rafmacalaba/datause-encoder-data huggingface dataset:
classification algorithms. Specifically, we take the average of each of the explanatory variables over the most recent period 2012-2014 included in the dataset, and feed these through all four classification rules to generate four alternative lists of countries “at risk” of conflict according to these models. For comparative purposes we also report the list of countries “at risk” of conflict based on the World Bank criteria for being a “fragile or conflict-affected state”. As noted earlier, this classification was based on having CPIA scores below a threshold of 3.2, and/or the presence of a UN and/or regional peacekeeping or peacebuilding operation, and we take the most recent classification of countries (i.e. the World Bank’s FY17 list).<sup>22</sup> Finally, we focus only on countries in which none of our three annual indicators of conflict is observed in any of the three years 2012-2014. This is to be consistent with the emphasis in our empirics on predicting transitions from non-conflict into conflict.

The results of this exercise are shown in Table 6. The rows of the table correspond to the 87 countries satisfying the criteria outlined above, the columns correspond to the four classifiers as well as the Bank’s listing of fragile and conflict-affected situations (FCS). In the table entries, ones indicate predictions of conflict, and zeros predictions of no conflict. The probit, linear, and threshold classifiers all predict conflict in 35, 43, and 37 of these countries respectively, while the random forest algorithm predicts conflict in 26 countries. It is useful to compare these predictions with the World Bank’s classification of fragile situations. There are 10 countries that appear on the FCS list and they are highlighted in light gray. Of these, five countries also have conflict predictions from all four classification algorithms (Burundi, Djibouti, Guinea-Bissau, Chad, and Zimbabwe). A further four countries have conflict predictions based on two or three of the algorithms.

Perhaps more interesting are cases where there is disagreement between the classification algorithms and the World Bank list. There is only one country on the World Bank list, Togo, for which none of the classification algorithms predicts conflict. In contrast, there are eight countries not on the World Bank list, but where all four conflict prediction algorithms do signal conflict and they are highlighted in dark gray: Bangladesh, Mauritania, Malawi, Nepal, Niger Senegal, Tajikistan, and Uzbekistan. Interestingly enough, in the latest replenishment round of IDA, an exceptional Fragility, Conflict, and Violence (FCV) Risk Mitigation Regime was established that would make countries who want to address FCV risks to be eligible for up to 1/3 of the country’s indicative allocations with a cap of

> 22 Note that a country can only appear on the World Bank’s list of fragile and conflict-affected states if its CPIA score was publicly disclosed, which is done only for countries eligible for borrowing from IDA.  For this reason, countries for which CPIA scores are not publicly disclosed are indicated with as missing values in the fifth column of Table 6.

28
Government of Honduras, Grupo Consultativo (2005). _Avanzando en la Planificacion Sectorial de Mediano Plazo: Plan Pluriennal de Ejecución de la ERP_ , Tegucigalpa, Honduras.

International Monetary Fund, IMF (2006). _Ghana: Debt Sustainability Analysis_ , Washington, DC.

Lofgren, Hans and Carolina Diaz-Bonilla (2006). “MAMS: An Economywide Model for Analysis of MDG Country Strategies – Technical Documentation.” Mimeo. World Bank.

Paxson, Christina H. and Schady, Norbert (2005). "Cognitive Development Among Young Children in Ecuador: The Roles of Wealth, Health, and Parenting," World Bank Policy Research Working Paper No. 3605.

Romp, Ward, and Jakob de Haan (2005). “Public Capital and Economic Growth: A Critical Survey,” in Infrastructure, Economic Growth, and the Economics of PPPs, EIB Papers, European Investment Bank, Vol. 10.

UN Millennium Project (2005). _Investing in Development: A Practical Plan to Achieve the Millennium Development Goals_ . New York.

World Bank (2001). _Honduras Public Expenditure Management for Poverty Reduction and Fiscal Sustainability,_ Washington, DC.

World Bank (2004). _Honduras Development Policy Review: Accelerating Broad-Based Growth,_ Washington, DC.

World Bank (2007). _Honduras Public Expenditure Review,_ Washington, DC.

20
**Table 2.7   Sectoral composition of KIBS sector in Singapore, 1990-2001**

||Establishments|Employment|Operating<br>receipts<br>($million)|Value<br>added<br>($million)|Value added/<br>Worker<br>($’000)|
|---|---|---|---|---|---|
|**1990**||||||
|IT and related services|338|5,524|581.4|240.8|43.6<br>|
|Legal Activities|446|4,884|362.9|269|55.1|
|Market Research, Business &|||||54.9|
|Management Consultancy Activities|1,032|6,512|631.7|357.3||
|Engineering services|339|3,477|274.2|144|41.4|
|Architectural, quantity surveying,|||||45.3|
|building & appraisal services|367|2,645|201.7|119.8||
|_Total KIBS sector_|_2522_|_23,042_|_2051.9_|_1130.9_|_49.1_|
|||Percenta|ge (%)|||
|IT and related services|13.4|24.0|28.3|21.3||
|Legal Activities|17.7|21.2|17.7|23.8||
|Market Research, Business &||||||
|Management Consultancy Activities|40.9|28.3|30.8|31.6||
|Engineering services|13.4|15.1|13.4|12.7||
|Architectural, quantity surveying,||||||
|<br>building & appraisal services|14.6|11.5|9.8|10.6||
|_Total KIBS sector_|_100.0_|_100.0_|_100.0_|_100.0_||
|**2001**||||||
|IT and related services|2716|24,296|4,802.6|1,771.4|72.9|
|Legal Activities|741|8,486|988.1|736.4|86.8|
|Market Research, Business &|||||69.1|
|Management Consultancy Activities|3,779|22,069|2,923.2|1,524.8||
|Engineering services|903|9,142|1,548.2|615.9|67.4|
|Architectural, quantity surveying,|||||59.6|
|<br>building & appraisal services|632|7,206|655.8|429.4||
|_Total KIBS sector_|_8,771_|_71,199_|_10,918.0_|_5,077.9_|_71.3_|
|||Percenta|ge (%)|||
|IT and related services|30.97|34.12|44.0|34.9||
|Legal Activities|8.45|11.92|9.1|14.5||
|<br>Market Research, Business &||||||
|Management Consultancy Activities|43.09|31.00|26.8|30.0||
|Engineering services|10.30|12.84|14.2|12.1||
|Architectural, quantity surveying,||||||
|building & appraisal services|7.21|10.12|6.0|8.5||
|_Total KIBS sector_|_100.0_|_100.0_|_100.0_|_100.0_||

_Note_ : KIBS data includes the following industries: IT and related services, legal activities, market research, business & management consultancy activities, engineering services, architectural, quantity surveying, building & appraisal services.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: Architectural, Engineering & Technical Services._ Singapore: DOS.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: IT & Related Services._ Singapore: DOS.

Singapore Department of Statistics (DOS). (2001). _Economic Surveys Series 2001: Professional Services._ Singapore: DOS.

15
to maintain credit balance on the Treasury accounts (Bhutan, The Gambia), to finance damages resulting from natural disasters and to obtain foreign currency (Namibia). In some states, borrowing purposes are specific in an annual budget law (Cape Verde) or specified and approved by the Parliament before a loan contract is signed (Maldives).

# **_Procedures for borrowing in domestic markets are relatively strong._**

Documented procedures for local currency borrowing in the domestic market was also found to be another area of relative strength in debt management capacity, with 11 small states meeting the minimum requirement again, in part, reflecting the institutional strength of central banks (that were responsible for issuing domestic debt in these small states). Progress has also been made in developing effective capacity to maintain a complete and up-to-date record of all holders of domestic government securities in a secure registry system.

On the other side of the balance sheet, the DeMPA determined that deficiencies in debt management performance in small states are many and widespread.

# **_Lack of capacity to monitor external borrowing and assess cost-effective terms and conditions_**

Small states score extremely poorly on dimensions of debt management that relate to determining the most beneficial and cost effective borrowing terms and conditions (DPI9.1) and the availability and quality of documented procedures for borrowing in foreign markets (DPI9.2). Only 3 small states met the minimum requirement for DPI9.1, while one has the appropriate quality of documented procedures for borrowing in foreign markets available. This is a glaring omission given that in the majority of small states external debt is the most important component of public debt and the state’s largest portfolio.

# **_Debt reporting_**

The quality of reporting debt data in small states is poor and there is an absence of timely and comprehensive information on public debt. Only 4 of the small states assessed met the statutory and contractual reporting requirement of central government debt to all domestic and external entities (DPI15.1) and none of them met the minimum requirement for the quality and timeliness of a publication of a debt statistical bulletin covering data on total central government debt, domestic and external (DPI15.3).

# **_Frequency of internal and external audit on debt management activities_**

The absence of audits results in lack of accountability and transparency and could result in malpractices, errors and corruption. Not one of the small states assessed met the minimum requirement in this dimension of debt management. In most instances the issue was not one of quality but rather the fact that neither internal nor external audits were being conducted.

# **_Limited use of market based mechanisms to issue domestic debt_**

17
# **Introduction**

In 2014, following a number of years of high growth, the gross domestic product (GDP) per capita growth in Papua New Guinea (PNG) reached 8.0 percent. However, despite the relatively robust economic growth, maternal and child undernutrition remain pervasive and damaging conditions in PNG. Undernutrition, which encompasses stunting, wasting, and deficiencies of micronutrients (essential vitamins and minerals), is one of the most significant causes of child mortality and morbidity. The stunting rate (low height-for-age, indicating chronic restriction of a child’s potential growth) for children under 5 in PNG was estimated at 46 percent in 2010. Despite GDP growth of 6.85 percent on average from 2005 to 2010 (Figure 1), the stunting rate remained basically stagnant from the 44 percent in 2005. Stunting in early life can have both short and longterm impacts on child and adult health. It is associated with poor cognition and educational performance in childhood. Stunting is also associated with low adult wages, lost productivity, and increased risk of nutrition-related chronic diseases when accompanied by excessive weight gain later in life (Subramanyam, Kawachi et al. 2011).

Figure 1: Stunting Rate for Children Under 5 and GDP per Capita Growth from 2005 to 2010 <!-- Start of picture text --> 60 2500<br>50 43.9 45.7 2000<br>40<br>1500 Overall<br>30 Male<br>1000<br>20 Female<br>500<br>10 GDP per capita<br>0 0<br>GDP per capita (current US$)<br>,% of children under 5)<br>Stunting ( height for age<br><!-- End of picture text --> Data source: GDP is from World Development Indicators; stunting rate in 2010 is from Household Income and Expenditure Survey (HIES) 2009-2010; 2005 stunting rate is from 2005 National Nutrition Survey.

The question arises as to why the general economic development in PNG has not led to improved nutritional outcomes. Economic development and poverty are key basic contributing factors for child malnutrition (Van de Poel, Hosseinpoor et al. 2007; Van de Poel, Hosseinpoor et al. 2008). UNICEF’s conceptual framework of malnutrition (UNICEF 1990) classifies the causes of malnutrition into three groups: immediate, underlying and basic causes. While dietary intake and health status are immediate causes of malnutrition; environmental, cultural and sociopolitical and economic contextual factors are all basic and underlying causes. General economic growth, measured by GDP per capita, predicts reduction in stunting rate in a reasonably large magnitude.

3
-34-

2.72 Second, there is evidence from both surveys that women farmers are much more diligent in following the recommended practices. The 1984 survey showed that women were twice as likely (52 percent versus 24 percent) to follow the recommended planting densities and were just as likely as men to purchase and apply fertilizer and hire labor for additional weeding. The data in Table 10 also shows that women were more likely than men to use fertilizer as a result of extension advice. 2.73 Finally, full-adopters of the maize package are benefiting considerably. A study of 1984 yields (MIDENO, 1985a) concluded that full adopters achieved an average increase in yields of 1.6 tons/hectare, an increase of over 80 percent. Partial adopters realized yields of over 70 percent. More recent estimates indicate that full-adopters attain a marginal increase of 1.5 tons/hectare throughout the province. At this rate, full-adopters are enjoying increased income of aproximately F CFA 70,000 (US$ 233) per hectare. For the province as a whole incremental production was estimated as aproximately 4000 tons in 1986, a marginal increase in value of about F CFA 370 million (US$ 1.2 million) (MIDENO, 1987b: A-ll).

Table 10: Analysis of Farmer ResDonses by Gender

||Percen|tage of Far|mer Response|
|---|---|---|---|
||Ye|s|No|
|Indicator<br>M|ale<br>F|emale<br>|Male<br>Female|
|Received advice on maize|74.4|91.8|25.6<br>8.2|
|Received advice on coffee|89.3|43.2|10.7<br>56.8|
|Received advice on other crops|74.1|81.1|25.9<br>18.9|
|Received advice on food crop||||
|farming methods (summary)|66.5|88.7|33.5<br>11.3|
|Replanted coffee because cf advice|93.8|63.3|6.2<br>36.7|
|Adopted maize package because of advice|68.7|90.7|31.3<br>9.3|
|Yields increased because of advice|75.4|83.5|24.6<br>16.5|
|Used fertilizer because of advice|74.6|86.6|25.4<br>13.4|
|Developed other farming skills||||
|because of advice|75.3|83.8|24.7<br>16.2|
|Received advice on vegetable production|72.6|82.1|27.4<br>1.7.9|
|Received advice on soya bean production|72.8|85.0|27.2<br>15.0|
|Received advice on soya bean preparation|<br>71.4|82.0|28.6<br>18.0|
|Received advice on storage of yams|75.2|78.0|24.8<br>22.0|
|Received advice on storage of potatoes<br>......<br>..............<br>...<br>i......n<br>.....<br>O<br>....................|76.0<br>.|77.5|24.0<br>22.5|

Source: PEM Division, HIDENO
tensions for water between agricultural and human needs and decrease yields, with particularly negative effects on soy and livestock farmers (World Bank, 2021).

Despite Paraguay’s relatively large rural population in comparison to other countries in Latin America, nearly 63% of its population was living in urban areas in 2021 (INE, 2021), which is projected to increase to 74% by 2050 (World Bank, 2021). Urban communities in Paraguay are increasingly exposed to flooding after intense rainfall events, and subject to property damage, loss of assets or negative health impacts due to inadequate infrastructure and draining systems (World Bank, n.d.-a). As urbanization rates grow, increasing pressure on dense urban structures and public infrastructure elevates urban populations’ vulnerability to weather shocks. In conclusion, Paraguay is highly vulnerable to the impacts of climate change due to a combination of the country’s economic structure, lack of resilient infrastructure and socio-economic activities that are particularly sensitive to climate risk.

# **4 Empirical Strategy**

## **4.1 Data**

**Household survey data.** We use household survey data from the Permanent Continuous Household Survey ( _Encuesta Permanente de Hogares Continua_ (EPHC)). The EPHC is an annual, cross-sectional survey that provides indicators related to labor markets, income, and other socio-economic characteristics that allow to analyze the evolution of well-being in the Paraguayan population over time (Instituto Nacional de Estadística, 2022). The EPHC also represents the main data source for official poverty estimations in Paraguay, with poverty being measured on an annual basis. For our purposes, we use data from 2004 to 2019, from which we generate a pooled cross-sectional data set of 379,170 individual observations (Table A.1 in Appendix A). Since 2017, the EPHC has been collected on a quarterly basis, while previously, the EPHC was collected only on the fourth quarter of each year. While data for the years 20202022 is available, the observed increases in poverty or decreases in incomes during the pandemic and subsequent economic recession may not be primarily attributed to weather shocks, and including such data may introduce contamination in our estimations. Therefore, our primary analyses in this study focuses on pre-pandemic years. It is also important to note that the poverty estimates used in this study are based on weights derived from national projections, 2015 revision. In 2022, the statistical office conducted a Population and Housing Census, and updated the weights for household surveys, but at the completion of this study, data for 2022 and 2023 were only available. Therefore, the historical series used in this study are not strictly

7
A follow-up survey was conducted in November 2008, i.e., about a year after the training sessions were completed. We attempted to visit the 167 enterprises in the sample of the baseline survey and obtained data of 139 enterprises. The attrition of 28 sample entrepreneurs occurred due to a variety of causes. One entrepreneur refused to cooperate with our survey, 13 enterprises had closed down. and the remaining 14 attritors were missing for unknown reasons.<sup>9</sup> These 28 attritors had not been invited to the training program. That is, no attrition occurred in the treatment group. We will discuss possible estimation bias due to the attrition in Section 5.

Although our original sample included foundry men casting metal, we do not include them in the sample used in the analysis below because the foundry business is distinct and because they received technical training from an aid agency in the same year. We also exclude from the sample several entrepreneurs who were ejected, after the training, from a prime location which they had occupied without permission. As mentioned earlier, the seven pre-selected participants are also excluded from the sample. The attrition and these adjustments reduced the sample size to 113.

# **4. Basic statistics**

As shown in Table 2, the treatment group consists of 47 entrepreneurs and the control group consists of 66 entrepreneurs. By the treatment group, we mean those entrepreneurs invited to attend the training program. In the treatment group, there are six refusers, who did not attend the training at all or only attended the first two days. The remaining members in the treatment group are called participants. The participants attended 14.1 days on average, and the majority recorded perfect attendance. The high 11
# The Heterogeneous Effects of Trade Policy Uncertainty: How Much Do Trade

# Commitments Boost Trade?

**Alberto Osnago Roberta Piermartini WTO WTO**

**Nadia Rocha**<sup>**1**</sup> **World Bank**

Keywords: Binding overhang, tariffs, policy space, non-tariff barriers, World Trade Organization JEL Codes: F10, F13, F14

**Disclaimer** : This paper is work in progress and should not be reported as representing the views of the WTO or the World Bank. The views expressed are those of the authors and do not necessarily reflect, officially or unofficially, those of the WTO or its Members, the World Bank Group, nor the position of any staff members. Any errors are the fault of the authors.  This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

> 1 We thank Valeria Groppo for facilitating access to the database on tariffs.  We also thank Jose de Sousa, Chad Bown, Nuno Limão, Aaditya Mattoo, Michele Ruta and the participants to the IMF/WB/WTO Workshop for their useful comments on previous drafts of the paper. Corresponding author: nrocha@worldbank.org.

1
**Table 4 Panel Cointegration Test Statistics**

||**‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐ Terms**|**of Trade (ToT**|**) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|<br>**‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐ Nomina**|**l Prices ‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|
|---|---|---|---|---|---|---|---|---|
||**‐‐‐‐‐‐‐ No**|**Trend ‐‐‐‐‐**|**‐‐**<br>**‐‐‐‐‐‐ Wit**|**h Trend ‐‐‐‐‐**|<br>**‐‐‐‐‐‐‐‐ No**|**Trend ‐‐‐‐‐‐**|<br>**‐‐‐‐‐‐ Wit**|**h Trend ‐‐‐‐**|
||**Pt**|**Pa**|**Pt**|**Pa**|**Pt**|**Pa**|**Pt**|**Pa**|
|**GDP measured at M**<br>|**arket Prices,**<br>|**constant U**<br>|**S$ 2010, total**<br>|<br>|||||
|World|‐6.00***|‐4.95|‐6.13|‐4.71|‐5.60*|‐4.21|‐7.26**|‐2.98|
|High income|‐6.01***|‐7.72**|‐6.72**|‐6.94|‐6.20**|‐4.01|‐8.52***|‐4.48|
|Middle income|‐7.52***|‐4.67|‐9.86***|‐6.71|‐8.20***|‐10.03**|‐6.32|‐9.72|
|Low income|‐6.45***|‐6.02|‐10.75***|‐20.66***|‐8.43***|‐10.82**|‐6.79*|‐13.92|
|**GDP measured at Pu**<br>|**rchasing Pow**<br>|**er Parity (P**<br>|**PP), total**<br>||||||
|World|‐6.57***|‐4.21|‐7.51***|‐3.93|‐5.68*|‐4.70|‐5.39|‐2.60|
|High income|‐6.10***|‐6.47|‐6.32*|‐8.26|‐5.93**|‐4.27|‐7.86***|‐4.31|
|Middle income|‐4.85*|‐2.54|‐10.23***|‐7.46|‐8.12***|‐9.20*|‐7.53**|‐21.14***|
|Low income|‐6.39***|‐5.96|‐10.71***|‐20.83***|‐8.44***|‐11.11**|‐6.86*|‐14.61*|
|**GDP measured at M**|**arket Prices,**|**constant U**|**S$ 2010, per c**|**apita**|||||
|World|‐6.28***|‐6.62*|‐6.84**|‐6.64|‐5.75*|‐7.99|‐6.17|‐4.60|
|High income|‐6.23***|‐8.21**|‐6.81**|‐7.94|‐6.39**|‐5.08|‐8.41***|‐6.12|
|Middle income|‐7.10***|‐5.87|‐11.89***|‐15.99***|‐8.54***|‐13.45***|‐8.23***|‐17.38***|
|Low income|‐5.33**|‐6.68*|‐10.75***|‐22.83***|‐9.53***|‐15.21***|‐6.99*|‐13.95|
|**GDP measured at Pu**|**rchasing Pow**|**er Parity (P**|**PP), per cap**|**ita**|||||
|World|‐6.72***|‐5.89|‐8.80***|‐7.53|‐6.11**|‐8.50|‐5.05|‐5.76|
|High income|‐6.20***|‐7.62**|‐6.28|‐8.65|‐6.10**|‐5.33|‐7.76***|‐5.72|
|Middle income|‐4.48|‐3.18|‐12.37***|‐18.3***|‐8.38***|‐12.16***|‐8.22***|‐17.41***|
|Low income|‐5.38**|‐6.67*|‐10.74***|‐22.81***|‐9.55***|‐15.14***|‐7.08*|‐14.29*|

**Notes:** Pt refers to the test statistics calculated using standard errors, and Pa refers to the test statistics calculated using Newey‐West standard errors. In the ToT model, the cointegration relationship between ToT and income is examined. In the nominal price model, the cointegration relationship among nominal prices, income, and manu‐ facturing prices are examined. One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respectively.

_19_