284|-0.056|-0.299|-0.023|-0.445|-0.039|-0.119|-0.015|-0.246|-0.030|
||(0.222)||(0.232)||(0.412)||(0.468)||(0.250)||(0.259)||
|Index of housing structure|0.178*|0.036|0.148|0.029|0.163|0.013|0.117|0.010|0.195*|0.024|0.186|0.022|
||(0.093)||(0.093)||(0.128)||(0.130)||(0.114)||(0.114)||
|Ubudehe 1 (=1)|||Ref.||||Ref.||||Ref.||
|Ubudehe 2 (=1)|||-0.273|-0.054|||-0.231|-0.020|||-0.195|-0.024|
||||(0.188)||||(0.253)||||(0.254)||
|Ubudehe 3 (=1)|||-0.552*|-0.109|||-0.746*|-0.066|||-0.339|-0.041|
||||(0.217)||||(0.345)||||(0.279)||
|Ubudehe 4 or higher (=1)|||-0.861***|-0.170|||||||-0.679*|-0.082|
||||(0.305)||||||||(0.363)||
|Constant|-0.224||0.297||-0.744||-0.137||-0.946||-0.534||
||(0.577)||(0.566)||(0.697)||(0.747)||(0.606)||(0.595)||
|N|615||615||583||490||615||615||
|Pseudo-R2|0.108||0.127||0.331||0.331||0.085||0.096||

_Notes:_ Robust, clustered standard errors in parenthesis. * p<0.10; ** p<0.05; *** p<0.01. _Source:_ VUP household survey, 2009, 2011, 2014.

22
|**Maritime**|25 km/h|25 km/h|
|---|---|---|
|**Upgraded rail**|50 km/h|75 km/h|
|**New rail**|–|75 km/h|

The choice of transportation mode is difficult to model and follows different logics and trade‐offs which are specific to the sector under consideration. It depends on the different elements that affect shipping costs such as the bulkiness of the good, subsidies, oil price, market structure and many others that cannot be modeled in a sensible way in our analysis. To account for the very high share of maritime shipping in international trade, we use a network preference to produce a maritime alternative where land connectivity exists. Within the optimal path algorithm, this preference is implemented in the following way: for any city pair that has access to both a maritime and a rail link, the algorithm selects the maritime option whenever the shipping time is lower than four times the shipping time incurred using the rail link. While such a criterion can be seen as a strong simplification of the actual trade‐off faced by many logistic companies, the imposition of such a preference for maritime links allows us to have a representation of the world that is very much in line with other data sources in terms of both modal shares and total shipping times at the city‐pair level. For example, in our pre‐BRI scenario with the global database, maritime transportation represents a total of 82.3% of all trade routes. In the following sub‐section, we explain in detail how we deal with the possibility of mode switching once all BRI projects are implemented.

On top of the time cost computed along the network elements, we account for processing times when a shipment is reaching / leaving ports, based on the estimates in Slack, Comtois, Wiegmans and Witte (2018). In their paper, Slack et al (2018) use data from Lloyds Intelligence Unit and base their estimates on a sample of 17,024 vessels in 2013. In our network, we use their regional averages reported in Table 2. In the context of our analysis, these processing times correspond to a “penalty” incurred when reaching a port, either from the land and from the sea, and correspond to the time it takes to load or unload the goods. In the improved scenario, when a project involves building a new port or upgrading an old port, we assume that the associated “processing time” decreases according to the following rule:

# **Improved Port Delay = Max (0.5*Baseline port Delay, Lowest Worldwide Processing Time)**

This rule allows us to account for a proportional decrease in port delay of 50 percent with a limit that no port can be more efficient than the worldwide lowest estimate of 17.2 hours.

_Table 2: Processing time in ports as estimated by Slack, Comtois, Wiegmans and Witte (2018)_

|**Region**|**Mean Ships Time In Port**<br>**(hours)**|
|---|---|
|Central and South America|23.5|
|South and South‐East Asia|26.5|

8
# **Differences in Household Composition: Hidden Dimensions of Poverty and Displacement**

# **in Somalia***

Lucia C. Hanmer<sup>[†]</sup> , Eliana Rubiano- ~~M~~ atulevich<sup>[‡]</sup> , Julieth Santamaria<sup>[§]</sup> _Keywords:_ households, conflict, internally displaced people, host communities, gender.

_JEL codes:_ D10, O15, D74, J16.

The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

> * This work is part of the program ‘Building the Evidence on Protracted Forced Displacement: A MultiStakeholder

Partnership’. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.
<!-- Start of picture text -->
-<br>Figure 4c Cross Sectoral Flows (S I) (Trends and Cycle): Mexico an Brazil<br>Mexico  Brazil<br>1987q1 1991q3 1996q1 2000q3 2005q1 1983q1 1988q1 1993q1 1998q1 2003q1<br>S-I I-S S-I I-S<br>1987q1 1991q3 1996q1 2000q3 2005q1 1983q1 1988q1 1993q1 1998q1 2003q1<br>S-I (HP) I-S (HP) S-I (HP) I-S (HP)<br>.24 .18<br>.16<br>.22<br>.14<br>.2<br>.12<br>.18<br>.1<br>.16<br>.08<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br><!-- End of picture text -->

Notes: The graph shows the transition rates between informal salaried (I=Informal Salaried) and the formal sector (F=Formal Sector). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

37
mobile “lab” (which consisted of four stations where our game facilitators would explain the activities and record the subjects’ actions) in the specified location in the village.<sup>10</sup> Of the 576 households sampled for the survey 475 sent a representative to the games session. We gathered data on observed behavior in the games mentioned below from these 475 subjects. We gathered information on these 475 subjects’ relationships with each other for our social network data. The survey always preceded the games session and the survey respondents were not invited to the game session until the survey was completed.

A baseline survey was conducted before we were brought on the project. Balance statistics for a variety of pre-treatment indicators taken from that survey are presented in Table 2. We report the mean in the untreated communities and the OLS-estimated difference between the treated and the control communities with village-clustered standard errors. In all cases the differences between the treated and control groups are statistically insignificant and, with the possible exception of water consumption substantively very small, thus indicating that excellent balance was achieved.

Our game invitation was extended to the person interviewed in the survey. Often due to work or other commitments the respondent would send another adult member of the household in his or her place. Thus strictly speaking our laboratory respondents are not a random sample but are selected by the household. We have no reason to suspect that households in the treated communities sent more (or less) pro-social members to the laboratory than did households in the control communities and so we do not think this small violation of randomization affects our results. Descriptive statistics of games participants and, where available, survey respondents are provided in Table 3. Our games participants were a bit more likely to be younger, single and female than our survey respondents but not significantly so. The larger percentage of females in the lab sample helps account for the

> 10Usually we set up our lab in a community building such as a school or community center but on a few occassions we had to set up the lab in the open air.

22
Table 9: Attrition at the cognitive and curriculum tests

||(1)<br>Core pupils tested<br>out of 30<br>Oral test|(2)<br>Core pupils tested<br>out of 30<br>Written test|(3)<br>Nr of pupils called<br>to reach 30 pupils<br>Oral test|
|---|---|---|---|
|Treatment|-0.39|-0.03|2.269|
||(0.39)|(0.03)|(0.35)|
|Constant|12.64|12.28|73.273|
||(17.86)**|(16.88)**|(15.56)**|
|Obs|49|49|46|
|R-squared|0.00|0.00|0.00|
|Notes:||||

The following method was used to sample the children who took the tests: The baseline was ...rst divided by school and age. Only children who were 4, 5 or 6 in 2000 were retained. Within each school-age group, the children were randomly ordered into a sampling list using a random number generator. The ...rst 10 children in each group form the "core" sample of children to be tested. Thus each school has 30 core children. The dependent variable in columns 1 and 2 is the number of core pupils who were actually tested. Core pupils who were absent on the day of the test were replaced by children further down on the sampling list, and replacement was done following the order of the list. The dependent variable in column (3) is the number of pupils in the sampling list who were called before the ...eld sta¤ had assembled a group of 30 pupils to take the test.

Three schools are excluded from regression (3), because the ...eld o¢ cer did not apply the procedure for replacing absent core children correctly. One school did not participate in the tests at all, because no ...eld sta¤ was available to test the children in the local language. Standard errors in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent In practice, children who were not enrolled or happened to be absent on the day of the test were not fetched home and thus they were not tested. From the original core sample of 30 pupils per school, on average 12.6 were administered the oral test, which implies an average attrition rate of 58 percent (Table 9, column 1). The attrition rate for the written test was similar (Table 9, column 2). On average, a total of 75 names were called before the test administrator had gathered 30 children to take the test (Table 9, column 3).

There is no signi...cant di¤erence between treatment and comparison schools in either of the two measures of attrition. Several factors make this plausible. First, the children who were parents who argued it was not fair that their child did not get tested while other children in the same class did get the test.

29