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Expenditure Survey,” _Food Policy_ , SI: Food Counts, 72, 72–80, 2017. doi:10.1016/j.foodpol.2017.08.014

- Farfán, G., M.E. Genoni and R. Vakis, “You Are What (and Where) You Eat: Capturing Food Away from Home in Welfare Measures,” _Food Policy_ , SI: Food Counts, 72, 146–56, 2017. doi:10.1016/j.foodpol.2017.08.020

- Ferreira, F.H.G., S. Chen, A. Dabalen, Y. Dikhanov, N. Hamadeh, D. Jolliffe, A. Narayan, E.B. Prydz, A. Revenga, P. Sangraula, U. Serajuddin and N. Yoshida, “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results,” _Journal of Economic Inequality_ , 14, 141–72, 2016. doi:10.1007/s10888-016-9326-6

- Gibson, J., K. Beegle, J.D. Weerdt and J. Friedman, “What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption?” _Oxford Bulletin of Economics and Statistics_ , 77, 466–74, 2015. doi: 10.1111/obes.12066

- Holden, K., and D.A. Peel, “On Testing for Unbiasedness and Efficiency of Forecasts,” _The Manchester School_ , 58, 120–27, 1990. doi: 10.1111/j.1467-9957.1990.tb00413.x

- Jäntti, M., V.M. Törmälehto and E. Marlier, “The Use of Registers in the Context of EU-SILC: Challenges and Opportunities,” Eurostat Statistical Working Papers, Publications Office of the European Commission, 2013.

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sup> In practice, NHIS has a long history of being slow to reimburse healthcare providers (Wang, Otoo, and Dsane-Selby 2017), leading some providers to refuse to treat NHIS members and others to charge them despite that they are insured—something to keep in mind as we examine the difference between reported OOP healthcare expenditures for NHIS members and nonmembers.

> 8 In 2017, the purchasing power parity conversion factor for cedis to international dollars was 1.75 cedis per dollar. We use this conversion factor wherever we report values in PPP dollars. The conversion factor was obtained from the World Bank’s World Development Indicators databank.

> 9 NHIS defines an “indigent” as an individual who (a) “does not have any visible source of income,” (b) “does not have a fixed place of residence,” (c) “does not live with a person who is employed and has [a] fixed place of - residence,” or (d) “does not have a consistent source of support from another person” (https://nhis.gov.gh/Faqs/the <u>benefits-of-the-national-health-insurance-scheme-2?msclkid=60d5acdcab8811ec95eecede25b33dfb; accessed on</u> March 24, 2022).

> 10 SSNIT is a statutory organization that administers a national social security scheme. The scheme offers old-age, disability, and survivor benefits to its members.

> 11 Thirty-six percent of all participating healthcare providers are private. Because staff salaries at private healthcare providers are not paid by the government, NHIS reimburses these providers more generously than it does public healthcare facilities, but not by enough to cover the entirety of provider salaries.

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. Similarly, men may use violence as a means of extracting their partners’ income (Bloch and Rao 2002; Bobonis, Gonz, and Castro 2013; and some qualitative evidence from Litwin, Perova, and Reynolds 2019). In either case, societies with fewer legal and police resources, such as in developing countries, may be unable to protect women from an increase in violence resulting from economic “empowerment.” Similarly, cultures with social norms that excuse violence against women (for instance, machismo) may be characterized by a greater portion of the population turning a blind eye to incidents of IPV (Cools and Kotsadam 2017).

This study contributes to the literature by examining the effects of natural fluctuations in the gender wage ratio on three administrative measures of violence against women in Brazil. It takes advantage of administrative microdata on the gender wage ratio measure and violence outcome measures. The data on wages are comprehensive microdata on the full universe of formal contracts in Brazil. The outcome data are taken from three administrative sources measuring violence at varying levels of severity. Previous studies on how women’s economic status influences IPV typically use only one data source to 2
goal of this section is to use the estimates of these effects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages).

The relevant information is collected in Table 10. We classify Venezuelan workers that _lack legal status_ on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15
rating. The findings indicate that the question-order of both corruption and business licensing and permit business environment ratings have a statistically significant effect on the perceptions of severe obstacles, but not on any obstacle (minor, moderate, major, and severe altogether). The implication is that more severe perceptions of corruption and business licensing and permits are susceptible to bias. The findings using the Ordered Probit are mixed. We find a statistically significant effect of question order for Corruption but not for Business Licensing and Permits.

# **7. Conclusion**

Firm-level data is scarce in developing economies. The ideal data ecosystem is one where researchers have access to comprehensive low frequency census data with short questionnaires, complemented with high frequency firm survey data with more detailed questionnaires to obtain a full picture of the private sector. However, such situations are rare. In many cases national statistical offices are understaffed and underfunded. This is evident in the difficulty of meeting the data demands of the SDG indicators (Desai et al, 2018). However, policy makers cannot wait for availability of perfect data and have to take decisive action on urgent concerns. The World Bank Enterprise Surveys (WBES) have been extensively used by policy makers, governments, and academics alike to understand the formal private sector, especially for poorer economies with limited or no data ecosystems. The use of survey questions that capture the perceptions of business owners and managers in the WBES are seductive as they provide broad statements on the business environment that are easy to grasp. This study advises some caution on how they are utilized, with the understanding in some cases that they are the only available option.

The study set up an experiment to explore whether perceptions of the business environment are stable or whether they are vulnerable to the ordering of questions in the Enterprise Surveys questionnaire. For corruption and business licensing and permits, the question order seems to matter. The findings stand even after accounting for a host of control variables. The swing in the percentage of variables identify corruption 17
× WW′ where, O , is the share of out-of-school by welfare quintile _q_ , for age group **_a_** , in country **_c_** (see figure 3 for the out of school variation across welfare quintile, per country income group) OO W’, is the share of children in welfare quintile **_q_** , in country **_c,_** after the income shock is applied, but considering the same cut-offs of each quintile as in the vector _W_ (table A.2.1 shows the transition probabilities per quintile from W to W’) If a country does not have a household survey available, we imputed the overall change in out-of -school rates of their income group.

At baseline, the within- and between-countries inequalities of access to school for the 4 to 11 age group are extremely high. While in high-income countries the range of out-of-school children from the poorest to the richest is close to 0.2pp, in low-income countries this range remains close to 15pp. However, this inequality rapidly falls to 12pp and 3pp, as we move to lower-middle-income and upper-middle-income countries, respectively.

For the 12 to 17 age group, the within country inequality by income group is almost the same (15pp) across all country groups. However, gender differences persist, with girls being less likely than boys to attend schools in low-income countries, and the reverse in high-income countries. Moreover, important between countries inequalities are evident. The poorest households in highincome countries have on average, a lower share of out-of-school female children (Q5=12%), than 42
# **Abbreviations & Acronyms**

|ADM|Administrative Unit|
|---|---|
|AR6|Sixth Assessment Report of the Intergovernmental Panel for Climate Change|
|CCKP|Climate Change Knowledge Portal|
|CR|Climate Risk|
|CRI|Climate Risk Induced|
|CPI|Consumer Price Index|
|EM-DAT|Emergency Events Database|
|FAO|Food and Agriculture Organization|
|GAUL|Global Administrative Unit Layers|
|GDP|Gross Domestic Product|
|HH|Household|
|IPCC|Intergovernmental Panel for Climate Change|
|IPCF|Per Capita Household Income|
|IRI|Idiosyncratic Risk Induced|
|LAC|Latin America and the Caribbean|
|LASSO|Least Absolute Shrinkage and Selection Operator|
|n.a.|Not available|
|PDSI|Palmer Drought Severity Index|
|PET|Potential Evapotranspiration|
|PI|Poverty Induced|
|PL|Poverty Line|
|PPP|Purchasing Power Parity|
|RI|Risk Induced|
|SEDLAC|Socio-Economic Database for Latin America and the Caribbean|
|SPEI|Standardized Precipitation-Evapotranspiration Index|
|SPI|Standardized Precipitation Index|
|TH|ThinkHazard|
|UNDHA|United Nations Department of Humanitarian Affairs|
|UNDRO|United Nations Disaster Relief Coordinator|
|UNDRR|United Nations Office for Disaster Risk Reduction|
|UNFCCC|United Nations Framework Convention on Climate Change|
|UNHCR|United Nations High Commissioner for Refugees|
|UNISDR|United Nations International Strategy for Disaster Reduction|
|USD|United States Dollar|

2
, not just commodities, using data from the IMF) to control for trade effects, and the lagged dependent variable to capture underlying growth unrelated to commodity prices. The country-specific commodity export price index is an index that weights commodity prices by their relevance in a country’s exports. This index is a better measure of a commodity price shock for a particular country than global commodity price indexes that might include goods not exported by the particular country.

> 16 In Table 5, the coefficient for EMDEs (0.085) in column (1) is about 3.5 times as much as the coefficient for advanced economies (0.024) in column (4).

9
expenditure intensity. More importantly, it allows using the employer-employee census to follow firms and workers over time and to match worker’s characteristics with those of the innovative and non-innovative firms. Exploiting these dynamics allows exploring transitions from noninnovative to innovative status, which is critical in the causality analysis of innovation and wages.

# **3.4 Methodology**

## **3.4.1 Measuring the innovation premium**

To identify the wage premium associated with innovation a canonical Mincer-type wage equation is estimated: where _i_ indexes individual, _j_ region, _f_ firm and _t_ years; _wijft_ is the hourly wage, which is regressed against our proxy of innovation (innovative) - the share of PoTec workers -and a set of personal characteristics: gender (male = 0), age, tenure, measures of education 9
848<br>|0.488|0.064|0.778|12.0|
|Livestock<br>|0.695<br>|0.094<br>|0.935|0.376|0.034|0.661|6.4|
|Rental|0.317|0.083|0.924|0.805|0.062|1.398|11.7|
|Total|-|1.000|-|-|0.532|-|100.0|
|(b) Rural Jordan||||||||
|Nonfarm<br>|0.685<br>|0.506<br>|0.567|0.760|0.218|1.046|53.5|
|Agricultural<br>|0.176<br>|0.085|0.919|0.439|0.034|0.979|8.3|
|Transfer<br>|0.913|0.273|0.726|0.560|0.111|0.987|27.2|
|Livestock<br>|-<br>|-|-|-|-|-|-|
|Rental<br>|0.848|0.136|0.535|0.625|0.045|0.812|11.0|
|Total|-|1.000|-|-|0.408|-|100.0|

Notes: All estimates are based on annual per capita household income.

(a) Source ginis **(GK)** are high because they include households with zero and negative incomes from different income sources. Source ginis can exceed unity if many of y, are negative.
headed even though the husband is present. In an attempt to nuance the definition of female-headed households to account for some of this heterogeneity, we disaggregate female-headed households into two categories: (i) those in which a male of labor age (15– 59) is present and (ii) those with no male of labor age. Where appropriate, we use this breakdown to present our results. As expected, the gaps are more pronounced among female-headed households without a male presence than among those with a male presence.

# **_Land_**

In all regions and in many countries, women have less access to land than men do.<sup>8</sup> For the 16 countries for which we have data, rural, female-headed households are more likely to own land than their male counterparts in only six countries (fig. 1).<sup>9</sup> These differences are much more pronounced in households without a labor-age male present, and they do not systematically decrease with GDP per capita (not shown in fig. 1).

**Figure 1**

<!-- Start of picture text --> Household land ownership in rural areas,<br>by headship type<br>FHHH no male FHHH with male MHHH<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Source: RIGA data, most recent year available.<br>Note: FHHH with male defined as FHHH with at least one working-age (15-59) male.<br>percent owning land<br><!-- End of picture text --> 6
farmers’ participation in cash crop cultivation were eliminated in the 1990s, smallholders’ limited resources (Ricker-Gilbert _et al._ 2014) and vulnerability to risk, particularly in the face of climate-related shocks (Sesmero _et al._ 2018), constrained their ability to diversify into other crops or adopt measures to manage soil fertility (Krah _et al._ 2019).

Agricultural production patterns are thus characterized by dominance of maize, a staple with limited scope for value addition and employment generation. Despite unequal coverage, fertilizer subsidies may further have crowded out more sustainable practices (Khataza _et al._ 2017) such as investment in organic manure (Holden and Lunduka 2012). Growing land scarcity has also triggered land disputes (van Donge 1999), often centered around inheritance, and land disputes are often associated with higher levels of tenure insecurity and quantitatively large productivity losses (Deininger _et al._ 2019), especially for women.<sup>4</sup>

> 3 While access to inherited land increased women’s level of self-employment and earnings in Ethiopia (Kumar and Quisumbing 2012), data from Demographic and Health Surveys for 15 African countries show that there are only two countries (Rwanda and Senegal) where widows and their children inherit most of a deceased’s assets whereas in more than half of the countries (Benin, the Democratic Republic of Congo, Guinea, Mali, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Tanzania, Uganda, Zambia and Zimbabwe), widows report no inheritance of assets whatsoever (Peterman 2012).

> 4 With 22% of farmers fearing potential land loss, annual losses due to tenure insecurity are estimated at US$14 million (Deininger _et al._ 2019).

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27 To create the fiscal space needed to finance these public sector contributions, the limitations of the existing public sector accounting frameworks must also be addressed in assessing possible trade-offs between the long-term payoffs of infrastructure expenditures and short-term financial stability. This assessment will be a challenge because there is not even an agreement among academics on the basic measures (for example, what is liquidity in the public sector?). In addition to a good framework, addressing the challenge will also require an investment in basic public sector data. Indeed, some of the basic fiscal debates still stems from the fact that the coverage of fiscal data is only tailored to meet a broad sense of fiscal status in relation to broad macroeconomic concerns but that it does not allow good assessment of sectoral policies.

For the poorest countries in which it is clear that the fiscal space is unlikely to be created any time soon, it seems reasonable to start considering the role of infrastructure grants—for access and sometimes for lifelines for a limited duration at least. The Swiss aid agency is a leader in this field and has a pilot program that needs to be analyzed much more carefully.<sup>31</sup> For many countries, the main challenge will actually be to come up with designs and choice of new financial instruments that will allow the public sector to leverage any fiscal space available. The need to connect infrastructure development with private financial markets as a way to leverage and mobilize more capital may be the most pressing and the most obvious. Bolivia’s capitalization experience and similar efforts in Eastern Europe have shown the possibilities as well as the challenges. Addressing these challenges requires better pooling of risks than in the past. This may require a willingness among donors to work together on the design of new and improved risk mitigation products and applications that allow national and subnational governments to pool risks across sectors and projects more effectively. It will also require, at the project level, improvements in risk allocation mechanisms to address currency and regulatory risks, which are among
# **Figure 3: Trust in the Tax Department (% of respondents)**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Somewhat A lot<br> Algeria Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Côte d ́Ivoire Egypt Gabon Ghana Guinea Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Morocco Mozambique Namibia Niger Nigeria Sao Tome and Principe Senegal Sierra Leone South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Zimbabwe<br><!-- End of picture text --> _Notes_ : Respondents were asked: “How much do you trust each of the following, or haven’t you heard enough about them to say: The tax department?” (% who say “Somewhat” and “A lot”). Afrobarometer R6 2014/2015 (Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d ́Ivoire, Egypt, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe). _Source:_ Isbell (2017) based on Afrobarometer.

The central challenge facing reformers lies in identifying innovative strategies to strengthen revenue mobilization, trust, and fiscal contracts. Recent research points toward potential elements of a strategy to strengthen the links between revenues and public benefits, including a greater emphasis on building trust and tax morale among taxpayers, expanded attention to the political challenges of reform, efforts to better tailor reform to local contexts and needs, and clearer focus on empowering taxpayers. However, these strands of research have been somewhat fragmented, and have yet to be
<!-- Start of picture text --> No or low risk High risk<br>EAP 595 m (25%)<br>SAR 370 m (19.5%)<br>SSA 171 m (14.8%)<br>ECA 128 m (13.9%)<br>LAC 95 m (13.8%)<br>MENA 75 m (16.3%)<br>USA & CAN 30 m (8%)<br>0 1,000 2,000 3,000<br>Millions<br><!-- End of picture text --> _Figure 4. Number of people exposed to significant flood risk by region (and as share of total regional population)_

# **5.2. Countries with the largest flood exposed populations**

**Absolute exposure headcounts by country.** Several countries stand out with particularly large populations directly exposed to high flood risk. In particular, the two most populous countries, China and India, also have the highest absolute population exposure to flood risk. Approximately one-third of all people exposed to flood risk globally reside in India or China. These two countries are home to approximately as many flood exposed people as in the next 18 highest exposure countries combined. Figures 5 and 6 present total headcount estimates at the country level.

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then the scorecard provides the resulting increase in resilience and decrease in risk. The socioeconomic resilience can also be used to translate in welfare terms the risk reduction benefits which are typically expressed in asset losses: if a new dike can reduce expected asset losses by $1 million per year in Malawi – with its 53 percent resilience – then welfare benefits can be estimated as equivalent to $2 million of additional consumption, assuming the dike protects the “average” population and is not biased toward non‐poor people, for instance.

These estimates can serve as an input to a cost‐benefit analysis that would also need to account for the cost of these options and their benefits unrelated to resilience. For instance, developing social protection brings benefits that go beyond increased resilience and include economic benefits even in the absence of shocks: an analysis of resilience cannot alone determine the desirability of such a policy. However, the scorecards can contribute to a discussion on a broad set of options to reduce natural risks and increase resilience, and ensure all options are discussed, from preventive actions like flood zoning to ex‐post options like insurance, contingent finance and social protection. The scorecard provides an integrated framework to discuss and compare these options, and could even help break the silos in governments and local authorities, where ministries or departments in charge of social protection, building norms and urban planning may not work well together or not even consider flood risks in their decisions.

Our analysis of Malawi and Sweden – and the 88 other countries – is a first‐round estimate using globally open data. It is a starting point for policy design and should be complemented by local studies (Aerts et al., 2014; Keating et al., 2014; Michel‐Kerjan et al., 2013). At the local or national level, for instance, the flood maps from the global model can be replaced by results from local analyses at higher resolution, including flash floods and small basins. Local data on flood protection and better exposure
with GenAI, but which will not be in position to do so due to the lack of digital infrastructure. Some 7 million such jobs are held by women and nearly 10 million are held by men (see Figure A6 in Appendix for country-level breakdowns).

# 4.3. Within-country patterns

In this section, we present a sample of within-country analysis, using the example of Colombia and Costa Rica (Figure 12). Detailed breakdowns by country are provided in the Appendix and made available in a dynamic way on our online portal.<sup>36</sup> Within each country, the plot can be read as a detailed breakdown of a given category on the vertical axis into the shares of employment represented in horizonal separations. For example, in the case of Colombia, among women, 5.5 percent of employment is exposed to the risk of automation, 11.3 percent of employment is in the augmentation category, and 22.5 percent in the big unknown. The remainder, not presented in the graph, contains all other female-held

> 35 We also implement a robustness check where we use the variable of ”computer ownership at home”, as selfreported by households in SEDLAC, to adjust for digitalization. In particular, we replace the imputed ”computer use at work” variable for this measure, to check if the patterns prevail. As seen in figure A.5 in the appendix, while the absolute values of exposure adjusted by digitalization are different across methods, the relative figures are very similar. In particular, among jobs exposed to GenAI augmentation, the share of workers with a computer at home tends to be higher in richer countries.

> 36 https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/

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