rafmacalaba/datause-encoder-data huggingface dataset:
In order to examine the resilience of treaties, we needed a measure of conflict and cooperation to use as a dependent variable. We developed such an indicator based on the water events stored in Oregon State University’s (OSU)’s International Water Events Database, created under the framework of the Basins at Risk project (BAR) (Yoffe et al., 2003), which was recently updated (De Stefano et al, 2010), to consider water events in international river basins worldwide (for the period 1948-2008). Each event includes a brief summary and source of information, and is coded by date, country pair (dyad), basin, issue area, and intensity of conflict/cooperation. Each event is coded also according to the type of event (conflictive or cooperative) and its intensity using the BAR scale, which ranges from –7, the most conflictive (war), through 0 (neutral events), and up to +7, the most cooperative (voluntary merging of countries).

To extract the events relevant to treaties examined in this study we first re-coded the events by country-basin dyad so that we could relate the events for a specific international basin with the treaties signed by each pair of countries. Secondly, for each treaty, we identified which events had occurred after the signature of the agreement and, among those, which were relevant to water quantity or allocation. In cases where a treaty was replacing a preexisting agreement, the relevant events were assigned accordingly. Moreover, in cases where a treaty affected a specific sub-basin within a wider transboundary basin, we identified, based on the content of the event summary, countries’ interactions relative to that specific sub-basin and assigned those to the corresponding agreement. The data processing produced a database listing the number and intensity (BAR values) of all the events relevant to each country-basin dyad and treaty issue. In order to treat the categorical BAR values as ordinal values, we use the anti-logged equivalent for each event intensity level. This is in line with previous studies using BAR events data (e.g. Yoffe and Larson 2001; Brochmann 2012).

# <u>4.1.1. Allocation mechanisms</u>

Based on a review of all available international water allocation treaties housed in the Transboundary Freshwater Dispute Database (TFDD) treaty databank and a joint TFDDInternational Water Management Institute (IWMI) categorization, a set of allocation mechanisms were identified.<sup>2</sup> These mechanisms were then further assessed as to their flexibility and

> 2 The list includes allocation mechanisms such as “fixed quantities,” “fixed quantities which vary according to water availability,” “fixed quantities recouped in the following period,” “percentage,” “prior approval,” “consultation,” “prioritization of uses.”

10
forcement following the terrorist attacks of 2001.<sup>1</sup> Ever since, deportations to El Salvador have maintained steady growth rates, reaching almost 50,000 individuals per year by 2018.

In principle, repatriation inflows can be understood as a pure labor supply shock as more workers enter the labor market. However, 76 percent of deportees to El Salvador spend less than one month abroad. Thereby, their effects are more likely to reflect the experience of detention, including, for example, trauma, potentially being exposed to individuals with criminal backgrounds, and being stigmatized since the reason for deportation is unknown by others back home. At the same time, repatriation inflows can represent a labor demand shock if deportees are more likely to engage in crime or other illegal activities; or if they are likely to join the informal sector, thereby affecting labor demand among formal firms facing increased competition from informal ones. Following this logic, we first examine the effects of repatriation inflows as a _supply shock_ , evaluating its effects on firm wages and employment. In a second step, we study the effects of repatriation inflows on outcomes that could be more directly linked to the _demand shock_ , including firm entry and growth (measured as number of branches), as well as increased competition from the informal sector.

We carry out our empirical analysis combining data from El Salvador’s migration authorities that include all registered repatriations with rich socioeconomic information about deportees with administrative information from the Salvadoran Social Security Institute ( _Instituto Salvadore ̃no de Seguridad Social_ , ISSS) containing the universe of firms and workers in the formal sector. Our period of analysis covers the years 2010 to 2018 and employs municipal and annual variation.

Considering that repatriated migrants may choose their location depending on local economic conditions, or that their location may have been forced on them by migration authorities, we cannot simply estimate a linear regression of firm outcomes on repatriation inflows. To account for these possible biases we use a shift share instrument that exploits two sources of variation: i) annual

> 1As documented by Rozo et al. (2020) after 2001, resources devoted to enforcing immigration law increased greatly. With the creation of the new Department of Homeland Security (DHS), resources devoted to both border enforcement (Customs and Border Protection, CBP) as well as interior enforcement (Immigration and Customs Enforcement, ICE) expanded.

2
_Centrality)._ We then use granular data to explore the co-occurrences of poverty indicators at the household level and build network maps for each country.

Using data from the Global Multidimensional Poverty Index (MPI), we apply these metrics in 67 developing countries to map the interconnected structure of multidimensional poverty and explore changes in its structure over time. Our findings reveal a remarkable similarity in the structure of the Poverty Spaces across countries and their stability over time. Specific indicators like cooking technology at home (cooking fuel, which is associated to living conditions at home) consistently emerge as nodes located at the core of this network, while indicators like child mortality tend to be located in the periphery. To bring a more dynamic perspective and capture pathways for spillover effects, we also explore the association between how central a poverty indicator is and the change in the incidence of people deprived in that indicator over time. We find that more central indicators in the Poverty Space also tend to experience a more significant reduction in their censored headcount ratio over time. Finally, to apply our findings in a policy context, we integrate the Poverty Space network into the Policy Priority Inference (PPI) framework (Guerrero and Castañeda Ramos, 2020). This integration enables us to explore how the structure of poverty could be associated with the potential effectiveness of targeted interventions. PPI serves as a forward-looking, agent-based model that leverages networks of structural relationships to prioritize policy initiatives, originally aimed at achieving sustainable development goals (Guerrero and Castañeda, 2024). By using the information offered by the Poverty Space on the structural relationships among poverty indicators, PPI can be implemented to prioritize different policy interventions, recognizing the potential cascading effects across various indicators of poverty.

Understanding the interconnected structure of multidimensional poverty is critical for maximizing the impact of policies to reduce it. By shedding light on the web of connections within the structure of multidimensional poverty, this paper shows how economic complexity methods can help us to better pinpoint the “key nodes” within that network. These key nodes represent the most interconnected dimensions within the poverty network and become pivotal areas of focus for interventions (Bloch, Jackson and Tebaldi, 2023). Just like in an acupuncture intervention, development strategies that target specific nodes could have a greater effect on the system as a whole.

The remainder of the paper is structured as follows. Section 2 reviews the literature to build a case for applying economic complexity approaches in the context of multidimensional poverty. Section 3 4
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Unlike elsewhere, the ICT sector’s contribution was relatively small—it added 0.69 percentage points to the per capita growth rate. The bad quality of the road network actually held per capita growth back, by - 0.56 percentage points. Infrastructure contribution in Niger was also among the lowest in West Africa, only after Guinea- Bissau and Sierra Leone, in a region where the highest contribution of infrastructure took place in Benin (1.6 percentage points) (figure 1a).

**Figure 1. Infrastructure’s contribution to economic growth: Benchmarking Niger against other Sub-Saharan countries**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic b. Potential contribution of infrastructure to annual per capita<br>growth in Central African countries, in percentage points, 2001–05 economic growth in Central African countries, in percentage points<br>5<br>5<br>4<br>4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>Roads Electricity Telecom Roads Electricity Telecom<br>Percentage Points Percentage Points<br>n a go r e i us<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T o Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau Niger Togo Nigeria Burkina Faso Guinea-Bissau Sierra Leone Benin Mauritania Gambia Senegal Ghana Guinea Cote d'Ivoire South Africa Maurit<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in Central African countries, in percentage points, 2001–05 _Source:_ Calderón 2009.

Poor infrastructure is a handicap for business in Niger. Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 35 percent of the productivity gap faced by Nigerien firms, the remainder being due to poor governance, red tape, and financing constraints (figure 2a). Firms’ perception of infrastructure as a bottleneck to growth in Niger is the worst among countries in West Africa. Customs clearance is the infrastructure constraint that weighs most heavily on Niger firms, with power deficiencies in second place (figure 2b). Niger is ranked 172 out of 181 economies on its ease of doing business indicators.

Looking ahead, Niger could grow by as much as 4.5 percentage points per capita (to the level of SubSaharan African MICs) if it expands and improves its infrastructure, particularly in the road sector (figure 1b).<sup>1</sup> Improving the condition of paved and unpaved roads would facilitate and increase trade within the country, boosting economic growth by 1.71 percentage points. Power would contribute around 1.51 percentage points to per capita growth if generation capacity and national access rates—which as of today are among the lowest in the region—were increased. ICT would also make an important contribution to economic prospects (1.31 percentage points), provided the mobile and Internet markets continue expanding.

1 The countries that are closest to Mauritius, whose infrastructure is the best in the region, will show the smallest infrastructure gap and will, therefore, have the smallest growth benefits.

3
- Sanchez, G. (2020). Migrant smuggling in the libyan context: re-examining the evidence. In P. Fargues and M. Rango (Eds.), _Migration in west and north Africa and across the Mediterranean: Trends, risks, development and governance_ , pp. 230–238. Berlin: IOM GMDAC.

- Shrestha, M. (2019). Death scares: How potential work-migrants infer mortality rates from migrant deaths. _Journal of Development Economics 141_ , 102368.

- Tjaden, J. and F. A. Dunsch (2021). The effect of peer-to-peer risk information on potential migrants–evidence from a randomized controlled trial in senegal. _World Development 145_ , 105488.

- UN-DESA (2020). International Migrant Stock Database. Department of Economic and Social Affairs of the United Nations Secretariat, New York (USA).

- UNHCR (2017a). _Global Trends: Forced Displacement in 2016._ United Nations Human Rights Council. Geneva (CH).

- UNHCR (2017b). _Mixed Migration Trends in Libya: Changing Dynamics and Protection Challenges._ United Nations Human Rights Council. Geneva (CH).

- UNHCR (2019a). _From hand to hand: The migratory experience of refugees and migrants from East Africa across Libya_ . United Nations Human Rights Council, Geneva, CH.

- UNHCR (2019b). _Mixed Migration Routes and Dynamics in Libya in 2018_ . United Nations Human Rights Council, Geneva, CH.

- UNSMIL (2018). _Desperate and Dangerous: Report on the human rights situation of migrants and refugees in Libya_ . Office of the United Nations High Commissioner for Human Rights (OHCHR) and United Nations Support Mission in Libya (UNSMIL).

- Wilson, R. (2021). Moving to jobs: The role of information in migration decisions. _Journal of Labor Economics 39_ (4), 1083–1128.

- Wittenberg, L. (2017). _Managing Mixed Migration: The Central Mediterranean Route to Europe_ . New York: International Peace Institute.

- World Bank (2015). _Labour Markets Dynamics in Libya_ . The World Bank. Washington D.C. (USA).

- World Bank (2018). _Asylum seekers in the European Union: Building evidence to inform policy making_ . Washington, D.C.: World Bank.

- World Bank (2023). _World Development Report 2023: Migrants, Refugees, and Societies._ Washington, DC: World Bank.

23
the closest formal firm) cannot be solely determined by the owner. It depends also on other factors such as the formal firm’s decision and the availability of specific locations. Finally, a large share of informal businesses in the sample operates from their own house, which is indicative of their limited ability to re-locate. To check the robustness of the results, additional analyses using different measures of distance and the subsample where informal firms operate in fixed premises are conducted. The additional results support the validity of the approach.<sup>11</sup>

# **c. Empirical Methodology**

The estimation of the spillover effects of ICT adoption from formal to informal firms uses a logit model. The specification of the estimation is the following: A A A 0 1 2 , (1) ii ii ii ii Where A A A denotes whether informal business AAnn = ββ + ββ DDAADDAADDnnD _i_ adopts a specific ICT technology (1 = yes, + ββ XX + εε and 0 otherwise), such as computers, tablets, cell phones, mobile money, and website. ii AAnn denotes the distance of informal business _i_ to the closest formal firm, which was captured either ii DDAADDAADDnnD by the standard WBES or the WBMES (see above for details). is a vector of controls that captures the characteristics of informal business _i_ that can affect the digital adoption decisions. Such characteristics include the number of workers in the last month, ii XX the number of years in operation, and a sector dummy as well as the owner's age, education, and prior experience in the same type of business. In an extension of the model, labor productivity is included.<sup>12</sup> Lastly, city dummies and clusters to control for city-specific effects of the three cities included in the surveys are incorporated.

In an extension, an interaction term is added to study how the distance effect varies by observable characteristics of informal businesses, which is formulated as follows: A A A 0 1 2 3 4 , (2) ii ii ii ii ii ii ii AAnn = ββ + ββ DDAADDAADDnnD + ββ HHDDAADDHHAAHHDDnnDDA HH + ββ DDAADDAADDnnD × HHDDAADDHHAAHHDDnnDDA HH + ββ XX + εε

> 11 See section 3b for details.

> 12 Labor productivity is calculated as annual sales divided by the number of workers in log. Missing values are filled with annual sales from two years ago.

9
- 23 -

Table 5

# Intra-Industry Trade for Selected Countries

||1967|1972|1977|1980|1982|
|---|---|---|---|---|---|
|Australia|.24|.35|.25|.29|.23|
|Brazil|.19|.21|.34|.44|.49|
|Canada|.55|.64|.65|.61|.65|
|France|.77|.80|.81|.82|.82|
|Germany (West)|.52|.61|.61|.66|.63|
|Italy|.59|.62|.61|.64|.60|
|Japan|.30|.29|.24|.27|.26|
|Korea (South)|.14|.33|.61|.40|--|
|Mexico|.19|.35|.30|--|--|
|Netherlands|.73|.74|.74|.76|.76|
|Norway|.45|.55|.52|.46|.52|
|Sweden|.66|.68|.71|.69|.68|
|United Kingdom|.64|.72|.78|.81|.77|
|United States|.58|.57|.61|.62|.59|

Note: This index of intra-industry trade was calculated on the basis of: Y = 1 _ Z lxi - Mil Z (Xi + Mi) where Y is an index ranging from zero to one, and Xi and Mi are exports and imports in the i-th industry.

Source: Reproduced from Geoffrey Carliner (1985), Table 2.
Table 3 highlights trends in the EU-15’s source of apparel imports over time. China is the market leader, with 24% of total EU-15 apparel imports in 2009, up from 9.6% in 2000. The next three top importers in 2009 are Turkey (6.3%), Bangladesh (4.7%), and India (3.9%). The shifting regional structure of EU-15 apparel imports between 1996 and 2008 can also be seen in Figure A-2 in the Appendix.

For the EU-15, it is important to note that all leading apparel suppliers, with the exception of China and Hong Kong, receive either duty-free or preferential tariff treatment. Tunisia and Morocco are part of the Euro-Mediterranean Partnership, and Romania, Bulgaria, Poland, Hungary, and Turkey are part of the EU-27 or EU Customs Union. To varying degrees, Indonesia, Thailand, Pakistan, Vietnam, India, Sri Lanka, and Bangladesh receive benefits from the Generalized System of Preferences (GSP) program. Whereas the United States excludes textiles and apparel items from its GSP agreements, the EU-15 includes textiles and apparel, thereby favoring many of the least-developed exporters in the global economy.

**Table 3: EU-15 Top 15 Apparel Import Shifts: 2000, 2005-2009**

[Values in Euros; % Represents Country/Region’s % of Year’s World Value]

||**2000**||**2005**||**2006**|**2007**|**2008**|**2009**||
|---|---|---|---|---|---|---|---|---|---|
||Value|**%**|Value|**%**|Value|Value|Value|Value|**%**|
|**World Totals**|64,517||73,909||80,392|84,172|86,935|81,300||
|EU15_INTRA|26,180|40.6%|29,544|40.0%|30,993|33,710|34,601|31,507|38.8%|
|China|6,190|9.6%|13,061|17.7%|14,789|16,865|19,139|19,491|24.0%|
|Turkey|4,437|6.9%|5,648|7.6%|5,730|6,109|5,739|5,137|6.3%|
|Bangladesh|1,907|3.0%|2,596|3.5%|3,381|3,208|3,536|3,800|4.7%|
|India|1,805|2.8%|2,455|3.3%|2,922|2,838|2,998|3,138|3.9%|
|Tunisia|2,496|3.9%|2,359|3.2%|2,386|2,500|2,526|2,196|2.7%|
|Morocco|1,822|2.8%|1,858|2.5%|2,007|2,165|2,089|1,809|2.2%|
|Romania|2,196|3.4%|2,881|3.9%|2,791|2,060|1,982|1,521|1.9%|
|Poland|1,539|2.4%|854|1.2%|812|890|1,185|1,335|1.6%|
|Vietnam|650|1.0%|522|0.7%|768|843|947|935|1.1%|
|Indonesia|1,281|2.0%|891|1.2%|1,052|899|899|865|1.1%|
|Bulgaria|722|1.1%|977|1.3%|1,088|1,054|1,035|823|1.0%|
|Pakistan|645|1.0%|697|0.9%|787|802|813|779|1.0%|
|Thailand|730|1.1%|663|0.9%|761|703|717|690|0.8%|
|Switzerland|377|--|519|--|528|636|642|548|0.7%|
|Sri Lanka|338|--|331|--|426|488|529|555|0.7%|
|Hungary|1,001|1.6%|687|0.9%|706|677|582|445|--|
|HongKong|1,885|2.9%|1,006|1.4%|1,557|1,005|510|258|--|

Source: Eurostat: Apparel Imports to Euro Area EU-15; Apparel represented by SITC 84

**Japan.** Like the United States and the EU-15, Japan relies heavily on apparel imports. In 2006, the estimated apparel import penetration ratio was 93% (Clothesource, 2008). Furthermore, Japan is highly dependent on one country, China, which represented 83% of total apparel imports in 2008 (WTO, 2009). The top 5 countries/regions (EU-27, Vietnam, Thailand, and Korea, plus China) accounted for 93.9% of total imports in 2008 (see Table 4).

6
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53
Table 6: Spillovers at the Polling Station Level

||(1)<br>W|(2)<br>omen|(3)<br>|(4)<br>Men|
|---|---|---|---|---|
|Panel A: Turnout|||||
|Share of Treated Women|0.864***|0.991***|-0.044|-0.149|
||[0.259]|[0.240]|[0.187]|[0.183]|
|R-squared|0.370|0.601|0.003|0.247|
|Mean of Dependent Variable|0.584|0.584|0.487|0.487|
|Panel B: Share of PPPP|||||
|Share of Treated Women|-0.614**|-0.611*|-0.335|-0.372|
||(0.277)|(0.314)|(0.326)|(0.290)|
|R-squared|0.205|0.237|0.127|0.080|
|Mean of Dependent Variable|0.621|0.623|0.630|0.630|
|Polling Station Controls|No|Yes|No|Yes|
|N. Observations|21|21|21|21|

Note: The dependent variable in Panel A is the share of valid votes cast by women (columns 1-2) and men (columns 3-4) over the total number of registered women/men. In Panel B, the dependent variable is the share of votes obtained by PPPP over the total number of valid votes. 'Share of treated women' is calculated by dividing the total number of women treated, in a polling station, by the number of registered women in the polling station. The symbols *, **, *** represent significance at the 10, 5 and 1 % respectively. Robust standard errors in parentheses.