rafmacalaba/datause-encoder-data huggingface dataset:
It would be tedious to characterize textually the many differences in approach we find in the IPAs considered here. Accordingly, we summarize them in Table 1, contrasting agreements involving the United States, the EU or EFTA, and others. The World Bank database identifies 130 detailed IPRs provisions, broken into 16 broad categories. These provisions are simply coded as either 0 (absent in the PTA) or 1 (present). In Panel A we list eight categories that seem relevant for studying potential trade impacts.<sup>5</sup> For example, there are 15 international treaties or conventions covering intellectual property that members of IPAs might be required to gain accession or ratify. The United States is far more likely to negotiate agreements demanding such activity, with the average IPA listing 11.5 conventions, compared to 3.3 for the EU/EFTA IPAs and 2 for the others. Indeed, US-involved IPAs stand out across the board, embedding far more provisions within nearly all categories than do the others. This difference is particularly stark in trademarks, patents, test data protection, copyrights, and enforcement.

In Panel B we turn to a subset of categories and, within those, list the number of provisions that are widely considered to be “TRIPS-Plus” in that they are considerably more prescriptive than what is found in TRIPS (Maskus, 2012). There are 32 such provisions, which we collectively call BTRIPS (for “Beyond TRIPS”) to avoid confusing the standard TRIPS-Plus terminology with the World Bank’s “WTO-Plus” designation.<sup>6</sup> These 32 provisions were selected because they feature prominently in debates over elevated IPRs norms, but the list is somewhat ad hoc. With this caveat in mind, there are five BTRIPS norms in patents, including such items as patents for new uses of known products, patent-term adjustments, and patent linkage. Similarly, there are five standards in test data protection, including protection for trial data in pharmaceuticals and biologics. The largest group is in enforcement, reflecting the fact that TRIPS essentially committed countries to a best-efforts approach, while some recent IPAs

> 5 The other categories focus on procedural questions (such as transparency and cooperation mechanisms), do not strengthen existing IPRs (such as incorporating existing IPRs treaties or recognition of the importance of biodiversity and traditional knowledge), or focus on non-trade issues.

> 6 The list of these BTRIPS standards, broken into “core” standards and “broad” provisions, is in Appendix Table A2.

9
The dichotomy in the literature about whether agriculture or non-agricultural growth has been most effective for poverty reduction reflects to some extent the lack of a multi-sectoral or general equilibrium approach to the issue. For example, Larson and Mundlak (1997) show that across countries the pace of inter-sectoral labor migration is determined by the magnitude of the difference in average incomes in farm and non-farm sectors. As labor leaves the agricultural sector, labor productivity in agriculture increases. Thus the very process of structural transformation in which agriculture plays a smaller and smaller role is in itself responsible for raising incomes and reducing poverty within the agricultural sector (see also Timmer, 1988).

Similarly, Foster and Rosenzweig (2004) model the rural economy as a threesector economy encompassing agriculture, non-farm non-tradable goods and services and non-farm tradable goods. Income growth is realized in two main ways: either by the increase in agricultural productivity due to technological change or by increasing urban or world demand for manufactured goods produced in rural areas. So both sources of productivity growth, agriculture and increased non-farm activities, can raise income and alleviate poverty. The main prediction of the model is that increased demand for manufactured goods can be the most pro-poor driver, as it both raises income and reduces local and spatial inequality. Empirically they also show, using panel data from India, those regions with the slowest growth in high yielding varieties of crops experienced the greatest increase in the rural non-farm tradable sector. This is because capital is mobile and seeks low wage opportunities in the agriculturally poorer regions. In other words, those rural areas which did not manage to raise incomes due to agricultural productivity increases found their productivity growth in the non-farm sector. This suggests that the natural and institutional factors that determine agricultural productivity growth also determine whether agriculture or the non-farm sector will be the principal pathways out of poverty.

Datt and Ravallion (1998) report compatible results. They confirm empirically that the main sources of poverty reduction in Indian states were agricultural productivity growth or divergence from the trend in non-farm output growth. But the initial endowment of human and physical infrastructure is found to be a crucial precondition for long-run impact on poverty reduction in India, as they can be seen as a prerequisite for 9
32 M. FAY, D. MARTIMORT, S. STRAUB TABLE I Infrastructure Projects with Financial Information

||Energy|ICT|Transport<br>Water|& sanitation|All|
|---|---|---|---|---|---|
|2015|93|0|14|9|116|
|2016|77|1|14|1|93|
|2017|117|1|29|5|152|
|Total|287|2|57|15|361|

TABLE II Share of Private Debt: Conditional Correlations

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|pcGDP<br>pcGDP squared|0.149***<br>(0.0333)<br>-0.00802***<br>(0.00251)||||||
|Resolving Insolvency||0.00684**<br>(0.00264)|||||
|Control of corruption|||0.161**<br>(0.0722)||||
|Regulatory quality||||0.280***<br>(0.0681)|||
|Cost of Public Funds|||||1.538***<br>(0.293)||
|Banking Concentration||||||0.00249<br>(0.00219)|
|Subsector F.E.|Yes|Yes|Yes|Yes|Yes|Yes|
|Region F.E.|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|326|278|326|326|141|323|
|R-squared|0.206|0.131|0.144|0.175|0.403|0.131|
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their service requirements. Since the age of national service closely corresponds to the age range at which individuals have the greatest propensity to migrate in many countries, these restrictions may prevent the young and mobile from being able to reap the rewards of migration.

**Table 8: Other Legal Restrictions on Emigration**

|_Government permission or an_<br>_exit visa required for_|_Restrictions on travel of Citizens_<br>_of National Service Age_|
|---|---|
|_all citizens to travel_|Algeria|
|Belarus|Azerbaijan|
|Cuba|Egypt|
|Ecuador|Eritrea|
|Equatorial Guinea|Israel|
|Iran|Kazakhstan|
|Laos|Lebanon|
|Libya|Singapore|
|Myanmar||
|North Korea<br>Ukraine||
|Uzbekistan||

source: Country reports of U.S. Department of State (2005)

# **7. Conclusions**

There is considerable scope for global gains from increased international migration (see Walmsley and Winters (2003) for one estimate of the size of the gains). Realization of many of these gains will indeed require greater willingness on the part of migrant receiving countries to explore mechanisms which allow for greater immigration, and for coordinated bilateral and multilateral arrangements. However, this paper has shown that there is also policy latitude on the part of many migrant-sending countries to enhance the ability of their citizens to emigrate. Reduction of the cost of a passport, faster processing times, and the removal of any legal restrictions on emigration offer the potential for reaping additional gains from migration.

- 20 -
programs, including the 30A poverty reduction program (World Bank, 2022).<sup>4</sup> Moreover, Viet Nam received substantial overseas development assistance amounting to nearly USD 50 billion during 2000- 2021 (World Bank, 2023).

Since 1998, Viet Nam has implemented two major programs focused on poverty alleviation. The first is the National Targeted Program for Poverty Reduction (NPPR), aimed at assisting impoverished households and communities in developing their production capabilities, increasing incomes, accessing social services, eliminating hunger, and reducing poverty. The second program, 'Socio-economic Development for the Communes Facing Greatest Hardships in the Ethnic Minority and Mountainous Areas’, commonly known as Program 135, provides support to nearly 2,000 communes located in poor and remote areas. These communes are characterized by a significant proportion of ethnic minority households and high poverty rates (CEMA and UNDP, 2009).

Since 2009, Viet Nam has implemented another major program dedicated to sustainable and rapid poverty reduction in the country’s poorest districts, which is also known as the 30A Program (because it was approved by Resolution No. 30a/2008/NQ-CP (Government of Vietnam, 2008)). The program specifically targeted 62 districts with a poverty rate exceeding 50% in 2006 (using the poverty rate estimated by the Ministry of Labor, Invalids, and Social Affairs (MOLISA)). Figure 1 presents the geographic distribution of poverty rates in those districts during 2006. The 30A Program districts are located in 20 provinces, primarily in the Northern midlands and mountain areas, North Central and Central coastal areas, and Central Highlands. These regions represent the poorest areas in the country, characterized by a high concentration of ethnic minority populations. In the 62 targeted districts, ethnic minorities constitute 90% of the population.

> 4 This sum roughly equals 10% of the country’s annual GDP during this period.

7
# **Tables**

Table 1. Sample Characteristics of Women Married Before 2002

||(1)<br>Other States|(2)<br>Gujarat|(2) - (1)|
|---|---|---|---|
|**Control Variables:**||||
|Hindu|0.75|0.92|0.16***|
|Muslim|0.12|0.07|-0.05**|
|SC/ST/OBC|0.75|0.74|-0.01|
|Male Household Head|0.87|0.89|0.02*|
|Wealth|2.90|3.35|0.45**|
|Rural Resident|0.71|0.64|-0.07***|
|**Outcome Variables:**||||
|Women’s Age at Marriage|17.72|17.98|0.26|
|Women’ Child Marriage|0.49|0.43|-0.05|
|Women’ Age at Cohabitation|17.97|18.24|0.27|
|Women’ Age at First Intercourse|18.03|18.13|0.09|
|Women’s Age at First Birth|20.37|20.59|0.22*|
|Women’s Years of Education|4.71|5.17|0.46|
|Husband Controls|0.48|0.47|-0.02|
|Husband is Emotionally Violent|0.14|0.11|-0.03**|
|Husband is Physically Violent|0.33|0.21|-0.12***|
|Makes Health Decisions|0.78|0.78|-0.01|
|Employed|0.90|0.86|-0.04***|
|Controls Own Income|0.84|0.79|-0.05***|
|Makes Household Purchase Decisions|0.78|0.79|0.02*|
|Makes Decisions About Visits|0.78|0.81|0.02*|
|Men’s Age at Marriage|22.35|21.40|-0.95**|
|No. of women married before 2002|174,226|6,104||
|No. of women married after 2002|227,593|7,518||

_Note_ : This table reports simple differences in means of the characteristics of women living in Gujarat and other Indian states using data from the NFHS-IV. The total sample represents the observations used for the age at marriage and education analysis, and the sub-sample represents observations used for analyzing women’s socioeconomic status. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

26
13 countries after the transition to market economies, but that pre-communist characteristics could reasonably be thought to be uncorrelated with our health and labor outcomes (measured about 45 years later) except by affecting the probability of SHI adoption. The relevance of our two instruments is assessed through a battery of tests, and Hansen tests are used to check the exogeneity of our instruments in the estimated models. Given the relatively small number of observations used to estimate some models, IV procedures (mainly GMM) potentially introduce small sample bias in the estimations; this adds to the—often sizeable—increase in standard errors expected when using IV methods relative to those obtained by OLS procedures. We therefore draw our basic empirical conclusions from the results of the DD-based models, and rely on the IV procedures mainly when SHI endogeneity seems to pose a problem even after controlling for country-specific effects and trends.

# **4. Data**

We use annual data on SHI status, health and labor market outcomes for the 28 ECA countries, from 1990 to 2004. Our dataset has been constructed using a variety of sources; the description in this section begins with our independent variable of interest, SHI status, and then continues for the variables included in our health and labor models, respectively. In the health sector models, data are generally available for most country-year combinations; fewer observations are available for the labor market models.<sup>18</sup>

> 18 In the case of Bosnia-Herzegovina, the period between 1992 and 1996 has been excluded from the analysis due to the lack of data for many dependent variables and the complete disorganization of the health system—which obviously included the SHI scheme—during the war period.
Policy Research Working Paper 9648

# **Abstract**

This paper proposes a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or primary selection units, are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or reduction of the necessary sample size, that is, a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty mapping, that is, spatial descriptions of the distribution of poverty, which are finely disaggregated in small geographic units, such as cities, municipalities, districts, or other administrative partitions of a country that are linked to primary selection units. Such information is then used to select primary selection units with systematic sampling by introducing further implicit stratification in the survey design, to maximize the improvement of the design effect. The proposed methodology has been implemented for the new 2021 Household Budget Survey in Tunisia, conducted under a cooperation project funded by the World Bank. The underlying poverty mapping is based on the 2015 Household Budget Survey and the 2014 Population and Housing Census.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vmolini@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1).

# **3. Simulation design**

The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3.1. The updated database from the pre-simulation analysis, which represents an integrated Levant in a peaceful alternative world, is the starting point for the simulation analysis of the Syrian conflict and the spread of ISIS as well as the disintegration of the deep regional trade ties. The design of the war and disintegration scenarios are presented in section 3.2.

11
needs to be implemented in order to understand what drives the World Bank performance, how Bank performance affects borrowers and project, and finally which reforms produced a real enhancing effect.

Though external validity warnings are to be made, we believe that the World Bank experience may be important for other multilateral development banks, international development institutions and, even, national governments. Selectivity is the key and every effort to promote development through infrastructure investment need to focus on picking up well-designed and solid projects: this may require a huge investment by financing institutions in project elaboration and selection, but this seems to be the most important micro step toward the realization of a “world free of poverty”.

# **References**

Agenor, Pierre R. and Moreno-Dodson, Blanca. (2006). “Public Infrastructure and Growth: New Channels and Policy Implications”, World Bank Policy Research Working Paper No. 4064.

Calderon, Cesar, Moral-Benito, Enrique and Serven, Luis. (2011). “Is infrastructure capital productive? A dynamic heterogeneous approach”, Banco de España Working Papers 1103.

Chauvet, Lisa, Paul Collier, and Margeurite Duponchel (2010). “What Explains Aid Project Success in Post-Conflict Situations?”. World Bank Policy Research Working Paper No. 5418.

Chauvet, Lisa, Paul Collier, and Andreas Fuster (2006). “Supervision and Project Performance: A Principal-Agent Approach”. Manuscript, DIAL.

Deininger, Klaus, Lyn Squire, and Swati Basu (1998). “Does Economic Analysis Improve the Quality of Foreign Assistance?”. World Bank Economic Review. 12(3):385-418.

Denizer, Cevit, Kaufmann and Kraay, Aart. (2011). “Good Countries or Good Projects? Macro and Micro Correlates of World Bank Project Performance”, Policy Research Working Paper No. 5646.

Dollar, David and Jakob Svensson (2000). “What Explains the Success and Failure of Structural Adjustment Programs?”. The Economic Journal. 110():894-917.

Dollar, David and Victoria Levin (2005). “Sowing and Reaping: Institutional Quality and Project Outcomes in Developing Countries”. World Bank Policy Research Working Paper No. 3524.

Dollar, David and Victoria Levin (2006). “The Increasing Selectivity of Foreign Aid: 19842003”, World Development, Vol. 34, No. 12, pp. 2034–2046.

17
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THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 20. Patterns of public spending on infrastructure**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>40 Operations and maintenance Capital investment<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Government Public enterprises<br>US$ million per year<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product; ICT = information and communications technology; WSS = water supply and sanitation.

ODA flows to the CAR averaged $42.6 million per year and were mainly channeled to the transport sector. But while ODA to the transport sector has been volatile over time, the other sectors have experienced different trends. The power sector received some development assistance in 2006 but it was minimal. ODA flows to the ICT sector have been rather constant, at about $0.1 million per year. Notably, the water supply and sanitation sector has attracted an important flow of ODA resources since the end of the conflict, reaching a peak of $6.8 million in 2007 and 2008, and declining since (figure 21).

The CAR has attracted very little private finance into infrastructure investment compared with its African peers. In fact, before 2007 the country did not attract any private investment. In 2007, $12 million went to the ICT sector, primarily for the mobile sector. Between 2008 and 2009 the CAR received just $4.5 million per year on average (table 20). Many African countries have done significantly better in this area (figure 22). Notable is the absence of private investments in the power and transport sectors. Countries such as the Democratic Republic of Congo, Liberia, Nigeria,

**Figure 21. ODA commitments by sector**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>ICT Transport WSS Power<br>US$ million per year<br><!-- End of picture text --> _Source:_ AICD Fiscal Matrix 2011. _Note:_ ODA = official development assistance; ICT = information and communications technology; WSS = water supply and sanitation.

Uganda, Kenya, and Senegal have captured between 1.8 and 2.5 percent of GDP. But in 2008 and 2009 the ICT sector attracted more than $80 million in foreign direct investment (FDI), most of it from mobile operators (World Bank 2009d).

42