rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2.** Diagnostic and treatment accuracy by country

||(1)<br>Diarrhea|(2)<br>Pneumonia|(3)<br>Malaria|(4)<br>Post-partum<br>hemorrhage|(5)<br>Neonatal<br>asphyxia|(6)<br>Tuberculosis|(7)<br>Diabetes|(8)<br>All|
|---|---|---|---|---|---|---|---|---|
|Kenya|0.69<br>(.67-70)<br>[4505]|0.65<br>(.63-.66)<br>[4491]|NA|0.71<br>(.69-.72)<br>[4481]|0.79<br>(.78-81)<br>[4476]|0.70<br>(.69-.71)<br>[4488]|0.49<br>(.47-.50)<br>[4489]|0.67<br>(.66-.68)<br>[26,930]|
|Madagascar|0.17|0.28|0.86|0.22|0.50|0.19|0.11|0.33|
||(.11-22)|(.22-.34)|(.82-.89)|(.16-29)|(.43-.57)|(.13-.25)|(.07-.15)|(.30-.36)|
||<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[642]|<br>[4,494]|
|Mozambique|0.68|0.45|0.83|0.30|0.57|0.51|0.23|0.51|
||(.64-.72)|(.41-.49)|(.79-.86)|(.26-.34)|(.53-.62)|(.47-.55)|(.20-.27)|(.49-.53)|
||<br>[725]|<br>[725]|<br>[725]|<br>[724]|<br>[724]|<br>[725]|<br>[725]|<br>[5,073]|
|Niger|0.84<br>(.79-.89)|0.42<br>(.35-.48)|0.78<br>(.73-.83)|0.19<br>(.14-.24)|0.48<br>(.42-.54)|0.21<br>(.16-.26)|0.07<br>(.05-.10)|0.42<br>(.40-.45)|
||<br>[519]|<br>[519]|<br>[519]|<br>[601]|<br>[601]|<br>[519]|<br>[519]|<br>[3,797]|
|Nigeria|0.23<br>(.22-.25)|0.29<br>(.27-30)|0.59<br>(.57-.61)|0.21<br>(.20-.23)|0.32<br>(.30-.33)|0.16<br>(.15-.18)|0.19<br>(.18-.21)|0.28<br>(.28-.29)|
||<br>[4693]|<br>[4572]|<br>[4669]|<br>[4387]|<br>[4383]|<br>[4669]|<br>[4609]|<br>[31,982]|
|Senegal|0.55<br>(.39-.70)|0.43<br>(.28-.58)|0.04<br>(.00-.08)|NA|NA|0.21<br>(.12-.30)|NA|0.31<br>(.24-.38)|
||<br>[152]|<br>[152]|<br>[152]|||<br>[152]||<br>[608]|
|Sierra Leone|0.89|0.45|0.92|0.67|0.85|0.33|0.21|0.62|
||(.87-.92)|(.42-.49)|(.89-.94)|(63-.70)|(.82-.88)|(.29-.36)|(.18-.24)|(.60-.63)|
||<br>[826]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[824]|<br>[5,770]|
|Tanzania|0.85|0.57|0.9|0.36|0.79|0.41|0.41|0.61|
||(.80-.90)|(.50-.63)|(.86-.93)|(.30-.42)|(.74-.84)|(.35-.47)|(.34-.47)|(.59-.64)|
||<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[542]|<br>[3,794]|
|Togo|<br>0.17|<br>0.57|<br>0.74|<br>0.09|<br>0.10|<br>0.11|<br>0.52|<br>0.33|
||(.08-.25)|(.45-.69)|(.64-.85)|(.02-.15)|(.04-.17)|(.07-.16)|(.39-.64)|(.28-.38)|
||<br>[302]|<br>[302]|<br>[302]|<br>[298]|<br>[298]|<br>[302]|<br>[302]|<br>[2,106]|
|Uganda|<br>0.29|<br>0.25|<br>0.61|<br>0.27|<br>0.50|<br>0.27|<br>0.39|<br>0.37|
||(24-33)|(21-29)|(56-65)|(23-31)|(45-55)|(23-31)|(35-44)|(35-39)|
||..<br>[709]|..<br>[709]|..<br>[708]|..<br>[709]|..<br>[707]|..<br>[709]|..<br>[709]|..<br>[4,960]|
|All|<br>0.54|<br>0.43|<br>0.70|<br>0.34|<br>0.55|<br>0.31|<br>0.29|<br>0.45|
||(.51-.56)|(.41-.46)|(.66-.73)|(.32-.35)|(.52-.58)|(.29-.33)|(.27-.31)|(.43-.46)|
||<br>[13,518]|<br>[13,381]|<br>[8,988]|<br>[13,120]|<br>[13,110]|<br>[13,477]|<br>[13,267]|<br>[88,862]|

Notes: Mean diagnostic accuracy: i.e., the percentage of health workers who correctly diagnose the condition, by country and condition, with all individual country means calculated using country-specific sampling weights, and the (unweighted) mean across conditions for each country in column 8. 95% confidence intervals in parenthesis and 26
district (Filipski et al., 2020). Local reports suggest that assistance provided at refugee camps could potentially influence individuals and households’ in-migration decisions.

Though there has been some increase in deforestation due to the expansion of refugee camps in Cox’s Bazar, the camps have provided shelter and support for hundreds of thousands of refugees fleeing violence and oppression in Myanmar. Today, nearly 1 million Rohingya refugees have shelters over their heads and appear to be relatively “protected” from escalating violence in Myanmar. The Government of Bangladesh, UNHCR, and several national and international stakeholders recognize that both refugees and their hosts’ fundamental human rights, including the right to life, settlement, and protection, should be safeguarded and respected, as enshrined in several international legal instruments.<sup>18</sup>

# **8. Conclusion**

Deforestation has been on the rise for nearly three decades in Cox’s Bazar district in Bangladesh. In 2017, over 740,000 Rohingya refugees from Myanmar sought asylum in the district. Humanitarian and socio-economic programs and foreign aid expanded as stakeholders worked to deliver essential services and goods to refugees and (in some cases) host community members. Considering the forest endowment of Cox’s Bazar, policy makers and concerned stakeholders inquired about the rate of deforestation as population and development activities increased. Using satellite imagery and remote sensing techniques, this study finds that 12,807 ha of forest cover disappeared across the entire Cox’s Bazar region between 2017 and 2020, including 1,337 ha that transitioned from forest to settlement within the 2,545 ha official refugee camp boundary.

Using a simple linear probability model with a difference-in-differences framework to capture changes in land-use / land cover trends in response to camp openings, we find an estimated 2,600 ha loss of forest cover within 1-5 km from the camps triggered by camp openings and local settlement expansions. Additionally, about 7,700 ha changed from non-settlement to settlement. Decomposing this result, we find about 1/3 of forest loss occurs within 1 km of camps, and the remaining 2/3 happens 1-5 km from refugee camps. Are refugees directly driving the results? Our evidence suggests that refugees’ direct natural capital extraction _may_ be taking place very close to

> 18 These include the 1948 Universal Declaration of Human Rights, the 1951 Refugee Convention and its 1967 Protocol, the 2016 New York Declaration for Refugees and Migrants, and more recently restated in the 2018 Global Compact on Refugees.

33
A key underlying requirement for successful fiscal decentralization is a uniform structure of subnational government accounts that are regularly and properly audited. There are problems with the availability of such a comprehensive data system in most developing countries. A census of government finances, reporting the actual financial outcomes for every subnational government, is essential information if the performance of the intergovernmental system is to be monitored. Yet, it is not common in developing countries to have an up-to-date information system that describes the finances of subnational governments in detail. Rarer yet is a fiscal analysis model that is used to track the performance of local government finances (Bahl, 1999b).

# **7. Breaches in Sequencing: Causes and Risks**

The primary question we raise in this paper is whether sequencing matters in developing and implementing a fiscal decentralization policy. Our answer is that it does, and that mistakes in sequencing can lead to significant resource costs and possibly to a loss in confidence in the decentralization initiative.

We also raise two secondary questions in this paper. First, we ask whether there is something akin to an optimal sequencing for decentralization policies and implementation. Our answer is that there is, and that following these sequencing rules might minimize the costs and the risks of implementing fiscal decentralization. Second, we ask about the extent to which countries follow these optimal sequencing rules. The answer would appear to be that in general, they do not.

What to make of this gap between theory and practice? One might consider a couple of conjectures. First, sequencing does not matter. Second, there is a lack of information or there are institutional and political constraints. In this section, we address in more depth these two possibilities.

# _7.1. Sequencing Does Not Matter?_

One might argue that the order of introducing decentralization policy does not matter, that sooner or later the structure of decentralization in a country will end up at the same place no matter how one goes about it. Look for example at the U.S., as strong an example of working fiscal decentralization as there is. There was probably never a comprehensive “white paper” on state and local government finance, yet the system grew up to be quite functional. De facto, this is exactly the stance on sequencing decentralization policies that seems to have been taken in many developing countries.

The question is whether this is a wise strategy for developing countries, most of which are in the early stages of decentralization. Certainly one can understand the appeal. Decentralization policy is after all a political decision, and the design and implementation of such a program will be driven by bargaining and political timing. Even so, we would argue against the “make it up as you go” strategy, for the following reasons: 34
Table 8: Impact of Conflict during School Age on Female Early Marriage – DHS.

|Explained Variable|(1)<br>Married by 15|(2)<br>Married by 15 -<br>here since 1996|(3)<br>Married by 15|(4)<br>Married by 15 -<br>here since 1996|
|---|---|---|---|---|
|Specification|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|
|=1 if 6-9 in 1996 x district<br>casualties|0.0351|0.0853**|0.0255|0.0726*|
||(0.0234)|(0.0400)|(0.0216)|(0.0394)|
|=1 if 6-9 in 1996 x Maoist<br>abductions|||0.0026***|0.0037***|
||||(0.0004)|(0.0011)|
|Constant|0.2634***|0.3504***|0.2649***|0.3478***|
||(0.0250)|(0.0383)|(0.0244)|(0.0371)|
|Panel variable|District|District|District|District|
|Region x Year of Birth FE|Yes|Yes|Yes|Yes|
|Observations|3217|1769|3217|1769|
|No. of Clusters|75|75|75|75|
|R-Squared|0.0549|0.3084|0.0574|0.3115|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Standard errors clustered at the district level are in parentheses. District casualties are expressed per 1000 inhabitants. Sample includes only individuals aged 6-9 (treatment group) or 16-19 (control group) years old at the start of conflict in 1996. *p<0.10, **p<0.05, ***p<0.01.

Table 9: Impact of Conflict during Marriageable Age on Female Marital Status – DHS and NLSS.

|Explained Variable|(1)<br>Married by 21|(2)<br>Married by 21 -<br>here since 1996|(3)<br>Married by 18|(4)<br>Married by 21|(5)<br>Married by 21 -<br>here since 1996|
|---|---|---|---|---|---|
|Specification|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|
|Dataset|DHS|2006|NLSS 2003|DHS|2006|
|=1 if 13-17 in 1996 x|-0.0334|-0.0274||-0.0339|-0.0229|
|district casualties|(0.0274)|(0.0263)||(0.0278)|(0.0266)|
|=1 if 13-17 in 1996 x<br>district casualties up to<br>NLSS|||0.0442<br>(0.0423)|||
|=1 if 13-17 in 1996 x||||0.0001|-0.0009|
|Maoist abductions||||(0.0008)|(0.0009)|
|Constant|0.8914***|0.9086***|0.6750***|0.8914***|0.9084***|
||(0.0174)|(0.0154)|(0.0326)|(0.0175)|(0.0152)|
|Panel variable|District|District|District|District|District|
|Region x Year of Birth<br>FE|Yes|Yes|Yes|Yes|Yes|
|Observations|3178|1535|1519|3178|1535|
|No. of Clusters|75|75|72|75|75|
|R-Squared|0.0149|0.1760|0.0457|0.0149|0.1764|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. The sample only includes women aged 13-17 (treatment group) or 23-27 (control group) years old at the start of conflict in 1996 and surveyed in the Nepal Living Standards Survey 2003/04 (Column 3) or Demographic and Health Survey 2006 (all other columns). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

44
WPS4016

# **Rules of Origin for Preferential Trading Arrangements**

**Implications for the ASEAN Free Trade Area of EU and U.S.  Experience**

Olivier Cadot<sup>*</sup> Jaime de Melo<sup>†</sup> Alberto Portugal-Pérez<sup>‡</sup>

## **Abstract**

With free trade areas (FTAs) under negotiation between Japan and the ASEAN Free Trade Area (AFTA) members and between the Republic of Korea and AFTA members, preferential market access will become more important in Asian regionalism. Protectionist pressures will likely increase through rules of origin, the natural outlet for these pressures. Based on the experience of the European Union and the United States with rules of origin, this paper argues that, should these FTAs follow in the footsteps of the EU and the US and adopt similar RoO, trading partners in the region would incur unnecessary costs. Using EU trade with GSP and ACP partners, the paper estimates how the utilization of preferences would likely change if AFTA were to veer away from its current uniform RoO requiring a 40% local content rate. Depending on the sample used, a 10 percentage point reduction in the local value content requirement is estimated to increase the utilization rate of preferences by between 2.5 and 8.2 percentage points.

JEL classification: F13, F15 _Keywords:_ Rules of Origin, preferential trade agreements, market access, NAFTA, PANEURO, AFTA, ASEAN _._ World Bank Policy Research Working Paper 4016, September 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ An earlier version of the paper was presented at the Boao Forum for Asia (BFA) “Regional Workshop on the Importance of Rules of Origin and Standards in Regional Integration” held in Hainan on June 27-28. We thank Paul Brenton, Antoni Estevadeordal, Céline Carrère , Bolormaa Tumurchudur and workshop participants for helpful comments on an earlier draft.

* University of Lausanne, CERDI and CEPR: e-mail: cadot@hec.unil.ch

> † University of Geneva, CERDI and CEPR: e-mail: <u>DeMelo@ecopo.unige.ch</u>

> ‡ University of Geneva,: e-mail: <u>Alberto.Portugal@ecopo.unige.ch</u>
# **4 Results**

This section presents the results from the simulations described above. First, we simulate the poverty trajectory towards 2030, both at the global and regional level, focusing on the poverty rate measured at $1.25/day. More detailed results, including for other poverty lines, are presented in Appendix 2.<sup>17</sup> Second, we explore some of the dynamic aspects of our simulations and the mechanics of the effect on poverty reduction. Third, we explore the distributional impacts of our simulations.

## **4.1 Impacts on poverty: Global and regional trajectories to 2030**

Figure 7 presents the trajectories for the global poverty headcount up to 2030 for the three different growth scenarios. In neither of the baseline simulations with distribution-neutral growth ( %) do we reach the World Bank’s first goal of reducing extreme poverty to 3% by 2030. The global poverty rate mm= 0 reaches between 4.7% and 6.7% in 2030, depending on the growth scenario (10 year historic growth, 20 year historic growth). This confirms the findings of World Bank (2015) that the poverty goal is unlikely to be achieved under distribution-neutral growth.

**Figure 7: Projections of poverty under different scenarios for shared prosperity**

<!-- Start of picture text --> 10 year historic growth 20 year historic growth<br>2010 2015 2020 2025 20302010 2015 2020 2025 2030<br>year<br>m=0% m=-1% m=-2% m=1% m=2%<br>Graphs by Growth scenario<br>20<br>10<br>0<br>Share of population in poverty ($1.25/day) (%)<br><!-- End of picture text --> The 3% poverty target looks much more achievable in simulations where growth of the bottom 40% is “boosted” to grow faster than the mean of the distribution (i.e. or ). With and the 10 year historic growth scenario, the 3% target is reached ahead of the 2030 deadline. With mm= 1% mm= 2% mm= 2%

> 17 Chen and Ravallion (2010) use $1.25, $1.45, $2.0 and $2.5. Global headcount figures are reported only for these poverty lines. The assumption of zero poverty in rich countries may not apply for higher poverty lines. Hence the Appendix presents results for higher poverty lines only for the regions available in PovcalNet.

14
human and physical capital accumulation, and stagnating or declining productivity.

# _III.1 Why the poor growth Record: the Dutch Disease?_

Traditional among explanations of poor performance in ORCs is the so called Dutch Disease (van Wijnbergen (1984a,b), Corden and Neary (1984)). This literature named after Holland’s poor record in managing its natural gas wealth in the 1960s, points out that spending out of oil wealth increases demand for non-tradeables and so draws productive resources into that sector. Since the presumption is that technological progress is faster in T-sectors than in NT-sectors, the explanation of low-growth naturally follows. If moreover some of the higher growth is related to effects that are not captured directly by private entrepreneurs, such as sector-wide learning by doing effects, there is a case for an explicit government supported economic diversification strategy (van Wijnbergen (1984b)).

However only when oil revenues are temporary, and, critically, capital market failures or misguided spending policies cause an associated temporary spending boom, a strong case for industrial diversification emerges. Countries following a Permanent Income rule, sharing the oil wealth with future generations and smoothing out expenditure into the far future, do not need to face a nearby future without oil wealth and with depressed economic activity, and therefore have no need to worry about future declines in exchange rates. In such circumstances there is no clear cut case in favor of intensified diversification policies after an increase in oil wealth (van Wijnbergen (1984b)). Nigeria has had periods of excessive spending, but also periods of underspending, and started an explicit expenditure smoothing policy in 20904. As long as that policy is maintained, there will be no real Dutch Disease problem now or in the foreseeable future.

Moreover, there are other difficulties in simply labeling Nigeria as another instance of an ORC succumbing to the Dutch Disease. In particular, the mechanism through which high spending out of what in essence is a tradeable resource leads to low growth is a fight for scarce resources drawing labor and capital out of the T-sector. Critical in this explanation is that there is resource scarcity at going factor prices, otherwise increases in NT activity can be met without decline in T-sectors. And that is where the story of Nigeria as a Dutch Disease victim falters.

In Figure 8 various measures of excess capacity are plotted against time. While capacity utilization surveys indicate higher capacity use in the early oil boom years than in later years, the utilization ratio never exceeds 75%, and, except 1980 and 1981, hovers at 60% or lower. Unemployment data that far back are unfortunately not available, but the figures that are available also point at considerable slack in resource utilization. Such numbers are at variance with Dutch Disease style pressure on scarce labor and 11
21 (originally _M_ 0) ought to be replaced by the drop in non-medical consumption caused by the medical expenditure, i.e. _x_ '- _NM_ 0 in Figure 6, while the denominator (originally _x_ ) ought to be replaced by the amount of non-medical consumption that would have been enjoyed in the absence of the health shock, i.e. _x_ ' in Figure 6. In the case of impoverishment, the pre-payment headcount ought to be assessed on the basis of _x_ ' rather than _x_ , while the post-payment poverty headcount is still to be computed using observed non-medical consumption, _NM_ 0. Factoring in dissaving, asset sales and borrowing also casts further doubt on the practice of including out-of-pocket payments in the consumption aggregate when measuring poverty.<sup>23</sup> A household may have been pushed above the poverty line in terms of its combined non-medical and medical expenditure by its medical outlays financed largely by dissaving and borrowing. A health financing reform that cuts out-of-pocket payments and reduces the need for households to dissave and borrow would end up increasing measured poverty!

Modifying estimates of catastrophe and impoverishment to take into account dissaving, asset sales and borrowing requires that one have an estimate of the counterfactual income _x_ '—the household’s income in the absence of the health expenditures. Most household surveys do not even ask how households financed their health expenditures (the _WHR_ is one of the few that does), let alone how much was raised by selling assets or borrowing. Such questions are sometimes asked in specialized vulnerability surveys, but rarely in health surveys. The 1995 _Indian National Sample Survey_ is an exception. In their analysis of the data, Flores et al.<sup>26</sup> find that heavy use of coping strategies, including drawing down of savings, asset sales, borrowing, and transfers. They find such strategies finance three-quarters of the cost of inpatient care in
<!-- Start of picture text --> 0.1<br>Autarky: d 0.2<br>0<br>1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 0.1<br>-0.1 nx:em<br>0<br>-0.2 Integration 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76<br>-0.3 -0.1 nx:d<br>-0.4 -0.2<br>Autarky: em<br>-0.5 -0.3<br>(a) Interest rate ( r ) (b) Net exports (as a fraction of out-<br>put)<br><!-- End of picture text --> Figure 4: A comparison of the integrated and autarkic equilibria: the interest rate on publicly backed claims and net exports (in the closed economy, net exports are 0).

at the same time, the return to liquidity increases, and the option of selling liquid claims abroad becomes more attractive relative to paying for domestic labor. The widening gap between the marginal product of labor and the wage is consistent with recent trends in the US.<sup>12</sup> The interest rate ( _r_ ) is declining over time. From the perspective of developed economies, financial integration is associated with an initial drop in _r_ from its autarkic steady state level of 3% to around 0. During the transition, the interest rate declines until reaching its steady state value of -19%. In principle, there are two competing forces affecting the interest rate trend: on the one hand, capital accumulation in both the emerging and the developed economies decreases the marginal return to capital, thus putting downward pressure on _r_ . In addition, as the emerging economy grows, its excess demand for liquidity increases. This channel further contributes to the decline in _r_ and to the increase in liquidity premiums. On the other hand,

> 12Calibrating the marginal product of labor as _MPL_ = <u>(1</u> _−lα_ <u>)</u> _Y_ with _α_ = 0 _._ 33, the “labor wedge”<sup>_<u>MP L</u>_</sup> _w_ has been increasing steadily in the US, from around 1.2 in 1990 to 1.3 in 2010. The calculations are based on the following data from the OECD, 2013. For _Y_ : B1 GA: Gross domestic product (output approach) National Currency, current prices. For _l_ : Annual Labor Force Statistics, Total Employment. For _w_ : Average annual wages, current prices in NCU.

29