rafmacalaba/data-use-mentions-extended huggingface dataset:
34

# **Notes**

1. Throughout this paper, CEE will denote Central and Eastern Europe and FSU--the states of the former Soviet Union.

2. The Berkeley-Duke studies of the "second economy" during the Soviet era are an exception. More recently, the qualitative importance of the unofficial economy in Russia is acknowledged in James Leitzel's **Russian Economic Reform** (London: Routledge Press, 1995).

3. Daniel Kaufmann, "Diminishing Returns to Administrative Controls and the Emergence of the Unofficial Economy," **Economic Policy,** 19, December 1994; idem, "Market Liberalization by Stealth: Curse or Blessing in Disguise?" in **Trade in the New Independent States,** Constantine Michalopoulos and David G. Tarr, eds., Washington, DC, World Bank/UNDP (United Nations Development Programme), 1994; Simon Johnson, Daniel Kaufmann, and Oleg Ustenko, "Winners, Losers and Survival Strategies in Ukraine," mimeo, The World Bank and Duke University, October 1995.

4. Istvan Dobozi and Gerhard Pohl, "Real Output Decline in Transition Economies--Forget GDP, Try Power Consumption Data," **Transition Newsletter,** World Bank, vol. 6, JanuaryFebruary 1995.

_5._ It is well known that many enterprises keep three sets of books: one for the enterprise's own management, one for potentially interested investors, and one for tax officials.

6. A rigorous formalization of this conceptual framework will be presented in a forthcoming paper.

7. This factor (type of activity) needs to be interpreted flexibly. In the FSU and CEE states almost any type of activity can operate unofficially. The difference is one of probability and the extent of doing so along a continuum.

8. Except for the fact that in the latter group there has been significant political liberalization. The relative sophistication and efficacy of this group's market and legal institutions could be expected
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(1992) finds a negative relationship between remittances and labor participation and a positive relationship with self-employment for both men and women in Nicaragua.<sup>15</sup> Studies like Airola (2008) use Heckman selection to address selection into the labor force, but do not address selection into the

> 11 Rural unemployment rates are about half that of urban rates.

> 12 Source: ILOSTAT. Underemployment LU4 definition is calculated as the share of the working age population that is unemployed, plus those who are employed but want to work more hours, and potential labor force participants currently out of the labor force: discouraged workers (available but not seeking), and seeking yet unavailable.

> 13 Source: ILOSTAT. Underemployment LU2 definition is calculated as the share of the working age population that is unemployed, plus those who are employed but want to work more hours.

> 14 The share of households (and adults) that report having received remittances fell for the three countries between 2006 and 2014. However, macroeconomic estimates of remittances in 2006 and 2014 do not report such significant drops, suggesting that the 2014 household surveys may suffer from higher levels of underreporting. This is a larger concern in the case of Guatemala, which records the share of adults in recipient households falling from 15 percent to 8 percent.

> 15 An increase in remittances of $100 decreases the probability of labor force participation by 2.1 percentage points for men and 5 percentage points for women while it increases the probability of being self-employed (conditional on being in the labor force) by 1.2 and 1.1 percentage points respectively.

5
<br>Pharmaceuticals, medicinal...|
|0.9%<br>Mining and quarrying, energy...|0.5%<br>Wholesale and retail trade;...|
|0.5%<br>Fabricated metal products|0.5%<br>Accommodation and food...|
|0.4%<br>Chemical and chemical products|0.5%<br>Education|
|0.3%<br>Pharmaceuticals, medicinal...|0.3%<br>Coke and refined petroleum...|
|0.3%<br>Other transport equipment|0.3%<br>Publishing, audiovisual and...|
|0.1%<br>Food products, beverages and...|0.3%<br>Land transport and transport...|
|0.1%<br>Warehousing and support...|0.3%<br>Rubber and plastics products|
|0.1%<br>Financial and insurance...|0.2%<br>Warehousing and support...|
|0.1%<br>Air transport|0.2%<br>Arts, entertainment and...|

Source: Own computations. Data: OECD TiE data, covering 51 countries and 45 sectors for the period 1995-2018. Income categories as of 1995 are used to classify countries into high- and low- and middle-income countries (see Appendix 7).

**Figure 13: Employment in exports, ten most contracting sectors, (% pt change), 1995-2018, emerging vs. HICs**

|**Ten most contracting sectors (% pt c**|**hange), LMICs**|**Ten most contracting sectors (% p**|**t change), HICs**|
|-
10 over that from the MICS as all other points in the stunting time series for Rwanda come from DHS); (2) survey sample size with a preference for larger surveys in order to minimize sampling error; (3) fit with the overall time series where points are dropped if they form outliers without a credible explanation; (4) preference for data points we computed from the micro-data ourselves over data points from publications.<sup>8,9</sup>

# _Complete replacement_

In some cases, we replaced points’ previous source(s) with an entirely new source. Most of the time, this occurred when we managed to obtain the microdata of a point that we had previously sourced from a publication – such as Kosovo’s 2013 skilled birth attendance rate that came from the MICS survey report in the 2018 HEFPI database and that we now computed from the microdata ourselves.

Encouragingly, in most cases, any changes in indicator values resulting from the changes in sources are small – the median change relative to the 2018 HEFPI indicator value is 3.9 percent. But sometimes, the changes are meaningful, such as for the United Kingdom’s 2003 inpatient care rate which drops by five percentage points to 8.3 percent when we exclude Eurobarometer data and exclusively rely on the General Household Survey (GHS).

> 8 For some of the points in the 2018 database which were computed as averages over multiple points, the underlying points came from the same survey. For instance, the 2018 database’s 2013 mammography rate for Belgium was computed as the mean over two rates from the European Health Interview Survey – our own microdata-based rate and the rate published by the OECD. The 2019 database uses the microdata-based rate only.

> 9 In some cases, sources changed because a source had been erroneously entered: For example, for the 2008 mammography rate for Canada, we mistakenly computed the 2018 HEFPI version’s data point as an average over a point from the 2002 Joint Canada/
however, and an important outstanding research agenda is to compare small area estimates of monetary poverty obtained using geospatial data to those obtained from standard census-based poverty maps directly from census data. This can confirm that the encouraging results presented above apply to monetary poverty.

A final caveat, which applies to small area estimation in general, is the challenge of measuring local transient shocks. In the context of geospatial data, key indicators used to generate model predictions, such as building counts, are not necessarily affected by local economic shocks. This also poses an issue for traditional census-based poverty maps, as the census predictions tend to rely heavily on indicators such as educational attainment and household size that may also fail to track local shocks. Empirical Best methods partly address this issue by incorporating welfare information from the sample. Nonetheless, future research is needed to explore which geospatial indicators better reflect local economic shocks.

Overall, the results from an evaluation exercise using data from two countries demonstrate major efficiency gains from combining survey data with geographically comprehensive geospatial data to generate small area estimates of non-monetary poverty. These efficiency gains came at no cost to coverage in Sri Lanka and a moderate cost in coverage rates in Tanzania, and the latter can be addressed through further refinements to the methodology. The financial cost of incorporating geospatial data is low, relative to the cost of achieving similar efficiency gains by surveying additional households. While there is room for further methodological improvement, these techniques can currently be applied at modest cost to generate more granular and informative estimates of non-monetary welfare.

> 43  Chambers and Das (2017) propose a similar strategy of adjusting traditional ELL estimates to correct for biased estimates of mean squared error.

> 44 See Belghith et al, 2020. This corresponds to the marginal R2 reported in Table 6.

42
the personal income tax by following the OECD’s assumption that income distribution is lognormal, and allowing for a distribution of income based on each country’s Gini coefficient (taken in most cases from the World Bank’s Poveal database) around average wage income (taken from national sources). We then located within this stylized income distribution the tax brackets as outlined in the tax code. The average marginal tax rate is the average across tax brackets of marginal tax rates weighted by total income in each bracket. As an example, Figure 2 illustrates the calculation of the average marginal income tax rate for Brazil in 2008. The figure shows the approximated cumulative distribution function (CDF) of earnings. At each wage rate along the X-axis, the CDF gives the percentage of total earnings in Brazil earned by workers with wages below the indicated wage. There were three income-tax brackets in Brazil in 2008: 0%, 15%, and 27.5%. Although 78% of workers in Brazil fell into the lowest tax bracket, their earnings comprised only 39% of total earnings. Similarly, 14% of the working population faced a 15% marginal tax rate; this group earned 25% of Brazilian income. Finally, the remaining 7% of the population, and 36% of national income, faced the highest marginal rate. The average marginal tax rate in Brazil in 2008 was calculated as the sum of the three marginal rates, each multiplied by the share of total wage income that faced that marginal rate. To assure the reader that our methodology is consistent with earlier estimates in the literature, Figure 3 compares the time series of the average marginal income tax rate calculated by Barro and Redlick (2009), to that obtained using our methodology. The two series are broadly similar, with <u>~ 1aez (2001) and others have argued</u> that the Pareto distribution better characterizes the actual income distribution in the United States than does the lognormal distribution. Lopez and Serven (2006
||||8|||
|R squared|0.4346|||||||
|F stat.|26.38|||||||
|Wald chi2|||659.15|||||
|Selection test:||||||||
|Wald chi2|||1.750|||||

# **VII. CONCLUSION**

42. In recent years, there has been renewed interest in access to jobs in relation to transport connectivity. In Sub-Saharan Africa, about 14 million working age people are added to the
terms of collecting and curating widespread data on changes in inequality (and for the moment is likely to remain so, since at least two datasets, separated by a time span of 5-10 years, are required to determine any such change). In this sense, the rising global interest in and policy stature of inequality, especially as it pertains to the world’s poorest people, _precedes_ the recent academic data revolution that has transpired for inequality research in high-income economies.

The world’s richest countries offer the best possible data for the study of income and wealth distributions and the intergenerational transmission of income and wealth. Making good use of these data, MDMIW offers a multitude of empirical insights. Allow us to highlight some (not being featured here is by no means a judgement of the study). First, returning to the observation highlighted in our opening paragraph, “there appears to be a broad trend across many countries toward an increase in wealth inequality” (Chetty et al., 2022; page 5). More specifically, the collective empirical findings point to recent increases in income inequality and a decline in intergenerational mobility in the high-income world, consistent with the Great Gatsby Curve (Corak, 2013). Chapter 3 (by McKinney, Abowd, and Sabelhaus) observes a recent increase in inequality and decline in income mobility in large metropolitan areas of the United States (over the period 1998-2017). Fisher and Johnson (in chapter 14) similarly observe an increase in inequality in the United States. Using Canadian census and tax returns data linked across

> 6 When discussing matters pertaining to economic life and comparing global trends, the World Bank uses the designation of ‘economies’ (rather than the more familiar term ‘countries’) in deference to those national territories whose formal status as a ‘country’ is contested or unwarranted.

5
days are less likely to exit while firms with a female top manager are more likely to exit. This provides some indication that women entrepreneurs may be vulnerable to extreme weather shocks.

# **3.4 Innovation, Digital Connectivity, and Exporters**

In table 5 we explore whether innovation, digital connectivity and exporting status have any influence in the relationship between extreme dry days and sales. We find that firms that have innovated through improved processes, or have technology licensed from foreign firms tend to debilitate the effects of extreme dry days on sales. This may be explained by more innovative firms being more able to adapt to climate shocks, and therefore are more resilient. Sales of firms that are more likely to own a website are less affected by extreme dry days than firms that do not own a website. While the data does not allow us to explicitly explain this relationship, one could hypothesize that digital connectivity allows firms to access more information that could help them adapt to extreme dry days. Finally, exporters are found to be more resilient to shocks than non-exporters. This suggests that demand from abroad is not affected by localized extreme weather events, and that as long as firms adapt, they may be more likely to survive. This is in contrast to findings in the literature that suggest that exporters are also more likely to be affected than non-exporters (Huppertz, 2023).

# **3.5 Green Firms**

The 2019 Enterprise Surveys wave for two regions - Eastern and Central Europe and Middle East and North Africa – has a special green module that captures whether firms adopt climate friendly measure (see table A1 for summary statistics). There is some evidence that firms exposed to extreme weather events are more likely to take steps to adapt by adopting climate-friendly measures (Benincasa et al., 2024). The destruction of physical capital due to extreme weather may result in new investments that lead to vintage effects where replenishment of capital means newer equipment with lower environmental footprint. Alternatively, firms 15
We construct loans and deposits by converting all reported LBS data (including restricted and confidential parts) into a bilateral country-to-country format. The LBS data are collected in a format where banks in each reporting country record their loans to and deposits from every counterparty country (all sectors). We transform this bank-to-country format into a country-to-country format by using mirror data available from the reported liabilities (Brei and von Peter 2018). In the latter format, deposits and loans from country _i_ to country _j_ need to include all sectors. The claims held by banks on country _i_ represent bank lending to all sectors (banks and non-banks) in every other country _j_ . In addition, what non-banks in country _i_ deposit with banks in country _j_ also represent claims of country _i_ on country _j_ and they can be inferred from liabilities that banks in country _j_ report owing to nonbanks in country _i_ . These mirror liabilities are reliable: banks know in which country the holders of bank deposits reside (unlike for holders of traded securities). Note that interbank positions appear twice whenever source and destination are both reporting countries. In those instances, we select the larger of two reported values _._<sup>25</sup> The use of mirror data has the benefit of maximizing coverage. Loans and deposits are available whenever the source or destination country reports information (recall the China example in subsection 2.2 in the main text). Despite the enhanced coverage thanks to mirror data, coverage remains incomplete when neither the source nor destination reports to the BIS. Incomplete coverage mainly afflicts the South-to-South block, although it has improved over time as more South countries have begun to report.

The treatment of unreported values in the loans and deposits data set is handled as follows. Bilateral positions between any two non-reporting countries are unobserved and thus treated as

> 25 Taking the maximum value addresses the general issue that incentives and reporting systems make underreporting more prevalent than overreporting. For instance, the set of reporting banks (‘‘internationally active banks
- Beazley, R., Marzi, M., & Steller, R. (2021) Drivers of Timely and Large-Scale Cash Responses to COVID19: what does the data say? Social Protection Approaches to COVID-19 Expert Advice Service (SPACE), DAI Global UK Ltd, United Kingdom

- <mark>Bloemendaal, N., De Moel, H., Muis, S., Haigh, I. D., & Aerts, J. C. (2020). Estimation of Global Tropical Cyclone Wind Speed Probabilities Using the STORM Dataset.</mark> _<mark>Scientific data</mark>_ <mark>, 7(1), 377.</mark>

- <mark>Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. (2020). Generation of a Global Synthetic Tropical Cyclone Hazard Dataset Using STORM.</mark> _<mark>Scientific data</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1), 40.</mark>

- Blumenstock, J E, N Eagle and M Fafchamps (2016), “Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters”, Journal of Development Economics, 120, 157-181 Bottan, N., Hoffmann, B., & Vera-Cossio, D. A. (2021). Stepping up during a crisis: The unintended effects of a noncontributory pension program during the Covid-19 pandemic. _Journal of Development Economics_ , _150_ , 102635. https://doi.org/10.1016/j.jdeveco.2021.102635

- <mark>Bottan, N., Hoffmann, B., & Vera-Cossio, D. A. (2021). Stepping up during a crisis: The unintended effects of a noncontributory pension program
|
||(0.01279)|
|bio1|-0.00406 ***|
||(0.00077)|
|bio4|-0.00021 ***|
||(4e-05)<br>|
|pet|0.00147 ***<br>(0.00016)|
|s2|0.02357<br>(0.24747)|
|rho|0.75524 ***<br>(0.00165)|
|Observations|765|
|adj. _R_<sup>2</sup>|0.558|

Table 4: Parameter estimates of SEM model with _τ_ = 0 _._ 54 t-statistics in parentheses: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01

# **4.2 Comparing poverty maps**

Before we inspect the estimates of poverty at the small area level, let us first evaluate the estimates at the national and the regional levels. Since the IHS3 survey is designed to be representative at these levels, the survey direct estimates provide a natural benchmark to compare our model-based estimates against. The ELL and SEM estimates are 23
# **V. THE WAY FORWARD: RECOMMENDATIONS FOR POLICYMAKERS**

Our review of the findings to date on financial literacy and financial education programs has suggested a number of practical implications for policymakers going forward. We conclude by highlighting some of the lessons from the literature, and also by noting gaps in our knowledge where future research is needed. We have divided the lessons into those for programs for consumers and those for entrepreneurs, though some lessons apply to both.

## _To improve financial education for consumers:_

**Start with awareness.** The FinScope surveys demonstrate that lack of awareness of financial products and institutions is likely a major barrier to the take-up of financial products, particularly insurance. Moreover, Carpena et al. (2011) indicates that it may be easier to design interventions to improve financial awareness and attitudes than traditional numeracy skills.

**Leverage social networks and peer effects.** Financial literacy interventions have been shown to have positive spillover effects in the social networks of those who participate in the intervention. For instance, Duflo and Saez (2003) find such effects for the colleagues of employees who attended an information session on a retirement plan, and Cai (forthcoming) finds that the friends of rice farmers in rural China who attended an information session on crop insurance were also more likely to take-up the product. Promoting such spillovers would be a smart and costeffective way of spreading the benefits of financial education. Particularly in the developing country context, related work by Conley and Udry (2010) on pineapple farmers in Ghana shows that informational networks are fairly strong, and potentially an important leveraging point for interventions such as financial education.

**Identify and target vulnerable populations.** Most surveys indicate that women, youth, the elderly, and those with lower incomes and educational attainment are less likely to be financially literate. Many surveys also indicate disparities between urban areas and rural areas, and geographical regions, and differences by race, ethnicity, employment status, and a variety of