rafmacalaba/datause-encoder-data huggingface dataset:
and finance, and projects that deliver education and training, whether general or sector-focused.

We include distributed aid flows for the same 16 years, 1990 to 2005, as for the trade flows.<sup>25</sup> Figure 1 shows the relative magnitudes of these categories of flows and how they change from 1990 to 2005. As shown, funding for **_broad trade facilitation_** (economic infrastructure and trade development) accounts for about 96 % of aid-for-trade facilitation in 2005. In the same year, **_hard trade facilitation_** projects account for about 70 % of aid-for-trade facilitation. We can see in Figure 1 that the shares of broad vs. narrow (and hard versus soft) have hardly changed over time. Although the importance of trade policy and regulation (our **_narrow_** category **)** increased through 2005 this category remains very small.<sup>26</sup> Figure 1: _Aid for Trade Facilitation by Category, 1990-2005_ <!-- Start of picture text --> Total Aid by Category<br>Economic Infrastructure Trade Development<br>Trade Policy & Regulation<br>10,000<br>8,000<br>6,000<br>USD Millions<br>4,000<br>2,000<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> The gravity equation approach that we use requires some additional variables. Overall, our data set combines information from four different sources: the World Integrated Trade Solution (WITS) database for trade flows and tariffs, the Centre d'études prospectives et d'informations internationales (CEPII) database for bilateral data (such as distances between trading partners) and country-specific data (such as a country's area),

> 25 The limitation to sixteen years stems from the availability of consistent trade data.

> 26 In fact, the Appendix table shows that both the amount and share of disbursements of this category of aid were smaller in 2007 than 2005.

8
<!-- Start of picture text --> Elasticity to Consumption Elasticity to Income<br>ad =<br>a=]= a]i=<br>3 3<br>ge Be<br>xa<br>5<br>oa= =o5<br>al al<br><< Cc<br>at at<br>65<br>C=aoa<br>Ww =<br>we Ww |<br>ao o<br>i] i]<br>3600 13600 23600 33600 43600 53600 6361 3600 13600 23600 33600 43600 53600 63600<br>PC Cons. (1000s) PC Inc. (1000s)<br><!-- End of picture text -->
**Table 8: Severe dry events in Tanzania’s key trading partners. The decadal occurrence of 1-in-10-year dry events in key trading partners is reported as the mean of the decadal occurrences of the individual climate model realizations. The no. of years in which a partner experiences a 1-in-10-year dry event but Tanzania does not is reported as a percentage of the respective decadal occurrences in each partner.**

|**Country**|**Metric**|**2000s**|**2010s**|**2020s**|**2030s**|**2040s**|**2050s**|**2060s**|**2070s**|**2080s**|
|---|---|---|---|---|---|---|---|---|---|---|
||dry years|0.8|1.0|0.8|1.0|1.3|1.3|1.5|1.2|1.6|
|AUSTRALIA|drybut TZA not|89.0|100.0|88.3|100.0|93.2|89.8|96.7|88.6|91.2|
||dry years|0.2|0.4|0.1|0.1|0.0|0.0|0.0|0.0|0.0|
|CANADA|drybut TZA not|100.0|100.0|100.0|100.0|n/a|n/a|n/a|n/a|n/a|
||dry years|0.8|1.2|0.9|0.6|0.4|0.2|0.1|0.1|0.1|
|CHINA|drybut TZA not|100.0|80.5|89.5|90.9|100.0|100.0|100.0|100.0|100.0|
||dry years|1.2|1.1|1.0|1.2|0.8|1.1|0.6|0.5|0.6|
|ETHIOPIA|drybut TZA not|84.7|78.1|82.0|100.0|100.0|90.5|93.2|80.0|100.0|
||dry years|1.1|1.1|1.1|1.0|0.7|0.5|0.5|0.6|0.7|
|INDIA|drybut TZA not|96.3|95.2|90.5|100.0|93.2|100.0|100.0|100.0|100.0|
||dry years|0.7|0.6|0.7|0.7|0.7|0.8|0.4|0.1|0.2|
|KENYA|drybut TZA not|56.2|81.8|47.1|75.3|80.9|82.9|63.9|100.0|77.8|
||dry years|0.9|1.4|1.1|0.9|1.4|1.5|1.6|1.6|1.7|
|MADAGASCAR|drybut TZA not|100.0|90.1|87.7|94.5|90.4|87.6|93.5|91.0|97.6|
||dry years|0.9|1.1|0.8|0.9|1.1|0.9|0.6|1.2|0.8|
|MALAWI|drybut TZA not|95.3|83.3|83.1|84.9|79.8|74.7|93.2|85.4|89.0|
||dry years|1.7|2.6|2.0|2.6|3.1|3.3|3.9|4.6|4.6|
|MEXICO|drybut TZA not|97.6|94.5|93.3|98.5|95.5|91.7|99.0|98.1|97.8|
||dry years|1.0|1.3|1.1|1.2|1.7|1.9|1.1|1.7|1.6|
|MOZAMBIQUE|drybut TZA not|95.0|86.4|79.8|92.4|81.0|83.3|95.2|92.3|91.5|
||dry years|0.4|0.1|0.1|0.1|0.0|0.0|0.0|0.0|0.0|
|RUSSIAN FED|drybut TZA not|100.0|100.0|100.0|100.0|n/a|n/a|n/a|n/a|n/a|
||dry years|0.9|0.9|1.1|1.5|1.8|1.9|2.3|2.1|1.8|
|SOUTH AFRICA|drybut TZA not|84.6|95.3|92.1|94.0|87.6|93.0|100.0|97.7|100.0|
||dry years|1.0|0.9|1.1|0.8|1.1|1.0|0.9|1.2|1.2|
|SUDAN|drybut TZA not|100.0|90.1|100.0|100.0|92.1|91.0|94.5|92.4|100.0|
||dry years|0.6|0.6|0.9|0.5|0.8|0.6|0.5|0.4|0.4|
|UGANDA|drybut TZA not|70.3|74.5|68.6|90.0|82.9|69.5|72.0|88.9|88.9|
||dry years|1.0|0.8|0.5|0.7|0.5|0.6|0.6|0.4|0.8|
|UK|drybut TZA not|82.0|88.3|90.0|100.0|82.0|85.9|90.9|88.9|89.0|
||dry years|1.1|0.7|0.8|0.7|0.9|0.9|0.6|0.7|0.5|
|USA|drybut TZA not|95.2|100.0|76.6|93.2|95.3|100.0|100.0|100.0|100.0|
||dry years|1.1|1.0|1.4|1.3|1.6|1.6|1.1|1.3|0.9|
|ZAMBIA|drybut TZA not|90.5|82.0|80.1|89.8|81.9|85.5|95.2|93.2|84.6|
||dry years|1.5|1.4|1.1|1.5|1.8|2.2|1.8|1.9|1.4|
|ZIMBABWE|drybut TZA not|93.8|96.5|87.2|93.8|85.2|93.7|100.0|93.0|93.6|

Source: Authors’ estimates from Meehl et al. (2007)
# _D. Thailand_

||||**Factors**<br>|||
|---|---|---|---|---|---|
|**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1Digital|Advanced|0.03|0.78|-0.04|0.39|
|2Digital|Intermediate|0.19|0.78|0.35|0.24|
|3Digital|Basic|0.71|0.32|-0.26|0.32|
|4Cognitive|Thinking|0.73|0.49|-0.18|0.19|
|5Cognitive|Communication|0.87|0.02|-0.05|0.25|
|6Cognitive|Organization|0.71|0.20|0.22|0.41|
|7Socioemotional|Relationships|0.67|0.31|-0.20|0.41|
|8Socioemotional|Emotions|0.73|-0.31|0.12|0.36|
|9Socioemotional|Personal growth|-0.07|0.09|0.95|0.08|

# _E. Vietnam_

||||**Factors**<br>|||
|---|---|---|---|---|---|
|**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1Digital|Advanced|0.06|0.78|-0.03|0.38|
|2Digital|Intermediate|0.28|0.81|0.09|0.26|
|3Digital|Basic|0.76|0.28|-0.32|0.24|
|4Cognitive|Thinking|0.75|0.43|-0.18|0.22|
|5Cognitive|Communication|0.88|0.08|-0.10|0.21|
|6Cognitive|Organization|0.72|0.30|0.20|0.36|
|7Socioemotional|Relationships|0.66|0.24|-0.31|0.41|
|8Socioemotional|Emotions|0.70|-0.36|0.10|0.37|
|9Socioemotional|Personal growth|-0.10|0.04|0.96|0.07|

_Sources_ : Authors’ calculations based on SEAD data. _Note_ : The factor analysis is based on varimax rotation for orthogonal factors.

53
In the case of Guatemala, a technique called “cross-validation” was used to guard against overfitting. Cross-validation splits the input data into parts, training the model on a subset of the data (75 percent of the dataset), then testing it on the remaining data (25 percent). This enables the diagnostic values from the testing set to provide a more accurate reflection of how the model would perform in a realistic out-of-sample scenario. The cross-validation subsets can be constructed multiple times in different ways, with the results averaged, to provide better estimates.

To evaluate the regression results, the accuracy of the machine-learning model is measured using R<sup>2</sup> , root mean square error, and the correlation between real and predicted values. R<sup>2</sup> measures the extent to which the model explains the variability of the response data around its mean, while root mean square error indicates the difference between real values and predicted ones. The quality of the classification techniques is analyzed using two measures: accuracy and F1 score. Accuracy reflects the percentage of testing samples whose predicted class is the same as their real ones. The F1 score is a metric that combines the precision and the recall of the method, i.e., the number of samples that are correctly classified and the number of samples for which a label is given. In general, stronger methodologies have higher values for both precision and recall.

# **_Results_**

All of the CDR models exhibited a significant degree of predictive value. However, the specifics of different models influenced how well they predicted poverty rates. The analysis yielded four overarching results:

**Result #1: CDRs can predict poverty rates in Guatemala**

Across all the models, CDR analysis consistently predicted poverty rates at the _municipio_ level, though their predictive value was limited under certain experimental settings. The best models predicted total poverty levels in 2006 with an R<sup>2</sup> of 0.76, implying that approximately 76 percent of the variation in _municipio_ -level poverty rates could be explained by 2013 cell phone data, and with F1 scores of up to 0.84 for the classification models, indicating that 84 percent of _municipios_ were classified according to the correct category when three separate categories (low, medium and high poverty rates) were considered. On the other hand, the prediction rates for urban data in 2006 showed R<sup>2</sup> values of 0.69 for regression techniques and 0.73 for classification, indicating weaker predictive value. The lowest prediction rates were for rural data in 2011, with R<sup>2</sup> results of 0.46 and 0.59 for regression and classification models, respectively. Classification results were slightly better than regression results across all models due to the fact that classifying poverty rates into three classes is a simpler prediction problem than attempting to approximate real values. Experimentally, as more classes were included, predictive accuracies for classification decreased

**14**
_Economic Theor_ y 37 (3): 395-416.

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<!-- Start of picture text --> Figure 12. Net cash position of households under alternative reforms<br>100%<br>2014 Baseline<br>80%<br>Includes Family 500+<br>60%<br>Family 500+ and Tax-free<br>amount PLN 5000<br>40%<br>Family 500+ and Tax-free<br>amount PLN 8000<br>20%<br>0%<br>-20%<br>-40%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market income deciles<br>Source: World Bank estimates based on HBS 2014.<br>Percent of original market income<br><!-- End of picture text -->

# **VII. Conclusions**

The analysis presented in this paper is complementary to existing work analyzing the distributional impact of taxes and benefits. As previous work has shown (Paulus, 2015; Myck and Najsztub, forthcoming), the combined effect of taxes and social spending helps to substantially reduce inequality in Poland, in line with other EU countries, with most of the reduction in inequality largely being achieved by pensions. This paper also shows the important role of in-kind transfers in the form of education and health. Moreover, we find that the combination of taxes and social spending also leads to an important decline in extreme poverty. However, in cash terms, households beginning in the second decile were net payers to the treasury in 2014, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population.

In terms of the specific fiscal interventions, we find that most direct taxes are progressive and equalizing with the exception of agricultural taxes and farmer contributions, which depend on land size rather than on the income generation capacity of households. In addition, although direct taxes and contributions are progressive, they are also poverty increasing as they pose a significant burden on the bottom of the distribution given that contributions do not have a minimum threshold. Even when old-age pensions are assumed to be deferred income, we find that the personal income taxes and health insurance contributions are poverty increasing. Moreover, indirect taxes are found to be regressive in line with previous work (Myck, 2015b). However, the paper notes that the burden of indirect taxes contributes to poverty and inequality increases, more so than what is observed in many developing countries. Both the results on direct and indirect taxes point as of 2014 to potential areas where Poland could improve, even if it were only to minimize the impact on 31
channels are underdeveloped (McMillan and Woodruff, 1999; Johnson McMillan and Woodruff, 2004; Allen, Qian, and Qian, 2005 and Cull, Xu and Zhu, 2007). The general idea in this literature is that trade credit can represent a viable substitute for formal bank credit and therefore a crucial determinant of firm growth for a country at the earlier stages of its development or during the transition towards a market based economy.

Cull, Xu and Zhu (2007) test whether Chinese firms have indeed used the trade credit channel to bypass or substitute for the lack of formal institutions to stimulate growth, but they do not find supporting evidence.<sup>4</sup> Instead they argue that the Chinese growth is fueled by a growing private sector with increasing competitive pressure. Competition is then likely to be an important motivation for the use of trade credit in line with our results. Given that widespread competitive pressures across sectors is a common feature of developed and market-based economies, our findings are not specific to China but have more general implications. A similar conclusion also holds for the matching story – the credit constrained firms in our sample are more likely to match contract terms of payables and receivables – given that relatively illiquid firms with limited access to bank loans are also widespread in developed economies, mainly among small and medium-sized enterprises (see Berkovitz and White, 2004 and Fabbri and Klapper 2008 for evidence on credit constraints among U.S. firms).<sup>5</sup> Finally, when we compare trade credit statistics in China and other countries, we find that the average use of trade credit in China is very similar to other developed and

> 4 They focus on differences in ownership structures, since in China it is not so much the lack of a formal financial system but rather its institutional bias in favor of state-owned enterprises that could give rise to trade credit among viable firms with restricted or no access to credit from state-owned banks.

> 5 Berkovitz and White (2004) find that the probability of a firm being credit constrained is 29% for noncorporate U.S firms and 26% for U.S. corporations. Fabbri and Klapper (2008) document that less than half of SMEs in the U.S. have a line of credit.

8
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Despite the heterogeneity in preferences, there is not much evidence of leader preferences reflecting those of one specific group more than another i.e. they do not seem to side with the wealthy or the poor, with those who lost more or less. We ran a multinomial regression (see annex) to further assess whether there exist systematic differences between the preferences of leaders and citizens. Data from the authorities and household respondents were pooled to explain the choice between various governance, security and socioeconomic initiatives which had been regrouped in seven categories: a development strategy that puts the north at par with the rest of the country, social services, rural development and employment, infrastructure, security, governance and jobs. We find that explanatory power is low: i.e. it is hard to predict preferences based on observed characteristics. Authorities generally represent the preferences of the population (with some slight preference for social projects in urban areas), rural households in Kidal are less interested in rural development and employment and those in rural Gao are particularly interested in infrastructure and jobs. Based on this, the general conclusion is that local leaders’ opinions are appropriate proxies for the citizens they represent.

# **V. Operationalizing the Priorities in the Peace Accord**

As mentioned above, Mali's Peace and Reconciliation Accord called on the World Bank, the African Development Bank and the Islamic Development Bank to carry out a Joint Assessment Mission (Mission d’évaluation conjointe, MIEC) to operationalize the Peace Accord. Using the various measures mentioned in the Accord, the MIEC was expected to prioritize and cost needs with an eye to ensuring a rapid recovery, to addressing poverty and promoting development, and to allowing the northern regions to reach the same level of development as the rest of the country within the next 15 years.

A prioritization framework was thus developed over the course of 60 days, along three pillars: 1) Strengthen peace, social resiliency and decentralized governance; 2) Restore and expand basic social services and social protection; and 3) Promote economic recovery, employment and infrastructures. Within each pillar, priorities were categorized based on whether implementation occurs immediately (year 1), in the short-term (year 2), or in the medium-term (3 to 6 years).<sup>27</sup> We find that, in broad strokes, priorities of households and local authorities are reflected in the pillars of the Report of the MIEC, although the MIEC demonstrates a strong preference for financing socioeconomic development activities instead of security and

> 27 Assessing Recovery and Development Priorities in Mali’s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 6.

24
# The Anatomy of Failure:

An Ethnography of a Randomized Trial to Deepen Democracy in Rural India Kripa Ananthpur (Madras Institute of Development Studies) Kabir Malik (The World Bank) Vijayendra Rao (Development Research Group, The World Bank) JEL Codes: O12, H79, C1 Keywords: Panchayats, RCT, Mixed-Methods <u>Acknowledgements: The authors are grateful to Wilfred D’Souza and H.S. Ashokanand who were then</u> with the Karnataka State Institute for Rural Development (KSIRD), and who led the intervention team, for their advice, openness and help with conducting this evaluation. They are also grateful to Jeff Hammer who was part of the initial research team and helped design many elements of this study, to Radhika Vishwanathan for superb research assistance, to Geeta Sethi and Yongmei Zhou from the World Bank’s operations team for facilitating the research, to Yamini Aiyar for advice and a crucial infusion of funds, and to Usha Rao for her important contributions in understanding the role of RPs. They are particularly indebted to Iranna Muragod, Narasimha Reddy, and the other members of the qualitative team for their brave, exemplary work. IMRB conducted the quantitative survey. Jonathan Fox, Saumitra Jha and Farah Zahir provided valuable comments on a previous draft. The research was funded by grants from the World Bank’s Research Support Budget, KCP Trust Funds, TFESSD Trust Funds from the Government of Norway, and the Accountability Initiative at the Center for Policy Research.
# **Appendix Table 1. ECOWAS Countries' Gross Domestic Product**

|||(In|units indicated)|||||
|---|---|---|---|---|---|---|---|
||Level (in<br>dollars)<br>|billion<br>|Share (in p<br>total)<br>|ercent of<br>|Average<br>annual<br>percent<br>|Per capita<br>dollars)<br>|(in current<br>|
||1974|2004|1974|2004|change|1974|2004|
|Total|48.0|124.7|100.0|100.0|3.2|431|490|
|Benin|0.4|4.0|0.9|3.2|7.6|143|494|
|Burkina Faso|0.5|5.1|1.1|4.1|7.8|93|399|
|Cape Verde|0.1|0.9|0.2|0.7|7.7|366|1,877|
|Cote d'Ivoire|3.1|16.0|6.4|12.9|5.7|487|897|
|Gambia, The|0.1|1.9|0.2|1.6|10.6|177|1,317|
|Ghana|4.1|8.9|8.5|7.1|2.6|409|409|
|Guinea|3.9|4.0|8.1|3.2|0.1|940|440|
|Guinea-Bissau|0.6|0.3|1.2|0.2|-2.5|905|176|
|Liberia|0.5|0.5|1.1|0.4|-0.1|326|152|
|Mali|0.4|4.8|0.8|3.9|8.6|67|368|
|Niger|0.7|3.0|1.4|2.4|5.2|128|225|
|Nigeria|31.1|64.4|64.8|51.6|2.5|542|500|
|Senegal|1.4|7.6|2.9|6.1|5.8|276|669|
|Sierra Leone|0.6|1.1|1.2|0.9|2.2|193|201|
|Togo|0.5|2.0|1.1|1.6|4.5|227|339|
|Memorandum items:||||||||
|WAEMU countries 1/|7.7|43.0|15.9|34.5|5.9|221|509|
|Non-WAEMU countries 2/|40.4|81.7|84.1|65.5|2.4|526|480|

Sources: International Financial Statistics, WEO; and staff estimates.

1/ Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo 2/ Cape Verde, The Gambia, Ghana, Guinea, Liberia, Nigeria, and Sierra Leone 25