rafmacalaba/data-use-mentions-extended huggingface dataset:
produce items, low cross-market price correlations owing to a lack of overall market integration, and weak price trends. The application cross-validates the imputation strategy under different designs in terms of numbers of markets, food items and time periods covered, and shows that when the survey is well-designed, imputations can achieve accuracy that is attractive when compared to costly– and logistically often infeasible–direct measurement. The localized statistics are shown to provide a new granular view on recent food price inflation dynamics in PNG leading up to and after the outbreak of the pandemic, and more recently the conflict in Ukraine.

The application builds on the original algorithm described by Andr ́ee (2021) but suggests methodological improvements that produce faster and more accurate results, particularly in lower data availability settings. The reduced computing time enables the paper to process a higher number of food items. The resulting estimates cover up to 27 fresh food items, across 8 markets, for the period from mid-2009 to July 2022. This is up from an average of 7 and maximum of 16 price items processed by Andr ́ee (2021). The paper also shows that the estimation methods can be applied to different price surveys, including those from country
approaches, quantifying the benefits of including conditional random effects when using machine learning models, probing the robustness of methods for estimating the uncertainty associated with tree-based machine learning estimates, experimenting with different geospatial features, and determining the age threshold at which census-based estimates become less accurate than geospatial estimates in different contexts. The question of which features best predict changes is also important. For example, changes in the rate of building construction, the characteristics of new buildings, or changes in crop types and forecasted yields have not yet to our knowledge been tested as correlates of welfare changes.

In addition to further research, further improvements to open-source tools are critical to make these techniques more accessible and educate users. This is particularly important to facilitate the adoption of more sophisticated methods in developing countries given the financial and technical constraints faced by national statistical offices and other practitioners. The R EMDI, SAE, SAEforest, and GRF packages available on CRAN are all examples of good practice, as the documentation for each is clear, comprehensive, and up to date. No such comparable user-friendly package exists for neural network models at this time. No matter how many new and improved methods are published by statisticians, they are unlikely to be used in practice without software that is accessible to non-specialists and thoroughly documented. User-friendly features automating common diagnostics and parallelizing across multiple cores to speed estimation, as implemented in the EMDI package, are very valuable. Finally, software that makes it simple to obtain and link publicly available geospatial indicators with survey data will also help facilitate data integration. As these tools are developed, small area estimates that combine survey data with publicly available geospatial data will inevitably become more popular worldwide, belatedly fulfilling the promise demonstrated thirty-five years ago by Battese, Harter, and Fuller (1988).
20 contributing most to that finding notwithstanding the tariff preferences provided to numerous low-income countries.

These results suggest that a multilateral move to global free trade would be good for developing country farmers. Whether that would be true too for a unilateral reform by any particular developing country depends heavily on the extent to which that country’s own policies effectively assist or harm that economy’s farm sector relative to its other sectors. Our measures of those effects depend very much on the levels of distortion in the GTAP database we use, as is clear from the sensitivity analysis of results reported above in Table 6. The agricultural distortions for highincome countries are reasonably reliable, thanks in large part to the carefully compiled protection estimates by the OECD (2005a and earlier). Currently available estimates of (particularly agricultural) trade distortions and subsidies in developing countries are less reliable, however. Nor are estimates provided in the GTAP database for all export taxes or tax equivalents of quantitative restrictions and bans on exports by developing countries. A new project at the World Bank is seeking to provide better estimates of that sort, building on the earlier work of Krueger, Schiff and Valdes (1988) and the OECD’s comprehensive PSE methodology and improved methods for measuring of exchange rate misalignment (Hinkle and Montiel 1999). Distortions to factor markets, particularly labor, may also have an important influence on the results for some countries if they were to be included in the model. Even more challenging is the task of improving estimates of distortions to services trade and foreign direct investment.

These are the next frontiers in improving our understanding of the impact of policies on agricultural and other sectors’ production, trade and value added. Only then will it be
the skilled data set defined for 57 sectors from the input-output GTAP table with the GVCs data derived from UNCTAD-Eora GVC Database which has 25 sectors. We focus on the impacts of skilled/unskilled labor contribution, migration and initial human capital endowment on forward and backward GVC participations. To our knowledge, this study is the first in the literature to assess the role of skilled and unskilled labor, net inflows of skilled/unskilled migrant workers, and the human capital index in GVCs integration. Importantly, this is the first study that focuses on SSA countries where skills related data are often very difficult to collect.

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and Jacques (2017) combine CDR and satellite data from Senegal with census data to predict non-monetary poverty across communes in Senegal. They use Gaussian process regression, a non-parametric machine learning method. They evaluate their estimates using 10-fold cross-validation. The estimates for non-monetary poverty across Communes achieved an out-of-sample R<sup>2</sup> of 0.83 and a rank correlation of 0.87.

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option multiplied the estimated proportion of non-poor by a constant in each region, chosen to ensure that the proportion of non-poor in the small area estimates matched the proportion of non-poor from the survey at the regional level. The final benchmarked head count estimates were then obtained by subtracting the estimated share of non-poor from 100 percent. Benchmarking the proportion of people that are not poor instead of the proportion of poor avoids generating poverty estimates that exceed 100 percent.

Estimates of mean squared error were calculated using the parametric bootstrap procedure implemented by the povmap package (Gonzalez-Manteiga et al, 2007). The Povmap package implements the bootstrap procedure in a way that takes the benchmarking procedure into account. As is standard when estimating uncertainty using this method, the estimates are assumed to be unbiased, which implies that the standard error is the square root of the estimated mean squared error (Tzavidis et al, 2018). This allows for the estimation of standard errors, and CVs for each target area.

Comparisons of the model-based and direct estimates at the regional level suggest modest amounts of model and sampling error. Comparing the estimated precision of the small area estimates with the direct estimates show a large efficiency gain from incorporating auxiliary data from the census. Unfortunately, due to the age of the census, and the fact the COVID – 19 pandemic happened between the census and the budget survey, the estimates will only slightly reflect the changes caused by the COVID – 19 pandemic and are better measures of long-term structural poverty.

# 5. Study Findings

Small area poverty maps The variables selected to predict per capita consumption as well as the resulting coefficients from the linear mixed model are provided in table A1 of the annex. Table 2 presents a set of model diagnostics relating to the fit of the model and the consistency of the normality assumptions with the sample data. Overall, the model explains 57 percent of the variation in log per capita consumption when not considering the conditional random effect, and 61 percent when the conditional random effect is included. The model was estimated
substantial constraints to the country’s growth and that an increase of trust in Morocco could yield an economic dividend of about $7.4 billion over the course of a decade. This cost also has social and political aspects. While Morocco stands out in the Middle East and North Africa (MENA) region for its relative stability, levels of institutional trust have remained around 40 percent (a median number for the MENA region and a globally low percentage) since the mid-2000s, with a sharp decline in 2018.<sup>5</sup> Morocco has witnessed a stream of social and political protests and upheavals since 2016. These episodes have ranged from geographically-localized protests in lagging regions to issue-based social mobilization by both low and middle classes. The COVID-19 pandemic also led to social unrest and protests including in the spring of 2021 when the government imposed a curfew during Ramadan and in late 2021 against the vaccination campaign.<sup>6</sup> ,<sup>7</sup> What, then, is the most e↵ective way to increase trust in institutions, and reap its developmental benefits? What are the drivers of institutional trust? In other words, what factors can drive individuals to update their belief about the trustworthiness of institutions? By presenting findings from a large-scale survey financed by the World Bank in Morocco in 2021, this paper contributes to a rich literature on the determinants of institutional trust.

Over the years, researchers have identified many correlates of trust and attempted to identify the extent to which they could be attributed a causal role in driving trustbuilding. One important correlate identified is the perceived or objective performance and quality of government to deliver public services (Keefer and Scartascini 2022 and Corbacho et al 2015) or to reduce economic inequality (Grimes 2017; Kettl 2017; Listhaug and Jacobsen 2017; Newton, Stolle, and Zmerli 2017). Many have also found strong negative correlation between trust and corruption (Braithwaite 1998; van der Meer 2010; van der Meer and Zmerli 2017
FCV 3.60 Resource-rich Non resource-rich<br>3.50 3.50<br>3.00 3.40<br>2.50 3.30<br>2.00 3.20<br>1.50 3.10<br>1.00 3.00<br>0.50 2.90<br>0.00 2.80<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> In this overall context of poor governance, corruption remains high in SSA, with little improvement in the last twenty years. Since 2000, the SSA region has consistently ranked at the bottom of world regions on the Worldwide Governance Indicator on Control of Corruption, a composite indicator that captures perceptions of the extent to which public power is exercised for private gain, including both petty and 13
**Figure 14. Kakwani indices by types of interventions in Moldova and peers.**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>SSC Direct Taxes Pensions Direct Transfers Indirect Taxes Indirect Subsidies In-kind Health In-kind<br>Education<br>Belarus Moldova Romania Russia Ukraine<br><!-- End of picture text -->

Note: Kakwani index is a measure of progressivity. The higher the value is the more progressive is the transfer. Source: authors’ calculations using HLCS-2016 data for Ukraine and respective CEQ analysis for other countries: Belarus – Bornukova et al., 2017; Poland - <mark>Goraus and Inchauste, 2016; Russia – Popova et al., 2018.</mark>

# **5. Conclusions**

This paper examined the fiscal incidence of the tax-benefit system in Moldova and its contribution to poverty and inequality reduction. The analysis reveals that the tax-benefit system in Moldova is quite pro-poor – 60 percent of the population are net recipients and only 40 percent are net payers into the system. Households at the bottom of the income distribution rely almost entirely on transfers – they receive as much as 96 percent of their final income as net benefits (including old age pensions); the top two deciles contribute 20 and 34 percent of their final income respectively into the redistributive system in Moldova. Across different household types, families with three or more children, as well as single-parent families and retirees-only households are among the key beneficiaries. The system also allows for some smoothing of spatial inequalities, as the main beneficiaries are concentrated in rural areas and small towns, while those living in big cities are net payers on average.

The overall redistributive effect of the tax-benefit
Heterogeneous Effects**

In seeking to examine the effects of the Employment Fund’s training program, it is important to keep in mind that different people within the intervention may respond differently to the same policy intervention—a possibility that researchers refer to as “treatment heterogeneity.” We might be concerned that people with particular social or demographic characteristics may fare differently than the average program participant.

Because treatment heterogeneity has important policy implications, we estimate heterogeneous treatment effects by estimating the impact both for the full sample of participants as well as for several subpopulations. For example, we may want to know how well the program works for the average participant or how well the program works specifically for women, young women, and different ethnic groups that are especially targeted by the EF. Different effects for various demographic groups can assist us in informing the policy debate regarding whether specialized investments are paying off or whether further strategies might be needed to ensure that various groups participate and benefit equally from the program. The AGEI sub-group ( _i.e.,_ young women aged 16-24), in particular, merits special attention because of the Employment Fund’s outreach activities towards this particular age group.

> 35 Conceptually, the ITT estimates are equal to or lower than the ATT estimates, since they include all the people to whom the program was targeted but chose not to participate, or who did not complete the program in the treatment group. ATT estimates may overestimate true program impact, since they represent the impact among a self-selected group of people who chose to take part and complete the program. It may be that those who are more motivated and capable are more likely to remain in the program, and the less capable drop out; on the other hand, it may be that the more capable decide that they don’t really need what the program provides.

> 36 Data on training program completion were obtained from the Employment Fund’s administrative records, and cross-checked with self-reported training program participation collected at follow-up.

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respondent or the male head of the household. On average, 9% of ultra-poor households in the control group reported an employer as a source of transfers for their household. The program led to a fall in the likelihood of receiving transfers from employers by 4 ppt. This effect is both statistically and economically significant (43% relative to the control group). The next two columns show the effect for the female and the male respondent(s) separately. Ultra-poor households were 4ppt less likely to receive transfers from an employer of the main female respondent, while the corresponding effect for the male respondents were 1.3ppt. While these effects suggest that the improvement in socio-economic conditions of ultra-poor households may have reduced their engagement in interlinked labor contracts, they may also be driven by the fall in wage-labor caused by the program. In fact, Table S1.7 in Appendix S1 shows that the program significantly reduced labor supply of both female and male respondents into wage-labor (although the effect on the extensive margin is insignificant for the latter). As such, it is possible that the fall in likelihood of

> 29Appendix Table S2.5 tests whether the treatments effects on reciprocity of ultra-poor’s transfer links differ between the midline and endline surveys. The results show that the treatment effects were significantly higher at endline relative to midline. This implies that the increase in reciprocity happened gradually over time, and was only fully realized by the endline survey. In other words, it took time for the ultra-poor households to establish reciprocal transfer links.

> 30See Table S1.3 discussed in footnote 19 above.

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In the estimation of _direct_ costs, annual incidence of malaria in each country and the cost for treating each incidence were considered. The annual incidence of malaria is published by the WHO for each country. Available data indicate that on average $5 is spent as a treatment cost for each incidence of malaria (MicrobiologyBytes, 2009). The incidence of malaria was multiplied by the treatment cost to estimate the total cost of treatment of malaria in each country. The results show that if malaria can be eradicated fully in the identified countries using DDT, it will result in a cost savings of $1.08 billion needed as the cost of malaria treatment. Since more than half of the malaria incidence occurs in the high malaria endemicity areas, the elimination of malaria from such areas with the use of DDT will reduce treatment costs by nearly $0.55 billion.

The estimate of _indirect_ costs or welfare loss from malaria induced morbidity and mortality is once again based on the DALY lost from malaria. Following the process discussed earlier in the DALY estimations from the use of DDT, the monetary value of the DALY lost from malaria in each country was evaluated by multiplying the lost DALY with each country’s per capita PPP GDP. Estimates based on the results show that the complete eradication of malaria with the use of DDT can lead to a reduction of indirect costs of $67.94 billion from lower morbidity and mortality in the affected countries. The elimination of malaria through the use of DDT in high malaria endemicity areas can result in reduction in indirect cost of more than $34 billion.

# **6.** **_Discussion and Conclusion_**

The estimates presented in this paper indicate that the use of DDT can provide large benefits from the effective control of mosquito vectors in malaria endemic countries.

# **[Insert Table 4]**

Current estimates of the economic losses from malaria show that such losses exceed $69 billion in 2010 U.S. dollar annually. A major part of the
section by presenting further robustness checks in Subsection 5.3.

# **5.1 Results from Disaggregated Bilateral Trade Data**

**Production versus Trade-specific Effect** Better contracting institutions may influence not only exports, but also domestic production. We start by highlighting the importance of distinguishing between two effects of institutions: (i) the effect on domestic production, and (ii) the trade-specific effect – i.e., the effect on export pattern over and above the effect on domestic production pattern. One way to understand the difference between the two is to think of a policy action implemented with the intention of making contracts more secure. This would reduce total trading costs thereby making it easier for non-exporting firms to embark on exporting and for those already exporting to scale up their operation. In addition, this policy action would also influence the incentive of incumbent firms to restructure their production and of potential firms to be attracted (more) towards a particular sector. While the latter will also ultimately affect the pattern of trade, it is the former effect that reflects the direct and immediate trade-specific effect of the policy reform.

As the OLS estimate in the first column of Table 1 shows, the coefficient of the interaction term _Rule of Law × Institutional-Intensity_ , which captures the effect of institutions on the pattern of trade, is 0.231 and statistically highly significant. However, part of this effect could represent the effect of improved institutions on domestic production which could subsequently boost exports. Column 2 isolates the differential trade-specific effect by controlling for the production effect using the (log) number of establishments. Comparing the two coefficients, it is clear that the effect of institutions on the pattern of export – over and above enhancing domestic production – makes up about 84 percent (= (0.193/0.231)x100%) of what we would have inferred without controlling for the effect on domestic production. This corresponds to an overstatement of the effect by around 20 percent (= ((0.
Table 10: Estimates by region

|_Panel A_|l|n simple t|arifs|
|---|---|---|---|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.142|-0.058|-0.075|
||[0.095]|[0.127]|[0.142]|
|ln rain_jt ∗_agric_i_|0.345***|-0.030|0.303**|
||[0.076]|[0.81]|[0.115]|
|Observations|15,151|8,248|13,512|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.528|0.611|0.589|
|_Panel B_|ln|weighted|tarifs|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.256*|-0.125|-0.113|
||[0.134]|[0.217]|[0.183]|
|ln rain_jt ∗_agric_i_|0.398***|0.274|0.388**|
||[0.128]|[0.316]|[0.155]|
|Observations|14,969|8,069|13,458|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.331|0.398|0.448|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _
of CEFE training was regularly provided by employment offices so that some control students may also have been able to take the training after graduating, although personalized coaching would not have been available to them. Administrative and survey data show that take-up of the CEFE training was low in the control group, with only twenty-nine students (or 3.4 percent of the control group) completing the CEFE training after graduation.

> 15 A student from Gabes, for example, reported that she was asked by the employment office to decide whether she wanted to remain in the traditional track or take up the entrepreneurship track before she fully understood what the latter entailed. As a result, she eventually decided to drop out of the entrepreneurship track.

> 16 Given the high expectations at the onset of the program, some students were disappointed in the training and dropped out. Other students dropped out due to exams conflicting with the twenty days of training when attendance was mandatory.

> 17 Data from a graduate tracer study in Tunisia suggest that graduates from Economics, Business, and Law were particularly prone to long unemployment spells after graduation (MFPE and World Bank, 2009).

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