rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Poverty in Mozambique: New Evidence from Recent Household Surveys**<sup>**1**</sup>

**Federica Alfani, Carlo Azzarri, Marco d’Errico and Vasco Molini**

_Keywords:_ Poverty lines; location specific poverty lines; measurement error; Mozambique JEL Codes: O15, I32, POV

> 1 Corresponding author, Vasco Molini, World Bank, <u>vmolini@worldbank.org; Azzarri: International Food Policy Research</u> Institute, <u>C.Azzarri@cgiar.org; Alfani: Food and Agriculture Organization, Federica.Alfani@fao.org; d’Errico: Food</u> and Agriculture Organization, Marco.DErrico@fao.org,. Comments on earlier versions by Peter Lanjouw, Kathleen Beagle, Bart van den Boom, Gero Carletto, Thomas Pave Sohnesen, Andrew Dabalen, Julio Revilla and Sean Lothrop are gratefully acknowledged. Special thanks are due to Antonio Nucifora for strongly encouraging and supporting the process of revising the poverty figures. The usual disclaimers apply. We gratefully acknowledge financial support from the World Bank. The views expressed here are those of the authors and do not necessarily reflect those of the funding organizations, and the institutions they are affiliated with.
(Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal. The weight of a food item when prepared, is however not equivalent to the weight when it is in its raw form. Beans for instance, or rice – another important staple in Brazil - absorb water and change in volume when cooked. We do not have conversion factors that would allow us to convert the prepared weight into the quantity in its raw form. Using kcal estimates in prepared form would also require strong assumptions about the way food was prepared before consumption. Working with the raw calorie intake we aim at assigning the potential calorie intake that could be obtained when prepared to a purchased food item. On the other hand, while non-edible parts of raw food items, such as fruits, vegetables, eggs and meat with bones, could be leading to an underestimation of the cost per calorie, we believe this to be of minor concern. Among the top-10 items as a proportion of total average food expenditures, none have non-edible parts.

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(China)<sup>3</sup> have complemented this work with their own health accounts. And four of the five systems—Singapore is the exception—have also been represented in a multi-system Asian comparative study on equity in health care finance and delivery, known as the Equitap project.<sup>†</sup> In addition, the last few years have seen several interesting policy reforms in the systems, some of which have been the subject of careful evaluation. These evaluations, as well as the data emerging from the health accounts and Equitap project, constitute the evidence base for this paper.

The paper begins in section II with the financing of health care, comparing the shares of GDP spent by the different systems on health care, and the revenue sources used in the financing of it. Also reviewed is evidence on the progressivity of health financing in the different systems, and the extent to which households—especially poor ones—are protected from catastrophic health expenses. The results are interpreted in the light of schemes aimed at cushioning the poor from copayments. The paper then turns in section III to the delivery and use of health services, reviewing the roles of the public and private sectors, and the ways providers are paid and their influence on provider organization. Also reviewed are recent reforms concerning provider payments, and the separation of drug prescribing and dispensing. The paper reviews in the light of this the evidence on utilization rates, comparing rates to OECD averages, and the extent of inequalities across income groups in use of different types of services. The final section—section IV—contains the conclusions.

> * Hong Kong (China)’s ‘domestic’ health accounts are available at http://www.hwfb.gov.hk/statistics/en/dha.htm. † The project’s homepage is at <u>http://www.equitap.org/.</u>
Although the risk of unintended health problems may vary by age and a number of other contributory factors, the RR has been used as an average measure of the increased risk for the exposed population in this study.

# **_3. Estimates of Population Exposed to DDT_**

A number of countries currently use DDT for malaria control.<sup>12</sup> In addition, the re-introduction of DDT for lowering the spread of malaria is under consideration in some other countries, especially in Sub-Saharan Africa. By and large the population of the geographic location where DDT is currently being used or where it may be re-introduced faces the risk of exposure to DDT and is the focus of this analysis<sup>13</sup> . In order to determine the population exposed to DDT from its continued use or potential future use, detailed geographic data of DDT use at a sub-country level are required. However, while production and usage data on DDT is generally available at country level, sub-country level DDT usage data are scarce. In its absence, this study focused on a proxy, the malaria endemicity across countries to capture the current and potential exposure of population to DDT within each country given that the use of DDT is restricted to malaria eradication.<sup>14</sup> The malaria endemicity in this study is based on the Global malaria atlas compiled by the Malaria Atlas Project (MAP) of the Oxford University. The data are based on 24,492 parasite rate surveys ( _Plasmodiumfalciparum._ 24,178; _Plasmodium vivax._ 8,866) from an aggregated sample of 4,373,066 slides prepared from blood samples taken in 85 countries. The MAP study employs a new cartographic technique for deriving global clinical burden estimates of 12 The following 13 countries reported using DDT for malaria control and prevention: Eritrea, Ethiopia, Gambia, India, Madagascar, Malawi, Morocco, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe (WHO 2001a).

13 LAC
the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 2 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months.

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# **Contestability Effects:  The Relationship Between the Pace of Creative Destruction and Productivity Growth**

The analysis above focuses on the direct contribution of the reallocation process for productivity growth (either with the dynamic or cross sectional decomposition). These decompositions lead one to think about the reallocation contributions and the within firm contributions as being alternative explanations of productivity growth. The reallocation contributions are often interpreted in the literature as reflecting the creative destruction processes while the within firm are interpreted as reflecting more traditional sources of productivity growth (the average firm becomes more productive with advancing technology. However, rather than being alternatives, these effects (within vs. reallocation) may be closely related. That is, the pace of the creative destruction process might be interpreted as a measure of the c _ontestability or competitive of markets._ As such, greater competitive pressures may induce incumbents to perform more efficiently.

A first look at such _contestability effects_ is provided in Figure 13. In this figure, we exploit not only the cross country variation but the variation across industries in order to be able to increase the number of observations (and since in principle the above arguments should apply across sectors as well as countries). In particular, we examine the relationship between the contribution of net entry in a country/sector (using time averages from the country/sector/year data) and the productivity growth from incumbents (the within term in the FHK decomposition). Interestingly, we find a strong positive and statistically significant correlation between the net entry contribution and the productivity growth of incumbents.

This finding is suggestive that there is a relationship between the creative destructive and within firm sources of productivity growth. If nothing else, this strong correlation suggests that these components are not orthogonal alternatives but rather closely related. It might be, however, that the correlation in Figure 13 is readily explained as reflecting the impact of technological advances for both continuing firms and for the creative destruction process. That is, it may be that with technological advances we observe incumbents
expensive to respond to and highlight the need for proper cybersecurity to avoid such breaches. A global study conducted by the Ponemon Institute on behalf of computer hardware developer IBM analyzed breaches involving the loss or theft of customer or consumer records during July 2018 to April 2019.<sup>64</sup> Expenditures on activities and resources enabling the company to successfully detect the severity and reach of a breach had an average cost of $1.22 million.<sup>65</sup> An average of $0.21 million was expended on resources enabling organizations to notify regulators such as the GDPR’s Supervisory Authorities, and affected data subjects of the relevant breach.<sup>66</sup> A Ponemon Institute survey found data breaches to be widespread among the companies surveyed: “About half of the respondents had GDPR data breaches that must be reported to regulators.”<sup>67</sup> This was consistent across the world: “39% of US respondents, 45% of European respondents, 36% of Chinese respondents and 33% of Japanese respondents say they reported a personal data breach to a regulator.”<sup>68</sup>

- A. Compliance Costs for EU Data Protection Law

# 1. Overall Costs of GDPR Compliance

As indicated earlier, estimates for average annual compliance costs for the GDPR range widely, depending on the size of the company, the nature of its business, and other factors. For large firms, the estimates are routinely in the millions of dollars each year. A study conducted in 2019 by the International Association of Privacy Professionals (IAPP) in conjunction with Ernst & Young, a global professional service network, found mean privacy expenditures for the companies at which its survey respondents worked to be $1 million in 2018, the year the GDPR first went into effect, and $622,000

> 64 IBM Security, _Cost of a Data Breach_ (Ponemon Inst. (2019).

> 65 _Id_ .

>
The health care system in rural Burkina Faso is relatively underdeveloped. In the 75 survey villages, not every village has a health clinic, but the median distance to the nearest health clinic is only two kilometers (average distance is 2.85 kilometers). This is below the Burkina Faso national average distance to a basic health care center, which is 7.5 kilometers (Ministry of Health 2007). In addition, there are no statistically significant differences across treatment and control groups in the average distance to a health clinic.

Access to basic health care remains poor in Burkina Faso. In 2007, only 36 percent of the population had access to basic health care (Ministry of Heath 2010). The available data also suggest that the health system is under-staffed. In 2009, the ratios of total population to health care professionals indicate there are 22,522 people per physician, 94,564 per pharmacist, and 2,892 people per nurse (Ministry of Health 2010). The poor access to health services translates into lower utilization of health care, especially preventive health care. In rural Burkina Faso, it is estimated that only 60 percent of baby deliveries are attended by a trained health care provider, with large variations across regions (INSD/ORC Macro 2010). Noticeable exceptions include immunization coverage (more than 80 percent of children in rural areas) and utilization of prenatal care (around 94 percent of pregnant women in rural areas).

In Table 2, we use baseline data to test the balance of the randomization experiment. We first present the mean of the specific variable measured at the baseline for the control group and each of the four treatment arms. In the subsequent columns, we present p-values from a Wald test comparing the treatment arm with control group. Finally, in the last column (under the control group heading), we present the p-value for an F-test of the joint test that the means of the five groups are equal. Results show balance across characteristics for children, parents,
compiled by the United Nations, which track actual movements of capital within given annual intervals. Moreover, in fDi Markets, missing figures for capital expenditure and employment are often estimated by the publisher, and are not attributed proportionally according to the equity participation of foreign investors. A recent evaluation of alignment with official sources has shown that aggregates produced from fDi Markets have high levels of correlation with actual flows (Shekhar Aiyar & Presbitero 2023). Regularly used in combination with official FDI statistics by international organizations (United Nations 2023, World Bank Group 2020) to analyze investment project dynamics, both sources may however have incomplete coverage of smaller countries at the low end of the income spectrum (Casella et al. 2023). We address this issue by using a minimum threshold of investors per year received by a host country to be included in our analysis.

# **3 Measurement and empirical framework**

**FDI Concentration.** Two measures are used to evaluate FDI concentration levels at a given time: i. Concentration Ratio corresponding to the share of the _N_ largest investing firms in total FDI flows and ii. The Herfindahl-Hirschman Index (HHI), based on the sum of the squares of each investing firm’s share in total FDI flows. While both have been widely used in the empirical literature, the former is less affected by measurement error or truncation at the bottom of the distribution, and generally preferred for its simplicity and ease of interpretation. Regional and income group aggregates correspond to cross-country averages weighted by the value of FDI flows in each country belonging to the group.

**Country and regional trends.** Trends over time are presented conditional on country and region-year fixed effects, in a panel data specification. Let _Yit_ denote a measure of FDI concentration (the concentration ratio of the top 5 investing firms or the Herfindahl-Hirschman concentration index) in host-country _i_ in year _t_ . Then, our trend equation is:

where _Trendt_ indicates a linear time trend and _Developingi_ indicates whether country _i_ is a developing
focusing on average effects is misleading when assessing climate-induced migration, since averages wash away the substantially heterogeneous responses of structurally different groups. Without thoroughly unpacking this heterogeneity puzzle, it is implausible to make statements _a priori_ on current patterns of climate mobility, let alone future projections. <mark>It follows that rigorously estimating heterogeneous treatment effects is an essential prerequisite for the optimal targeting of</mark> climate and development policies.

Guided by these reflections, we couple a causal machine learning approach based on a data-driven search for treatment effect heterogeneity (Athey & Imbens, 2016; Wager & Athey, 2018), tailored to a conceptual framework bridging the New Economics of Labor Migration (NELM) and the poverty traps literature, with longitudinal, multi-topic household survey data collected in Nigeria with support from World Bank's Living Standards Measurement Study (LSMS). When we refer to a ‘data-driven search for treatment effect heterogeneity’, we mean that we go beyond estimating average treatment effects. Instead, we calculate the impacts of weather shocks at the household level. These individual impacts are then aggregated into group-average treatment effects. Importantly, the groups are identified through data-driven sample splits based on specific household characteristics before the shock. Our non-parametric estimates of treatment effect heterogeneity suggest that some key variables —namely, pre-shock asset levels, _in situ_ adaptive capacity, and cumulative exposure to weather shocks—can alter not just the magnitude, but even the sign of the causal impact of growing season weather anomalies on the (im)mobility outcomes of Nigerian agricultural households. Overall, our analysis provides empirical evidence that suggests the presence of climate-induced immobility traps.

Among recent works related to this paper, <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) leverage high-frequency data collected from the International Organization for Migration in 17 West and Central African countries over the period 2018-2019, and estimate that droughts occurring during the growing season reduce international migration. As they find these effects only in middle-income areas (and not in rich nor poor
fast and cheap for businesses in treatment groups. 82 percent of businesses that benefited from the program and formalized declared that they did not pay anything in the process (those who paid something in the treatment groups formalized with a different status than the _entreprenant_ status).

Qualitative work conducted few days or weeks after the businesses received a visit from the CGA suggests that the program understanding was relatively good, given the complexity of the intervention. However, data from our endline survey suggest that one and a half to two years

> 8 Bank data did not include sufficient information besides names that could be used for the matching. As a result, matching between study data and bank data was not perfect and only 70 percent of the _entreprenant_ accounts were found in the study data. Therefore, 6.2 percent represents a lower bound of the number of _entreprenant_ bank accounts opened by study participants.

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# **Box 1. Financial inclusion in Turkey** : **Insights from the 2017 Global Findex Survey**

- 69 percent of adults in Turkey have an account, up from 57 percent in 2014.

- 83 percent of men but only 54 percent of women have an account, a gender gap that is roughly three times the average in developing economies.

- 64 percent of adults make or receive digital payments, up from 48 percent in 2014. Part of the increase may reflect the increased use of debit cards, which 39 percent of adults use, up from 24 percent in 2014.

- 23 percent of adults save at a formal financial institution, up from 9 percent in 2014. On average, men are 9 percentage points more likely than women to save.

- 19 percent of unbanked adults cite religious concerns as a reason for not having an account— the same percentage that cites high costs. Just 1 percent of adults report religious concerns as their sole reason for not having an account.

- 72 percent of unbanked women (and 51 percent of unbanked men) indicate that one reason they do not have an account is that one of their family members already has one. In the developing world overall, a quarter of the unbanked cite this reason, with no difference between men and women.

- 89 percent of unbanked women (and 65 percent of unbanked men) are out of the labor force.

- 5 percent of unbanked women are wage‐employed and 6 percent are self‐employed.

- Roughly 15 million unbanked adults in Turkey have mobile phones—including 88 percent of unbanked women—suggesting possible opportunities to increase financial inclusion by moving routine cash payments into accounts.

- About 1 in 10 unbanked women in Turkey—1.3 million women—send or receive domestic remittances in cash or through an over‐the‐counter service. Expanding the reach of mobile remittances could have benefits that go beyond convenience. Research shows that mobile transfers help people survive financial shocks by making
outcomes are similar across the different treatment groups (see Appendix Table A1). The followup surveys (described below) put in place strict protocols to ensure consistency in which household member was interviewed, ensuring the main remittance receiver responsible for household financial decision-making in the migrant’s absence was interviewed. Table 1 shows the balance across treatment groups in the individual characteristics of the respondents at the time of the follow-ups.

Household respondents in the study are on average 41 years old at baseline, with 33 percent female, and 85 percent married. They are typically the spouse, parent, or sibling of the migrant. Education levels are lower than the migrant’s levels on average: 14% have not completed primary school, 36% have only completed primary schooling, 28% secondary schooling, 19% senior high school, and 3 percent have a higher degree. Average household income is approximately US$150 per month.

Only half of all households report having any savings at baseline, and only 3 percent record income and expenditure. Despite 92 percent of respondents saying they discuss financial issues with family, only 40 percent had heard of the term financial budgeting, only 39 percent knew what an exchange rate was, and only 29 percent had heard of the TKI insurance that all legally registered migrant workers must have. These baseline levels suggest potential scope for financial literacy training to build knowledge and develop savings behaviors.

# **2.4 The Financial Literacy Intervention**

Training was generally conducted at the Singosari Training Center in Malang, which is a complex managed by the Malang’s Manpower & Transmigration Office. Aside from classrooms, workshops, meeting halls and dining halls, it also rents out rooms. Training sessions were also conducted directly at the offices of the PPTKIS when an individual PPTKIS had sufficient workers about to embark. These locations were very convenient for the migrant workers, since they typically live in facilities run by the PPTKIS for two to six months prior to departure, and the PPTKIS arranged transportation from their locations
chose loan clients in this age bracket because Partner felt that business and financial education could have a particularly large impact on this group. Youth unemployment is high in Bosnia, about 58 percent according to the 2007 Labor Force Survey, and self‐employment provides a viable solution to this problem. In this type of environment, it is particularly important to explore strategies to promote the entry, survival, and growth of youth‐led businesses.

We limited our study sample geographically to areas around Tuzla,<sup>5</sup> where Partner is headquartered, to facilitate the logistics of the business training. Moreover, we dropped clients who had not taken out a loan for business purposes from our sample in order to target clients who were either running a business or planning to start a business. We also did not include clients who were delinquent on their loan payments according to Partner’s definition.<sup>6</sup> All 2,274 Partner clients meeting these criteria received an initial screening phone call, asking them whether they would be interested in participating in a business and financial education training course. About 500 clients could not be reached over the phone. Among the 1,783 clients who were reached, half reported being interested in participating in the course. Table 1

> 5We limited the sample to clients living in the municipalities of Banovici, Gracanica, Gradacac, Kalesija, Lukavac, Sebrenik, Tuzla, and Živinice.

> 6 Partner’s definition of delinquent loans is either being more than 15 days late on the current payment or having a cumulative number of late payment days over 15. The reason for not including these clients in the sample is that it is Partner’s policy not to offer any programs or new loans to delinquent clients.

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