rafmacalaba/data-use-mentions-extended huggingface dataset:
affected areas experiencing up to 1.2% greater negative impacts than non-conflict areas. Similar patterns were observed in Mozambique, where cyclones led to a 1.39% reduction in economic activities in conflict-affected populations compared to their non-conflict-affected counterparts. As a potential next step, a long-term difference in difference regression analysis, as well as the use of observational data that could be more nuanced and contextual, could explore, in addition to the short-term impact identified here, the long-term impact of disasters dynamics in conflict settings.

To fully comprehend these dynamics, it is essential to conduct further research to pinpoint the causal factors that exacerbate vulnerability in conflict-stricken settings. Our findings, however, emphasize the critical need for investment in disaster risk reduction and the prioritization of recovery initiatives in conflict-affected regions. Inhabitants of these areas suffer more significant impacts and face extended recovery periods in the aftermath of disasters, underscoring the necessity for focused interventions to reduce their vulnerabilities and bolster their resilience.

# Conclusion

has been given to understanding how conflicts influence disaster impacts and recovery. By employing a difference-in-difference method alongside geospatial and satellite imagery datasets, this paper seeks to address this literature gap by estimating the impacts of a disaster in conflict settings and contrasting them with impacts in non-conflict settings.

This study represents one of the initial attempts to quantitatively compare the diverging impacts of a disaster between conflict and non-conflict affected regions. Our findings suggest that in the aftermath of the floods of interest, conflict-affected areas in Nigeria and Mozambique experienced the most severe negative impacts following the flood event, up to 1.2% and 1.39%, respectively. These results indicate that conflict-affected areas bore a heavier burden of the floods compared to non-conflict areas. Additionally, the negative impacts in conflict settings continued to be larger than in non-conflict affected regions for the six months following the event, which gives an indication that these floods prolonged the recovery period for conflict-affected populations.

In conclusion, despite existing methodological constraints, our study demonstrates that the impacts of disasters are significantly more pronounced in conflict settings
|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. All variables are described in Table 2, Table 3, and Table 4. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

the stock market turnover (the value of stock market transactions relative to the market value of all listed shares). While stock market capitalization measures the size of the stock market relative to the size of the economy, the stock market turnover captures liquidity or activity of a stock market in a given country. Otherwise, the specifications correspond to Table 5.

Columns (1) to (3) of Table 6 explore the impact of active stock markets on the export survival of products with high investment needs. The interaction term including 27
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12. To empirically estimate the impact of transport connectivity on agricultural production, a simple crop production function is considered: where _Qij_ is the amount of crop _j_ produced by household _i_ , using production inputs _Xij_ . Although our household data are not comprehensive in terms of input variables, _X_ , four types of inputs are included following the agricultural economics literature (e.g., GyimahBrempong, 1987; Bravo-Ortega and Lederman, 2004): labor, fertilizer, improved seeds, and intermediary services.

13. As usual, the production levels may also depend on household characteristics, _Zi_ , for example, the age of the head of household and ownership of certain assets, such as vehicles and motorcycles. The crop-specific fixed-effects, _cj_ , are also included to control for unobservables across different crops. _ε_ is an idiosyncratic error distributed independently and identically under the cluster-robust variance-covariance assumption.

14. The main hypothesis of the paper is that the level of production is affected by the market accessibility, _Mi_ . This contains a set of locational characteristics of household _i_ , which is common across crops. The impact of transport connectivity is supposed to be captured by _β2_ . This specification implicitly rules out subsistence farming with few advanced inputs used, which is in fact the case in the Comoros. As will be shown below, only 10 percent of the surveyed households used some advanced inputs in our sample. On the output side, on the other hand, about half were involved in some market transactions. In our model, the accessibility variable therefore captures the generic impact of access to input and output markets.<sup>2</sup>

> 2 The paper will also examine the market sales equation, in which the accessibility variable may be able to more explicitly capture the impact of access to output markets. See below.
# Abstract

In this paper, we investigate individuals’ exposure to indoor air pollution (IAP). Using new survey data from Bangladesh, we analyze exposure at two levels: differences within households attributable to family roles, and differences across households attributable to income and education. Within households, we relate individuals’ exposure to pollution in different locations during their daily round of activity. We find high levels of exposure for children and adolescents of both sexes, with particularly serious exposure for children under 5. Among prime-age adults, we find that men have half the exposure of women (whose exposure is similar to that of children and adolescents). We also find that elderly men have significantly lower exposure than elderly women. Across households, we draw on results from our previous paper (Dasgupta, et al., 2004), which relate pollution variation across households to choices of cooking fuel, cooking locations, construction materials and ventilation practices. We find that these choices are significantly affected by family income and adult education levels (particularly for women). Overall, we find that the poorest, least-educated households have twice the pollution levels of relatively high-income households with highly-educated adults.

Overall, we find that young children and poorly-educated women in poor households face pollution exposures that are four times those for men in higher-income households organized by more highlyeducated women. In our previous paper, we recommended feasible changes in cooking locations, construction materials and ventilation practices that could greatly reduce average household pollution levels. In this paper, we consider measures for narrowing the exposure gap among individuals within households. We focus particularly on changes for infants and young children, since they suffer the worst mortality and morbidity from indoor air pollution, but our findings also apply to women and adolescents. Our recommendations for reducing their exposure are based on a few simple, robust findings: Hourly pollution levels in cooking and living areas are quite similar because cooking smoke diffuses rapidly and nearly-completely into living areas. However, outdoor pollution is far lower. At present, young children are only
# **Sifting through the Data:**

**Labor Markets in Haiti through a Turbulent Decade (2001-2012)**

**Thiago Scot**

**Aude-Sophie Rodella**

**Working Paper**<sup>**1**</sup>

_Keywords: labor markets; poverty; female labor markets; youth employment; fragility, conflict and violence;_ _JEL codes: J01; J24; O17_

> 1 Poverty Global Practice. This paper builds on analysis conducted by the authors in the context of the Haiti Poverty Assessment (2015) and the Haiti Systematic Diagnostic (2015) in collaboration with Facundo Cuevas. The authors are grateful for the comments received from: Facundo Cuevas, Raju Singh, Oscar Calvo-Gonzalez, Bernardo Atuesta and the LAC Poverty team for comments received during the 2015 Summer Internal Research Seminar Program (SIRPIG).
” _Review of Income and Wealth_ , 2022, _68_ (1), 263–288.

- **Bosch, Mariano and Jarret Guajardo** , “Labor market impacts of non-contributory pensions: The case of Argentina’s Moratorium,” Technical Report, IDB Working Paper Series 366 2012.

- **Cavallo, Alberto and Manuel Bertolotto** , “Filling the Gap in Argentina’s Inflation Data,” Available at SSRN: https://ssrn.com/abstract=2782104 May 2016.

21
However, he questions the causal link between relieving financial constraints and the higher levels of investment because he observes a rise in private transfers among households. In regard to unconditional transfers or noncontributory pensions, there is evidence that supports the contribution of pensions to relieving credit constraints as in the case of blacks in South Africa (Berg, 2013). Moreover, the randomized control trial conducted by Haushofer and Shapiro (2013) sheds light on how regular unconditional transfers, as opposed to lump-sum transfers, are more well suited to encouraging long-term investment. There are four papers which are probably the closet to our study. First, Soares, Ribas, and Osório (2010), who evaluate Brazil’s Bolsa Família and find that it raised female labor force participation by 4.3 percentage points, even though they do not discuss this result further. Fogel and Paes de Barros (2010) find for the same program, using aggregate data of Brazilian municipalities, that labor force participation increased by 2-4 percentage points for males. Results for females are also positive, yet not significant and smaller in magnitude. Next, Skoufias, Unar and González de Cossio (2013) exploit the experimental design of Mexico’s Food Support Program (Programa de Apoyo Alimentario, PAL) on labor supply. Although they find no significant effects of the PAL on total labor market participation, their estimates show that PAL have a significant negative effect on the participation of males in agricultural activities which translated into a shift into participation in non-agricultural activities. They argue that PAL provide partial insurance (reduce down-side risks) for food consumption that allows program beneficiaries to allocate less time into agricultural production and more towards more risky non-agricultural activities. Finally, Martinez (2004), in an unpublished paper, finds that Bolivia’s Bonosol Program boosted food consumption among rural households by more than what could be purchased with the amount of the transfers. He concludes that the additional funds are likely being used to invest in agricultural inputs.

Most other papers on the labor market impacts
|92-08|92-08|92-08|92-13|92-13|05-13|
|Within Country R-Sq.|0.769||||||
|Within R-Squared|0.209|0.139|0.137|0.192|0.128|0.118|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes._ “HSW” denotes Henderson et al. (2012)’s baseline result, which appears in Table 1, Column 1 (p. 1,012) and Table 2, Column 1 (p. 1,015). All specifications include country and year fixed effects. Robust standard errors, clustered by country, are in parentheses. Both reported R-Squared statistics are adjusted for sample size; “Within R-Sq.” is within all fixed effects. Note, Penn World Tables v. 6.0 had a country count of 188, while v. 10.0 has a reduced count of 183. In this analysis, Hong Kong, Macao, and Taiwan are included as part of China for consistency with existing shape files commonly used to process worldwide data. We adjust all Penn World Tables numbers accordingly, and thus obtain a final data set of 183-3=180 countries with both GDP and night lights data available.

S10
**Figure 6:** Average years of schooling in MENA among women and men 15-24 years old.

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>1940 1960 1980 2000 2020<br>Women Men<br>Average years of schooling<br><!-- End of picture text --> Source: Own calculations using the Barro and Lee (2013) dataset. We use population weights to obtain the regional average.

**Figure 7:** Gender Employment Gaps by Skill Level

# Employment-to-Population Ratio (ER) by Skill and Gender

<!-- Start of picture text --> 0.96 0.97 0.96<br>0.91<br>0.80<br>0.75<br>0.73<br>0.66 0.64 0.68 0.66<br>0.62 0.60 0.62<br>0.56 0.58 0.58<br>0.53 0.53<br>0.48<br>0.45<br>0.43<br>0.39<br>0.32 0.29 0.33<br>0.19<br>0.14<br>0.10 0.11<br>0.06 0.04<br>S U S U S U S U S U S U S U S U<br>ARE DZA EGY IRN PSE QAT TUN YEM<br>Female Male<br>Note: U=Unskilled (not college educated) and S=Skilled (college educated).<br>Plots the employment-to-population ratio for four groups, skilled-women, skilled-men, unskilled-women,<br>unskilled men (all of working age). For example ER(s,
informal. In the bottom half of the distribution, imputed tax income (“reported income”) is significantly lower than survey income (“true income”). This probably reflects the underreporting of income in the tax data. At the 75<sup>th</sup> percentile of the distribution, the income value is approximately equal in all three data sources. In the top quarter of the distribution, tax income and imputed tax income are significantly higher than survey income. This may reflect the underreporting of top incomes in survey data or the systematic undercoverage of richer households (e.g., Brzeziński et al., 2020; Flaichere et al., 2022). The survey might completely miss respondents from the extreme right tail of the income distribution either because the survey design does not target high-income earners or because the wealthiest individuals refuse to participate.

**Figure 1. The distribution of observed gross labor income in the EU-SILC.**

<!-- Start of picture text --> 0 20000 40000 60000<br>EU-SILC Imputed tax income<br>.25<br>.2<br>.15<br>Fraction<br>.1<br>.05<br>0<br><!-- End of picture text --> _Note: The first red line indicates the annual minimum wage for employees without higher education, and the second red line indicates the annual minimum wage for employees in the construction sector. Both distributions are censored at the 99th percentile to keep the graph readable._ _Source: Own estimation based on tax data, EU-SILC, and imputation methods._ This **Our calculation of annual tax income relies on the tax income reported in July multiplied by 12.** decision is informed by the fact that, in July, the mean income closely aligns with the annual mean income. Notably, despite the challenges posed by COVID-19, Romania witnessed a 4% increase in mean 20
intent-totreat impacts of the cash transfers. We discuss this approach in more detail in Section 4, where we present the estimation strategy and the composition of the follow-up sample.

The interventions were implemented between February 2013 and April 2015. Program implementation was closely monitored. The delivery of the monthly cash transfers started in February 2013 in the Dosso region and in March 2013 in the Maradi region. Cash transfers were delivered monthly until the final payments took place (in March 2015 in Dosso and April 2015 in Maradi). More than 98% of households selected for the cash transfer program received benefits. The accompanying measures were rolled out

> for all children identified by the household survey team. Data collection for the child survey took place in the household through interviews with the main caregiver for each child. The anthropometric measurements and cognitive test took place in presence of the caregiver. Thorough quality control procedures were put in place, with systematic verifications of questionnaires by enumerators and supervisors. Additional verifications, including random back-checks, were undertaken by the coordination and quality control teams throughout the survey.

11
**Population.** We retrieve population data for each neighborhood from the Housing and Population Census (2015). Figure 1 describes the distribution of population across neighborhoods and the relationship between population density and neighborhoods’ distance from the center. The first panel shows a scatter plot relating population density and distance. The plot shows a negative relationship, suggesting that people concentrate in the center of the city. The second plot confirms this finding by grouping neighborhoods into deciles according to their distance to the center. The third plot is a map which shows the population density for each neighborhood. The map show that some of the neighborhoods in the outskirts also have high population density, suggesting that Amman has some resemblance to a poly-centric city.

**Employment.** We use employment data by sector at the matiqa level from the Establishment Census (2018). We impute this data to the neighborhood level based on the withinmatiqa distribution of building volume. Figure 2 shows the distribution of employment across neighborhoods and the relationship between employment density and neighborhoods’ distance from the center with three plots that are analogous to Figure 1. We see that the highest employment density is in the center of the city, with a particular concentration in the center-west.

Figure 3 shows the distribution of the employment density separately for each sector: we see that jobs in tradable services are most spatially concentrated, with a clear cluster in the center-west of the city. Non-tradable service jobs are somewhat more dispersed: they have high density in the same areas (center/center-west), but they additionally have higher density in the southern and eastern parts of the center. Manufacturing is less centralized, with distinct clusters in the east, south, and west.

**Housing Volume and Zoning Restriction.** We collect data on total built floor space in each neighborhood from satellite data from the European Space Agency. In addition, we

8
. Internet penetration is also below average considering the nation’s status as an upper-middle-income economy. Botswana’s fixed-line penetration was some three times less than the upper-middle-income average in 2008, the peak year. Growth has been stagnant, due the popularity of mobile phones as well as tariff rebalancing (raising local call charges and reducing international call tariffs). Although fixed-line competition is legal, in practice there is no strong

> 7 - See International Telecommunication Union (ITU) Mobile Cellular Statistics, available at: http://www.itu.int/ITU <u>D/ict/statistics/.</u>

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A special case is the development of cash exchange outlets. So long as cash remains a critical part of the system then the establishment of a network of cash merchants to exchange cash and BEMs will be central to the functioning of the payments system. A network is both expensive and time consuming to develop and the ability of service providers to have exclusive relations with merchants may be necessary for them to invest in the creation of such networks. A requirement for competitors to be able to access a merchant network on particular terms or for merchants to act as agents of more than one service provider may limit the development of the agency network. We thus question policies that restrict exclusivity of agent networks. Moreover, new entrants into the account provision and transfer market are always free to establish competing networks and anyone can, in principle, set up a merchant business.

When services are unbundled and no longer part of a traditional bank the question arises as to which organization should regulate them. Financial regulators are the most competent for banking and financial services, and depending on the country prudential regulation and business conduct regulation may be under the same roof or carried out by different agencies (the “twinpeak” model of regulation). However, where competition issues arise in network industries, it is typically sensible to allocate responsibility to sector-specific regulators. Special rulings on matters like interconnection pricing and rulings on exclusivity arrangements may be better made by sector-specific regulators that have a grasp of the technical and organizational intricacies of the problem at hand. In some cases the regulatory system may require co-ordination between several different sector-specific regulators, for example when co-coordinating registration requirements for SIM cards and accounts. Most of the time, rulings by telecom regulators on competition issues in the telecom network and rulings on competition in the payments network by financial regulators can be made separately but some level of consultation about the nature and timing of decisions may be required.

Beyond financial inclusion, the M-PESA experiment provides important insights into the regulation of financial services in developed economies. There is an active
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The state of Cameroon’s infrastructure

Despite the country’s rich endowment of natural resources, Cameroon’s economic growth has been sluggish, and poverty levels remain high. While GDP per capita increased from $680 in 2000 to $1,050 in 2007, average poverty remained unchanged at 40 percent over the same period and actually increased in rural areas—where more than 55 percent of rural households are poor (figure 2b).

Cameroon produces about 32 million bbl/year of crude oil, yet extractive industries account for only 8 to 10 percent of Cameroon’s GDP. Significant gas and mineral reserves (bauxite, iron, uranium, platinum, gold) remain unexploited (figure 2d). An oil-exporting country, Cameroon felt the impact of the global economic crisis and recently obtained a $144 million disbursement under the IMF’s Exogenous Shock Facility.

Cameroon ranks 164 of 181 in the World Bank’s Doing Business Index, and governance issues are important deterrents to increased investment. Corruption is ingrained at all levels of society, with 79 percent of Cameroonians admitting to paying bribes. The country ranks below the 25th percentile on all criteria of the Kaufmann-Kraay Governance indicators, significantly lagging its peers, and ranks 141 out of 180 countries in Transparency International’s 2008 Corruption Perception Index. Enforcing a contract takes 43 steps and 800 days. Improving governance is a priority of the government of Cameroon’s revised development policy.

Cameroon’s 19.5 million people (as of 2009)sparsely populate the country’s 475,440 km2 (figure 3a).Whereas the average density is 35 inhabitants per square kilometer, there are important differences among regions. In the south and east, the average density is 5 inhabitants per square kilometer , whereas in the west and north population density exceeds 200 inhabitants per square kilometer.

Following the distribution of economic activity and population, the country’s roads, power, and ICT backbones are concentrated in urban areas