rafmacalaba/datause-encoder-data huggingface dataset:
the same sample as in the IMR estimates. The dependent variable, on the vertical axis, is the residual of literacy after district and year of birth fixed effects. The graph has two horizontal axes: IMR in the first year of life, along the top, and categories of caste and religion, along the bottom. The local kernel regression plots the association between the literacy residual and IMR: there is no relationship, and the prediction is never statistically significantly different from zero. In contrast, there are large differences in literacy according to caste and religious groups. It appears that social groups and hierarchy – much more than ability as shaped by early-life disease – shaped the allocation of education for Indian men of the cohorts studied. If social hierarchy indeed limited the ability of the Indian economy to optimally allocate investments in human capital, then the effects of early-life health on adult wages that we estimate may be less than the effects that would have been otherwise possible.

# **3.5 Controlling for Historical Female Literacy**

We have shown a robust association between the early-life disease environment and adult wages that does not appear to spuriously reflect other time trends, correlated effects of contemporary labor markets, adult occupation, educational achievement, or social heterogeneity. Could our result, however, merely reflect a different dimension of the early-life environment? Although many dimensions of heterogeneity in historical Indian districts have already been accounted for by our empirical strategy, here we focus on a particular measure of human development that is available in historical census data, important for child human capital accumulation, and distinct from the disease environment: average levels of female literacy.

This method has the further advantage of being a verification of our technique of interpolating independent variables from historical census data; in other words, similarly constructing an additional variable from similar historical sources helps us verify that the association we find with adult wages is not mechanically implied by our procedure (although there is no particular reason to suspect this). Therefore, we construct district level annual time series of female literacy by interpolating linearly between census rounds, and match adult males in the IHDS with district level female literacy in the year of their birth.

Table 5 presents results of adding female literacy to our analysis. Column 1 first checks 12
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# **B. Overtime Changes in Relative Earnings**

Before we **turn** to the discussion of the time trends of the rate of return to schooling, let us examine the behavior of earnings differentials in Latin American countries. Owing **to** the availability of data, calculation of earnings differentials over time is only allowed for twelve countries. Table 4 presents the earnings differentials in absolute and index values by level of education, and Table 5 provides the corresponding structure of the labor force by level of education.

N.

As seen in Table 4 and depicted in Figure 2, the relative earnings of individuals with either secondary or university education decreased over time in most conntries with the exception of Bolivia, Guatemala and Panama. This is expected since the number of workers as a percentage of the labor force with post-compulsory schooling increased during the same time period in all countries expect Hondurasl'. This implies a strong relationship between schooling and income distribution in society. Earnings differentials narrow as the educational attaintment of the labor force increases.

_I/_ The overtime comparability of the Honduras, Peru and Uruguay data sets is limited given the fact the later surveys refer only to urban areas.
The classification of enterprises into HEs and MEs is done primarily based on information provided in the enterprises module. Usually, analysis only defines MEs, and does not include HEs as a sub-category. Here they are separated, in recognition of importance of the ability to hire workers from outside the household as an indicator of managerial ability and growth potential, and therefore the probability of a different response to a particular policy or program from MEs compared with HEs (see de Mel et al, 2008 and 2009).

We include enterprises in the study _regardless of registration status_ . In classifying an enterprise as _informal_ , standard practice (ILO, 2011) requires that it meet (i) an ownership criteria (unincorporated, owned by household members) an _d either_ (ii) a size criteria (below a specified level of employment, e.g. 5 or 10 employees depending on the country), and/or (iii) a legal status criteria (non-registration of the enterprise or its employees). We have adopted the first criteria, but not the legal status. We find that the rules on registration differ by country, and within countries there may be several levels of registration -e.g. national as well as sub national. And the meaning of registration is different by country. In some countries, (e.g. Uganda), registration implies a certain level of legitimacy from the state, but in other countries (e.g. Rwanda, Tanzania), it does not. In addition, in some countries it is legal to do business in one‟s own name _without_ registration. This means that the concept of registration is not defined or implemented consistently across sample countries, and so we do not use it as a sorting variable. On this point, our study differs from those using the ILO definition (Grimm et al, 2011; ILO, 2011; Nguyen et al, 2011).

The core analysis is based on nationally representative household survey data from the following countries: Burkina Faso, Cameroon, Republic of Congo (urban only), Ghana, Mozambique, Rwanda, Tanzania, and Uganda.<sup>4</sup> Survey instruments differed by country but we have developed consistent variables for the economic activities of household members over the age of 15 to the extent possible. Variables include; type of activity, and for non-farm activities, time spent in the activity (any unit), and earnings per time period worked. The respondent‟s stated primary activity is used for the employment analysis, while both primary and secondary information is used for the analysis of all household activities (livelihoods) and the analysis of HE owners. For Ghana, Rwanda, Tanzania, and Uganda, the nationally representative household survey data was supplemented by qualitative and quantitative data collected from field interviews with HE and ME owners. These data are not nationally representative. The fieldwork focused on HE owners‟ motivations, perceptions about their work, their opportunities, and the constraints they faced.

> 4 See Annex Table 1 for a list of countries and data sources.

6
civil law system such as many countries in Africa (Mali, Madagascar, Togo, Chad, etc) and some in the Middle East (Morocco, Tunisia, Algeria, Syria, etc), face similar issues since security interests for businesses are registered in the ―Registre de Commerce et du Credit Mobilier‖ (RCCM), limiting registration of security interests to legal persons described in the commercial code. The recommended practice is to create a single depository of security interests for all types of legal and natural persons.

**Types of assets.** A secured transactions law is most useful when it broadly defines the scope of permissible collateral to include tangible and intangible property of any nature, assets that do not yet exist or are owned by the debtor (future assets), and a changing pool of assets. A single, unitary concept of security interest should be adopted for granting a real right in any movable property from the debtor to the creditor in order to secure an obligation of the debtor. Moreover, creditors should be able to describe the collateral generically to allow the possibility of creating security interests in future assets and fluctuating assets, a prerequisite for modern inventory and receivables financing. Requiring more specific descriptions prevents future and fluctuating assets from being used as collateral, and is cumbersome even for existing assets that are not uniquely identifiable (e.g., raw materials). Table 4 illustrates examples of what could be used as movable property in jurisdictions with modern secured transactions systems.

**Table 4: Types of Assets Typically Used as Collateral in Countries with Modern**

**Secured Transactions Systems**

|**Tangible movables**|**Intangible movableproperty**|
|---|---|
|Machinery and equipment: industrial and non-agro<br>machinery, agro-machineryand equipment|A single accounts receivable|
|Inventory, i.e.,goods for sale|Multiple accounts receivable|
|Motor vehicles|Investment property (stocks and securities, options and<br>futures, bonds, derivativeproducts, etc)|
|Agricultural products: fertilizers, crops and other<br>agriculturalyields, livestock, fish farm, etc.|Intellectual property (e.g., patent rights, trademarks)|
|Consumer goods: office equipment, computers, servers,<br>screens,projectors, furniture, etc.|Insurance policies|
|Minerals, hydrocarbons, timber|Membership and partnership interests in business<br>entities and cooperative shares|
|Medical equipment|Futures (e.g., crop futures, future acquisitions of<br>collateral described in the agreement, and unborn<br>livestock)|
|Jewelry,precious stones|Bank accounts|

In MENA, immovable property is still used as the prevalent form of collateral. The majority of banks in the MENA region accept movable property as collateral. A recent World Bank survey to financial institutions in the MENA region shows that on average 67% of banks accept 13
# **Under What Conditions Are Data Valuable for Development?**<sup>**1**</sup>

Dean Jolliffe Daniel Gerszon Mahler Malarvizhi Veerappan Talip Kilic Philip Wollburg

**Keywords** : Data, development, statistics **JEL codes** : C81, C83, O10, O20

> 1 The author ordering was constructed through American Economic Association’s randomization tool (confirmation code: qhPJIjX8dDgr). All authors are with the Development Data Group of the World Bank. Jolliffe (djolliffe@worldbank.org) and Veerappan  (mveerappan@worldbank.org) are the co-director and the manager, respectively for the _World Development Report 2021: Data for Better Lives_ , while Kilic (tkilic@worldbank.org), Mahler (dmahler@worldbank.org), and Wollburg (pwollburg@worldbank.org)  are among the core team members. Jolliffe is also a Research Fellow with the Institute for the Study (IZA) of Labor and a Fellow with the Global Labor Organization (GLO). The authors are thankful for comments and feedback received from Andrew Dabalen, Gero Carletto, Kathleen Beegle, Lucas Kitzmuller, Luis Alberto Andres, Paolo Verme, Robert Cull, Tim Kelly, Umar Serajuddin, and Vivien Foster. In addition, they are thankful for the many colleagues who provided and reviewed case studies including AnnSofie Jespersen, Aparajita Goyal, Audrey Ariss, Benjamin David Roseth, Emilia Galiano, Elizabeth Goldman, Florence Kondylis, Frederic Meunier, Hana Brixi, Isis Gaddis, Joao Pedro Wagner De Azevedo, Madalina Papahagi, Marc Tobias Schiffbauer, Marelize Gorgens, Maria Poli, Natalia Baal, Natasha Rovo, Megumi Kubota, Paul Andres Corral Rodas, Sabina Alkire, Sonali Vyas, Stephane Hallegatte, Stephanie Jamilla, Tea Trumbic, Theresa Beltramo, Utz Johann Pape, and Zelalem Yilma Debebe. The authors gratefully acknowledge financial support from the UK Government through the Knowledge for Change Program.
And finally, Cargill"n acquisition of Citrobrasil<sup>in 1976 marked the arrival</sup> of a major new player in the industry.

In 1977, a freezi in Florida initi ted a period<sup>of much higher world</sup> prices for frozen concentrated orange jul;e<sup>(table 6.3). By 1978, average</sup> wholesale prices for frozen concentrate<sup>in tle U.S. market had increased by</sup> roughly 71 percent over 1976 levels (Irias<sup>1981, 20). Brazilian companies</sup> responded rapidly, expanding their production<sup>and increasing their exports to</sup> the U.S. market. The U.S. share of Brazil's<sup>exports of frozen concentrated</sup> orange juice increased from an average<sup>of 14 percent during 1970-76 to 22</sup> lercent in 1977 and 44 percent in 1978. By<sup>1978, the United States was the</sup> largest importer of Brazilian concentrate.' The climate shocks suffered by the Florida<sup>orange crops in the 1980a</sup> were the primary factor that shaped this<sup>third stage of development of the</sup> Brazilian citrus industry. The United States<sup>became a net importer of frozen</sup> concentrated orange juice, and Brazil<sup>became the dominant world producer,</sup> largely because of its increasing share in<sup>the U.S. market (table 6.4). At the</sup> beginning of the decade, Brazilian processors<sup>had produced the equivalent of</sup> 91 percent of Florida's frozen concentrate<sup>output; by the second half of the</sup> 19809, Florida's production represented less than £0 percent of Brazil's output.

The Brazilian frozen concentrated orange *e industry also increased its share in the country's total exports,<sup>from 0.5 percent in 1970 to 4.7</sup> percent in the 1980s. The industry's share<sup>in the economy as a whole is much</sup> smaller, however. Even after two decades<sup>of rapid growth, industry revenues</sup> are equivalent to only 0.56 percent of<sup>Brazil's GDP, a reflection of the</sup> strong outward-orientation of the sector. The industry has a much<sup>greater</sup> relevance to the economy of the state<sup>of Sao Paulo, however, since most (96</sup> percent) of Brazil's processing capacity<sup>is located there, and citrus-related</sup> activities employ an estimated 150,000 workers<sup>in the statA.</sup>

-5-
- Fisher, G. (2013, January). Investment choice and inflation uncertainty. Mimeo, london school of economics.

- Friedman, M. (1969). The optimum quantity of money. In _The Optimum Quantity of Money and Other Essays_ , Volume 1. Aldine Pub. Co.

- Galor, O. and J. Zeira (1993). Income distribution and macroeceonomics. _Review of Economic Studies 60_ (1), 35–52.

- Gordon, R. and W. Li (2009, August). Tax structures in developing countries: Many puzzles and a possible explanation. _Journal of Public Economics 93_ (7-8), 855–866.

- Holland, A. S. (1993, August). Inflation regimes and the sources of inflation uncertainty: Comment. _Journal of Money, Credit and Banking 25_ (3), 514–20.

- Lucas, Robert E, J. and N. L. Stokey (1987, May). Money and interest in a cash-in-advance economy. _Econometrica 55_ (3), 491–513.

- McKinnon (1973). Money and capital in economic development. _The Brookings Institution_ .

- Rajan, R. G. and L. Zingales (1998, June). Financial dependence and growth. _American Economic Review 88_ (3), 559–86.

- Sidrauski, M. (1967). Inflation and economic growth. _Journal of Political Economy 75_ , 796.

- Smith, R. T. and H. van Egteren (2005, July). Inflation, investment and economic performance: The role of internal financing. _European Economic Review 49_ (5), 1283–1303.

- Stockman, A. C. (1981). Anticipated inflation and the capital stock in a cash in-advance economy. _Journal of Monetary Economics 8_ (3), 387–393.

24
# **ACCOUNTS AND PAYMENTS**

Worldwide, 50 percent of adults report having an account at a formal financial institution—a bank, credit union, cooperative, post office, or microfinance institution.<sup>1</sup> For most people, having such an account serves as an entry point into the formal financial sector. A formal account makes it easier to transfer wages, remittances, and government payments. It can also encourage saving and open access to credit.

These benefits accrue to account holders around the world. But beyond these commonalities are many differences across regions, income groups, and individual characteristics—in the prevalence of accounts, in potential barriers to their use, in the purposes of their use. And in the developing world especially, many people rely on alternatives to formal accounts.

## **How does account ownership vary around the world?**

Not surprisingly, account penetration differs enormously between high-income and developing economies: while it is nearly universal in high-income economies, with 89 percent of adults reporting that they have an account at a formal financial institution, it is only 41 percent in developing economies. Among regions, the Middle East and North Africa has the lowest account penetration, with only 18 percent of adults reporting a formal account (figure 1.1).

In several economies around the world— including Cambodia, the Democratic Republic of Congo, Guinea, the Kyrgyz Republic, Turkmenistan, and the Republic of Yemen—more than 95 percent of adults do not have an account at a formal financial institution (map 1.1). Globally, more than 2.5 billion adults do not have a formal account, most of them in developing economies.<sup>2</sup>

### **Account penetration**

Adults with an account at a formal financial institution (%) <!-- Start of picture text --> 89<br>GLOBAL<br>AVERAGE<br>55 50%<br>45<br>39<br>33<br>24<br>18<br>HIGH-INCOME EAST ASIA EUROPE & LATIN SOUTH SUB- MIDDLE EAST<br>ECONOMIES & PACIFIC CENTRAL ASIA AMERICA & ASIA SAHARAN & NORTH<br>CARIBBEAN AFRICA AFRICA<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> What explains the large variations in account penetration? Why do more than 99 percent of adults in Denmark have a formal account while virtually none do in Niger? Does account penetration depend simply on an economy’s income level? Or are there other determining factors? And if so, what are they?

**11**

MEASURING FINANCIAL INCLUSION
prospective clients when shopping for financial products at the time of the experiment.<sup>10</sup> These materials combined pictures with information about the terms, but each institution had its own design, making comparisons across similar products difficult. The second disclosure treatment used key facts statements (KFS) that institutions were required to give customers after contracting a product. The SBS regulated the minimum number of terms that had to be disclosed, but the design and whether to show the terms in fine print was left to the financial institution. As a result, these market-designed KFS had different layout of information, again making comparisons across products difficult. The third and final disclosure format used a standardized key facts statement designed jointly by SBS and us. This format presents the more relevant information in the top right corner using a large font and because the information is standardized, a given term will always be in the exact same place for every institution thus facilitating comparability. In Mexico, the first disclosure treatment also used brochures. The second disclosure treatment used a standardized key facts statement designed jointly by CONDUSEF and us that is similar to the standardized KFS used in Peru. The remaining treatments used comparative tables with either 5 or 10 products that varied the number of financial terms presented. The complex treatment presented information for 5 products with 8 terms for credit and 12 terms for savings ( _Complex 5_ ). The simple treatment also presented information for 5 products, but with 4 terms for credit and 3 terms for savings ( _Simple 5_ ). Finally, the long, simple treatment presented the same terms as the Simple 5 treatment just described but provided information about 10 different products ( _Simple 10_ ). Online Appendix OA4 contains examples of all the disclosure formats used in the experiment.

The order in which formats were presented to participants was randomized in each session to avoid learning effects. Given the objective of comparing the performance of different formats, all materials had to have key information about the APR / YPR and user fees to make the informational content comparable across formats. This meant that terms of the product had to be added to the materials if these were missing in the original one, which was typically the case for brochures.

> 10 From all the marketing material collected by CONDUSEF and SBS, we chose the 5 materials with the highest amount of information about the product.

11
group were steadily falling further behind the rest of the village in terms of their living standards – even though within the Jatab community there existed differences across individuals and households. The analysis in Lanjouw and Rao (2010) thus found strong support for the perception that caste based inequality in Palanpur was significant and durable.<sup>11</sup> Table 11 revisits the calculation undertaken in Lanjouw and Rao (2010) and updates the analysis to include the 2008/9 data. The evidence points to a clear turnaround with the Elbers et al (2008) statistic registering a significant decline between 1983/4 and 2008/9, from 50% to 29%. Whereas in the survey rounds leading up to 1983/4 the picture is one of the Jatabs as a group falling ever further behind the rest of the population to the point where even the richest Jatabs have lower incomes than the poorer most non-Jatab households, the 2008/9 data indicate a substantial attenuation of this pattern. The evidence from this decomposition exercise thus accords well with story offered earlier in this paper, of the Jatabs registering significant upward mobility since 1983/4, the result of markedly improved access to non-farm earning opportunities for at least a subset of Jatab households.

**Table 11:  Growing “Isolation” of Jatabs Has Reversed Since 1983/4**

|Jatabs versus the rest<br>of the village|Overall<br>Theil<br>L<br>Measure of Inequality|Elbers et al (2008)<br>partitioning index|Inequality<br>Contribution<br>from<br>“Classic”<br>Decomposition|
|---|---|---|---|
|1957/8|0.188|7%|3%|
|1962/3|0.282|10%|4%|
|1974/5|0.169|39%|9%|
|1983/4|0.174|50%|22%|
|2008/9|0.336|29%|12%|

# _The Contribution of Non-Farm Income to Overall Inequality_

The analysis above confirms that widening group differences are unlikely to explain the significant increase in income inequality between 1983/4 and 2008/9. Much more plausible is

> **11** Lanjouw and Rao (2010) also documented that while caste based inequality in Palanpur was significant, preliminary analysis based on other criteria for group differentiation –  education of household head, and household landownership – failed to reveal sizeable between group differences, whether based on the Elbers et al (2008) statistic or the standard between group decomposition.

23
**Figure 7: Global Corporate Vulnerability Index**

<!-- Start of picture text --> CVI<br>0.2<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Global country median. 75 th and 25 th percentiles in dotted lines. Source: Own calculation<br><!-- End of picture text -->

**Figure 8: Intensity of Corporate Vulnerability**

Debt at Risk for X or more indicators (DaR>=X) <!-- Start of picture text --> DaR>=X<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>DaR 1 or more DaR 2 or more DaR 3 or more DaR 4 or more<br>Global country median. Source: Own calculations<br><!-- End of picture text --> Trends across regions have not been uniform. In ECA, corporate vulnerability increased significantly in 2007 and has remained elevated since, while LAC experienced a steep rise in 15