rafmacalaba/datause-encoder-data huggingface dataset:
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA kernels with bandwidths of 1.0 and 0.5. The below IPL PPPs converge relatively fast as well. In this case, the uniform kernel with bandwidth 1.0 converges fully. The only two indexes that produce oscillations are based on the uniform kernels with bandwidths 0.5 and 0.25.

The reason why one country could have such an effect lies in the lumpiness (i.e., discontinuity) of actual household survey expenditure data. For example, if a certain number of households in some country around the poverty line exhibited a rather unusual weight structure combined with an uneven density of the probability density function, it would result in oscillations for the below-IPL index. This has nothing to do with the methodology employed, but rather reflects imperfections of the original household expenditure data. Smoothing the input data would solve the problem and at the same time would make the household expenditure data more realistic, without affecting much the resulting PPPs.

How critical are the oscillations in these PPP calculations? It turns out they are quite insignificant. From Figures 2 and 3 we can see that in the worst case scenario—uniform kernel with bandwidth 0.25— the oscillations are around 0.01% (!) on average, which is several orders of magnitude better than expected precision of the PPP computation.

It also turns out that all bandwidth of kernel based PPPs produce very similar results, with most countries having results with a standard deviation (SD) of around 0.1–0.2%.<sup>12</sup> Only the Gabon poverty weighted-PPPs have an SD of 0.4% (see Figure 4), with the bi-weight poverty-weighted PPPs systematically higher than their uniform kernel based counterparts (see Table A1 (annex)). This is probably related to some peculiarities of Gabon’s probability distribution function.

<!-- Start of picture text -->
1.100<br>Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5)<br>Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0)<br>Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25)<br>1.050<br>1.000<br>0.950<br>0.900<br>PPP Ratios<br>Burkina Faso Burundi Cameroon Cape Verde Congo, Rep. Congo, Dem. Rep. Côte d'Ivoire Ethiopia Gabon Ghana Guinea Kenya Liberia Madagascar Malawi Mali Mozambique Namibia Niger Nigeria Rwanda Senegal South Africa Swaziland Tanzania Togo Uganda Zambia<br><!-- End of picture text -->

FIGURE 4. EFFECT OF KERNEL TYPE AND BANDWIDTH ON POVERTY-WEIGHTED PPPS, 28 COUNTRIES

# _4.3 Poverty-reweighted PPPs according to various methods_

## _4.3.1 Effect of removing one country_

We have seen the effect of removing Guinea on the convergence of poverty-weighted PPPs, now let us look at this effect on the regular consumption PPPs based on expenditures from the national accounts (“SNA-based PPPs”). The results for SNA-based PPPs are shown in Figure 5. The scale is intentionally

> 12 SD is estimated with respect to the regional unweighted geometric mean in order to remove the base country effect.

8
teraction between product- and firm-level characteristics. For instance, while Bernard, Redding, and Schott (2006a) model firm- and product-specific competencies and Eckel and Neary (2006) assume that how good a firm is at producing a particular product depends on how far this product is from the firm’s core competencies, in Nocke and Yeaple’s (2006) model firms are heterogeneous but products are symmetric.

The objective of this study is to inform the theoretical literature by shedding some light on the firm-product dynamics, particularly in the context of export decisions. We are able to do so thanks to a unique dataset at the firm-product level, covering 85% of Mexican industrial output and several thousands of products during the period 1994-2003. The data give us a window on a period of profound reforms and increasing international integration of Mexico after the introduction of NAFTA. These irreversible trade reforms together with a dramatic exogenous shock, a 300% devaluation of the peso, expanded the access of Mexican firms to the US market and stimulated sharp responses at the firm- and product-level. In fact, the 300% increase in Mexican exports between 1993 and 2002 and growing export diversification in the post-NAFTA period have already been documented by studies relying on aggregated trade statistics (Romalis 2007, Feenstra and Kee 2007).

In the study, we present five main findings. First, we show that multi-product exporters are much less prevalent in Mexico than in the US, which contrasts with the observations of Bernard, Redding, and Schott (2006b).

Second, we document a significant degree of churning at the product level taking place in response to declining trade costs and find evidence suggesting that firms’ decisions to introduce and drop products are influenced by what appear to be their “core competencies”. These observations confirm the predictions of the latest theoretical models (Bernard, Redding, and Schott 2006a, Eckel and Neary 2006) stating that the range of products produced within a firm is another margin of adjustment to changes in trade policies. Moreover, our data support the prediction that exports of core products tend to expand in response to improved access to foreign markets.

Third, we observe that new exporters tend to “start small” in terms of both values and number of exported products. This observation supports the prediction of Rauch and Watson’s (2003) model, in which search costs and information asymmetries lead heterogeneous firms to start small in order to “test” the credibility of the foreign partner. In an environment where firms are heterogenous and there is incomplete information about the reliability of new trading partners, the seller needs to gain a minimum degree of credibility with the buyer before being requested a large order. For this reason, when the relationship 3
In this paper, we use data on economic rates of return (ERRs) from a set of 1,276 public and private investment projects to try to answer these questions. This unique data set has a number of advantages. It has a wide coverage across countries and over time; it was calculated according to a relatively uniform methodology; and it measures productivity from an economic standpoint. Most importantly, by using a microeconomic unit of observation as the dependent variable, the problems of spurious causality and simultaneity often faced by the empirical work in the aggregate growth literature are greatly reduced. In addition, this data provides insights on how policies affect returns to investment at the micro level--insights that are lost in aggregate statistical analysis. This data set also has one major disadvantage: these projects are not a random (nor necessarily representative) sample of typical investment projects in developing countries. This problem must be addressed in the statistical analysis.

This paper is organized as follows. Section Il summarizes the project and country-wide data used in this analysis and the basic statistical results. Sections III and IV present the main econometric results, and section V addresses sample selection biases. Section VI presents a set of proposed mechanisms for the linkage between the policy enviromnent and investment productivity. Section VII concludes with a discussion on project selection in the developing world.

# II. The Data and Basic Results

# A. Project data.

From the World Bank's Operations Evaluation Department (OED) and the evaluation unit of the International Financial Corporation (IFC) , we assembled a data set of public and private sector projects in 61 developing countries, implemented from the late 1960's into the early 1990's. The data include reestimated economic rates of return (ERRs)--as well as other project specific information<sup>2</sup> --from 1,163 investment projects financed by the Bank and implemented by public agencies in developing countries and from 113 private projects financed by the IFC. The analysis in this paper includes all projects in tradeable sectors--agriculture, industry, and tourism--and non-tradeable sectors--transport, infrastructure, energy, water, and urban--for which such ERRs have been calculated and for which a minimum set of country-specific policy indices was available.<sup>3</sup>

> 2 For an analysis on the divergence between _ex-ante_ and _ex-post_ ERRs, see Pohl and Mihaljek (1 992). 3 Kaufmann and Wang (1995) examine the performance of social sector projects--which receive a binary "satisfactory/unsatisfactory' rating from OED but no ERR--as the dependent variable in a Probit specification. They find that the probability of project failure in the social sectors is positively and significantly associated with policy distortion indicators such as the fiscal deficit, the foreign exchange parallel market premnium, and the degree of price distortions.
12 -

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which water charges or benefit taxes may be levied. OPN 2.10 goes on to observe that: "There Is no prkna face reason why any particular share of costs, such as OLM costs, should normally be recovered." A further note on financing operations and maintenance (OLM) was Issued by OPS In 1984. This note asked that assurances be required (at the appraisal stage) that sufficient funds would be available for OWM. It also specifled that there should be an analysis of how the fiscal system affects farmer Incentives. Perhaps most Interestingly, the 1984 note did not require that OW8 costs be covered by direct cost recovery from proJect beneficlaries. It should be noted that none of these Instructions asked for an analysis of the consistency of the many government Interventions In agriculture %Ith the proposed scheme for Irrigation service fees. In particular, there was no reference to an analysis of direct and Indirect taxation of agricultural outputs. Nor was any justification ever given for using irrigation water fees as a vehicle for Income redistrlbution when much more esfficlen' means for targeting the poor were avallable.

# V. A **BREF** tEVEW OF **BSA** EXPERENCE **WITH** uRIGATIDN COST RECOVERY CONDITODNALITY

This sectin summarizes documented Bank experience and Is based on the aforementioned OED review of June 1986. As noted above, the review covers the period up to 1976, when relatively unambiguous policy guldelines were In place. The iong gestation period for kIplementatlon of irrlgatlon projects and the lack of consistent guidelines over the 1976-88 period make It likely that the appearance of a snimla **r** eview for that period Is some years away.

Since the overall assessment of Bank experience given In the summary of the 1986
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46
economic conditions is low in this locality group. If we take this as our main result, our impact effect is somewhat bigger than previous evidence. Among other things, this could be due to the fact that our cohort of children fully exposed to the program was, not only exposed since birth, but also exposed for 4 years.<sup>25</sup> Therefore, this group of children experienced the maximum level of exposure one could get from this intervention, including pre-birth exposure for some of them. Additionally, it could be that the documented problems in the initial implementation of the nutritional component of the program were fixed over time, or that there is a learning process happening at the household or community level. We are measuring impact effects seven years after the program started.<sup>26</sup> The only existing evidence from the urban areas analyzes the effect after two years of exposure on children incorporated in 2002 (Leroy et al., 2008). The study only finds a positive effect on height for children who were younger than 6 months at baseline (0.41 z-score). When the authors divide the sample by socioeconomic status, they find a positive effect (0.27 z-score) on children in the poorest tercile. The closest estimate we have to compare this result with is our “ _Full relative to Partial exposure_ ” estimate in community-group 2. In that case, we are approximately estimating the same length of exposure – this estimate represents an average of two additional years of exposure. Additionally, since children exposed for longer are in the program since birth, they can be closer compared with the subsample of children for which previous estimates found positive effects (younger than 6 months). Finally, even though we are comparing children fully exposed to children partially exposed (as opposed to children not exposed), our middle cohort of children was

> 25 This exposure is not on average. Every child in this cohort was exposed for four years, as virtually all are born after the program is in place, and they are all measured in 2005 when they are over 5 years old.

> 26 Another difference could come from sample composition effects. The Oportunidades data sampled households in 1997, while our survey represents the population of these localities in 2002.

31
Given these value-added trade statistics in addition to total exports, we measured the share of each country’s exports that is part of a multi-stage trade process (i.e. a global value chain) using the definition from Aslam et al. (2017). Aslam et al. (2017) define country c’s GVC participation in sector s at time t as: ������ The first component measures the share of country c’s total exports of products in ����� ��������� sector s that are produced within the country (indirect value-added, DVX���). This is “forward participation” as it computes domestic value-added incorporated in the exports of other countries (the downstream perspective). The second component ������ is the share of imported ����� ��������� (foreign) value-added that is incorporated into the exports of country c, also referred to as “backward participation” (the upstream perspective). Whereas the first component is the share of a country’s exports that contributes to GDP, the second component specifies the share of a country’s gross exports that is made up of inputs produced in other countries. This GVC participation rate measures the extent to which a country’s exports are linked to international production networks. Consequently, the larger is the participation ratio for a given country, the greater is its involvement in GVC.

In conclusion, our GVC data capture backward and forward participation at sector level for 115 countries worldwide including 23 SSA countries (see the Appendix for the list of SSA countries included in the data). We also focus on manufacturing GVCs and classify exports into 19 subcategories as presented in a concordance table in the Appendix.

# **_3.1.2 Skill data_**

Human capital is potentially an important determinant of country participation in global markets. However, data on human capital are often very difficult to obtain especially for SSA countries. This study derived skills data using information from the Global Trade Analysis Project (GTAP) input-output tables. We also used the recently published Human Capital Index developed by the WBG (2018) as an indicator of human capital endowment. This index measures the knowledge, 10
||(0.032)|(0.029)|(0.029)|||||
|---|---|---|---|---|---|---|---|
|Families without children|0.633***|0.648***|0.352***|||||
||(0.034)|(0.030)|(0.030)|||||
|**Female headship**||||||||
|_De facto_FHH||||-0.061**|0.591***|0.203***|0.267***|
|||||(0.024)|(0.031)|(0.032)|(0.026)|
|_De jure_FHH||||0.228***|-0.054|0.517***|0.309***|
|||||(0.027)|(0.033)|(0.035)|(0.028)|
|Observations|4,749|2,359|2,359|2,359|2,359|2,359|2,359|
|R-squared|0.556|0.872|0.714|0.504|0.782|0.297|0.145|

_Source:_ Own elaboration based on Somali HFS 2017-18. _Note:_ ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. Standard errors in parentheses.

# **7. Conclusions**

Our findings show that even when poverty rates are very high, there are important differences between poverty risk according to gender and displacement status. Distinguishing between different types of households beyond the gender of the household head improves our understanding of poverty in situations of forced displacement. The association between a household’s demographic composition, its income profile and poverty risk shows that women’s lack of economic empowerment and their caring responsibilities elevate poverty risk. If we had simply stopped at female headship, we may have concluded that – aside from widows - women are better off in Somalia.

Our analysis extends the gender and poverty literature by establishing a link between the demographic and income profiles of households and the risk of experiencing poverty in situations of displacement. Using data that is typically available in household surveys, we showed how this can be done. Our starting point is that women as individuals or households dependent on women’s income may be at greater risk of poverty due to two interrelated gender norms. These two norms are first, the norms assigning women a disproportionate responsibility for care work and the provision of household services, and second the norms that restrict women’s ability to access paid 26
Appendix Table A5: Aggregate Measures by Follow-up Round

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Financial<br>Awareness<br>Round 1|Financial<br>Awareness<br>Round 2|Financial<br>Awareness<br>Round 3|Applied<br>Financial<br>Knowledge<br>Round 1|Applied<br>Financial<br>Knowledge<br>Round 2|Applied<br>Financial<br>Knowledge<br>Round 3|Financial<br>Numeracy<br>Skills<br>Round 1|Financial<br>Numeracy<br>Skills<br>Round 2|Financial<br>Numeracy<br>Skills<br>Round 3|
|Migrant-Only Training (A)|-0.007|0.040|0.088*|0.007|-0.009|0.030|0.031|-0.027|0.044|
||(0.046)|(0.049)|(0.050)|(0.031)|(0.022)|(0.024)|(0.040)|(0.038)|(0.039)|
|Family-Only Training (B)|0.112**|0.120**|0.138***|0.024|0.046*|0.008|0.102***|-0.032|0.037|
||(0.046)|(0.050)|(0.051)|(0.031)|(0.027)|(0.021)|(0.038)|(0.038)|(0.038)|
|Migrant and Family Training (C)|0.200***|0.187***|0.187***|0.080**|0.040|0.025|0.108**|-0.015|0.026|
||(0.047)|(0.049)|(0.050)|(0.033)|(0.028)|(0.023)|(0.042)|(0.039)|(0.040)|
|R-squared|0.063|0.044|0.060|0.019|0.021|0.021|0.025<br>|0.004|0.027|
|Sample Size|392|375|365|392|376|365|392|376|365|
|Number of Clusters|392|375|365|392|376|365|392|376|365|
|Mean of Dependent Variable in Control Group|0.384|0.419|0.389|0.434|0.263|0.246|0.383<br>|0.446|0.344|
|Test: A-B = 0 (p-value)|0.013<br>|0.115|0.334|0.576|0.038|0.363|0.087<br>|0.897|0.849|
|**0 (**<br>**l**<br>**)**<br>Test: A-C = p-va ue|**0** **000**<br>.|**0** **003**<br>.|**0** **0** **0**<br>. 5|**0** **02**<br>.<br>7|**0** **08**<br>.<br>4|**0** **860**<br>.|**0** **088**<br>.|**0** **6**<br>.7 5|**0** **6** **8**<br>. 4|
|Test: B-C = 0 (p-value)|0.070<br>|0.177|0.329|0.093|0.829|0.441|0.876<br>|0.674|0.774|
|Test: A = B = C = 0(p-value)|0.000<br>|0.001|0.002|0.077|0.099|0.547|0.014<br>|0.842|0.690|

This table presents regression analysis for aggregatedmeasures of Financial Awareness, Applied Financial Knowledge, and Financial Numeracy Skills, by follow-up survey round. Standard errors in parentheses. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).