rafmacalaba/data-use-mentions-extended huggingface dataset:
is the second bimonth of 2018 and afterwards for the fiscal variables or the first quarter of 2018 and afterwards for the labor market variables. Table A13 and table A14 show the estimates of _α_ along with the weights that each of the northern states receive in forming synthetic control state. The covariates used to match for the fiscal variables include index for the service sector from IBGE and the cost of making per square feet, while the covariates used for the labor market includes gender ratio, youth dependency, household size, average age and fraction of white and college educated from the pre-2018 time period. Results that remain consistent are that there is a social assistance spending shock due to the inflow of Venezuelan refugees and migrants in Roraima but revenue collected from indirect taxes also increased, while the population shock led to an increase in unemployment, suggesting the in the short-run, the labor market in Roraima may be finding it difficult to absorb the excess labor supply. The estimates can be interpreted as the average treatment effects as explained in Abadie et al. (2010).

# **7 Conclusion**

The inflow of forcibly displaced Venezuelans into Brazil is a humanitarian crisis but has created a development challenge since most of them have entered through Roraima, the state of Brazil bordering the Rep ́ublica Bolivariana de Venezuela at the north. The economy of Roraima is small and is mainly dependent on the public sector and although it is relatively easy for forcibly displaced Venezuelans in Brazil to get labor cards and work in the private sector, jobs in the public sector are out of their bounds. This paper uses a regression discontinuity design to study the impact of the population shock caused by the influx of the forcibly displaced Venezuelans on Roraima and finds that although in the short-run they have a negligible effect on Roraima’s fiscal balance and is not only incurring additional social assistance expenditure but also may be contributing through indirect taxation, the labor market is finding it challenging to absorb the excess supply. It finds that in the immediate short-run, the influx of
institutions endorse rent-seeking and production as competing activities, leading investments to be allocated into unproductive activities, hence, resulting in poor growth performance. This channel of causation has not only been empirically supported by the authors, but also by others using different measures of institutional quality and natural resources endowment (Boschini et al., 2013).

While institutional quality may either operate indirectly or interactively with resource wealth on economic performance, the role of local institutions has relatively been neglected in the resource curse debate (Lawer et al., 2017). Notwithstanding, local institutions have borne increasing responsibilities since the wave of decentralization reforms that have taken place on the African continent in the last decades. In resource-rich communities, as in others, decentralization is intended to strengthen local authorities as they complement national authorities in ensuring that these communities benefit from resource revenues through improved welfare. As advanced by Mitton (2016), the distinction between national and sub-national entities is important to consider, as they may not operate the same way. Even though national policies may promote accountability and transparency of resource revenues, Lawer et al. (2017), in a qualitative analysis, denotes that decentralization and by extension local government institutions do not necessarily improve living standards in mining communities, but rather gives more rooms for rent-seeking behaviours when local institutions are weak in nature.

To date, and to the best of our knowledge, no empirical and quantitative studies have explored the local effects of natural resources through the lens of decentralization, sub-national governments taxing rights, and their legal attribution in revenue mobilization. Therefore, as part of our second contributions, this paper assesses the mediating role of local governance and decentralization in the relationship between natural resources endowment and local socioeconomic outcomes.

# **3. Data Description and Sources**

Our empirical analysis relies on geo-referenced Afrobarometer surveys data for the period of 2005 to 2014. The Afrobarometer constitutes a large pool of public opinion surveys and a reliable source of information on various political and socio-economic characteristics of residents and local communities in Africa
risk management strategies due to their small size and low, irregular earnings (Guven et al. 2021). They also tend to not be connected to social protection systems, particularly in urban areas where the prevalence of safety nets and cash transfer programs has been meager as compared to rural Africa (Gentilini et al. 2021, Bossuroy et al. 2017). Finally, they are more affected by global economic fluctuations, compared to rural households, because of the nature of their economic activities being more exposed to international markets.

**Within the NFHE sector, and the informal sector more generally, certain demographic groups may have experienced particular hardship.** In the SSA region, 78 percent of employed women work in the non-farm informal sector, compared to 67 percent of men (ILO 2018). Employed women’s work tends to be concentrated in face-to-face services (Madgavcar et al. 2020), including food service, hospitality, retail and wholesale, all of which were disproportionately affected during the shutdowns and mobility restrictions. Women were further saddled by an increase in home care<sup>1</sup> – tending to children whose schools had closed, caring for family members who were sick with COVID-19, and home production of goods and services that may have been difficult to procure due to the shutdowns and disrupted supply chains – that required diverting time and financial resources away from their businesses (Goldstein et al. 2020). Indeed, preliminary evidence from phone surveys from 40 developing countries finds that women were disproportionately affected by employment loss (Kugler et al. 2021). The same study suggests that

> 1 https://blogs.worldbank.org/developmenttalk/global-state-small-business-during-covid-19-gender-inequalities

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1 percent level. Also, the networking parameter captured by political motive of the fund remains positive and significant at the 1 percent level. This suggests that SWFs with a political bias are more likely to invest in Africa. We also find that the coefficient associated with the share of christians has a positive and significant effect on SWF investments. Interestingly, the coefficient associated with the proximity to the technology frontier also enters positively and statistically significantly different from zero at the 5 percent level. This shows that the share of SWF investments in Africa increases with the level of technological innovation. In terms of institutional variables, we use the level of corruption. The results are shown in columns (6) and (7). The signs and significance of our coefficients of interest remain unchanged. Our main results remain robust to the introduction of inflation, macroeconomic variables, trade, corruption and the proximity to the technology frontier as controls, and confirm at the same time the results listed above, and our theoretical implications.

# **3 Conclusion and policy implications**

Using available data on SWF in Africa, we observe that 47 percent of SWFs that invest in Africa are from minerals (extractive commodity), while 53 percent are from non-commodity sources. In addition, we find that African funds invest less domestically, in contrast to Asian funds.

In addition, we show using an enriched mean-variance portfolio model that the determinants of SWF investments in Africa are excess return, risk and networks. We test our theoretical predictions empirically, using an econometric specification regressing the logarithm of the in18
parents, requesting them to pay tuition fees in order to maintain the center. But this was not successful.”_ Indeed, our quantitative data suggest that a strategy employed by many playgroups to sustain their programs was to charge fees. As shown in Appendix Table 2, only 32% of playgroups that would eventually close were charging fees in 2010. In contrast, 51% of playgroups that would remain open were charging fees in 2010. The proportion of playgroups charging fees rapidly expanded between 2010 and 2013, for both those that would eventually close (59%) and those that would remain open (70%). Moreover, project playgroups 8
basis of two regressors: a “growth effect”, given by average income growth times the theoretical growth elasticity of poverty calculated under the lognormality assumption, and an “inequality effect”, given by the change in inequality (as measured by the standard deviation of log income) times the theoretical inequality elasticity of poverty as derived under the lognormality assumption as well. Under the null of lognormality, both regressors should carry coefficients equal to unity.

When this empirical approach is implemented on a sample of developing-country growth spells, with the change in headcount poverty as dependent variable, Bourguignon (2003) rejects the null of lognormality but still finds that the lognormal specification provides a good empirical approximation to actual poverty changes. When the dependent variable is instead the change in the poverty gap, the null hypothesis is still rejected, and in addition the fit of the regression is quite poor.

There are, however, two problems with this approach. The first one, noted by Bourguignon, is that the elasticity-based approach is valid only for infinitesimal changes in poverty and its determinants. Applying it to discrete changes can result in large approximation errors, especially given the long duration (ten years and over) of some of the spells in Bourguignon’s sample. The second problem is the implementation of the approach using poverty databases, which tend to be relatively small, typically include a considerable number of outliers,<sup>7</sup> and often involve substantial measurement error. The latter is further exacerbated by first-differencing the data for the regressions, which raises the noise-to-signal ratio. Below we present a new test of lognormality of the distribution of income that is not subject to these concerns.

# _II.1 Empirical approach_

In spirit, our approach is closest to that employed by Rutherford (1955). As noted above, for several countries (one at a time) he compared the observed quintiles of the distribution of income with their theoretical counterparts derived under the null hypothesis of lognormality. Formally, we exploit the one-to-one mapping that
contrast, the presence of fully-owned foreign firms is found to have a negative impact on local firms, due to technology intensity of multinationals crowding-out local producers within the same sectors (Abraham et al. 2010).

In addition, the _length of foreign presence_ of a multinational in the host country also influences FDI spillovers. Focusing on FDI spillovers from old versus new firms in 17 Central and Eastern Europe transition economies, Turkey and the Commonwealth of Independent States, Gorodnichenko, Svejnar, and Terrell (2007), for example, find significantly positive forward and horizontal FDI spillovers from older firms (i.e. firms that were established before 1991), while these effects cannot be confirmed for newer firms (i.e. firms that were established in or after 1991).

4
_forecasting future Covid-19 hotspots with sufficient timeliness and accuracy to be useful for contingency planning._ Satisfaction of the latter condition can be established only after a model is implemented and tested. Otherwise, the choice seems clear: The best feasible alternative at present is a gravity-modeling approach that uses freely-computable travel times instead of geographic distances. In the following sections, we develop, estimate and test the forecasting accuracy of a model that can be applied _right now_ in any setting where spatially-disaggregated infection data are available. All other data requirements can be met from open and freely-available public sources.

# **3.  Theoretical Specification**

Our specification of Covid-19 spread draws on models that are commonly employed in empirical studies of trade, population migration, epidemiology and technology diffusion. The core model views the spread of Covid-19 between locales as a function of their populations, infection rates, and the effective distance between them as measured by travel time.

- (1) i i where ii NN = ρρnn II

# **_Transmission of Infection between Two Locales_**

_N_ ij = People infected in locale i by interactions with people in locale j ρ = Probability that interaction with an infected person will lead to an infection Pj = Population of locale j) _I_ ij = Number of interactions between inhabitants of locales i and j

- Pi , Pj = Populations of locales i and j

tij = Travel time between i and j 5
<!-- Start of picture text --> Figure 11 - Regional Differences in Consumer Protection Legislation and Implementation<br>47 37 37 42 55<br>56 56 30 54 31<br>46 42 29 83 33<br>65 60 32 42 38<br>50 59 46 50 50<br>76 44 46 67 48<br>86 63 44 81 59<br>% of countries<br>Fair Treatment Disclosure at Opening<br>Periodic Disclosure Recourse Mechanism<br>CP Unit<br>Source: Financial Access Database<br>Regional means are displayed.<br>Sub-Saharan Africa<br>South Asia<br>East Asia & Pacific<br>Europe & Central Asia<br>High income<br>Middle East & North Africa<br>Latin America & Caribbean<br><!-- End of picture text -->

* Refer to notes in Table 1 for definitions of Fair Treatment and Recourse Mechanism. Disclosure at Opening is an indicator variable that takes a value 1 if a country has at least half of the measures for disclosure requirements at account opening shown in Figure 5. Periodic disclosure is an indicator variable that takes a value 1 if a country has at least half of the periodic disclosure requirements shown in Figure 6. CP Unit is an indicator variable that takes a value 1 if the regulator has a specific unit designated to work on financial consumer protection.

26
on better capturing labor outcome indicators in the country. Consequently the information content differs across surveys in terms of scope and content. This has meant that the methodology used to compute variables on the measurement of labor market outcomes - levels and trends –changed over time as well, posing challenges for analysis.

Table 2 below summarizes how the five Uganda UNHS surveys since 1992 have measured (i) labor force participation rate, (ii) unemployment, and (iii) characteristics of _all_ economic activities undertaken, including earnings. It shows where key measurement recommendations for these indicators in low-income countries discussed in the previous section - such as screening questions and long recall periods to ensure that all economic activities are captured, careful measurement of unemployment, and use of both individual and household level modules to collect complete information on hours worked and income of the venture (farm or non-farm) - were and were not used.

> 12 The guidelines for LSMS surveys can be found in Grosh and Glewwe, and online at www.web.worldbank.org (go to LSMS website).

> 13 UNHS questionnaires, documentation, and manuals are available at www.ubos.org.

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is not sufficient for theoretical explanations to merely explain why emerging market firms may be investing in (ostensibly) less risky Northern markets, but also why they may choose to invest in other economies that are otherwise very similar to their own (insofar as their level of development is concerned), rather than investing at home.

This paper seeks to examine the factors associated with the flow of M&A investment originating in emerging market economies.<sup>1</sup> The empirical analysis relies on bilateral outbound cross-border M&A data for firms based in 61 emerging markets, collected for the period between 1997 and 2010. This coverage includes economies from all major developing regions, which makes this, to our knowledge, one of the most comprehensive analyses of bilateral M&A activity by the Global South.

The picture of Southern cross-border M&A that emerges from our paper is a fairly sophisticated one. Consistent with other forms of cross-border economic activity, M&A deals reflect standard gravity components, such as economic size and distance. But the strength of existing trading and investment relationships also matter, and for acquisitions in advanced economy targets, the informational advantages gleaned from such prior economic relationships appear to overcome frictions due to physical distance. Moreover, deals in advanced economies tend to

> 1For the purposes of this paper, emerging economies are defined as 61 (mostly middle-income) economies traditionally classified as emerging markets by the financial community. The full list is provided in the annex.

2
- Fishbein, D. H., Herman-Stahl, M., Eldreth, D., Paschall, M. J., Hyde, C., Hubal, R., Hubbard, S., Williams, J., and Ialongo, N. (2006). Mediators of the stress–substance–use relationship in urban male adolescents. _Prevention Science_ , 7:113–126.

- Fletcher, J. M. (2008). Adolescent depression: diagnosis, treatment, and educational attainment. _Health Economics_ , 17(11):1215–1235.

- Frank, E. and Levenson, J. C. (2011). _Interpersonal Psychotherapy._ American Psychological Association.

- GBD 2019 Mental Disorders Collaborators (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. _The Lancet Psychiatry_ , 9(2):137–150.

- Goldberg, D. and Williams, P. (1988). General Health Questionnaire.

- Guthold, R., Johansson, E. W., Mathers, C. D., and Ross, D. A. (2021). Global and regional levels and trends of child and adolescent morbidity from 2000 to 2016: an analysis of years lost due to disability. _BMJ Global Health_ , 6(3):e004996.

- Hardaway, C. R. and Smalls, C. (2009). Poverty and socioeconomic disadvantage in adolescence. _Handbook of Adolescent Psychology, Volume 2: Contextual Influences on Adolescent Development_ , 2:444.

- Haushofer, J., Larreboure, M., Lowes, S., and Mait, L. (2023a). Cash transfers and social preferences of children. Working Paper 31720, National Bureau of Economic Research.

- Haushofer, J., Mudida, R., and Shapiro, J. (2023b). The comparative impact of
# **I. Introduction**

With a per capita gross domestic product (GDP) of US$449 (2014), Madagascar is one of the five poorest countries in the world. To finance the country’s development, there must be a significant increase in the state budget. With this end in view, the tax revenue ratio, which is one of the lowest in Africa (around 10% of GDP), should increase.

In order to increase domestic revenue mobilization, Madagascar revenue collection authorities (i.e., tax and customs administrations) should notably combat fraud more effectively. The fight against tax evasion involves that revenue collection authorities should detect more to deter more. To this end, the analysis of discrepancies in international statistics may be very helpful. Zucman (2013) uses the differences between international assets and liabilities to evaluate the degree of tax evasion by households. Based on Bhagwati (1964), Raballand et al. (2012) use discrepancies in trade statistics to identify and assess customs frauds. In a perfect scenario,<sup>2</sup> any statistical anomaly—that is, any difference between the declared export value (price, volume, weight) and the declared import value for the same trade flow, is suspect. When exports are not taxed, the exporting company has no incentive to make a non-compliant declaration. In such a situation, assuming that the gap is due to the importer is therefore a reasonable assumption.

Using original customs data, this paper aims to identify, in Madagascar, some high-risk products and high-risk operators (importers and brokers). The proposed method is complementary to risk analysis methods based on compliance (see e.g. Geourjon and Laporte (2012)). A two-step procedure is adopted. Based on discrepancies in trade statistics (mirror statistics), the paper presents products or sectors in which customs fraud is deemed to be significant. A quantification exercise of customs losses is provided. Then, through the use of
substantially complete monthly data on imoney market rates _(60B)_ is available, of which seven share a comnmon rate in the West African Monetary Union. Excluding all **but** one of the latter, this gives a total of 28 developing countries for which complete data on the movements in wholesale rates can be analyzed. These countries are: Argentina, Brazil, Cote d'Ivoire, Fiji, Ghana, Guyana, India, Jamaica, Kenya, Korea, Malaysia, India, Sri Lanka, Mexico, Malawi, Mauritius, Nepal, Pakistan, Papua New Guinea, Philippines, St. Lucia, Sierra Leone, Singapore, Swaziland, Thailand, Trinidad and Tobago, Uganda, South Africa, Zimbabwe. Although a microstate, St. Lucia is included as a representative of the ECCB. C6te d'Ivoire represents the UMOA. The following 12 industrial countries, for which monthly data on _60C_ exist, were included as controls: Australia, Belgium, Canada, Germany, Ireland, Italy, New Zealand, Portugal, Spairn, Sweden, Switzerland, United Kingdom, United States.

For the expected or _ex ante_ real interest rates, a Hodrick-Prescott filter with parameter **16500** was applied to the log-change in each coulmtry's CPI, and the result subtracted frorn the nominal exchange rate (cf. Edison, 199[6]).

3" In a few cases short stretches of missing data were filled by interpolating available quarterly figures, or by using regression relationships with available data.

35 i.e. excluding those who were members of the OECD throughout. In this definition Korea and Mexico are included with the developing countries, as they were not members of the OECD for most of the sample.

40
sup> _by method and in or out of sample_

|Country|Indicator|In-sampl|e|Out-of-S|ample|
|---|---|---|---|---|---|
|||EBP|XGboost|EBP|XGboost|
|Madagascar|Asset Index|0.83|0.87|0.62|0.76|
|Malawi|Asset Index|0.79|0.91|0.54|0.71|
|Mozambique|Asset Index|0.84|0.91|0.70|0.81|
|Sri Lanka|Asset Index|0.80|0.83|0.64|0.81|
|Malawi|Estimated headcount<br>poverty|0.75|0.91|0.63|0.78|

4. Small area estimates using geospatial data can help improve on poor targeting systems A key application of small area estimation is assisting the identification of the poorest households through geographic targeting. Traditionally, cash transfer programs use Proxy Mean Tests (PMT) as a way to identify the poorest households, which utilize a registry of verifiable characteristics to assign each household a score (Coady, Grosh, and Hoddinot 2004). The weight applied to each characteristic is typically determined through by regressing these proxy welfare indicators on log per capita consumption. However, registries are typically very costly and time-consuming to collect and update. In
equal to €176.5 billion;

- Taxes on commodities, composed of other net taxes on products from national SAM, are redistributed across regions using the demand for the respective commodity as a share. We took data from the national SAM 2016, which report a total national amount of taxes on commodities equal to €9 billion;

- VAT is regionalized using data from the DF microsimulation model on VAT (Cirillo et al., 2021). Only the VAT relating to households is regionalized. The total amount (€102 billion) was re-proportioned using the totals from the ISTAT national accounts (i.e., those reported in the 2016 national SAM);

- Excises from national SAM (€59,2 billion) reproportioned on the national demand for the respective commodity;

- Taxes on imports are regionalized as regional tariffs and the total amount is equal to €2.2 billion.

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