rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 5 explores the distribution of returns in our global bond portfolio. We examine the distribution because the standard deviation can understate the degree of risk due to a skewed distribution with fat tails. The return distribution does not look overly skewed. Despite some positive and negative outliers, the plot for the full sample (Panel A) is roughly in line with a normal distribution (which is also true for the monthly data). We nevertheless compute adjusted Sharpe ratios, to account for volatility, skewness, and kurtosis (the differences are small; see, for example, Section 6).

Figure 5: Distribution of bond returns and bond prices, 1815-2016 Panel A: Bond returns - full sample Panel B: Bond returns - (real, arithmetic average total returns) default vs non-default spells <!-- Start of picture text --> 15 Mean: 6.85Median: 6.58 30 Non-default years: Mean: 6.05<br>13 Scewness: 0.26 Median: 4.85<br>Skewness: 1.09<br>25<br>Default years:<br>10 Mean: 2.37<br>20 Median: 1.98<br>Skewness: 0.17<br>8<br>15<br>5 10<br>3 5<br>0<br>0<br>-40 -20 0 20 40 60 -100 0 100<br>Total returns (yearly, real, arithmetic avg.)<br>Total returns (yearly, real, arithmetic avg.)<br>normal distribution Non-default years Default years<br>Panel C: Bond prices – default vs non-default spells<br>20 Non-default years:<br>Mean: 88.47<br>Median: 95.00<br>Skewness: -1.14<br>15 Default years:<br>Mean: 35.09<br>Median: 25.00
2|5.0|
|1992-93|1308.0|-|542.3|600.4|(-0.7)|<br>2450.0|1040.|8|5.5|
|1993-94|1424.0|-|424.9|579.7|57.4|<br>2486.0|918.7||5.3|
|1994-95|1486.0|-|433.0|573.0|-|2492.0|865.6||5.4|
|1995-96|1848.1||625.0|624.9||3098.0|1059.|0|6.0|
|1996-97|2273.0||606.2|788.8||3668.0|1244.|0|6.5|

*Other commodities include rice, lentils, chicken, frozen fish and frozen meat.

**Total food subsidies include financial losses of food marketing companies.

***Total food subsidies in real terms calculated by deflating nominal costs by CAPMAS Consumer Price Index for urban areas (1 986-87 = 100).

n.a. = Not available.

Sources: Food subsidy data: 1970-71 to 1980-81 from Harold Alderman, Joachim von Braun and Sakr A. Sakr, Egypt's Food Subsidy and Rationing System: A Description, Research Report 34 (Washington, DC: International Food Policy Research Institute, 1982), Table 2.

1981-82 to 1996-97 from unpublished data, Ministry of Planning, and Ministry of Trade and Supply.

# Total government expenditure data:

1970-71 to 1979 from Alderman, von Braun and Sakr, op. cit.

1980-81 to 1991-92 from unpublished data, Central Agency for Public Mobilization and Statistics. 1992-93 to 1996-97 from unpublished data, Ministry of Trade and Supply.
surrounded by (empowered) sisters in treatment communities might raise their competitiveness in response to increased sibling rivalry over parental resources.<sup>4</sup> Second, the program might also affect the influence that sisters have on their brothers’ competitiveness through its social empowerment impacts. By challenging prevailing gender norms, the program might have threatened boys’ sense of traditional male identity [Akerlof and Kranton 2000]. This is particularly true for boys with relatively more sisters, as they are more likely to be indirectly exposed to the program. This gender identity threat can trigger an opposing response aimed at restoring the damaged self‐image [Bénabou and Tirole 2011]. If being competitive is part of the male identity under traditional gender norms, boys surrounded by sisters in treatment communities can subsequently become more competitive as a form of cognitive dissonance reduction.

# **3. Sampling and Lab Experimental Design**

## **3.1. Sampling**

The participants in our experiment were randomly drawn from the sampling frame of households surveyed by Bandiera _et al._ [2015], stratified to include both control and treatment communities for the randomized control trial impact evaluation of the ELA program. A total of 700 adolescents participated in the experiment, 40 percent of whom came from control communities and 60 percent from treatment communities. About 55 percent of the participants were girls, both in treatment and control groups.

At the end of each experimental session, participants completed a short exit survey asking basic background socio‐demographic related questions such as their age, education, marital status, and whether they have any children. The survey also contains sibling sex composition information on participants, namely the number of brothers and sisters they have. Table 1 presents summary statistics on these characteristics for the full sample of participants and separately for girls and boys in control and treatment communities.

Four points are of note. First, participants in our experiment are in their late adolescence and early adulthood: the average participant is 17 years old
# **Abstract**

This paper analyzes poverty in rural and semi-urban areas of Mexico (localities with less than 2,500 and 15,000 inhabitants, respectively) and it provides guidance on a social agenda and poverty alleviation strategy for rural Mexico. The analyses are based on INIGH and ENE datasets for 1992-2002. Monetary extreme poverty affected 42 percent of the rural population in dispersed rural areas and 21 percent in semi-urban areas in 2002, slightly less than one decade earlier. Most of the rural poor live in dispersed rural areas and 13.2 million people live in poverty in rural Mexico with less than 15,000 inhabitants. It is disproportionately a feature of households whose heads main job is in the agricultural sector, as self-employed farmers or rural laborers, and that have at most a primary education. However, the incidence of extreme rural poverty has declined since 1996 but at a slower pace than the decline in urban poverty. Hence, the rural-urban poverty gap increased in recent years and in some places extreme poverty is at least four times higher in rural than urban areas. Moreover, not only is the income gap in urban areas increasing, but also the gap between richer and poorer segments of the population in the rural areas is growing. Finally, the gap between rich and poor regions is still large.

ii
# **1. Introduction**

Infrastructure is key to delivering several essential services and plays a central role in achieving the United Nations’ Sustainable Development Goals.<sup>2</sup> Goal 9 explicitly articulates the importance of building resilient infrastructure by promoting innovative sustainable technologies. However, it has been shown that “infrastructure either directly or indirectly influences the attainment of _all_ of the Sustainable Development Goals (SDGs), including 72% of the targets” (Thacker et al., 2019).

Developing countries face significant infrastructure needs if they are to deliver on the 2030 policy agenda (Doumbia et al., 2019; World Bank, 2019). There have been few rigorous attempts to quantify these spending needs, and until recently most of them were based on high-level cross-country panel econometric studies (such as Fay and Yepes, 2003), or limited in scope to individual regions or sectors (for instance Foster and Briceno Garmendia, 2009). A more recent study takes a micro-economic approach to modeling individual components of infrastructure needs, which are then built-up into an aggregate picture (Rozenberg and Fay, 2019). This research finds that, when combined with good policy choices, investments of 4.5 percent of GDP, combined with operations and maintenance expenditure of 2.7 percent of GDP, will together enable low and middle-income countries to achieve infrastructure-related Sustainable Development Goals and stay on track to full decarbonization by the second half of the century. These headline estimates can be further disaggregated at the sector level (see table 1).

**Table 1: Overview of estimates of infrastructure spending needs for energy and transport**

|% of GDP of LMICs|Investment needs|Operations and maintenance needs|Total expenditure needs|
|---|---|---|---|
|Energy|2.2|0.6|2.8|
|Transport|1.3|1.3|2.6|
|Water|1
setting to estimate the causal impact of intervention programs or collective behaviors. The seminal paper by Jalan and Ravallion (2003) uses PSM to estimate the effects of piped water on children’s diarrhea incidence and duration in India. With a cross-sectional data set, the authors match the households with and without piped water based on propensity scores estimated from observable household characteristics. They further stratify the cross-section by household income per capita and the highest education of a female family member, and then estimate the treatment effects with PSM in each subgroup. Imbens (2015) illustrates the application of PSM using non-experimental data with examples such as the lottery winnings effects on labor market outcomes and the job training program impact on subsequent earnings. Chiputwa et al. (2015) use PSM to identify the impact of food standard certification on poverty among coffee farmers in Uganda. Fischer and Qaim (2012) employ PSM to match farmers that form and do not form farmers groups to analyze the impact of farmer collective action on income in Kenya. Also using a cross-sectional household data set from Uganda, Kassie et al. (2011) use PSM to find that adopting improved groundnut varieties (technology) significantly increases crop income and reduces poverty. We follow the same methodology to study the role of e-commerce participation on household income growth in Taobao Villages.

# **_3.2 Logit model of participation_**

We start by estimating a logit model of participation decision on a set of observed characteristics. At the household level, we include the following variables:

1. _Gender of the household head_ . This is a dummy variable that equals one if the head is male and zero if female. E-commerce allows more flexible working hours and location (many people work at their own homes during periods they choose) and is often less manually demanding than traditional farming. This might make it especially suitable for female participation. We expect the coefficient to be negative.

2. _Age of the household head_ . As e-commerce is technology-intensive and new to rural areas, the younger
_stability_ , and _rule of law_ . _Finance_ and b _usiness environment_ have high intercepts which make up for their negative slopes. While it is encouraging to find that there is aid that is flowing into these two areas (high intercepts), it seems like donor countries are not targeting the right countries (negative slopes), especially when it comes to _finance_ related aid. The _labor_ area seems to be the most neglected; although it has a positive slope there is very low levels of aid that flow into any country with the objective of educating and training the workforce and/or to improve labor regulations.

# **Does More Aid Lead to Better Firm Perceptions?**

In the previous section we found that aid indeed targets those areas which firms in recipient countries have identified as obstacles for their growth. Now the question is whether aid actually improves those areas so that fewer firms find them to be obstacles for their growth. Once the aid disbursement has been received and the project has been implemented we expect that the firms’ perceptions for that area would be better than if no aid has been received. In this section we attempt to answer the question: are firm perceptions better in those areas that have received more aid in the past?<sup>10</sup> With this objective in mind we run regressions of the following form: _Perception_ , the dependent variable in this case, is the natural logarithm of the percentage of firms in a recipient country _r_ that have identified the area _j_ as an obstacle in year _s_ . _Aid_ is the natural logarithm of the amount disbursed in millions of USD dollars from donor country _d_ to recipient country _r_ , for obstacle _j_ , and year _t_ . Since we want to identify

> 10 We would like to analyze whether aid _improves_ aid perceptions. For this we need to analyze the _change_ in firms’ perceptions; however, there is no panel data available for this specific question on the Enterprise Survey. We are left we the second best analysis which is identifying
# **Figure 6: Level of education attained by income quintile**

<!-- Start of picture text --> Primary Secondary University Postgraduate<br>81.7%<br>68.0%<br>60.2%<br>49.7%<br>42.1%<br>38.4% 39.2%<br>24.9% 25.8%<br>19.5%<br>12.5%<br>11.1% 11.3%<br>5.3%<br>0.0% 0.0% 3.6% 3.8% 2.0% 0.9%<br>Q1 Q2 Q3 Q4 Q5<br>Source: ENIGH 2007<br><!-- End of picture text --> In order to estimate private pocket contributions to education, we may recur to national account figures<sup>7</sup> , where final private consumption in education amounted 17,147 RD$ million in 2005, which represented 1.37% of the GDP that year. As we argue, this may be a good proxy for private pocket expenditure in education (enrolment, fee for services, materials) as, by definition, it does not include capital formation (investment in infrastructures).

Overall, total private spending in education is believed to represent around 1.7% of GDP nowadays. In this mixed model (public and private centers), resources do not seem to be optimally allocated, as poor performance of Dominican children in tests demonstrates. Families have been contributing to the financing of education an average of 38% between 1995 and 2005, with enrolment in private schools amounting two thirds of expenditures in this field.

By looking at expenditure in education as a percentage of total family expenditure, it is possible to observe that households from different income levels allocate a similar share of
researchers. There have always been close connections between IDA and researchers interested in development economics. At least since the early 1970s, research on development has been informed by practice and, with continuous feedback from evaluation, IDA has been a learning institution. The operations prepared by IDA staff have integrated lessons and findings from research. It is doubtful whether, without the influence of research and the close connections between research, policy advice and policy practice, IDA would have become a premier development organization.

The World Bank has been a pioneer in the measurement of poverty and has conducted many surveys (household surveys or health and demography surveys). Many advances, such as poverty mapping and research on equity, also originate with the World Bank. Research at the World Bank—with its close links with academia around the world—has been the interface and intermediary between cutting edge intellectual advances and informed operations. The Bank’s research staff—the quality of which, over the years, is attested by its rankings and citation counts—has excelled at publishing relevant research and helped their colleagues in other departments by participating in IDA/Bank operations.

The allocation of IDA funds is an important area that has been influenced by changes in development thinking. First, the dominant thinking on development has influenced IDA in terms of sector priorities, putting emphasis first on infrastructure and increasingly on agriculture, then in the 1980s on macroeconomic adjustment and policy reform, and finally in the 1990s on governance and institutional factors.

Second, starting in the late 1980s, IDA assistance became less determined by political considerations and more subject to selectivity, i.e., the idea that assistance would have a greater impact on poverty reduction if it were focused on countries with stronger economic institutions and policies. The formula now used by IDA attempts to balance needs and performance, and gives a lot of weight to governance. Governance and institutions—increasingly viewed by economists as one of the major causes of underdevelopment—represent a real challenge for IDA, and it is
flood events, especially remotely sensed data from NASA, the Japanese Space Agency, and the European Space Agency, which provide frequent updates of worldwide surface water conditions. It records floods with ―significant damage to structures or agriculture, long (decades) reported intervals since the last similar event, and/or fatalities.‖ The DFO Archive is used more often than EM-DAT by flood researchers, as it provides more detail on flood events, including flood magnitude and GIS data on specific areas affected within a given country, and has a reputation for strong quality control.

In addition to exploiting a new data source, we take steps to identify the effects of income and governance more rigorously than previous disaster studies have. Although most studies have analyzed panel data, only one has exploited this data structure to include country and year effects that control for unobserved heterogeneity in spatial and time dimensions (Kellenberg and Mobarak 2008). The exception separately analyzed countrylevel data on fatalities caused by earthquakes, landslides, windstorms, and extreme temperature in addition to floods, and unlike our study it found that income had an inverted U-shaped impact on flood fatalities. It excluded governance indicators, however, so this effect might not be an unbiased estimate of the income effect. Several other studies have included both income and governance but not country or year effects (Kahn 2005 for the same five types of disasters; Anbarci et al. 2005 and Keefer et al. 2010 for earthquakes). Kahn applied instrumental variables to a subsample of the countries in his model, but this caused all the income and governance variables to become insignificant at 5%, and not just because the standard errors rose. Keefer et al. allowed income and governance to have nonlinear effects, but not in the same model.

4
qualifications, and the survey report notes that estimates of teacher academic credentials also reflect the impact of policies implemented more than a decade ago (UNICEF 2022).

This difference in teachers’ qualifications is confirmed by data supplied by Ministries of Education to UNESCO Institute of Statistics (UIS). In these data, countries report on the percentage of their teachers who have met their country’s “minimum required qualifications”.<sup>4</sup> The data shows that in East Asian countries, almost all primary education teachers have met the minimum qualifications required to enter the profession, with a few exceptions. However, many Pacific Island countries have a significant shortage of qualified primary teachers (again, as defined by countries’ own definition of what are these qualifications). For instance, in the Solomon Islands, around 25% of primary teachers do not have the minimum qualifications, while in Micronesia, two-thirds of teachers lack the necessary requirements.

Figure 1. Share of teachers with minimum required qualifications in primary education, 2015–2019, median, % <!-- Start of picture text --> 99.5 99.7 100.0 100.0 100.0<br>97.0<br>100.0 95.3<br>92.3 92.9 93.3<br>90.0<br>78.3<br>80.0 74.2 75.4 75.8<br>70.0<br>60.0<br>50.0<br>38.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>%<br><!-- End of picture text --> Source: Authors’ calculation using UNESCO Institute of Statistics database. Note: Blue: East Asian countries; Yellow: Pacific Island countries.

Taken together, many
sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (and square term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete sec- ondary, postsecondary, and adult education. No migrant from rural excluding rural migrant, but include migrations between urban areas.

Table 10: Summary of 2SLS Estimation: Market Accessibility Index (MAI)

||Self-Emp|loyment|Formal:|License|Underem|ployment|Nominal|Earnings|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||OLS|2SLS|OLS|2SLS|OLS|2SLS|OLS|2SLS|
|MAI|-0.050<sup>_⇤⇤⇤_</sup>|-0.109<sup>_⇤⇤⇤_</sup>|0.021<sup>_⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|-0.018<sup>_⇤⇤_</sup>|-0.047<sup>_⇤⇤⇤_</sup>|0.064<sup>_⇤⇤_</sup>|0.164<sup>_⇤⇤⇤_</sup>|
||(0.019)|
Policy Research Working Paper 6316

# **Abstract**

Africa’s economic performance has been widely viewed with pessimism. In this paper, firm-level data for around 80 countries are used to examine formal firm performance. Without controls, manufacturing African firms perform significantly worse than firms in other regions. They have lower productivity levels and growth rates, export less, and have lower investment rates. Once geography, political competition and the business environment are controlled for, formal African firms lead in productivity levels and growth. Africa’s conditional advantage is higher in low-tech than in high-tech manufacturing, and exists in manufacturing but not in services. The key factors explaining Africa’s disadvantage at the firm level are lack of infrastructure, access to finance, and political competition.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at lxu1@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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure 27. Fiscal Spending - Central <!-- Start of picture text --> Rest Anatolian Tigers<br>2006 20072008 2009 2010 20112012 2013 20062007 2008 2009 2010 20112012 2013<br>Developed West<br>2006 20072008 2009 2010 20112012 2013<br>Graphs by Reg<br>East West<br>2006 20072008 2009 2010 20112012 2013 20062007 2008 2009 2010 20112012 2013<br>Personnel Social Security<br>Purchases Interest Payments<br>Current Transfers Capital Spending<br>Capital Transfer Lending<br>Substitute Payments<br>Graphs by IWest<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>Per Capita Expenditures 2005PPP, (thousands) .5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>Per Capita Expenditures 2005PPP, (thousands)<br><!-- End of picture text --> _Notes_ : Monetary values are in thousands and 2005PPP _Source_ : Ministry of Finance – General Directorate of Public Accounts 40