rafmacalaba/data-use-mentions-extended huggingface dataset:
political processes when these do not function well enough (e.g., Estache and Martimort (2000), Jordana and Levi‐Faur (2006), Maggetti 2007; Maggetti et al (2013), Majone (1997), Thatcher (2011)). When this happens, IRAs can be quite effective in improving performance. In the rest of this paper, we ignore these process related matters and focus on the simple payoff to the creation of a separate regulatory agency. We recognize that the degree of independence of the agency is an important dimension but it goes beyond the scope of this paper.

# **3. How many water IRAs are there in the world?**

To be able to assess the impact of IRAs on PPP, two sets of data at least are needed: data on the existence of IRAs and comparable data on PPPs. PPP data are available from the World Bank, but global information on IRAs is not available, at least publicly. This is why this paper started with a data collection effort on the existence of national water and sanitation IRAs. The main output of this effort is reported in Table 1. We managed to collect data for 177 countries in 2015.<sup>3</sup> The detailed country specific coding is reported in Annex 2.<sup>4</sup> The data are recorded as a dummy variable, i.e. if the country has an IRA, it is coded as 1 and otherwise as 0. The information has been collected from a systematic search of the official websites of the countries’ governments, United Nations agencies and multilateral development banks’ project documents. In the process, we also documented the extent to which the country had any access to some significant water or sanitation delivery contract with the private sector. The country is considered to have some form of public‐private partnership (PPP) in 2015 if during that year there is an ongoing divestiture, greenfield or brownfield PPP in the water
**Figure A2. Share of natural gas in the total consumption of goods and services by various sectors in 2012 (%)**

Households 0.4% Service 0.5% Transport 4.6% Trade 1.5% Mining 4.0% Gas 0.0% Electricity Construction 0.1% Other manufacturing 5.2% Machinary & Equipment 0.5% Metals & products 4.0% Non Metalic Minerals 6.3% Petroleum 4.0% Fertilizers & Chemicals 32.1% Jute, textile, Leather 0.0% Food & Beverage 0.4% Forest & products 1.5% Agriculture 0.0% <!-- Start of picture text --> 68.9%<br><!-- End of picture text --> Source: Social accounting matrix used for this study 35
% (France and Germany) and 8.1% (Portugal). Em-

> 8See http://www.cia.gov/cia/publications/factbook.

> 9Clearly it would be best to have the ‡ow of migrants between 2000 and 2010 but this will be available only in a few years as the Censuses from the 2010 round are collected, processed and made public.

10
to slightly decrease for the whole sample of countries, increase in some countries, but decrease in others. However, the top 20% will remain by far the main contributor to carbon emissions.

**Literature review** This paper is related to several strands of literature. It first complements the many studies that have analyzed the determinants of household transport expenditures. Most papers have focused their research on the impacts of change in fuel prices and on transport mobility for poor households (Ferdous et al., 2010). This literature can be classified into three categories: (i) works that describe the trend and composition of household transport spending (see e.g., Kauppila, 2011; Venter, 2011; Nie et al., 2016; Anowar et al., 2018), (ii) works that investigate the determinants of transport demand (see e.g., Gandelman et al., 2019), and (iii) works that analyze household transport spending jointly with other types of expenditures (see e.g., Choo et al., 2007). Most studies show that overall, the share of household transport expenditure over total expenditure varies from 10% to 20%, and that private transport spending represents the main component of transport expenditures with a share up to 80% in Australia or 85% in Canada. Furthermore, there are significant differences in transport shares between rich and poor households in low- and middle-income countries, especially in Africa (see e.g., Olvera

> 1More detailed mobility surveys would be needed to look at the environmental impacts of rising income on the substitution between public transport services and private transport.

> 2More work would need to be done to assess the size of the reduction in emissions intensity of transportation in low- and middle-income countries to improve the simulations.

4
Policy Research Working Paper 0

# **Abstract**

Using the management and operational practices survey in the Russian Federation, this paper finds that an average Russian manufacturing firm adopts 43 percent of the structured management practices (a score of 0.43), a value that is far from the frontier (for example, the United States scores 0.62). This average mask the wide heterogeneity in practices, where a large share of firms adopt few structured management practices and only 3.5 percent of them have a score over 0.75. Consistent with the findings in other countries, better managed firms in Russia show stronger firm performance, measured as gross revenue per employee, value added per employee, total factor productivity, and employment growth. Improving the management score from the 10th to the 90th percentile is associated with an increase in sales per worker by 87 percent, value added per worker by 30 percent, and total factor productivity by 13.5 percent. What drives better management capabilities? Russian firms are similar to those in other countries, such that exporters and firms with foreign linkages are better managed. Switching from operating purely in the domestic market to being globally linked is associated with a significant increase in management capabilities. However, unlike the results in other countries, management capabilities in Russia are not associated with firm age, implying that firms do not learn to be better managed over their life cycle. This result points to the possibility of inefficient allocation of resources, such that learning and selection mechanism does not weed out the badly managed firms, perhaps due to the lack of pro-competitive forces.

This paper is a product of the Finance, Competitiveness and Innovation 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 agrover1@worldbank
building behavior. In the context of the multi-faceted poverty reduction program in rural Niger, we hypothesize that representing independent models of agency will increase women’s intrapersonal beliefs, such as self-efficacy, but that representing interdependent agency intervention will additionally influence relational affordances and, ultimately, women’s economic development outcomes.</mark> In Study 1, we provide <mark>descriptive data on the sociocultural context and illustrate specific methods for</mark> designing ‘culturally wise’ social-psychological interventions. We find that, in rural Niger, models of women’s economic agency were more interdependent than independent, situating relational factors like social harmony and respect as more important than intrapersonal 4
While the stunting rate in Madagascar stands at 40%, our research has unveiled new policy relevant insights after appending the two surveys at the commune level. For instance, 17.9% of stunted children reside in non-poor households, suggesting that using household poverty levels as a targeting tool for identifying stunted children is ineffective. Similarly, 21.3% of non-stunted children live in impoverished households, underscoring that malnutrition is not solely a product of destitution, reinforcing Sen’s (1981) argument that famine or malnutrition is not the results of lack of food or poverty, but a result of entitlement failure.

The remainder of this paper is structured as follows: In the next section, we delve into the nutritional landscape of Madagascar, presenting key statistics on macronutrient and micronutrient consumption, as well as the distribution of caloric intake across the country. Section 3 provides a literature review to contextualize the paper's contributions. Section 4 outlines the data and methodology employed to implement the small area estimation approach for deriving nutrition and poverty indicators at the commune level. Our findings are presented in Section 5, and we conclude the paper in Section 6, offering policy recommendations.

# 2. Nutrition in Madagascar

## Caloric intake

Table 1 provides information on the distribution of calorie intake in rural and urban areas of Madagascar for the year 2021-22, as well as the overall figures for the country. The data is categorized based on per capita real consumption (PCER) deciles, which divide the population into 10 equal groups based on their calorie intake and poverty status. In rural areas, the lowest PCER decile (1st) has an average calorie intake of 707 kcal per person per day, while the highest decile (10th) has a significantly higher intake of 3,827 kcal. This suggests a substantial disparity in calorie consumption between the most impoverished and least impoverished individuals in rural settings. The trend shows a gradual increase in calorie intake across the deciles, with each decile having a higher average intake than the previous one.
) may be lower bounds of the ASP’s total impact due to the presence of spillovers from the program.

To calculate spillover effects, we have to use the sample of nontreated students. However, a plausible concern in this estimation is that students assigned to C group can be more motivated than their nonenrolled peers, proxied by their registration decision, and may have better socioemotional skills or behavior at school. Thus, by measuring spillovers on the C group we may be underestimating them, since the motivation effect seems to go in the same direction as the spillovers. Therefore, to capture the total indirect effect while accounting for a nonobservable motivation effect, we use only the sample of nonenrolled children.

Are the enrolled and nonenrolled samples similar on observables? Using administrative data on academic performance and misbehavior at school, we compare the characteristics of these two groups and find the groups are similar regarding academic performance and behavior at school 18
indicated as above). There are no significant differences for any characteristics between the sample and the frame, suggesting that the balanced sampling approach performed well, and the Nigeria HFPS sample retains the representativeness present in the frame.

**Table 4.2: Sample representativeness in Nigeria HFPS**

||**Nigeria (G**|**HS-Panel)**<br>**With**|**HFPS**|**Samle**|
|---|---|---|---|---|
||**All**|<br>**Contact**<br>**info**||**p**<br>_Di †_|
||_Mean_|_Mean_|_Mean_|_fference_<br> _(3) -(2)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|
|**Sample size(number of households)**|_4,976_|_4,934_|_3,000_||
|Rural|0.686|0.683|0.687|-0.001|
|Average household size|5.528|5.515|5.530|-0.002|
|Dependencyratio|1.016|1.014|1.028|-0.012|
|Female head(%)|0.186|0.187|0.186|0.001|
|Age of head|48.797|48.775|48.646|0.151|
|**_Religion of head_**|||||
|Orthodox|||||
|Muslim|0.425|0.422|0.425|0.000|
|Christian|0.557|0
# **6 Mechanisms**

In this section, we explore potential mechanisms driving the effects of the survey methods presented in Section 5. We test if the LOA and ESR can improve the reporting of women’s and young men’s labor indicators based on prevalence of peers’ informal employment or by social norms related to domestic obligations. Although our findings are suggestive, we believe they warrant further exploration, since they can serve as a starting point for new avenues of methodological and policy-relevant research.

## **6.1 Prevalence of Local Informality**

Women and young men are more likely to engage in informal work in developing countries (ILO 2018). For example, in low- and middle-income countries, 92% of women are engaged in informal employment compared to 87.5% of men. In El Salvador, the gap is wider: 72% of women are engaged in informal work relative to only 56% of men (UN Women, 2023). Standard labor surveys fail to accurately measure informal work or employment because when respondents are asked about income generating activities, they automatically think only about formal employment or work. In contexts where informal employment is high, respondents’ reference of labor market indicators may be even more biased. For example, a proxy respondent in a community with high levels of informality may be more likely to report that a woman or youth living in his household are not working because the respondent observes activities that he does not categorize as work or employment.

To test this hypothesis, we first estimate a measure of prevalence of peers’ informal employment at the community level to which each respondent is exposed. This measure is defined as the share of employment in the informal sector<sup>16</sup> in each EA by sex, excluding the respondent’s employment status.<sup>17</sup> Within our sample, the average informal employment to which the respondents are exposed at the EA-level is 76%, with an average of
|United Kingdom|20.1|22.1|17.3|0.933|–|0.940|0.958|
|---|---|---|---|---|---|---|---|
|**ECA (country average)**|**18.0**|**21.1**|**25.9**|**0.904**|**90.3**|**0.932**|**0.945**|
|**ECA (population-weighted average)**|**18.4**|**22.5**|**25.6**|**0.894**|**91.4**|**0.927**|**0.938**|

_Source:_ Data on obesity, smoking, and alcohol consumption are from the European Health Interview Survey, <mark>Health Equity and Financial Protection Indicators,</mark> and the World Health Organization. The ECA average for the share of children not stunted is calculated based on countries for which data are available only.

_Note:_ HCI = Human Capital Index; ECA-HCI = Europe and Central Asia extension of the HCI.

- Not available.

- a. Includes consumption of smokeless tobacco.

# **_5. Estimation of the ECA-HCI_**

The ECA-HCI is the product of three components: The three components are defined as follows: The estimates of the ECA-HCI in table 8 show that countries in the region can achieve large increases in their long-run productivity if they reduce the distance between the expected educational attainment and adult health status of children born today and the benchmarks of complete education and full health. The average country for which the ECA-HCI is calculated has a value of 0
The top panel of Table 2 displays the proportion of the population in different quantiles of the true per capita consumption quantiles (columns) deemed eligible for Samurdhi benefits by Model 1. The PMT cutoff scores (rows) are set at the 20<sup>th</sup> , 30<sup>th</sup> and 40<sup>th</sup> percentiles of the true per capita consumption distribution, implying that around 20, 30, and 40 percent of the population with scores below the respective cutoffs would be eligible for benefits.<sup>12</sup> As is evident from the table, the share of households eligible for benefits increases monotonically as the cutoff rises. According to the HIES, 40.2 and 37.1 percent of population in the bottom decile and quintile, respectively, were beneficiaries of Samurdhi in 2016. By applying a PMT with the weights estimated in Model 1, around 75 percent of the bottom decile, and 65 percent of the bottom quintile, would be eligible if the PMT cutoff score were established at the 30<sup>th</sup> percentile of the true consumption distribution. This rule would select around 24 percent of the population overall.

Table 2. Comparative Targeting Performance of Model 1

|Quantiles of the true consumption distribution=>|Bottom<br>Decile|Bottom<br>Quintile|Top Quintile|Top<br>Decile|All|
|---|---|---|---|---|---|
|_Eligible share_||||||
|Existing Samurdhi beneficiaries<br>PMT cutoff score*|40.2|37.1|3.8|2.2|18.8|
|Score: 856 (10th percentile)|17.5|12.0|0.0|0.0|2.9|
|Score: 868 (15th percentile)|34.
of this paper is that we take advantage of recent methodological advances to provide a credible answer to a long-standing policy-relevant question:

> 11This is supported by our cohort-wise estimates that show signicant changes in sibling correlation over time. Nicoletti and Rabe (2013) provide evidence that sibling correlation in academic attainment declines sharply as more distantly spaced siblings are considered.

> 12Thus, the maximum age dierence between siblings in our sample can be 12 years. Bingley and Cappellari (2019) also use a maximum of 12 years age gap to dene their sample. This implies that the cohort variations in our analysis primarily come from dierent survey years of dierent waves of DHS for a given country. We note here that the main conclusions are robust to alternative age ranges. These alternative results are available from the authors.

> 13It is well understood in the literature that the degree of sample truncation due to coresidency declines as we focus on younger children. See, for example, the recent inuential analysis on intergenerational educational mobility in Africa by Alesina et al. (2021) where they use census data (coresident samples) from IPUMS and restrict age of children to 18-24.

14The only other paper we are aware of that provides estimates of sibling correlation for multiple developing countries is Dahan and Gaviria (2001) who report estimates for 16 Latin American countries. But their estimates are not comparable to the other estimates available in the literature because they use a dierent measure. They focus on the educational failure (lack of grade progression) of a child rather than educational attainment.

4
Table 3. Accounting for Selective In-migration

|Trimming using the|All-Time In-m|igration Rate|1980 In-m|igration Rate|1985 In-mig|ration Rate|
|---|---|---|---|---|---|---|
||Wealth Index|Wealth Index|Wealth Index|Years of Education|Years of Education|Years of Education|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Guerrilla control|-0.101***|-0.260**|-0.121***|-0.277**|-0.121***|-0.274**|
||(0.0353)|(0.107)|(0.0358)|(0.109)|(0.0358)|(0.109)|
|Observations|3,630|3,637|3,630|3,637|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|-0.0330|6.538|-0.0280|6.570|-0.0280|6.565|

_Notes:_ The results follow the specification of equation 1 for the Wealth Index and Years of Education outcomes. However, we trim the dependent variables by using different in-migration rates. In Columns 1 and 2, we use the all-time in-migration rate to trim the 10.4 percent most educated and wealthy people and the 3.3 percent least educated and wealthy from the treated and control groups’ respective distributions. In Columns 3 and 4, we use the in-migration rate from 1975 to 1980 to trim the 0.4 percent most
for African standards - but also a distribution of health workers that is biased towards urban areas.<sup>8</sup> Health workers in Ethiopia face a labor market with specific characteristics. Most important is the central role of the government. Wages are set by policy makers and therefore may not reflect the market value of labor. The government also plays a direct role in the allocation of health workers.<sup>9</sup> Students who have been funded by the government – which is the vast majority - have an obligation to serve time working for the funding government agency (regional or federal) and are randomly allocated to posts through a lottery, with those funded by a regional government allocated to a post in that region.<sup>10</sup> Although the allocated job cannot be changed in principle, we find evidence that students bend the rules.<sup>11</sup> Only after having served the obligatory term in a government facility can health workers receive a release certificate from the public sector. This certificate is legally required to work in the private health sector. Private facilities at the clinic level are now common place, but only in urban areas. In general, the Ethiopian health sector is dominated by the public sector - except for the pharmacies and drug shops.<sup>12</sup> Our analysis is based on a survey with final year nursing and medical students in their last year of study. The sample includes 219 nursing and 90 medical students, sampled from eight clinical nursing schools and three medical faculties all over the country.<sup>13</sup> The nursing students were in the final year of their training while the medical students were about to enter their internship. Our sample of medical students represents 49% of the 2003/4 cohort, while our sample of nursing students represents an estimated 16% of the 2003/4 cohort. The statistical analysis is based on data from three sources: (i
|High Skill and Technology Intensive<br>manufacture|22.1|17.2|8.4|8.3|5.5|6.4|

Source: World Bank and UNCTAD

# **3.3. EMPLOYMENT AND POVERTY REDUCTION**

Senegal’s per capita GDP rose by 1.81 percent during 1995-2005 and by 0.38 percent during 20062013, compared to averages of 0.97 and 1.32 for WAEMU and 1.59 and 2.00 for SSA over the same respective periods. A sectoral decomposition shows that the services sector’s contribution to per capita growth in Senegal rose from 61 percent in 1995-2005 to 75 percent during 2006-2013.<sup>9</sup> Meanwhile, the sector’s contribution increased from 49 percent to 61 percent in SSA and fell from 44 percent to 38 percent in WAEMU.

9 There seems to be a disagreement between the country national accounts and the World Bank database in the construction of GDP per capital growth rates. Using data from country sources, the services sector contributed 69 percent and 76 percent to GDP growth during 1995-2005 and 2006-2013, respectively. Average population growth was 2.59 percent and 2.83 percent during the respective periods. Based on these, one would expect services’ contribution to per capita GDP growth rates to be around 66.4 percent and 73.2 percent for the two periods. However, the numbers from WDI are 61 percent for 1995-2013 and 75 percent for 2006-2013, so that population had more than the expected effect on per-capita GDP growth in the earlier period and less than expected effects in the latter period. There is a potential explanation for this. Keeping output constant, a capital intensive production system will yield higher output per worker than a labor intensive technology. Thus, the observed difference is a reflection of technological change that minimizes the population growth effect significantly.

16
BRI and non-BRI countries to investigate how the determinants of investment flows might vary between BRI and the rest of the world.

Table 9 shows that most of the empirical regularities established in previous studies hold in our broad cross-country data. Most of the factors that have been considered in the literature exert significant and expected effects. As in earlier studies, transportation costs between source and host countries are found in general to reduce FDI, but the effect varies significantly by the mode of transportation. A 1-percent increase in air distance is associated with 0.7-1 percent decrease in the volume of FDI. Land transportation cost, measured by either driving distance or driving time, is also found to exert a significant and negative effect: a 1-percent increase in driving time, for instance, leads to a close to 1-percent decrease in FDI. In contrast, the effect of sea distance is weaker (insignificant in some specifications): a 1-percent increase in sea distance is related to around 0.1percent decrease in FDI flows.

Infrastructure quality is found to matter: the results show a significant and positive relationship between each of the three infrastructure quality measures, namely, railway, airway and port qualities, and FDI inflows. Countries with higher railway and airway indices and better ports tend to attract a greater volume of FDI. For example, improving the level of railway index from BRI countries (11.8) to the level of high-income countries (18.5) is associated with 4.3 percent increase in FDI flows.

26