rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Fiscal Space in SSA Countries, 2010-13 vs. 2015-16: Balance Sheet Composition and External Debt**

||Conc<br>(%|essions Ext.<br>GG Gross De|Debt<br>bt)|Short‐t|erm Externa<br>(% Total)|l Debt|Short‐t<br>(% Inte|erm Externa<br>rnational Re|l Debt<br>serves)|Exte|rnal Debt S<br>(% GDP)|tocks|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Fiscal Indicators|2010‐13<br>Median|2015‐16 <br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16 <br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16 <br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16 <br>Median|Difference<br>(p‐value)|
|Industrial Economies|..|..|..|34.8|32.8|(0.409)|991.1|862.5|(0.906)|192.2|175.3|(0.409)|
|Developing countries (excl. SSA)|22.4|16.7|(0.314)|13.6|13.3|(0.876)|34.5|33.5|(0.926)|44.5|51.8|(0.299)|
|Sub‐Saharan Africa (SSA)|42.3|40.1|(0.972)|6.2|5.2|(0.875)|11.6|13.0|(1.000)|27.3|31.6|(0.037)|
|SSA ‐ Resource Abundance|||||||||||||
|Non‐resource rich|56.0|45.7|(0.406)|7.8|5.1|(0.733)|14.5|13.8|(0.841)|28.6|31.6|(0.123)|
|Non‐oil resource rich|41.2|41.3|(0.754)|6.6|3.6|(0.934)|20.3|8.4|(0.268)|28.1|31.7|(0.402)|
|Oil resource rich|15.2|17.6|(0.920)|0.9|0.6|(0.920)|1.2|2.1|(0.920)|18.8|27.2|(0.920)|
|SSA ‐ Growth resilience|||||||||||||
|Bottom Tercile|37.4|24.6|(0.330)|4.0|3.0|(0.957)|5.0|5.8|(0.843)|25.6|31.7|(0.299)|
|Middle Tercile|56.2|50.9|(0.271)|9.9|6.1|(0.199)|22.1|17.1|(0.323)|27.9|34.1|(0.199)|
|Top Tercile|55.5|51.0|(0.447)|2.0|2.5|(0.688)|8.0|13.0|(0.923)|26.1|31.1|(0.229)|

_Note: The null hypothesis of the median equality test is that of no statistical difference across periods. GDP = gross domestic product; SSA = Sub-Saharan Africa. The p-value reported in this table is that of a two-tailed test. Source: Calculations based on data from Kose et al. 2017._ 1
# **Preface**

This report is part of a project on risk-based supervision of pension funds coordinated by the Financial Sector Operations and Policy Department of the World Bank, and initiated in response to a growing number of requests from member countries for assistance in the area of pension supervision. The project is being implemented in collaboration with the International Organization of Pension Supervisors (IOPS), and involves the identification of good practices in risk-based supervision of pension funds, based on a number of carefully selected country studies. These good practice guides are expected to benefit a large number of supervision agencies in developed and emerging countries, and contribute to a better performance of private pension systems.

The project will draw on the experience learned in countries that have implemented a risk-based supervision approach and apply this knowledge to assist developing countries. Models of risk-based supervision demonstrate benefits to be gained from moving away from a focus on individual transactions and strict compliance with a range of specific rules towards greater awareness of the risk profiles of supervised entities. A risk-based approach provides encouragement to supervised entities to place a greater focus on risk management in their daily operations, and promotes a safer and sounder financial system. It is expected that over time moving to a risk-based approach to supervision will create scope for supervisors to focus resources on areas of highest risk and, over time, result in a more efficient use of supervisory resources.

The Danish case provides a model of risk-based supervision as applied in a voluntary occupational system that has achieved a high degree of coverage through collective agreements. Danish funds operate on a defined contribution basis but offer guarantees that result in defined-benefit type of arrangements. The model demonstrates how the move towards a risk-based supervision can be a gradual process and need not involve the development of an holistic risk rating model. The “traffic light” model provides a stress test which can feed into a broader and more subjective assessment of pension funds. Nevertheless the results are still used to guide the intensity and scope of supervision.

This report is a condensed version of a more detailed analysis of the Danish system undertaken by Rein van Dam and Erik Brink Andersen. Rein Van Dam was formerly the head of pension supervision in the Netherlands. Erik Brink Andersen was formerly at the World Bank. The paper was extensively edited and restructured by Dimitri Vittas, former World Bank staff, and Greg Brunner of the World Bank. Most valuable and insightful comments were provided by Charlotte Moller of the Danish Financial Supervisory Authority (DFSA), Peter Skjødt of the Danish Insurance Association (DIA) and Roberto Rocha of the World Bank. The authors alone are responsible for the documentation, analysis and conclusions of the report, which do not represent the views of the World Bank, the DFSA or the DIA.
technology transfer from the United States is noisier than in the unweighted estimations that emphasize more developing and emerging countries.

The second column presents the reduced-form estimates where ∆ ln( _M itk_ ) predicts ∆ ln( _x_  _ijtk_ ) using a format similar to equation (3). In both panels, there is substantial reduced-form link of technology flows to export volumes. The interquartile range in the reduced form, conditional on the fixed effects, can account for around 6% of the interquartile range of export growth using a 0.8 coefficient estimate that sits in between panels A and B.

The third column provides the second-stage estimates from equation (3) having used ∆ ln( _M itk_ ) to predict ∆ ln(<sup>_z_</sup>  _itk_ ) . In panel A’s estimation, the weighted elasticity is 1.6, suggesting a 16% increase in export volumes for every 10% increase in labor productivity. In panel B’s unweighted estimation, the 10% increase in labor productivity is linked to a 24% increase in export volumes. The second-stage elasticity in panel B is larger than in panel A, as the IV estimates provide the reduced-form scaled up by the first-stage effects. Thus, even though the unweighted reduced-form estimate in column 2 is smaller than the weighted reduced-form estimate, this ordering reverses once scaled-up by the first stages.

This study does not overly favor one set of estimates. The weighted and unweighted approaches both have merits and liabilities. Instead, the conclusion from this work is that the instrumented elasticity is in the neighborhood of two. While it is impossible to differentiate among the various reasons as to why the IV estimates are larger than the OLS estimates, a very likely candidate is that OLS suffers from a substantial downward bias due to measurement error in the labor productivity estimates, especially with the substantial differencing embedded in equation (3). While it is likely that omitted factors or reverse causality influenced the OLS estimations as well, these appear to have been second-order to the measurement issues.<sup>11</sup>

> 11. Costinot et al. (2012) adjust export volumes for trade openness using the import penetration ratio for a country-industry (to link observed productivity to “fundamental productivity”). The estimates are very similar when undertaking this approach, being 1.163 (0.457) and 2.513 (1.138) for weighted and unweighted specifications, respectively. The unadjusted and adjusted first differences have a 0.93 correlation. This approach is not adopted for the main estimations due to worries about mismeasurement in the import penetration ratio when combining UNIDO and WTF data.
# **Table 10. Trade Partners Likely to See the Largest Decreases in Exports to Belarus**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage of Total 2012 Belarus**<br>**Imports from Partner**|
|---|---|---|
|Russia|-61.3|-0.2|
|Georgia|-0.4|-1.1|
|Kazakhstan|-0.2|-0.1|
|Azerbaijan|-0.02|-0.1|
|Russia|-61.3|-0.2|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 37
wheeler motorized vehicles. Cars include all type of four-wheeler motorized vehicles. Microbuses also include Coasters in Amman, vans in Beirut, Tomnaya in Cairo. Train and Metro are only available in Cairo.

Table 5 now presents the distribution of commuting time by city and gender for all modes of transport (both public and private). A sizeable share of commuters spends 20 minutes or less for a one-way trip from home to work (note that the sample only includes households residing inside the metropolitan area). On average, commuters in Beirut have shorter trips but with similar time for men and women, while in Amman and Cairo, women tend to have somewhat shorter commuting trips than men. Comparing the accessibility to jobs by public transport only (presented earlier) to the observed commuting times accounting for all modes of transport (presented here) show that private modes of transport offer a clear time advantage when getting to and from work.

**Table 5: Commuting time (including all modes of transport), by city and gender (percent of total)**

||Amm<br>|an<br>|Bei<br>|rut<br>|Cai<br>|ro<br>|
|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|
|Less than 10 min.|21.2|14.4|20.4|16.8|22.4|24.5|
|10 - 20 min.|26.8|28.1|40.4|34.6|24.1|28.5|
|20 - 30 min.|15.9|24|21.3|29.3|18.7|17.3|
|30 - 45 min.|19.9|23.3|14.3|16.2|19|17|
|45 - 60 min.|5.3|5.5|2.4|3.1|8.7|6.1|
|60 - 90 min.|7.1|4.1|1.2|0|6.4|5.8|
|More than 90 min.|3.8|0.7|0|0|0.7|0.7|
|Average (in minutes)|26 min.|23 min.|17 min.|18 min.|25 min.|23 min.|
|_Observations_|693|292|587|191|669|277|

Finally, Table 6 presents the share of total respondents with private vehicle ownership and driving licenses (it does not restrict the sample to those who commute from home to work). The differences across cities that we have observed in Table 4 in terms of use of private cars for commuting, is also reflected here. In Amman, 66% of households own a car. In Beirut, vehicle ownership is very high with 72% of households owning a car. On the contrary, in Cairo, vehicle ownership is very low, with only 16% of households owning a car. This is also reflected in the share of people with a driving license. This difference is consistent with the finding that as cities become larger and denser, more people rely on public transport to get to places (thus reducing vehicle ownership). In every city, the share of men with a driving license is higher than the share of women.

**Table 6: Vehicle ownership, driving license and use of public transportation**

|_Share of households who:_<br>**Amman**<br>**Beirut**|**Cairo**|
|---|---|

27
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46 Table 4

# Annual Average Capital Flows to Highly Indebted Countries by Period, 1983-91 ($ billions)

||1983-85|1986-88|1989-91|
|---|---|---|---|
|Public||||
|Multilateral Banks|3.9|4.2|8|
|Bilateral|1.6|1.9|4|
|IMF|3.8|-0.9|2|
|Total|9.3|5.2|14|
|Private||||
|Banks|9.8|4.6|9|
|Direct Investment|14.5|8.1|10|
|Total|33.6|17.9|33|

Source: Table 3; IMF, World Economic Outlook October 1988, p. 115 (for "non-debt-creating" flows).
Hsieh, Chang-Tai and Peter Klenow (2009) “Misallocation and Manufacturing TFP in China and India”, _Quarterly Journal of Economics_ 124(4): 1403-48.

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27
of the targeted villages as well as the target population. By looking at the heterogeneity of impact we hope to improve our understanding of the channels through which the program affects outcomes, and to highlight the role of complementary inputs in achieving nutritional gains.

We address these questions in the context of a large scale community based nutrition program: SEECALINE, in Madagascar. The ex-post evaluation design builds on two nationally representative surveys administered before and after the program, longitudinal at the community level. As common in any non-experimental setting, our identification strategy needs to carefully address the non-random selection of communities into the program, due both to purposive targeting of the program to the most malnourished districts of the country, as well as to potential unobserved heterogeneity arising from the decision of the communities to participate in the program [Pitt, Rosenzweig, Gibbons 1993]. The data collection effort was designed to revisit the same communities in a follow-up survey seven years apart, and as such allows us to difference out any selection bias arising from time-invariant characteristics.

Our empirical strategy combines difference-in-difference methods with matching estimation techniques, to measure the effect of program availability at the community level on child nutritional outcomes ( _intention-to-treat_ ). This represents the lower bound on the full effect of the program on the participants.

Our results show that the program helped the participating communities bridge the gap in weight for age z-score by 0.15-0.22 standard deviations and reduced the incidence of underweight by 5.2-7.5 percentage points. The results also indicate that the program had significant effects in terms of longer term nutritional outcomes. The program had a protective effect, preventing Seecaline sites from an increasing trend in stunting. The result is particularly important in light of the fact that SEECALINE communities had a higher incidence of shocks and higher food security constraints. Furthermore, we provide suggestive evidence of the impact of the program on a set of child care practices that document the intervention channels of the program. The program effects were obtained through significant improvements in feeding and hygiene practices such as exclusive breastfeeding, timing of weaning, and child care during diarrhea episodes.

While there have been a few well documented successful applications of such approaches based on case-studies or small-scale programs<sup>3</sup> [Allen and Gillespie, 2001], there is

> 3 Notable examples include the Iringa project in Tanzania (Gillespie et al 2003), the Indonesian Nutrition Development Program and the Weaning Program in Indonesia (Favin Griffiths 1999), the ANEP program in the Dominican Republic (USAID 1988), the Nutrition Communication Project in Mali (Ross 1997). There are also

3
desirable outcome in most LICs. Of course, it is difficult to determine what the optimal level of revenue should be in each country, and there is no guarantee that more revenues will automatically lead to productive public expenditures and better development outcomes. Nevertheless, given that the governments in many LICs are often unable to meet even the most basic development needs, it seems very unlikely that increasing government revenues (including by raising tax-to-GDP ratios) would not be desirable in most LICs.

At the same time, it is important to note that countries with lower income levels also tend to feature less favorable economic conditions for generating government revenues. Figure 5 shows the average value of the DEA input index for each income group in 2016-2019 together with the disaggregated values for each of the four included input factors. Unsurprisingly, countries with lower income levels tend to feature smaller values of the DEA input index, indicating less favorable conditions for revenue mobilization. Simple averages of the DEA input index for income groups are: LIC (0.09), LMIC (0.15), UMIC (0.21), and HIC (0.40). Individual economies with the highest values of the DEA input index include Singapore (1.00), Luxembourg (0.90), and Hong Kong SAR, China (0.83). Countries featuring the lowest values of the DEA input index include Niger (0.02), Uganda (0.03), and Tanzania (0.04).

**Figure 3.** Revenues Over Time, by Income Group and Region

<!-- Start of picture text --> Panel A: Income groups Panel B: Regions<br>40<br>40<br>30<br>30<br>20<br>20<br>LIC<br>10 LMIC 10 EAP ECA<br>UMIC LAC MENA<br>HIC SA SSA<br>1980 1990 2000 2010 2020 1980 1990 2000 2010 2020<br>Revenue, excluding grants (% GDP) Revenue, excluding grants (% GDP)<br><!-- End of picture text --> _Notes:_ The sample consists of 182 countries. _Source:_ Authors’ analysis based on the variables and data sources described in Section III.

**Figure 4.** Ranges of Revenues (incl. Median) by Income Group, 2016-2019

12
# **Structural Change in West Africa: A Tale of Gain and Loss**<sup>**‡**</sup>

Fiseha Haile _The World Bank Group_ <mark>JEL:</mark> O11, O47, O55.

Keywords: Structural change, labor productivity, growth, Benin, Burkina Faso, Côte d’Ivoire

> ‡ <mark>This paper has been prepared under the overall guidance of Lars Christian Moller (Practice Manager, World Bank) and Jacques Morrisset (Lead Economist and Program Leader, World Bank). Thanks are due to Michael Weber (Senior Economist, World Bank) for kindly providing survey data on employment for Burkina Faso. The author would also like to thank Patrick Premand (World Bank) for kindly sharing the labor market data used for the World Bank (2017) report “</mark> _<mark>Towards Better Employment and Productive Inclusion of the Poor in Côte d’Ivoire</mark>_ <mark>.” David Cal MacWilliam, Boulel Toure, Samba Ba, and Marim Diop kindly provided assistance with data collection. Useful comments were received from Jacques Morrisset. The views expressed in this paper are solely those of the author and do not represent the opinions of the Word Bank Group.</mark>
Policy Research Working Paper 5334

# **Abstract**

The Special Safeguard Mechanism was a key issue in the July 2008 failure to reach agreement in the World Trade Organization negotiations under the Doha Development Agenda. It includes both price and quantity-triggered measures. This paper uses a stochastic simulation model of the world wheat market to investigate the effects of policy makers implementing policies based on the Special Safeguard Mechanism rules. As expected, implementation of the quantity-triggered measures is found to reduce imports, raise domestic prices, and boost mean domestic production in the Special Safeguard Mechanism regions.

However, rather than insulating countries that use it from price volatility, it would actually increase domestic price volatility in developing countries, largely by restricting imports when domestic output is low and prices high. This paper estimates that implementation of the quantity-triggered measures would shrink average wheat imports by nearly 50 percent in some regions, with world wheat trade falling by 4.7 percent. The price measures discriminate against low price exporters—many of whom are developing countries—and tend to increase producer price instability.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to understand the implications of proposed policy reforms for the agricultural sector. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmartin1@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