rafmacalaba/data-use-mentions-extended huggingface dataset:
Household characteristics are similar across program and comparison schools (Table 2): there are no significant differences in terms of parent education, number of siblings, or the ownership of a latrine, iron roof, or mosquito net (using data from the 2002 student surveys), indicating that the randomization was largely successful in creating comparable groups.<sup>10</sup> Further evidence is provided by comparing the 2000 (baseline) test score distributions, which are nearly identical graphically for cohort 1 girls in Busia (Figure 2). More formally, we cannot reject the hypothesis that average baseline test scores are the same across program and comparison schools for students in the restricted sample, as discussed below.

Another estimation concern is the possibility of cheating in program schools, but this appears unlikely for a number of reasons. First, district records from external exam invigilators indicate there were no documented instances of cheating in any sample school during either 2001 or 2002 exams. Several findings reported below also argue against the cheating explanation: test score gains among cohort 1 students in scholarship schools persisted a full year after the exam competition, when there was no longer any direct incentive to cheat, and there were substantial, though smaller, gains among program school boys ineligible for the scholarship. There are also program impacts on several objective measures of student and teacher effort – most importantly, school attendance measured during unannounced enumerator school visits.<sup>11</sup>

# **<u>4.2 Sample Attrition</u>**

Teso district primary schools had higher rates of sample attrition than Busia schools in 2001. The gap in attrition across program versus comparison schools was also greater in Teso district, and students who attrited from the sample in Teso appear to be systematically better students than those who participated in the program. These patterns all complicate causal inference in Teso district.

> 10 This comparison in Table 2 relies on the assumption that the household characteristics (i.e., parent education,
# **D Detailed Acknowledgments**

The preparation of the Firm-level Adoption of Technology survey questionnaire involved the contribution of several sector experts within and outside the World Bank.

First, we would like to thank the following World Bank Group colleagues: Erick C.M. Fernandes (Lead Agriculture Specialist), Holger A. Kray (Manager, Agriculture), Michael Morris (Lead Agriculture Economist), Madhur Gautam (Lead Agriculture Economist), Robert Townsend (Lead Agriculture Economist), Ashesh Prasann (Agriculture Economist), Aparajita Goyal (Senior Economist, specialized in Agriculture), Harish Natarajan (Lead Financial Sector Specialist), Erik Feyen (Lead Financial Sector Economist), Laurent Gonnet (Lead Financial Sector Specialist), Arturo Ardila Gomez (Lead Transport Economist), Victor A Aragones (Senior Transport Economist), Edson Correia Araujo (Senior Health Specialist), Irina A. Nikolic (Senior Health Specialist), Brendan Michael Dack (Chief Industry Specialist at IFC), Emiliano Duch (Lead Private Sector Specialist), Blair Edward Lapres (Economist), Kazimir Luka Bacic (Operations officer), Justin Hill (Senior Private Sector Specialist), Etienne Raffi Kechichian (Senior Private Sector Specialist), Justin Yap (Private Sector Specialist), Austin Kilroy (Senior Economist).

Similarly, we would like to thank several external experts. From Embrapa-Brazil, we would like to thank Alexandre Costa Varella (Head of Embrapa research unit in the South region, expert on livestock), Flávio Dessaune Tardin (Researcher, Maize and Sorghum), Edison Ulisses Ramos Junior (Researcher, Soybean), Isabela Volpi Furtini (Researcher, Rice and Beans), Alberto Duarte Vilarinhos (Researcher, Cassava and Fruits), Carlos Estevão Leite Cardoso (Researcher, Cassava and Fruits, Technology transfer), and other participants of the internal seminars to validate the sector-specific questionnaire for agriculture and livestock. We also would like to thank a group of senior consultants from Patina Solutions, who contributed with the preparation and validation exercise for several sectors, including
> Extensions have focused on relaxing the assumption on the distribution of errors (Barrett and Li, 2002), complementing the analysis with trade flow data (Barrett 2001; Barrett and Li 2002), and shifts in probabilities (Park et al. 2002; Negassa and Myers 2007). In this paper, we propose a modification of Baulch’s original contribution (Baulch 1997a) and apply the proposed approach to the Malawi maize market.

5
Table 3 in the Annex shows women’s share and number of children in urban areas. From this table, it follows that a large majority of teachers at primary and lower-secondary school levels are female both in urban and rural areas. At the upper secondary school level, men and women are equally represented. Only a third of the teachers at the tertiary school level are female. While a large share of the teaching labor force is female, less than a third work in other occupations or in agricultural activities. More than half of the _Professionals with an University Degree in Education not teaching_ are females. Female teachers' salaries can be a primary source of income for the family, since 24 percent of female teachers in primary public schools are household heads. Fourteen percent of the females in _the mixed-skilled_ group are household heads.

# _Hours-worked_

“Teaching time” is sometimes used as a proxy indicator of the workload of a teacher. Based on the ENEU, Tables 4 and 5 in the Annex show weekly working hours and adjusted weekly working hours (two month vacation) for 1988, 1994 and 1999, public and private teachers and other counterparts. Teachers have worked substantially less number of hours than _Professional with a B.A in Education not teaching_ .

Working hours did not substantially increase from 1988 through 1999 (Fig. 1). The teacher group has worked consistently less than their counterparts. Additionally, the _mixed-skilled group_ exhibits a higher variation in the number of hours-worked through time. Primary public school teachers work fewer hours than primary private school teachers. These trends become even more pronounced when using adjusted weekly working hours.

9
Policy Research Working Paper 10609

# **Abstract**

Economic integration of the African continent rests on two pillars: the ratification of an ambitious trade agreement and massive investment in transportation infrastructure. Leveraging a newly created city-level database on African exporters’ transport times, transport route optimization and general equilibrium modeling of international trade, the paper quantifies the impact of greater trade and transport integration in Africa. A pan-African agreement, such as the African Continental Free Trade Area, would increase African countries’ exports by an average of 3.4 percent and increase gross domestic product by 0.6 percent. Complementing trade integration by reducing transportation time on roads, ports and border posts would increase exports by 11.5 percent and increase gross domestic product by 2 percent. Major transport investments are necessary to reap the full benefits of the African Continental Free Trade Area.

This paper is a product of the Office of the Chief Economist, Africa Region and the Infrastructure Chief Economist Office. 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 mlebrand@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
457|3,457|<br>3,457|3,457|3,457|
|R-squared|0.015|0.025|0.033|0.032|0.031|<br>0.032|0.034|0.031|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are from the six visits of the GHS and telephone survey for conflict. No weights are used in any of the regressions. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xviii
largest number of countries. To correct for the fact that, for some countries, the time frame slightly diverts from these two reference years, we report results in terms of average annual changes. Moreover, to achieve comparability with the Millennium Development Goals (MDG) reports, we also repeat the analysis for a subset of countries with high initial rates of extreme poverty (more than 10 percent of the population with income/expenditures below 1.25 dollars a day), and look at changes between 1990 and 2008.

In our main analysis, out of the 84 countries available in the _Povcal_ (including the Latin American ones), we exclude those that had poverty rates below 5 percent in 1995, since our approximation would capture with high errors the actual poverty changes in these countries. We also exclude Georgia from the analysis, as with poverty rates more than doubling in the period, it is a clear outlier. We are thus left with 74 countries (see Table 1). For the same reason, in the analysis focusing on the MDGs, we only 7
and sex crime. Second, in Panels B and D, the first-stage Kleibergen-Paap F statistic is large, eliminating concerns about weak instrumentation. For brevity, we focus on the 2SLS (panel B) results, since those estimates control for total crime and robust to endogeneity concerns. As such they are, in our view, the most robust results. In particular, the results of column (6) show that in the months after the roll-out of the PEP program, municipalities that had double the number of PEP holders (per 100,000 inhabitants) saw an increase of 0.1 in the rate of crimes reported by Venezuelans, on average. Considering that the rate of total crimes reported by Venezuelans is 0.79 (as documented in Table 2), this constitutes an increase of 12.5 percent, a sizable effect. In terms of specific types of crimes, we observe statistically significant effects of the program on reports of domestic violence, and sex crimes. Considering the point estimates for the outcome of reports by Venezuelans, the results are also economically significant. For example, a city with double the number of Venezuelan migrants who received the PEP program compared to the mean (relative to the local population), experienced an increase in the rate of sex crime reports by Venezuelans of 0.05. Since the average rate of reports by Venezuelans of these types of crimes is 0.08, this corresponds to more than an 62.5 percent increase in reporting after Venezuelans received regular migratory status. The same number for domestic violence crime reports is about 53 percent.

Note that the purpose of controlling the total number of reports _CrimeReports_<sup>_TOT_</sup> _ct_ —a proxy for _all_ crimes committed in that municipality every month—implies that our main estimator in this specification, when positive, does not represent an _increase in crimes_ but rather an increase in crime reports by Venezuelans. Naturally, the estimators for _CrimeReports_<sup>_TOT_</sup> _ct_ are all positive
only to look at the migratory flows causally linked to weather anomalies and climatic disasters but also, and perhaps especially so, to the relationship between such events and the presence of potential immobility traps associated with adaptation failures. A useful tool in this respect would be an _ad hoc_ section on migration-as-adaptation embedded within a broader Adaptation Module, with particular emphasis on questions investigating the reasons (e.g., the role of liquidity constraints) that hampered the implementation of household- or individual-level coping strategies, including those migration-related.

Migration can be seen as one among a pool of household adaptation strategies in the face of environmental change. The study of _in-situ_ adaptation strategies, therefore, should be seen as complementary to the adaptive decision to migrate. McCarthy (2011) provided a set of key indicators and modules to supplement LSMS and LSMS-ISA survey instruments based on a taxonomy of household agricultural practices and investments that can contribute to adapting to climate change (the so-called sustainable land management, SLM). These include agroforestry investments, reduced or zero tillage, use of cover crops, and various soil and water conservation structures. There are often long-term benefits to households from adopting such activities in terms of increasing yields and reducing the variability of yields, making the system more resilient to changes in climate.

The implementation of a comprehensive _Adaptation_ module capturing these aspects as well as other insights from resilience and vulnerability studies (Magrini et al., 2018; D’Errico et al., 2019) would represent a clear added value for household surveys. It would allow one to distinguish between migration as adaptation mainly implemented as a strategic risk diversification strategy by richer and better-endowed households _vs_ survival migration adopted as a last-resort option by the poorer households, and hence enhance our understanding of the nature of migration as voluntary or involuntary. It would also have important implications for studying adaptation policies to climate change in rural developing contexts in Sub-Saharan Africa. For example, the systematic inclusion, in the roster of potential answers to the question on coping strategies, of the option
**Figure 1. Fiscal space indicators**

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Differences in the quality and quantity of infrastructure endowments and services across countries as well as differences in needs within countries and sectors make the identification of infrastructure needs quite complex in the region. Moreover, under a business as usual scenario, the gaps are likely to magnify as demand for infrastructure grows with population and income growth, and countries face challenges related to water and energy conservation, efficiency, and climate change.

When little detailed data are available to conduct a bottom-up approach, the assessment of infrastructure needs can be based on the estimation of econometric models of demand for each infrastructure subsector. Fay and Yepes (2003) and Yepes (2008) are among the best known papers illustrating the method across regions of the world. In this paper, we improve their earlier estimates for the MENA region by: (i) including data on infrastructure stocks for countries of the region from national sources as compared to relying on extrapolations from international databases; (ii) updating the data from original international sources; and (iii) including high income countries (GCC) in the definition of MENA. Demand for infrastructure is assessed by regressing per capita stocks of infrastructure against per capita income, the share of GDP derived from agriculture and manufactures, population growth and density, urbanization rate,
_permanent_ effects on the rate of economic growth. Empirically, however, neither in the United States nor in other OECD countries does economic growth seem to exhibit such an effect. Incidentally, albeit for different reasons than in the OECD countries, these stationarity properties seem to be equally true for schooling and income growth in the Guatemalan data, as demonstrated in the Augmented Dickey-Fuller (ADF) tests in the Appendix.

# **3.3 More Evidence on Education and Growth**

Empirical studies usually take the form of regression analysis and typically look at many more explanatory variables than human capital. A large number of papers have found one or more variables that correlate with growth. In fact, their number is very large and the question arises which combinations of these variables are actually robust. In the context of the present study, some of these findings will be outlined next.

# **_Weak Correlations_**

While there is strong theoretical support for a key role of human capital in growth, Sala-i-Martin (2002), Easterly (2001) and in particularly Pritchett (2001) argue that the empirical relationship between education and growth is weak.<sup>9</sup> However, more specifically, Temple (2001) points out that fragile correlation in cross-country data may be due to measurement error and influential exceptions. Also, some kinds of relationships are more robust that others. For example, what is less clear and weak is the relationship of educational _growth rates_ on output growth, the role of different education levels, and differences in effects of female and male education on growth. By contrast, some measures of health seem to be positively correlated with growth. In addition to human capital, many other factors have been found to be important for growth. Following Barro (2001), these include institutions, such as free markets, secure property rights and the rule of law. Similarly, more open economies and countries with lower initial inequality appear to experience higher growth.

# **_Conditional Convergence_**
within their family or community, formal insurance offers additional benefits. Formal insurance products can pool risk over a much larger population, which affords households broader coverage than they would have if they relied on their own savings, credit, or community. This is especially true for adults in low-income households with limited assets. Moreover, formal insurance products can provide protection from common risks faced by individuals in the same

> 8 Unfortunately, the 2014 Global Findex database does not collect data on the use of insurance and, to the best of our knowledge, no similar data exists for the use of insurance.

17
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We focus on the sample of male-owned enterprises as, similarly to the SLMS, Fafchamps et al. (2014) find no impact on female-owned businesses.

Firms were surveyed twice before treatment, in October-November 2008, and in February 2009. Then, firms that completed the baseline rounds were randomized into either a control group (396 firms), or a cash or in-kind treatment groups (198 firms each). Firms were then surveyed quarterly in May 2009, August 2009, November 2009, and February 2009. Following Fafchamps et al. (2014), we group these four follow-up rounds together. An additional long-term follow-up survey was conducted in March 2012, approximately three years after the intervention.

## <u>Variable details:</u>

- indicator to one if the still owns and

- Self-employment: equal respondent operates a business (survey introduction question 6), or if he is operating a different business (introduction question 11 ”What it your main activity now?”).

- Any employment: if the respondent is self-employed (see previous point) or wage worker (introduction question 11).

- Migration: all business owners participating in the study are initially living in either Accra, the capital city, or the nearby industrial city of Tema. We therefore code migration as moves outside the Greater Accra Region. We take migrants’ location from introduction question 6 (”If [the household is not living at the same address as the previous round], record the new address of the household”). Instances of migration mainly involve moves to the towns of Kasoa, to Nsawam or to Manhyia.

# **C.7 Nigeria Youth Enterprise With Innovation in Nigeria (YouWin!)**

The YouWin! competition was launched in late 2011 by the Nigerian government, attracting approximately 24,000 applications that presented ideas to start a new business or grow an existing one. The prize of the competition were grants averaging $50,000. Top applications were chosen to attend a
COVID-19 spread mitigation measures. Business closure due to COVID-19 and lack of customers (which could partially be attributed to the pandemic) are the most widely reported reasons both by male and female-headed households for loss in enterprise income (Figure 7).

Female-headed households who earn their main livelihood from enterprise income have particularly experienced income losses (Table 6, Column 3). Compared to male-headed households, a larger proportion of female-headed households reported non-agricultural business or enterprise income (47 percent) and help from others as their main source of income. These are also income sources from which the largest proportion of total households reported having decreased income (around 70 percent). Of the male-headed and female-headed households who reported enterprise income as their main source of income, a statistically significant larger portion of female-headed households reported decreased income (81 percent) compared to male-headed households (72 percent).

The significant coefficient for female-headed households reporting income loss from non-farm enterprises during COVID-19 is not unexpected. From Ngatia et al. (2021), which uses ECOSIT4 (household budget survey), we know that although women own 57 percent of the non-farm enterprises, they make 77 percent less profit than male-owned enterprises. Female entrepreneurs, on average, have a lower level of education and have less access to electricity, running water, machinery, or telephone for their business. Female entrepreneurs who do have access to electricity or phone have lower returns from owning them, indicating underlying discrimination. Although their analysis is not exclusive to female heads of households,<sup>13</sup> but to all women, we could assume that female heads of households face similar constraints in running their business if not additional and more severe constraints. These existing vulnerabilities could render them more at risk of reduced income during the COVID-19 pandemic. For example, if customers are unable or unwilling to visit the enterprises during COVID-19 to buy commodities physically, they may alternatively place orders through the telephone; and women have less access to such technologies.

> 13 In fact, 27