rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2.5 Breaking down PPPs in MENA**

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1. Total Investment by Country<br>(Millions of U.S. dollar)<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>2. Total Investment by Sector 2005 - 2016<br>(Billions of U.S. dollar)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Telecom Electricity Seaports Treatment Natural Gas Airports Water Utility Railroads<br>plant<br>Sources: World Bank, Private Participation in Infrastructure Database; and World Bank staff calculations.<br>Note: Total value of investment and number of projects are in accordance to year 2016, or latest year available before that,<br>unless otherw ise noted.<br><!-- End of picture text -->

# **The political economy of implementation and management in MENA**

MENA states have had a poor track record in both the delivery and the management of public utilities. The are many reasons.

A primary factor is the so-called curse of financing. The excess profits from oil have turned governments into principals and citizens into agents. It is the reverse in economies in which taxpayer monies are the main sources of government revenue. As a result, there is little pressure on governments to do better. Moreover, the excessive reliance on the traditional source of financing (entirely from public finance) also played a role in inducing inefficiency. Public financing of infrastructure without internal checks and balances allows for overpricing and capture. It also prevents efficient pricing of risks and risk sharing with other partners such as the private sector and MDBs.

Another factor in the poor track record of MENA countries is the concentrated nature of bureaucracies that make decisions from
lowest at 17%, followed by firms in services at 22% and this was 10 percentage points lower than the decline for firms in agriculture. Within manufacturing and services, recovery has been most remarkable for firms in heavy industrial manufacturing and knowledge-intensive services. Firms in each of these sectors experienced a 12% decline in total sales and the lowest predicted probability of falling into arrears of 27-28% (Figure 10). The recovery has been weakest for hospitality services with a 44% drop in sales and a 52% predicted probability of falling into arrears. The evidence from the BPS data confirms systematically discussions in the popular press on the strong recovery of firms in business services contrasting with the poor performance of those in hospitality services.<sup>15</sup>

> 15See https://www.ft.com/content/8075a9c5-3c43-48a5-b507-5b8f5904f443 https://www.ft.com/content/ac6266ed-3977-4d51-88bf-d60768ee8c7a https://www.economist.com/finance-and-economics/2021/06/05/covids-unequal-effect-on-companies https://www.economist.com/britain/2021/09/02/britains-economic-recovery-from-the-pandemic-is-far-from-smooth

21
household enterprise are more often missing in the labor module than household members aged 25 to 65.

# 4.2. Paid and unpaid economic activities

A key difference between labor modules is related to the approach used to measure unpaid and paid work. We have identified four approaches. Two approaches, the single keyword question and the activity list, identify whether the person is employed in general or in specific activities. The two other approaches, the stylized questions followed by questions about the main and second wage job and time diaries, measure hours worked in the last 7‐days/12‐ months or at specific moments during the day. Recently, some surveys, like the survey 11
,178.0<br>|10.78|365.7|6,041.1|6.05|
|Iron**and Steel**|32,836.9<br>|80,838.0|40.62|24,276.1<br>|51,525.3|47.11|
|**Radio and TV Receivers**|4,854.5<br>|12,570.4<br>|38.61|2,983.3|9,003.6|33.13|
|**Motor Vehicles**|39,425.6|125,104.0|31.51|18,362.1|57,791.5|31.77|
|**Total in our sample**|202,002.0|487,315.0|41.44|138,908.6|297,828.9|46.64|

**Source: Figures in prices of the other country derived from tables** 4.2 and 4.3; **figures in national currencies derived from table** 4.1.
|
|||B40<br>T60|B40<br>T60|
|1|LIC|24.23<br>16.94|19.73<br>20.18|
|2|LMC|19.33<br>10.51|19.90<br>21.24|
|3|UMC|16.08<br>8.09|21.08<br>22.39|
|||3. Average number of births<br>per woman|4. Share of women who do not want to<br>become pregnant again but not using<br>contraception, percent|
|||B40<br>T60|B40<br>T60|
|1|LIC|6.12<br>4.68|27.39<br>24.95|
|2|LMC|4.74<br>3.14|24.27<br>19.29|
|3|UMC|3.97<br>2.52|18.87<br>13.24|

_Source:_ World Bank staff.

_Note_ : Data are from Demographic and Health Surveys. B40 refers to households in the bottom 40 percent of the wealth distribution, while T60 refers to households in the top 60 of the wealth distribution. Unmet need for family planning is defined as the percentage of women who do not want to become pregnant but are not using contraception As the household’s child dependency ratio falls and the share of working-age people increases, per capita income is likely to increase. This in turn relaxes the social and household budget constraints. Families who have fewer children will have more per capita resources at their disposal for consumption as well as investment. An increase of 1 percentage point in the share of working age population is estimated to be associated with a reduction of 0.75 percentage point in the poverty rate (Table 10).
at midline (Premand et al., 2016). Table 1 in the annex presents the average baseline characteristics of the treatment and control groups, as well as differences between the two for the sample of individuals effectively surveyed at endline, i.e. excluding attritors in the 2014 followup survey. Overall, results indicate that the two groups remain well-balanced, suggesting that attrition did not affect the internal validity of the randomized design. There are few systematic differences between participants and non-participants and the differences are quantitatively small. The empirical analysis will control for the few characteristics in Table 1 that are found to be statistically different between the two groups at baseline.

The study relies on randomization among applicants to the entrepreneurship track. As such, results may not be generalizable to the entire population of university students. Employment outcomes in the control group at midline in 2011 can be compared to employment outcomes among young and recent university graduates of similar ages (22-24 years old) in the 2012 nationally representative labor force survey.<sup>8</sup> Compared to program applicants, the overall population of young graduates exhibit slightly higher unemployment and inactivity rate and a lower employment rate. The overall population of graduates is also slightly more concentrated in wage jobs, and less likely to be in self-employment. This suggests that applicants to the entrepreneurship track have stronger predisposition to self-employment than the national population, consistent with positive self-selection into the entrepreneurship track.

In considering the external validity of the results, it is also important to underline that although the entrepreneurship track took place before the Tunisian revolution, the follow-up surveys took place after it. The Arab Spring instilled a sense of optimism and hope for many young people, but perceptions of economic opportunities did not seem to last. Data from the control group in

> 8 The comparison is only indicative as the employment indicators are measured slightly differently. Still, approximately 70 percent of graduates in the control group were unemployed or inactive (63 percent of males and 74 percent of females), with approximately
|Soil moisture|Soil Water Index from Copernicus Global<br>Land Service|Soil moisture standardized anomaly (based<br>on the average over the first three months<br>of thegrowingseason)|
|---|---|---|
|Availability of water<br>for crops|FEWS NET|End of season WRSI as anomaly (as a ratio<br>of the WRSI to the median historical WRSI)|

_Source:_ World Bank.

_Note:_ CHIRPS = Climate Hazards Group InfraRed Precipitation with Station; FEWS NET = Famine Early Warning Systems Network; MODIS = Terra Moderate Resolution Imaging Spectroradiometer; NASA = National Aeronautics and Space Administration; NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index.

The historical coverage of the data differs. In general, the analysis uses data from the period 2000/01 to 2019/20 at dekadal or monthly intervals to reflect the shocks faced within the context of a changing climate. For example, the NDVI data utilized are a product of the MODIS (Terra Moderate Resolution Imaging Spectroradiometer) satellites and are available for February 2000 and after. However, the precipitation data are from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) global rainfall data set and are available from 1981. Only the soil moisture data cover a shorter period, starting in 2007. Despite the limited history, however, soil moisture data are rapidly proving useful in estimating drought conditions, and they have a memory effect that rainfall data lack.

The hazard data were extracted at a uniform grid resolution of 0.05° by 0.05° (about 5 km by 5 km near the equator) and a crop coverage mask (Copernicus Global Land Cover Layers, 2019) was applied to exclude hazard values in areas that would not affect crop yields. Data on seasonal timing were taken from the UN Food and Agriculture Organization (FAO) for each country. To aggregate the hazard data
**Rapid Economic Growth but Rising Poverty Segregation: Will Viet Nam Meet the SDGs for Equitable Development?**

Hai-Anh H. Dang, Shatakshee Dhongde, Minh Do, Cuong Viet Nguyen, and Obert Pimhidzai<sup>*</sup>

# **JEL** : C15, D31, I31, O10, O57

Keywords: poverty, inequality, pro-poor growth, convergence, household surveys, Viet Nam

* This is forthcoming with Review of Development Economics. Dang (hdang@worldbank.org; corresponding author) is a senior economist in the Living Standards Measurement Study Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Dhongde (shatakshee.dhongde@econ.gatech.edu) is a professor of economics, Georgia Institute of Technology; Do (minh.nn.do@gmail.com) is a visiting lecturer with University of Economics and Business, Vietnam National University, Hanoi; Nguyen (vietcuong@vnu.edu.vn; corresponding author) is a researcher in International School, Vietnam National University, and Thang Long Institute of Mathematics and Applied Sciences (TIMAS), Thang Long University, Hanoi, Vietnam; Pimhidzai (opimhidzai@worldbank.org) is a lead economist with the Global Poverty Practice, World Bank. We would like to thank the editors Sanghamitra Bandyopadhyay, Andy McKay, three anonymous reviewers, Judy Yang, World Bank colleagues, and participants at Review of Development Economics’ “Inequality in the 21st century: Symposium” workshop and University of Michigan’s Sustainability and Development Conference (Ann Arbor) for useful comments on an earlier draft. We are grateful to the UK Foreign Commonwealth and Development Office (FCDO) for funding assistance through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.
prices of spot and futures prices for no. 2 heating oil in the New York Harbor. The first number, “n,” in “n-m” stands for the number of past months for averaging prices, and the second the number, “m,” of months in the futures contracts. For example, “6-3” is the average of the past six months and the average of the next three months, taking the futures contract prices for one, two, and three months ahead. Returns = differences in logarithms of successive prices; coefficient of variation = ratio of standard deviation to average; n.a. = not applicable.

The results show that, as expected, increasing the averaging period reduces price volatility: the coefficient of variation, which is one measure of volatility, is largest when daily prices are taken with no averaging, and smallest when the prices from 12 months—the previous nine months and the next three months—are averaged. Cumulative losses increase with decreasing volatility. Inclusion of future prices, however, reduces cumulative losses markedly without sacrificing the benefit of volatility reduction. The number of months when world and domestic prices move in the opposite direction—which could be politically awkward—is also reduced substantially when futures contract prices are included. For example, adding the futures prices for the next three months to the past nine months in the averaging basket reduces the percentage of the time when world and domestic prices move in the opposite direction from 50 to 30 percent. Although not shown, adjusting prices daily rather than monthly increases volatility and decreases the cumulative loss for the same averaging period.

When there is no strong trend in the underlying international prices, smoothing schemes can operate without incurring an excessive fiscal burden for the government in the long run. Even in that case, however, the pattern of oil price changes can result in the scheme running a deficit for a lengthy period. The choice of moving average is important. The longer the moving average, the lower will be the
Second, it allowed them to actively participate in the reorganization procedure. The law also aimed at increasing the eciency of the bankruptcy system by reducing the cost and time of both reorganization and liquidation, leading to an increase in the value of distressed rms. It improved on existing rules by providing in- and out-of-court options to reorganize, and by allowing the conversion of reorganization proceedings into liquidation. Using data from Brazilian and non-Brazilian rms, Araujo et al. (2012) estimated the eect of the bankruptcy reform on debt variables. In particular, they compared Brazilian rms to non-Brazilian rms from Argentina, Chile and Mexico with respect to the behavior of debt related variables. They found a reduction of approximately 8 percent in the cost of debt and increases of 10 percent and 23 percent in the amount of both total debt and long-term debt in Brazil, respectively. Since secured creditors have beneted more from 26
## **Table 11. Potential Growth Effects of Improving PM 2.5 Levels for African Firm**

Here, we only report those “policy instruments” with the following characteristics. First, the instrument must be statistically significant for the specific growth result for the African countries. Second, the instrument must be something that the government might be able to do something.

||**African coeff.**|**Improve by one**<br>**Africa-SD**|**% African SD in the outcome**<br>**explained by one Africa-SD**<br>**improvement in the instrument**<br>βaaa∗σX,aaa<br>σY,aaa|
|---|---|---|---|
|**For L-Growth**:<br> <br>||||
|σY,aaa= 0. 14<br> <br>||||
|<br>PM 2.5(at x=mean)<br>|0.0032|8.064|14.89%|
|<br> <br>PM 2.5(at x=40)<br>|-0.0199|-8.064|92.99%|
|<br> <br>**For LP-Growth:**<br> <br>||||
|σY,aaa= 0. 51<br> <br>||||
|<br>PM 2.5(at x=mean)<br>|0.0041|8.064|5.48%|
|<br> <br>PM 2.5(at x=40)<br>|-0.0479<br>|-8.064<br>|64.78%<br>|
|
boundary,” which governs their (lack of) access to public spaces alone as well as their social interactions with individuals outside the household, similar to the Pakistani context examined in Cheema et al. (2019). Our BAF voucher intervention addresses these mobility barriers not by changing the social norm (although we observe a small but significant increase in women’s ability to visit clinics alone), but by enabling women to “bypass” these normative conditions by visiting clinics with their female peers, who may be less likely to constrain their choices than husbands or mothers-in-law.

Finally, prior research based on collective models of the household (Chiappori and Mazzocco, 2017) has mainly focused on spousal interactions, particularly when examining fertility and FP decisions (e.g., Ashraf et al. (2014); McCarthy (2019)). However, a growing body of work in economics (Anukriti et al., 2020; Gupta et al., 2021; Khanna et al., 2015; McKelway, 2020) and a large literature in other disciplines has emphasized the importance of other household members, such as the mother-in-law, in household decision-making (Gram et al., 2018; Rassam, 1980). Our study is unique in showing that vouchers and peer connections can be leveraged to improve women’s reproductive autonomy by overcoming the constraints imposed by mothers-in-law in patrilocal societies.

# **2 Experimental Design and Data**

In this section, we describe our study sample, the experimental design, the interventions that were implemented as part of the randomized experiment, the data, and the landscape of FP service provision in our study area.

## **2.1 Sample**

Our study is based in 28 villages of Jaunpur district in the state of Uttar Pradesh (Figure A.1). According to the 2015-16 Demographic and Health Survey (DHS) of India, the total fertility rate in rural Uttar Pradesh (3.8) is high relative to other states; 20
survey is very similar in its relative ranking of governorates based on food access as compared to the face-to-face Emergency Food and Nutrition Security Assessment that was undertaken during the survey period.<sup>20</sup> Additionally, the regions that the WFP survey identifies as receiving the most assistance roughly align with the population-level estimates of the share receiving food assistance.<sup>21</sup>

# **4. Summary Statistics of Food Assistance and Food Access**

Table 1 reports average characteristics from the mVAM survey and illustrates a dire food security situation in Yemen over the course of the conflict. The share of respondents with a poor FCS, as defined by the WFP, was approximately 20 percent; the average prevalence of the food coping strategies collected were between 52 and 68 percent; households consumed fruit, vegetables, and protein less than half the days of the previous week and primarily relied on staples; and approximately one-third of the respondents received food assistance in the past month.<sup>22</sup> The majority of food assistance takes the form of in-kind assistance, and the vast majority of food assistance is being provided by the WFP.<sup>23</sup> Table 1 further demonstrates how food access has changed over the course of the conflict. The estimates demonstrate that food consumption and food coping strategies were already poor in 2015. However, measures of food access continued to worsen in 2016 and 2017 before improving in 2018.

Importantly, the data allow us to further report food access and food assistance by each region’s 2017 IPC classification.<sup>24</sup> Figure 2 reports the average FCS, rCSI, and the average share of respondents that received food assistance in each of the four different IPC classifications in the 2017 IPC announcement. The figure reports the averages of the variables in the months immediately before the 2017 IPC announcement.

There are three notable patterns apparent in Figure 2. First, the regions that were classified with the least severe
from sectors comprised of more intangible assets<sup>6</sup> are higher when countries have stronger property rights (Claessens and Laeven, 2003; Hur, Raj, and Riyanto, 2006). These studies have utilized data with limited coverage of countries from the MENA region: Hur, Raj, and Riyanto’s (2006) study of finance and trade across 42 countries includes only one MENA country (Syria). Svaleryd and Vlachos (2005) examine only OECD countries and do not include any MENA countries. While Beck’s (2003) study of financial dependence and international trade does include five MENA countries (out of 56 countries total), it does not examine the aspect of asset tangibility.

This paper combines aspects of the analysis focused on external financing as a determinant of export performance (Beck, 2003; Rajan and Zingales, 2001) and aspects of the literature focused on sector asset tangibility and governance (Braun, 2003), to assess the role of financial sector development and governance on export performance and specialization. Inclusion of more MENA countries in the analysis allows for MENA-specific characteristics to be discussed.

Improving the understanding of what stimulates and inhibits exports in MENA is vital, as a number of empirical studies have shown that this region exports below its potential.<sup>7</sup> Twenty-two percent of MENA firms report that customs and trade regulation are a major constraint to business operations (Figure 1, Enterprise Surveys<sup>8</sup> ). Yet, the potential for trade is great. The region is strategically located, nested between European, African, and Asian markets. MENA is also endowed with human capital, experiencing a growing base of educated youth looking for work. In terms of the business climate, the Global Competitiveness Index ranks the MENA region better than upper-middle-income countries in the areas of infrastructure, health, primary education, and higher education.

The literature finds that the presence of trade agreements between MENA countries and the rest of the world
. We also decompose model CO2 predictions into five broad source categories for each city: Industry, Fires, Income (the component that is uncorrelated with Industry), Population and Climate. We find particularly important roles for Industry in India, China and other East Asia & Pacific countries; Fires in Sub-Saharan Africa; Income (the component uncorrelated with Industry) in Latin America & Caribbean, North America and Western Europe; Population in Sub-Saharan Africa, India and other South Asia countries; and Climate in the former Comecon countries.

Our results can also inform the discussion of policy instruments for CO2 emissions reduction. Many policy analysts who support Pigouvian pricing also argue for a non-Pigouvian supplement: coordinated public investment in low-carbon land development, energy and transport that will accelerate the transition to low-carbon economies. We explore this proposition for subway investments, drawing on a recent global survey of subway systems. We divide our 1,236 regression residuals into deciles, with the largest negative residuals in the first decile, and identify the subset of 132 global subway cities in each decile. We find that subway cities are four times more numerous among first-decile cities than among tenth-decile cities. We also find that representation of subway cities declines steadily across deciles. While these results provide strong suggestive support for the non-Pigouvian view, they are subject to potential endogeneity that should be explored in future research.

To conclude, this paper offers a response to the World Bank’s new climate change mandate, which requires new metrics for judging progress in CO2 emissions reduction. We demonstrate that satellite-based CO2 measures can contribute by enabling rigorous analysis and performance assessment for all global cities and regions. We have estimated a CO2 emissions model for 1,236 cities with populations greater than 500,000, but our 10 km grid covers all terrestrial areas of the 35