rafmacalaba/datause-encoder-data huggingface dataset:
Bruner, 2011). These business services correspond to ISIC Rev. 4 numbers 45-56 as well as 64-66, 6875 and 77-82.<sup>12</sup> Together, this grouping is referred as Digital+, as shown in Annex Table A2.

Of all bilateral trade relations that exist in the TiVA database, the analysis captures around 63 separate exporters and importers. Admittedly, by selecting TiVA over other sources of trade such as the ITPD-E, it omits a large group of developing countries. However, compared to the ITPD-E database, TiVA records more consistent bilateral trade data for the smaller group of developing countries covered. Consequently, this results in a higher number of total observations. Moreover, the share of digital services trade covered by countries sharing the same data model only increases by a small share when using the ITPD-E database, as can be seen when comparing Figure 1 with Figure A1 in the Annex.

# **3.3.1 Data models and digital services trade**

Figure 3 shows that a share of 53 percent of digital services trade takes place among country-pairs sharing a similar data model when considering the component of cross-border flows of data. This share is slightly lower when considering the component of domestic processing of data, namely 47 percent. This difference is due to the fact that, as explained, countries can be categorized into two different models across the twin components.<sup>13</sup> The rest of the digital services trade takes place between countries following different data models.

Within the portion of digital services trade covered by country-pairs that share the same data model, the vast majority of trade happens between countries that follow the conditional model. This share is around 79 percent for the component of cross-border data transfers, which increases up to 92 percent for countries following the conditional model for domestic data processing, as shown in Figure 2. A smaller fraction of trade within that portion takes place between countries sharing the open model of cross-border transfers and processing of data, with a share of respectively around 20 percent and 4 percent. Only a minority of trade happens between country-pairs sharing the limited model. The share is about 1.5 percent considering the component of cross-border data transfers and 4.5 percent considering the component of domestic data processing.

> 12 Typically, digital trade also covers trade in digital goods such as computer, electronic and optical products (ISIC Rev. 4 code 26), which has been left out from this study because of its focus on digital services trade. 13 In what follows, the categorization of countries is based on the component of cross-border data transfers, given that this component more directly connected to trade.

12
based on both the size of oil and gas discoveries and the size of the economy. For example, initial finds in São Tomé and Príncipe were small in absolute terms but big for such a small country. Conversely, there were some finds in Botswana, but they are very small compared to the size of the economy.

These two criteria clearly delineate the 12 countries at the center of our study, which we present in Table 1. As we show in more detail in Table 5 in the appendix, our selection of countries is robust to adjusting these thresholds in different ways, as there is a large gap between the countries included and excluded from our study for both indicators.

|_Table 1. Countries of focus against the selection criteria_<sup>_6_</sup>|
|---|

|**Country**|**Share of oil and gas**<br>**resource wealth**<br>**discovered in 2001-**<br>**2018**|**Forecast average annual**<br>**gross value of resources**<br>**discovered in 2001 to 2018**<br>**as percentage of 2000**<br>**GDP**|
|---|---|---|
|Ghana|97%|44%|
|Guinea-Bissau|100%|8%|
|Kenya|99%|12%|
|Liberia|100%|22%|
|Mauritania|100%|76%|
|Mozambique|97%|480%|
|Niger|90%|31%|
|São Tomé and<br>Príncipe|100%|85%|
|Senegal|88%|77%|
|Sierra Leone|100%|42%|
|Tanzania|96%|46%|
|Uganda|100%|54%|

_Source: Rystad Energy’s Ucube database; authors’ calculations_ In each of the 12 countries reviewed, we identified a trigger event—the announcement of a specific discovery, sequence of discoveries or field reevaluation—that led to a significant shift in expectations regarding oil or gas prospects in the country.<sup>7</sup> As we will discuss in later sections, shortly after these trigger events, companies, governments and international experts showed increased interest in further exploration, started planning for the quick ramping up of petroleum production and began preparing for a transition to a petroleum-producing economy. Table 2 sets out some of the details of these events.

> 6 These criteria are based on resources found rather than proven reserves.

> 7 This list of events builds on the discoveries presented in the World Bank Africa’s Resource Future dataset.

3
# **_India (Moghul) around 1750, and India (British) 1947_**

**<u>India–at the end of the Moghul  rule (around 1750)</u>**

|Social group|Percentage of<br>population|Percentage of<br>total income|Income in terms<br>of per capita<br>mean|
|---|---|---|---|
|Nobility, zamindars|1|15|15.0|
|Merchants to sweepers|17|37|2.2|
|Village economy|72|45|0.6|
|Tribaleconomy|10|3|0.3|
|_Total_|_100_|_100_|_1_|

**<u>India–at the end of the British rule (1947)</u>**

|Social group|Percentage of<br>population|Percentage of<br>total income|Income in terms of<br>per capita mean|
|---|---|---|---|
|British officials, traders|0.06|5|83.3|
|Nobility, Indian capitalists|0.94|9|9.6|
|Petty traders, govt & industrial<br>workers|17|30|1.8|
|Village rentiers|9|20|2.2|
|Working land proprietors|20|18|0.9|
|Sharecroppers, tenants|29|12|0.4|
|Landless peasants|17|4|0.2|
|Tribaleconomy|7|2|0.3|
|_Total_|_100_|_100_|_1_|

Note: Zemandars are large landowners. The data refer to the entire Indian subcontinent (today’s India, Pakistan and Bangladesh).

**Income distribution data.** The source of both  data is Maddison (2002) which is turn based on Maddison (1971: pp. 33 and 69). Maddison (2002) gives only population and income shares, but if we combine this information with Maddison’s own estimates of GDI per capita for India (see below), we can calculate $PPP income estimates for each social group. Indian Moghul data present a particular problem because there are only 4 social classes given. Since their incomes are vastly different, and the largest group (72 percent; village economy) is in the middle of income distribution, probably spanning people with very different incomes, Gini2 is unusually some 27 percent higher than the minimum Gini (G2 is 48.9 vs. Gini minimum 38.5).<sup>24</sup>

_Discussion:_ Note that a part (but only a part) of high Indian inequality around the time of the independence from Great Britain is caused by very high incomes of the British in India. According to Maddison, 0.06 percent of the population (British officials and businessmen) received 5 percent of total income which made their average per capita income more than $PPP 51,000 per year (and would place them in the top 5 percent of

> 24 For the definitions of  G1 and G2, see the main text.

55
**Table 14.** Robustness Checks: Montecarlo Simulations.

|||**Shares of hou**<br>|**sehold inco**<br>|**me spent on:**<br>||
|---|---|---|---|---|---|
||(1)<br>Food|(2)<br>Education|(3)<br>Health|(4)<br>Durables|(5)<br>Adult|
|**OLS**|-0.104|-0.011|-0.020|-0.032|0.062|
||(0.079)|(0.033)|(0.028)|(0.066)|(0.016)|
|**TSLS**|-0.113|-0.084|0.068|-0.042|0.14|
||(0.348)|(0.141)|(0.124)|(0.283)|(0.075)|
|_Coverage_|[0.969]|[0.964]|[0.964]|[0.967]|[0.951]|
|_F_statistic|10.669|10.723|10.719|10.654|10.624|
|Hansen J Statistic - p value|0.502|0.503|0.502|0.993|0.504|
|**LIML**|-0.113|-0.092|0.074|-0.043|0.146|
||(0.393)|(0.164)|(0.139)|(0.319)|(0.084)|
|_Coverage_|[0.968]|[0.965]|[0.968]|[0.966]|[0.956]|
|_F_statistic|10.669|10.723|10.719|10.654|10.624|
|Hansen J Statistic -pvalue|0.504|0.506|0.505|0.957|0.508|

**Notes:** Standard deviations in parenthesis. Number of iterations = 1,000. _Coverage_ is the frequency with which the hypothesis of equality between coefficient estimated from actual sample and coefficient estimated from simulated sample has not been rejected. Controls included. Standard errors robust to village level clustering.

# VI. CONCLUDING REMARKS

This paper explores the effects of parental migration on investments on children left behind. The main concern is that migration of one of the spouses may be associated with a shift of resources away from the children due to a moral hazard problem: as the migrant loses the ability to observe the behavior of the spouse left behind, this creates for the latter incentives to shift resources away from the common goods, which include investments on children, onto the private ones.

I showed that in a model in which first the two spouses cooperatively choose whether and who should migrate, then the migrant decides how much to send back home in the form of remittances and, finally, the spouse left behind chooses how to allocate the available budget within the household, the subgame perfect Nash equilibrium is one in which the share of total income devoted to children is the same no matter which of the parents migrates. This is because remittances act as a device in the hands of the migrant to offset the decisions of the spouse left behind.

I tested the predictions of this model on data from Indonesia, where female migration is particularly widespread. To account for endogenous selection into migration, I exploited information about previous migrants’ earnings and built measures of expected returns and risks of, respectively, male and female migration to use as instrumental variables. I thus showed that households prefer to send away the member who is expected to earn more upon migration and whose earnings will be less volatile. The estimation results revealed 27
# ACCOUNTS

_Worldwide, 62 percent of adults have an account. For most people, owning an account provides an entry point into the formal financial system. An account makes it easier and often more affordable to pay bills, to receive payments, and to send or receive remittances. It also offers a safe place to store money and so can encourage saving. And it can open access to credit from a financial institution. In short, having an account is a marker of financial inclusion._

## Ownership of accounts

For the 2014 Global Findex database, account ownership is defined as having an account either at a financial institution or through a mobile money provider. The first category includes accounts at a bank or another type of financial institution, such as a credit union, cooperative, or microfinance institution.<sup>1</sup> The second consists of mobile phone–based services used to pay bills or to send or receive money.<sup>2</sup> To identify people with a mobile money account, the 2014 Global Findex survey asked respondents about their use of specific services that are available in their country—such as M-PESA, MTN Mobile Money, Airtel Money, or Orange Money—and included in the GSM Association’s Mobile Money for the Unbanked (GSMA MMU) database. The definition of a mobile money account is limited to services that can be used without an account at a financial institution. People using a mobile money account linked to their financial institution are considered to have an account at a financial institution.<sup>3</sup> The question on mobile money accounts was asked only in the 74 economies—among the 143 included in the survey—where the GSMA MMU database indicates that mobile money accounts were available at the time the survey was carried out.<sup>4</sup>

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

Not surprisingly, account ownership varies widely around the world. In high-income OECD economies account ownership is almost universal: 94 percent of adults reported having an account in 2014. In developing economies only 54 percent did. There are also enormous disparities among developing regions, where account penetration ranges from 14 percent in the Middle East to 69 percent in East Asia and the Pacific (figure 1.1; map 1.1).

<!-- Start of picture text --> FIGURE 1.1<br>Account penetration<br>Adults with an account (%), 2014<br>94<br>69<br>World<br>51 51<br>46 Mobile money<br>34 account only<br>Financial institution<br>and mobile money<br>14 account<br>Financial institution<br>account only<br>East Asia Europe High-income Latin Middle South Sub-Saharan<br>& Pacific & Central OECD America & East Asia Africa<br>Asia economies Caribbean<br>Source: Global Findex database.<br><!-- End of picture text --> Globally, nearly all adults who reported owning an account said that they have an account at a financial institution: 60 percent reported having a financial institution account only, 1 percent having both a financial institution account and a mobile money account, and 1 percent a mobile money account only.

> THE GLOBAL FINDEX DATABASE 11
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# _7.2. Decomposing the change in crop prices_

Decomposing changes in crop prices involves numerous factors, some affecting demand whereas some affect supply. We limit our analysis to four such factors; namely, biofuel, income, exchange rate, and energy prices. Other factors that need to be considered, but are outside the scope of this work, are productivity shocks, trade policy, and speculation. We report below the change in prices (due to the shocks) in a certain year compared to actual or observed prices in that same year.

## _7.2.1. Corn_

The results of four different shocks, applied one at a time for the period 2002-2007, are depicted in Figure 22. In particular, the figure presents the decline in the price of corn, if demand for corn did not grow because of the GNP growth, biofuel was not mandated, energy prices did not increase, and the US$ was not devalue. Note that the results in table 6, and different from the figures, project the inverse relationship, the increase in food commodity price because of economic growth, introduction of biofuel, higher energy prices and the depreciation of the US$. Throughout the text, as mentioned above, the figures and the tables present the results of our simulated scenarios from two vantage points. The table shows the price increase because of a change in factor _i_ (i.e., ), whereas the figures represent the price reduction if the observed change in factor _i_ would not have happened (i.e., ) – equation (21) shows the relation between and .

Two dominant factors affect corn prices: The introduction of biofuels and economic growth (see Figure 22 and Table 6). Whereas in the absence of an increase in demand for corn, prices would have been 15% lower in 2007, prices would have been 10% lower without the increase in biofuel production (Table 6). If we ignore the fact that the co-product of corn ethanol production, namely, distillers grains, is a substitute for corn, then biofuels appropriate a larger quantity of corn that is traditionally consumed as feed and, as a result, become responsible for about 12% of the price increase in 2007. The single largest use of corn is feed grain for animals, which is used for meat and dairy. Furthermore, meat consumption tends to increase with income, resulting in higher demand for corn in emerging economies. As per FAO statistics, in China, which witnessed average growth rate of 8.5% between 1990 and 2003, per capita meat consumption increased 150% from approximately 20 kg per person per year in the year 1985, to approximately 50 kg per person per year by the year 2000. Furthermore, increase in per 45
**Table 3:** Data density by sector, region

||**2000**|**-2003**|**2004-**|**2007**|**2008-**|**2011**|**2012-**|**2016**|
|---|---|---|---|---|---|---|---|---|
||#|%|#|%|#|%|#|%|
|o|bservation|s<br>coverage<br>|observations|<br>coverage|observations|<br>coverage<br>|observations|<br>coverage|
|||**Gover**|**nment spend**|**ing on health**|||||
|Overall|729|96%|737|97%|742|98%|920|97%|
|East Asia & Pacific|114|98%|116|100%|116|100%|145|100%|
|Europe & Central Asia|192|96%|193|97%|196|98%|245|98%|
|Latin America & Caribbean|128|100%|128|100%|128|100%|160|100%|
|Middle East & North Africa|77|96%|80|100%|80|100%|90|90%|
|North America|8|100%|8|100%|8|100%|10|100%|
|South Asia|30|94%|32|100%|32|100%|40|100%|
|Sub-Saharan Africa|180|92%|180|92%|182|93%|230|94%|
|||<br>**Governm**|<br>**ent spendin**|<br>**g on educati**|<br>**on**||||
|Overall|492|65%|464|61%|506|67%|583|61%|
|East Asia & Pacific|65|56%|52|45%|69|59%|77|53%|
|Europe & Central Asia|167|84%|165|83%|174|87%|197|79%|
|Latin America & Caribbean|84|66%|65|51%|69|54%|82|51%|
|Middle East & North Africa|46|58%|47|59%|38|48%|38|38%|
|North America|7|88%|6|75%|8|100%|5|50%|
|South Asia|19|59%|24|75%|29|91%|35|88%|
|Sub-Saharan Africa|104|53%|105|54%|119|61%|149|61%|
|||**Governmen**|**t spending o**|**n social prot**|**ection**||||
|Overall|||||21|3%|102|11%|
|East Asia & Pacific|||||5|4%|12|8%|
|Europe & Central Asia|||||1|1%|26|10%|
|Latin America & Caribbean|||||1|1%|17|11%|
|Middle East & North Africa|||||3|4%|6|6%|
|North America|||||0||0||
|South Asia|||||4|13%|3|8%|
|Sub-Saharan Africa|||||7|4%|38|16%|

Overall, the evidence presented in this section suggests that data availability is increasing over time, although modestly and with a drop in the period 2012-2016. Average trends hide heterogeneity across sectors and regions, with some worrying time trends in coverage for some regions. For education and social protection, increasing data availability represents an essential step for meaningful comparisons.

8
8 could result from constraints in raising appropriate amounts of debt. In addition, the nature of these constraints may differ for firms of different sizes. For instance, Figure 7 suggests that only mega firms can issue bonds, and that micro firms get only tiny bank loans compared to their balance sheet size. To a detailed analysis of these issues the paper now turns.

**Figure 7:  Financing Structure of Chilean Firms by Size, 2003**

<!-- Start of picture text --> Financing structure of firms Corporate leverages, 2003<br>100% Average debt to equity ratio<br>80% 2.5 2.2<br>2.0<br>60%<br>2.0<br>40%<br>1.5 1.3<br>20%<br>1.0<br>0%<br>0.5<br>% bank loans % bonds -<br>% equity % supplier credit Chilean US firms Colombian<br>% intra company loans % other fin. resources firms firms<br>Source: own calculations based on FECUs, Bizstats, Superintendencia de Empresas de Colombia<br>Mega Large Medium Small Micro<br><!-- End of picture text -->

# **2.  Firm Access to Capital Market and Bank Financing**

# **_Foreign Capital Markets_**

Access to foreign capital markets is restricted to mega large firms. Firms with access to foreign capital markets (bonds or equity) are concentrated in three sectors - utilities (including the privatized utilities and the road concessions which issued infrastructure bonds), manufacturing and industry. They include exclusively mega large firms and nearly exclusively firms based in Santiago.

# **_Domestic bonds_**

The high cost of issuing bonds and minimum rating requirements by institutional investors create a minimum size for bond issues higher than in neighboring countries. As Figure 8 shows, the cost of issuing bonds is twice as high in Chile as the cost of issuing equity. In fact, in terms of transaction cost, it is cheaper for Chilean firms to issue debt
from the Freedom House "Freedom in the World Countries" database<sup>15</sup> . "Freedom in the World Countries" is a database containing an annual comparative assessment of the state of political rights and civil liberties in 192 countries and 18 related and disputed territories . The lower is the index the more economic freedom the country has. We expect that countries with greater economic freedom are more open and more likely to trade.

To capture _historical linkages_ between trading partners two zero-one type dummy variables were included in the regression. FrenchExcolonyij and NonFrenchExcolonyij take the value of 1 if the exporting country (i) was a colony of France or other partner country(j). Colonial links often reflect not only historical ties but also that the traders of the two partner countries can speak the same language. If a country was an ex-colony of its trading partner trade between the two countries would probably necessitate lower transaction costs thus more trade. A separate dummy is included for non French ex-colonies and French ex-colonies, because we expect that there might be differences in the intensities of the trade links for French ex-colonies.<sup>16</sup> A variable measuring the hypothetical value of preferences (PREF), which is the difference between the third country duty and the preferential duty multiplied by eligible imports, was included in equation (7). This variable allows us to take into account the importance of the EU’s preferential treatment on its bilateral trade with ACP countries. This variable captures the potential value of the preferential scheme if all eligible trade would have requested and obtained preferential treatment. Given that the preferences were not usually fully utilized in most of the cases, the real value of the preferences is almost always lower than the ‘hypothetical’ value of the preferences. To construct the variable the difference between the third country duty and the preferential duty payable was calculated and multiplied by all eligible trade. The data on preferential and MFN tariffs was obtained from the European Commission.

In many aspects, such as size of the economy or the level of development, South Africa differs from most of the other countries in our sample. In order to avoid that specificities of South Africa is driving our results a dummy taking the value 1 if the exporting country is South Africa is included in the regressions. Dutydifferencek is the difference between MFN and preferential

> 15 See further details on the methodology of the index: http://www.freedomhouse.org/research/freeworld/2003/methodology.htm. 16 Only four countries were not colonies in our sample.

18