rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7 Conclusion**

This paper aims to contribute to the ongoing debate on ways to improve the accuracy and timeliness of welfare statistics in developing countries experiencing severe budget constraints. Only a few developing countries have the capacity to collect annual data on income or expenditure, therefore, indicators such as inequality or poverty rates can be computed in these countries only when Household Budget Surveys are available, about every four to five–and sometimes as many as seven–years. To overcome this problem, methods have been developed to compare these indicators over time from surveys that are little comparable. These techniques (SSITs) have proven effective at predicting poverty indicators, but are much less accurate when used for inequality indicators.

To illustrate this limitation, we conducted several simulations based on data from Moroccan Household Budget Surveys and Labor Force Surveys. Our results indicate that the predicted inequality measures are, at best, one-third smaller than those directly estimated from the data. Other regression methods were implemented, but none seems to significantly reduce the negative bias in the Gini, Theil, and HCR indices. Our theoretical explanation for this points to two main limitations of the Standard SSIT models based on linear regressions: the overly stringent assumption of the residuals’ normality distribution and the expectation that regression-based models predict distribution compressed around the mean and with thin tails. Unfortunately, the shape of the tails is crucial to correctly estimate inequality. Thus, almost by design, these models tend to produce estimates that are far below the correct values.

The method we propose is based on an algorithm that minimizes the sum of the squared differences between a certain number of direct estimates of an index and its empirical version obtained from the predicted values. With this algorithm, we reduce the bias and obtain results that are not systematically biased. When the estimated results are compared with those directly estimated on the original sample, the bias is negligible. Furthermore, the estimates of inequality indices for the years in which only labor force data are available seem to be consistent with
attending school). **Appendix Figure A3** describes the composition of the different household wealth measures used.

As described in Alix-Garcia et al. (2018), our social capital measures included elements generally considered to be relevant both globally (Grootaert, 2004) and locally (Merino and Martinez, 2014). We included actions that indicate cooperation (participation), investments that benefit the whole community (infrastructure), and attitudes demonstrating the foundations of social capital (trust or functions). We also measured institutional structures that support social capital, including the range of decisions made by the community assembly (governance), and whether a wide variety of community members participated in decisions (inclusion). **Appendix Figure A4** describes the composition of the social capital indices. The full survey instruments are published as part of the supplemental information in Alix-Garcia et al. (2018).

To reduce dimensionality and multiple-hypothesis testing concerns due to the large number of outcomes, we aggregated into indices the land-cover management activities, the household-level socio-economic measures, and the community-level social capital measures. We used a variety of methods, including simple summation of the presence or absence of particular characteristics, principal components, polychoric principal components (which takes into account categorical variables), and inverse proportion weighting (which applies higher weight to rare items). The results were similar across different aggregating methods. We report the results of simple aggregation, but other results are available upon request.

This paper also explores impact heterogeneity for a series of time-invariant covariates where theory suggests there may be differential impacts. In particular, we expect greater program impact where there is higher deforestation risk. Risk may also be (but is not necessarily) indicative of higher participation costs and hence lower economic gains. We also test for differential impacts across regions for similar reasons.

# _Comparison between Study Samples and Universe of Applicants_

A key concern for external validity is the extent to which the studied samples are representative of the population from which they were drawn. In
**Figure 1: Change in welfare indicators between 2007 and 2008**

<!-- Start of picture text --> 1800 0.60<br>1600 0.40<br>0.20<br>1400<br>0.00<br>1200<br>-0.20<br>1000<br>-0.40<br>800<br>-0.60<br>600<br>-0.80<br>400<br>-1.00<br>200 -1.20<br>0 -1.40<br>2007 2008<br>Income Food consumption Consumption<br>Weight-for-age BMI-for-age Height-for-age<br>Meticais Z-score<br><!-- End of picture text -->

**Table 1: Change in household welfare indicators between 2007 and 2008 in Mozambique. Sample means.**

||**2007**|**N**|**2008**|**N**|**Difference**|**p-value**|
|---|---|---|---|---|---|---|
|**Income**|1588,96|896|1001,07|896|-587,89|**0,02**|
|**Consumption**|1278,96|861|1137,67|872|-141,29|**0,05**|
|**Food consumption**|670,90|861|673,24|872|2,34|0,96|
|**Weight-for-age z-score**|-0,08|3314|-0,43|3807|-0,35|**0,00**|
|**Height-for-age z-score**
<!-- Start of picture text --> 3<br>2<br>0<br>2,500 5,000 10,000 20,000 40,000<br>GDP per capita, log scale<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>15<br>10<br>0<br>2,500 5,000 10,000 20,000 40,000<br>GDP per capita, log scale<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>2,500 5,000 10,000 20,000 40,000<br>GDP per capita, log scale<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>2,500 5,000 10,000 20,000 40,000<br>GDP per capita, log scale<br><!-- End of picture text --> <!-- Start of picture text --> MEE Engineering (Managerial<br>ME «Support services [EE Production<br>HE Others<br><!-- End of picture text -->
5,924|
|National|24.8%|6.2%|9.9%|-3.7%|8,599|7,366|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

_Table 7: Evolution of the real price of a calorie from 2015 to 2017/18_

|Region|Price of calorie in<br>2015|Price of calorie in<br>2017/18<br>(2015 prices)|General CPI<br>(2015<br>to<br>2017/18)|
|---|---|---|---|
|Metropolitan|0.00393|0.00388|1.59|
|Lower Urban|0.00369|0.00349|1.65|
|Lower Rural|0.00362|0.00342|1.66|
|Upper Urban|0.00376|0.00366|1.61|
|Upper Rural|0.00375|0.00366|1.63|
|Border|0.00407|0.00364|1.65|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

The evolution of poverty was heterogeneous across different regions. Putting aside the Border regions, where the smaller sample size provides a less reliable picture, Table 6 shows that the largest increase in poverty took place in the Metropolitan area, where the poverty rate doubled. This is indeed the region in which total expenditures per capita underwent the largest decrease, from about 12,000 Egyptian pounds in 2015 to about 9,600 Egyptian pounds in 2017/18. As a result, poverty increased from 11 percent in 2015 to 25 percent in 2017/18. Poverty also increased in
444|0.561|0.606|
|Between R-squared|0.088|0.224|0.322|0.120|
|Overall R-squared|0.096|0.280|0.352|0.179|

_Notes:_ Robust standard errors in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

_Source:_ Authors’ own elaboration using World Bank’s data.

27
children and 61% of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8% of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signi�icantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11% of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents' educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020).

_Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy)_ <!-- Start of picture text --> 68% 68%<br>61% 59% 59%<br>50%<br>35% 36%<br>31%<br>26% 26%<br>23%<br>Would like to continue living Would like to move back to Feel strongly connected to Feel strongly connected to<br>in Italy Ukraine Italy Ukraine<br>Caregivers (N=283) Children between 9 and 14 years old (N=141) Children between 15 and 20 years old (N=96)<br><!-- End of picture text --> Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25% of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. The lack of certainty means children are trying to prepare
Policy Research Working Paper 10429

# **Abstract**

Recent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing subnational machine-learning-based poverty estimates with survey-based, direct estimates. Although unbiased, survey-based estimates at a granular level can be imprecise measures of true poverty rates, meaning that it is unclear whether the validation procedures used in machine-learning approaches are informative of actual model performance. This paper examines the credibility of existing approaches to model validation by constructing a pseudo-census from the Mexican Intercensal Survey of 2015, which is used to conduct several design-based simulation experiments. The findings show that the validation procedure often used for machine-learning approaches can be misleading in terms of model assessment since it yields incorrect information for choosing what may be the best set of estimates across different methods and scenarios. Using alternative validation methods, the paper shows that machine-learning-based estimates can rival traditional, more data intensive poverty mapping approaches. Further, the closest approximation to existing machine-learning approaches, using publicly available geo-referenced data, performs poorly when evaluated - against “true” poverty rates and fails to outperform tradi tional poverty mapping methods in targeting simulations.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pcorralrodas@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,
consumption model and, vice-versa, estimated the 2007 poverty rate using the 2001 consumption model. In both cases, we were able to closely match the official poverty rates with the imputation-based estimates. Whether we used the _forward_ approach or the _backward_ approach, we effectively obtained the same poverty estimates.

The application provides a number of new insights for Morocco. The imputation-based estimates show that poverty consistently declined between 2001 and 2007, and that the decline continued beyond 2007 and up to 2010. This confirms that Morocco has been able to withstand the global financial crisis, arguably due to the favorable agricultural production during that same time period. The estimates also show an urban-rural convergence in poverty, with rural poverty falling faster than urban poverty, thereby reducing the urban-rural gap. Interestingly, poverty rates in Morocco have not declined everywhere; disaggregating by region, we see both upward and downward trends in poverty that previous statistics for 2001 and 2007 were not able to capture. The quarterly poverty estimates have provided an entirely new perspective on the study of poverty in Morocco.

The potential for extensions and applications of this work is also promising. The estimated quarterly poverty series can be used for further cross-section and panel econometric work, for forecasting, and for simulation of policy reforms and economic shocks. These applications have the potential to substantially expand the toolkit of the welfare economist.

The paper is organized as follows. The next section describes the macroeconomic context, poverty trends and government policies in Morocco over the period 2000-2010. Section three illustrates the cross-survey imputation methodology adopted. Section four explains the HESs and LFSs data used. Section five, shows the empirical model, section six carries out some validation tests and section seven discusses the poverty estimations obtained. Section eight discusses possible extensions and applications of the methodology proposed and section nine concludes.

# **2. Growth, poverty and policies 2000-2010**

As an emerging economy that has increasingly opened to trade during the last decade, Morocco has become more dependent on the global economy. Global shocks that include the
|13245|10878|
|<br>R-squared|0.153|0.131|0.143|0.115|0.110|0.111|0.106|0.106|0.110|0.112|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Germany’s export shares refer to the period 1994-2004. Standard errors in parentheses clustered at 2-digit industry level. TFPR is estimated using the method in Levinsohn and Petrin (2003). Column (1) excludes entries and exits after 2004. Column (2) excludes the period 2000-2004. Column (3) considers only firms with more than 50 employees. Column (4) considers only firms born before 2004. Column (5) uses Germany’s export shares for the period 2000-2004. Column (6) excludes the Chemical and Food sectors. Column (7) excludes firms that switch sector over the period. Column (8) includes Poland’s input and output tariffs. Column (9) includes EU’s external tariff. Column (10) includes tariffs and excludes the Chemical and Food sectors. *10% level **5% level and ***1% level.

36
Table 6: Robustness V, Survival Sanitary Risk and Product Perishability. The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS4 (columns 1-2) and across HS8 (columns 3-4). We control for alternative product characteristics, their perishability (perishabilityk). We interact the level of ...nancial development in country c with tthe perishability index (FDc�perishabilityk). In addition we include number of non EU partners to control for altrenative markets where exporters can sell their products. Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, *** **, and denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|FDc x sanitary_riskk||-0.154**||-0.263**|
|||(0.073)||(0.108)|
|FDc x perishablek|-0.537|-0.321|0.703|0.978|
||(0.897)|(0.890)|(0.927)|(0.915)|
|sanitary_riskk||0.020***<br>(0.007)|||
|perishablek|0.296**|0.279**|||
||(0.135)|(0.131)|||
|initial_exportcikj
24 companies include rent to their managers, which may increase this component in the budget, relative to ATP 1.

The financial performance of the five ATP2 companies is presented in Table 10. The calculated average gross margin is nearly 25%, which indicates a sustainable level of profit. However, one company has a very low level of performance (8%) that is mainly explained by a low level of producer fee collection.

Based on the data in Table 10 and Table 12, the per producer cost of extension provision by ATP2 varies between $53 and $77 per year. This range is not too far apart from the estimated cost (between $30 and $66) of extension provision by ATP1, that was calculated earlier.

Another aspect of the financial status of the private companies that provide extension services to producers under the ATP2 program can be found in Tables 1 1 and 12 below.

Comparison of the private firms data in Table 11 suggest major differences in resource allocation for visits of farmers. While firm 1 allocates 24 working days per month for each extensionist, of which 83% (_20 days) are spent on producers' visits, firn 5 allocates 22 working days per month for each extensionist, of which 77% (_ 17 days) are spent on visits. Other interesting results are the big variation between the firms in both technician training and office work. If data on producer performance is available at the extension provider level, It could be used to estimate more accurate efficiency differences among the firms.

Table 10: Financial performance of five private companies in the period 8/95-6/96

|Item|Company I|Company 2|Company 3|Company4|Company 5|
|---|---|---|---|---|---|
|Incomefrom INTA|646583.97|613707.98|713207.76|626676.56|621872.28|
|Income from Producers|127957.48|
strong competition in export markets, firms wishing to export considerably improve their productivity before entering export markets. Second, in some cases, there is a learning-by-exporting process thanks to which feedback from foreign buyers and competitors helps to improve the performance of exporting companies after export (Camino-Mogro et al., 2023; Greenaway et al., 2002; Bigsten et al., 2000). Whether the improvement in firms' performance is ex-ante or ex-post, entering export markets is associated with better productivity which can improve wages and therefore the standard of living.

Hence, it becomes important to understand how electricity access, which is one of the main obstacles to firms' growth in SSA, helps manufacturing companies to move from domestic to export markets.<sup>4</sup> As such, this paper examines the role that access to reliable electricity could play in the orientation of manufacturing companies towards export markets at the expense of domestic markets. Using the World Bank Enterprise Surveys (WBES), we explore a sample of 13,025 manufacturing firms in 39 SSA countries surveyed between 2006 and 2022. We consider a treatment variable describing whether a firm has access to reliable electricity or not. We then assess how this treatment – a dummy taking 1 if the firm declares not experiencing power outages and 0 otherwise – affects our two measures of ISSC.

By using the entropy balancing method, we first create a counterfactual group containing firms experiencing power outages but as similar as possible to those in the treated group (the group of companies not experiencing power outages). Then, in a second step, we determine the difference in terms of ISSC between the two groups, which difference is attributed to electricity access. Compared to other

> 3 In this paper, the term output corresponds to companies' sales, since the database we use approximates production to sales.

> 4 In this paper, the word "electricity access" refers to the availability of reliable electricity.

2
Since _ex post_ AIT estimates represent a lower bound of the impact on households that actually _received_ treatment (for reasons explained earlier), the _ex ante_ estimates of program impact from BFL should also be seen as a _lower bound_ for the actual impacts of the program on beneficiary households.

# _The impact of Bono de Desarrollo Humano (BDH)_

As before, we compare the _ex ante_ estimates of program impact from the BFL model with _ex post_ findings from Schady and Araújo (2008), henceforth SA. For _ex ante_ simulations, we use the baseline survey of BDH collected in Carchi, Imbabura, Cotopaxi and Tungurahaua and described in section IV above, which is also used by SA as the baseline data in analyzing impact. It is relevant to note that by the authors’ own admission, the results from SA should be treated with some caution as they are not based entirely on random assignment.

26
with the highest levels of infrastructure coverage and quality (output) concerning their public capital stock (input). Countries positioned at the forefront of infrastructure coverage and quality, relative to their public capital stock and income per capita, serve as benchmarks for efficiency and receive a PIE-X score of 1. Other countries receive PIE-X scores ranging between 0 and 1, contingent upon their vertical distance from the frontier in comparison to peer best performers. The efficiency frontier is defined using three metrics: (i) A physical indicator that uses data on the volume of economic and social infrastructure; (ii) A survey-based indicator drawn from a World Economic Forum survey that measures business leaders' perceptions of infrastructure service quality; (iii) A hybrid indicator that gives equal weight to the physical and survey-based metrics.

The index, which is computed for more than 100 countries and includes both advanced economies and EMDEs, shows that there is a positive relationship between the efficiency of public investment and income per capita. IMF (2015) also finds that that the slope of the efficiency frontier declines as the public capital stock increases, indicating diminishing marginal returns to additional investment.

A limitation of the <mark>of PIE-X is that the physical indicator is based on volume and fails to comprehensively capture quality, while the survey-based indicator is inherently subjective.</mark> The IMF’s Public Investment Management Assessment (PIMA) evaluates the decision-making process of public investment across three key stages: (i) planning; (ii) allocation; and (iii) implementation. Each stage is assessed using five indicators. For planning, the focus is on indicators that recognize the importance of robust institutional frameworks in ensuring the fiscal sustainability of public investment and institutions that facilitate coordination across sectors, governmental levels, and between public and private entities.<sup>35</sup> For allocation, the indicators translate the idea that optimal allocation of capital spending requires a comprehensive, unified, and medium-term perspective on capital budgeting. This involves employing objective criteria and