rafmacalaba/datause-encoder-data huggingface dataset:
- Security buy-backs to pre-finance a particular issue shortly before maturity to reduce roll-over risk. Investors participating in buy backs are expected to reinvest in on-therun issues.

- Securitiy switches to replace older illiquid issues with new, more liquid on-the run issues. They can also be used to lower refinancing risk.

**Tunisia is using re-openings but without an explicit strategy to build benchmarks; while Morocco and Egypt have already incorporated systematic re-openings to build liquid benchmarks.** In Morocco, despite the greater use of re-openings and increased size of individual lines of securities, there is still room to build larger benchmarks. In Egypt, the next step should be to utilize buy-backs and switches to smoothen the maturity profile of the larger issues and to support market liquidity by replacing less liquid issues with on-the-run issues. Tunisia should upgrade their re-opening practice by incorporating a strategy to gradually build benchmarks at all points of the yield curve.

**Jordan and Lebanon do not apply re-openings and have the most fragmented debt structure.** Consolidation of issues at the shorter end of the yield curve through a carefully designed re-opening strategy should be the first step, together with more predictable issuance calendars. The strategy should assess the adequate issue size that balances the need of larger volumes favoring market liquidity and re-financing risk. Consolidation of issues would be particularly important for Jordan given its lower debt volume in absolute terms and therefore a bigger challenge to boost liquidity. Additionally, the overfunding strategy suggested in the primary market section may also be more relevant to countries with a smaller size of domestic Government debt, as it would increase the size of investible debt.

**Table 6: Main features of primary markets**

||**Egypt**|**Morocco**|**Tunisia**|**Jordan**|**Lebanon**|
|---|---|---|---|---|---|
|**Placement mechanism**|MPA|MPA|MPA|MPA|MPA|
|**Preannounced**<br>**calendar**|Yes|Yes|Yes|No|No|
|**Compliance with**<br>**calendar**|High|Medium|Medium|n.a.|n.a.|
|**Re-openings**|Yes|Yes|Yes|No|No|
|**Benchmark building**<br>**dynamics**|Medium|Low|Low|Low|Low|
|**Bid-to-cover ratio in**<br>**2008**|1.5|7|3|1.4|1.5|
|**Auction participation**|15 exclusive<br>Primary<br>Dealers|Banks and 6<br>non exclusive<br>Primary<br>Dealers|12 banks and<br>1 non<br>exclusive<br>Primary<br>Dealer|Any<br>financial<br>institution|Banks|

MPA: Multiple Price Auction Source: Ministries of Finance and Central Banks (annual reports and webpages) 26
Do Nat **al** Resource-Based Industrialization Strategies Convey Important (Unreco' ._zed) Price Benefits for Commodity<sup>Exporting Developing Countries?</sup>

# Alexander J. Yeatsl

# I. Introduction

Developing countries have long had two policy objectives relating to comnmodity price stability and domestic processing. Concerning the former, "buffer stock" agreements or export<sup>earnings stabilization</sup> schemes like STABEX have been designed to offset unstable primary commodity prices which are purported to have detrimental effects on<sup>both producers and consumers.</sup> Second, developing countries have attempted to remove constraints--like escalating tariffs in export markets--to further processing of domestically produced commodities. The rationale for such efforts is that domestic commodity processing can have important employment<sup>creation effects, it may increase trade</sup> contacts and provide benefits associated with "outward-oriented" trade poli.cies, there may be important linkage effects to other<sup>sectors of the economy, and it</sup> may produce an increase in export revenues<sup>and foreign exchange earnings.2</sup> lPrincipal Economist, International Trace Division, The World Bank, Washington. I would like to thank Bela Balassa, Julio Nogu6s and Kenji<sup>Takeuchi</sup> for comments and suggestions.

> 2 See Chapter 3 in Yeats (1981) for a survey of major issues relating to commodity processing and trade. Helleiner and Welwood (1978) or Roemer (1979) provide useful overviews of problems<sup>and prospects for natural resource-based</sup> industrialization. Section D of the Punta del Este (Uruguay Round) Ministerial Declaration states that the "negotiations sha]l aim to achieve the fullest liberalization of trade in natural resource-based products, including in their processed and semiprocessed forms. The negotiations shall aim to reduce or eliminate tariffs and nontariff measures,<sup>including tariff escalation."</sup>
but the average value per exporter is not affected. They argue this is strong evidence that quality standards act as a fixed cost to trade. Relative to these studies, our novelty is to examine the integration of regulatory barriers in PTAs and to show that PTAs alleviate the regulatory burden with a particular benefit for small firms.

The remainder of the paper is structured as follows. Section 2 describes the data sources. Section 3 describes our empirical approach. Section 4 describes our main results while 5 presents results from several robustness checks and extensions. Section 6 concludes.

# **2 Data and Descriptive Statistics**

## **2.1 Deep Trade Agreements Database**

Our analysis is based on the novel database on the content of preferential trade agreements described in Mattoo et al. (2020).<sup>10</sup> The database contains detailed information on the commitments included in 18 policy areas across 283 PTAs signed and notified to the WTO between 1958 and 2017.<sup>11</sup> SPS and TBT provisions are covered in, respectively, 269 and 263 PTAs signed between 1960 and 2017.<sup>12</sup> The templates used for the mapping of these provisions cover areas such as (a) the reference to the WTO rules; (b) the type of integration approach (harmonization versus mutual recognition) for standards, technical regulations, and conformity assessment procedures, (c) transparency; (d) institutions or mechanisms to administer the agreement and solve disputes; and (e) cooperation among regional partners on standards-related issues.<sup>13</sup> A set of essential provisions are identified which comprise substantive provisions, that is,

> 10See https://datatopics.worldbank.org/dta/index.html.

> 11The policy areas are: anti-dumping, countervailing duties, competition policy, environmental laws, export taxes, investment, intellectual property rights, labor market, migration (visa and asylum), movement of capital, public procurement, SPS, services, state-owned enterprises, subsidies, TBT, rules of origin, and trade facilitation.

> 12For more information on the mapping of the content of SPS and TBT provisions in PTAs, see Espitia et al. (2020) and Stone and Casalini (2020).

> 13The templates used for the mapping of SPS and TBT provisions are provided in Appendixes B.1 and B.2.

8
respectively in 2030 from the respective BAU emissions. If the peaking of global emissions is advanced by 5 years to 2020 (i.e., global emissions start declining from 2020, Scenario 2), Annex I and non-Annex I countries require to lower their total CO2 emissions in 2030 by 42 percent and 19 percent, respectively from their BAU levels. Global CO2 emissions in 2030 would be 24 percent and 29 percent smaller from their BAU levels under Scenario 1 and Scenario 2, respectively.

**Table 5: Percentage Reduction of GHG Emissions from their BAU Level**

|Country/Region|P|ercentage|Changei|nCO2Em|issions||
|---|---|---|---|---|---|---|
||S<br>|cenario1<br>||S<br>|cenario2<br>|<br>|
||2010|2020|2030|2010|2020|2030|
|United States|0|-38|-50|0|-38|-50|
|Canada|-31|-39|-48|0|-39|-48|
|Mexico|0|0|-8|0|0|-15|
|OECD EU|-34|-41|-48|-34|-41|-48|
|Japan|-25|-31|-36|-25|-31|-36|
|South Korea|0|0|-6|0|0|-11|
|Australia/New Zealand|0|-44|-54|0|-44|-54|
|Russia, Other Europe & Central Asia|0|-7|-15|0|-7|-15|
|China|0|0|-11|0|0|-22|
|India|0|0|-10|0|0|-20|
|Other Asia|0|0|-9|0|0|-17|
|Middle East|0|0|-7|0|0|-14|
|Africa|0|0|-7|0|0|-14|
|Brazil|0|0|-9|0|0|-16|
|Other Latin America|0|0|-7|0|0|-15|
|Annex IGroup|-11|-32|-42|-11|-32|-42|
|Non-Annex IGroup|0|0|-10|0|0|-19|
|Global|-6|-15|-24|-6|-15|-29|

-26-
**_LIBERALIZATION:_** liberalization index. It is a weighted average of three indices that respectively account for liberalization in the internal markets, liberalization in external markets and for private sector development. The index assumes values from 0 (no liberalization) to 1 (complete liberalization). Source: De Melo et al. The indices have been update for 1997 on the basis of EBRD (1997).

**_WAR:_** dummy for war torn countries.

# **INITIAL CONDITIONS:**

**_FREEDOM1993:_** initial freedom, measured by the (reversed) Freedom House index in 1993. Source: Freedom House.

**_FSU:_** dummy for countries of the Former Soviet Union.

**_INITDEVELOP:_** measure of initial overall level of development, constructed by De Melo et al. (1997). Linear combination, obtained using the method of principal components, of variables reflecting structural development factors (per capita income, urbanization, over-industrialization, etc.). Source: De Melo et al. (1997).

**_INITFREEDOM:_** initial freedom, measured by the (reversed) Freedom House index. For CEE countries initial freedom is measured in 1989, for FSU countries the reference year is 1990. Source: Freedom House.

**_INITMA CRO:_** measure of initial macroeconomic conditions, constructed by De Melo et al. (1997). Linear combination, obtained using the method of principal components, of variables reflecting the degree of initial macroeconomics distortions (trade dependence, repressed inflation and black market premium). Source: De Melo et al. (1997).

**_INITREGIME:_** dummy equal to 1 for countries where, at the start of the transition, non communist governments were in power. Source: see above.

**_LOCATION:_** dummy for countries geographically close to market economies. Source: De Melo et al. (1997).

**_STATE:_** dummy equal to 1 for countries that were independent states before 1989and equal to 0 for countries that were members of a federation.

**_YEARPLANN:_** number of years under central planning. Source: De Melo et al. (1997).

27
their time (or more time) to the activities with the highest earnings? One reason may be limited access to inputs and assets (e.g., high-return livestock) and lower human capital endowments (i.e., salaried/formal employment) among UP women. For instance, more nonUP than UP households own cows (28% vs. 9%) and fewer own chickens (26% vs. 40%). NonUP households also hold a larger number of animals per type (not shown). The TUP program aims to relax these constraints.

**Figure 3. Earnings per Hour for Main Income-generating Activities, by Gender (USD PPP)**

<!-- Start of picture text --> Men Women<br>2.03<br>Paid Labor: Other<br>0.78<br>2.89<br>Formal/Salaried Employment<br>2.39<br>1.85<br>Paid Labor: Agriculture<br>0.98<br>Own Livestock Rearing (HH) 1.14<br> - 0.50 1.00 1.50 2.00 2.50 3.00 3.50<br>USD PPP per hour<br><!-- End of picture text --> _Notes._ This figure is derived using non-UP and UP control group survey data at follow-up in 2018. It presents earnings per hour in different activities by gender. Other paid labor includes maid services, non-agricultural activities outside of the household, and other paid work. All monetary amounts are PPP-adjusted USD terms, set at 2018 prices using the Afghanistan CPI and PPP conversion factor from the IMF, unless otherwise stated.

# 3. THE TUP PROGRAM

The TUP program combines the transfer of a productive asset with structured training, mentoring, a basic cash stipend, and other complementary services for a defined period. The original TUP program was designed and implemented by BRAC in Bangladesh. The program targets UP women who can manage an enterprise but have no productive assets in the household and are not connected to a microfinance institution. The aim is to help them move out of extreme poverty and ultimately be able to engage in formal financial opportunities.

The Afghanistan TUP program we study here was implemented under the World Banksupported “Access to Finance” program. The program covered six provinces in Afghanistan between 2015 and 2018, supporting 7,500 households. The impact evaluation focuses on Balkh province, where 1,500 households were supported under the program. The 10
# **I.  Introduction**

Public policies in developing countries are often articulated in terms of poverty reduction objectives. Resources for such purposes are invariably scarce relative to the number and magnitude of competing claims. Spending priorities must be defined, and it is often desirable to target social transfers to those beneficiaries whose needs are most urgent. Coady and Morley (2003) survey experience with such targeted transfer programs and show that errors of inclusion and exclusion are unavoidable consequences of such targeting efforts. Efforts aimed at improving targeting of public spending generally focus on reducing either one, or sometimes both, of these types of errors.

Because the precise economic circumstances of households can be difficult to ascertain it is not easy to define who should be eligible to receive a government transfer. Nor is it straightforward to design an administrative mechanism to ensure that the transfer actually reaches the intended beneficiary. In practice governments often exploit geographic variability in the design of targeting schemes: poverty is typically thought to be more concentrated in some areas of a country than others and most countries have an administrative structure that disaggregates to different levels. For example, the central government, located in the capital city, may rely on state or provincial governments to implement government policies at the state or province level. These administrations might rely, in turn, on counties or districts, which may themselves rely on yet lower levels of administration. Resources aimed at poverty reduction can thus be directed to those localities where poverty is concentrated and administration of these transfer schemes can be carried out at the relevant local level.

3
Table 1. New GDP per capita compared to the “old” WDI (World Development Indicators) data (13 most populous countries in the world; year 2005; PPPs year 2005)

||GDP per capita<br>based on “old” PPPs|GDP per capita<br>based on “new”<br>PPPs|Revision (in<br>percent)|
|---|---|---|---|
|Vietnam|3106|2143|-31.0|
|Philippines|4991|2956|-40.8|
|Mexico|10356|11387|+10.0|
|Japan|31262|30290|-3.1|
|Nigeria|1200|1520|+26.7|
|Bangladesh|2025|1068|-47.3|
|Russian Federation|11053|11858|+7.3|
|Pakistan|2437|2184|-10.4|
|Brazil|8854|8474|-4.3|
|Indonesia|3898|3209|-17.7|
|United States|42454|41813|-1.5|
|India|3536|2222|-37.2|
|China|6666|4088|-38.7|

Note. Both GDPs per capita are expressed in 2005 international dollars.

The importance of these revisions does not lie solely in the fact that for the year in which the ICP is conducted, the national accounts of the countries are affected. The true, and at times dramatic, revisions ensue for the past years, because national account aggregates (in particular GDP, with which we are concerned here) are recalculated – based on the 2005 level—for all previous years. The recalculation is done is a such a way that the previous years’ GDP levels are obtained by applying to the “new” 2005 level the national growth rates from the earlier years.<sup>7</sup> An obvious implication is that India and China will now be shown to have been poorer than previously thought for all the years since the late 1940s or early 1950s when their national accounts start. As this is a level effect (which of course varies from country to country), it will not impact countries’ growth rates, but it will impact a number of other calculations. The new calculations of world poverty for the period 1981-2005, based on the 2005 ICP results, were presented by the World Bank in August 2008 (see Chen and Ravallion, 2008). Predictably, these results led to an upward revision in the number and proportion of world poor, but to little

> 7 This procedure has always been done although it is not strictly speaking entirely accurate: the PPP basket of goods is different from the basket of goods  used by each individual country in calculating  its own (physical) growth rate. But this is the only way that both spatial and temporal comparisons between countries’ national account aggregates can be made.

6
Some schooling subsidies and elderly assistance were in effect prior to March 2009, but SP1 vastly increased the scope and targeting of such programs. We used the detailed data in the SES on household socioeconomic characteristics as well as participation in other government programs to determine whether households in the SES were eligible to participate in these SP1 programs based on the eligibility criteria described above. Because SP1 was introduced in March 2009, we interact the eligibility status for each program with a post-March 2009 dummy.

Table 1 provides summary statistics on household participation across different government programs over 2006-2010 (stimulus and non-stimulus programs), including the share of households that were targeted for the three stimulus programs under SP1 above. Participation in most government programs was fairly flat throughout 2006-2010, except for social assistance for the elderly, which rose in both urban and rural areas after 2008. This is likely highly correlated with the component of SP1 focused on elderly assistance. Among the stimulus programs, roughly 80 percent of households in urban and rural areas were targeted across any one of the three programs in 2009. Free education had the largest share of targeted households (45 percent in urban and 60 percent in rural areas). Elderly assistance targeted about 30 percent of urban households and 39 percent of rural households. Transfers to workers contributing to Social Security was more lopsided, with about 15 percent of rural households eligible compared to 30 percent in urban areas.

As mentioned earlier, while SP1 was a large program, individual transfer programs spread out over a large targeted population led to interventions that were relatively small as a percentage of increases in capital expenditure. Using each program‘s budget outlay/structure of transfers, we calculated that the free education and elderly assistance programs constituted about 2-3% of households‘ monthly per capita expenditure, and a one-time 10% increase in consumption from the Saving the Nation‘s Checks program. For this reason, and also since households could participate in 10
# **1. Introduction**

Three million children were born in Pakistan in 1990, the majority in rural regions. By 2005, these children were at the doorstep of adulthood and 80 percent had completed all the formal schooling they would receive during their lifetime. The better educated arrived at this threshold with numerous advantages—they would earn higher wages in the labor market and enjoy better health outcomes, and the educational and health outcomes of their children would be substantially better than their own. More educated girls, although they may not participate actively in the labor market, would command greater autonomy and marry into higher socioeconomic groups. Even among children with the same level of formal schooling, those with more learning (as measured through test scores) would be at an advantage—a study of adult workers in Pakistan showed that literacy and numeracy had a higher return than primary school completion (Nasir and Nazli 2000).

Part of what determines a person’s educational status is the time she spends in school, and this has been the focus of most educational research in Pakistan. Recent studies point to low educational participation, both in absolute terms and relative to the average income of the country (World Bank, 2003, World Bank 2005). With an adult literacy rate of 44 percent, Pakistan compares poorly to the South Asian average (54 percent), and in 2001-02, net-enrollment was 51 percent compared to 83 percent for India, 90 percent for Sri-Lanka and 70 percent for Nepal. For the country’s level of income, the forecasted net enrollment rate (based on a regression of primary net enrollment on log per-capita income and the square of log per-capital income for 138 countries) is 77 percent: Pakistan’s net enrollment is below what one expects for its level of income. Research also suggests that this low participation has a strong gender component (girls are less likely to go to school), is linked to the distance from schools (more so for girls than boys), and to the wealth and educational attainment of the child’s parents (Holmes, 2003, Lloyd, Mete and Sathar, 2005, Alderman, Orazem and Paterno, 2001).

While we do know how many years children spend in school (and what determines the extent of their participation), we know little about how much they learn during this time. Household surveys reveal who joins schools, which children drop out and when, but say less about the basic competencies in language and numeracy that children acquire in school. Data requirements partly drive this emphasis—it is easier to ask about school enrollment than to test children. But it is also driven by educational policy. For example, the Millennium Development Goals call for universal

- 2 -
# **Findings and Policy Responses**

# **_Findings_**

# Themes arising from the PPAs

Very specific findings vary from site to site, but there are a number of common themes that can be identified:

- A near-unanimous recognition that economic wellbeing in rural areas has improved over recent years;

- A strong demand for a greater range of opportunities to develop sustainable livelihoods, particularly those which reduce the dependence on landholdings of reducing size;

- A strong sense of vulnerability to both household-level and community level shoclk s, with ill health being the single most significant shock which poor households enduire;

- Identification of a range of coping strategies which households use to deal with hardship and shock, many of which can be destabilizing for poor households in the longer term;

- A voiced concern about the lack of information about initiatives, plans and programs which affect their livelihoods and a sense of alienation from decision-making processes;

- A number of issues related to intra-household inequity which highlight the vulnerability of children and women;

- A range of gender-related dimensions of illbeing;

- A number of specific policy issues: the status of migrants; the role of the off-farm private sector in rural areas; the way in local fees and contributions are levied; the costs of healthcare and education; the limitations of current mechanisms to consult poor households; and, the limited outreach of formal safety nets.

# Links between the PPA findings and household survey data

The PPAs were analyzed and synthesized at the same time as the quantitative household survey data from the second round of the VLSS were being analyzed. There were strong links between these exercises, with much of the analysis for both of these exercises taking place within the World Bank.

Much of the value of the PPAs lies in covering research territory that is more difficult to probe with household survey data. This is demonstrated in the themes identified above. many of which are issues that would not have emerged from the quantitative analysis alone. There is also a useful overlap, though, in the research domains of the PPAs and **1he** household surveys. Where the PPA research themes and VLSS data overlapped, findings from the quantitative surveys and the participatory research usually coincided closely - with a few notable exceptions. Where the findings from the PPAs and the VLSS do apparently contradict each other, there are clearly understood reasons for the difference in results. As such, the differences are not undermining for either the participatory or quantitative research. This opportunity to triangulate the findings from the PPAs with tbie quantitative data and the general consistency of findings gave both sets of information more credibility, but this reinforcement of key messages is doubtless more significant for 18