rafmacalaba/datause-encoder-data huggingface dataset:
surveyed firms are what they call “pure-players” (i.e. not retailers or wholesalers).<sup>16</sup> Most of these independent firms (72%) have fewer than 10 employees and 84% have fewer than 20 employees, and two-thirds engage in cross-border e-commerce. In developing countries, anecdotal evidence taken form UNCTAD (2015) shows that when facing substantial challenges while engaging in e- commerce, many micro and small enterprises rely on existing online platforms. Such platforms propose a variety of service that micro and small firms would not be able to provide on their own such as advertising, product management, shipping, and most importantly payments via credit cards, PayPal, or bank accounts.<sup>17</sup> We first develop a simple analytical model to analyze the role of small firms in explaining the impact of online exports on the wage gap. We then empirically assess whether the impact of online exports on the wage gap is stronger in countries with a larger share of employment in small firms.

# **5.1 A conceptual framework**

Building on Ekholm and Midelfart (2005) and Yeaple (2005) it can be shown that a reduction in the fixed cost of exporting faced by small firms leads to a reduction in the wage gap between skilled and unskilled workers, and that this reduction is larger in the presence of a relatively larger share of small<sup>18</sup> On the demand side we have a representative consumer with CES preferences over two imperfectly substitutable composite goods _Xh_ and _Xl_ who maximizes her utility with respect to her budget constraint _E_ . Each composite good is also a CES aggregate of individual varieties produced by monopolistically competitive firms. The aggregate production and corresponding price index in each sector is given by:

> 16 Reports are available at `https://www.ecommerce-europe.eu/` .

> 17In developing countries it is also important to recognize the role played by mobile phones in facilitating the access by micro and small business to e-commerce. According to WTO (2013), “ _The most popular ICT in developing countries and one which is progressing very rapidly in Africa and Asia, in particular India, is the mobile phone. Mobile phones are increasingly playing a larger role in the expansion of e-commerce in developing countries, especially among users without terminal connections. [..] In many developing countries, [...] they are increasingly being used for data application such as m-commerce and m-banking. In a number of African countries, notably Kenya, South Africa, Tanzania and Zambia, mobile telephones are being used to do personal banking services_ ”.

> 18 We develop a simple closed economy model, but it can be easily reinterpreted as an open economy model by either having consumers and producers being located in different countries, or by adding a second set of consumers in a different country in which a fixed cost needs to be paid in order to reach them.

11
16 However, Sierra Leone stands out with the average rural expenditure share being twice as high. Among the poor, urban expenditure shares were higher in 15 countries, although the numbers of households in the sample are too small for meaningful results in many countries. Expenditure shares were higher for female‐ headed households in 19 out of 22 countries. Uganda was exceptional in that there were no households among the urban poor who had purchased electricity; 0.8 percent of the urban poor reported receiving free electricity.

Table 2: Share of total household expenditures spent on electricity

||A|ll househol|ds||Poor||Househ|old head|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Urban|Rural|Total|Female|Male|
|Angola|4.0|2.9|4.0|5.6|4.2|5.5|3.1|4.2|
|Botswana|6.2|7.6|6.5|9.5|11.1|9.8|6.8|6.2|
|Burkina Faso|4.6|4.1|4.5|6.2|3.3|5.2|4.6|4.5|
|Cote d'Ivoire|2.5|2.4|2.5|3.1|3.0|3.1|2.5|2.5|
|Ethiopia|2.8|1.3|2.2|4.6|2.4|3.7|2.8|2.0|
|Ghana|2.6|2.0|2.4|3.1|2.4|2.7|2.5|2.4|
|Madagascar|4.4|4.1|4.3|4.1|3.7|3.9|4.6|4.3|
|Malawi|0.3|0.3|0.3|0.4|0.4|0.4|0.3|0.3|
|Mali|2.9|1.4|2.2|2.4|1.2|1.4|2.6|2.2|
|Mozambique|3.7|4.0|3.7|6.1|7.5|6.4|4.2|3.6|
|Niger|3.4|2.4|3.2|2.8|1.9|2.2|3.6|3.1|
|Nigeria|2.6|2.3|2.5|3.1|2.7|2.9|2.8|2.4|
|Rwanda|1.4|1.6|1.5|2.3|3.3|2.9|1.6|1.4|
|São Tomé and Príncipe|2.0|1.9|2.0|2.3|2.1|2.2|1.9|2.1|
|Senegal|3.7|3.6|3.7|3.7|3.5|3.6|3.7|3.7|
|Sierra Leone|4.5|9.1|4.6|4.9|13.2|5.2|4.5|4.6|
|South Africa|5.3|5.2|5.3|7.3|6.0|6.6|5.5|5.1|
|Swaziland|9.5|9.1|9.3|13.9|10.9|11.9|9.7|9.0|
|Tanzania|3.0|3.3|3.1|1.9|5.8|3.1|3.4|2.9|
|Togo|3.1|3.1|3.1|3.6|3.3|3.5|3.1|3.1|
|Uganda|2.4|1.9|2.4|—|3.6|3.6|2.6|2.3|
|Zambia|5.4|6.3|5.5|8.3|5.4|7.8|5.7|5.4|
|Median|3.3|3.0|3.1|3.7|3.4|3.6|3.3|3.1|

_Source_ : World Bank staff calculations using household survey data. _Note_ : Only households with positive expenditures on electricity are analyzed. In Ghana, South Africa, Uganda, and Zambia, freely provided electricity is excluded. — = No households with positive cash expenditures on electricity.

Because the amount of cash available is an important determinant of the household’s ability to pay for electricity, Table 3 re‐computes the shares by excluding these imputed values from household expenditures in the 18 countries where cash expenditures were computed. The corresponding results by quintile are given in Table A.5 in the annex. As an indication of how much rural households rely on freely acquired food, the urban expenditure share was larger in only 5 out of 13 countries (38 percent), in contrast to 59 percent found in Table 2. Sierra Leone and Uganda stand out with rural expenditure shares being about two‐and‐a‐half times higher. Among the poor, in addition to these two countries, the rural expenditure share is three‐and‐a‐ half times higher in Tanzania. In absolute terms, the expenditure share appears exceptionally high in rural Sierra Leone, Botswana, and Swaziland, but this is in part because of the small sample sizes reducing representativeness of the households reporting positive expenditures. The pattern with respect to the
- _51_ -

# 6. CONCLUSIONS

Starting from the premise that agriculture should play a pivotal role in the process of structural transformation and economic development in Sub-Saharan Africa, this paper addressed two inter-related issues in this regard. The first issue is the extent to which policy induced distortions tax agriculture by causing the structure of incentives to be biased against the sector in Sub-Saharan Africa. Here the paper distinguished between two types of policy induced distortions: the direct one which is induced by micro and sectoral policies directly deployed on agriculture, and the implicit or indirect set of distortions caused by policies directed at the macroeconomic management of the economy. The second issue considered in the paper is the consequence for the growth of agriculture of these distortions given other relevant growth fundamentals. The potential deleterious effects on economic growth of such distortions have been the subject of study in the recent endogenous growth literature (e.g. Easterly (1990) and Easterly and Wetzel (1989).

In the introduction section of this paper, preliminary evidence on the extent of economic distortions in Sub-Saharan Africa is reviewed. The evidence on agricultural prices in six major SubSaharan African countries show that despite variations across countries, farm gate prices remain generally lower compared to their equivalent border prices. Furthermore, worsening macroeconomic conditions in Sub-Saharan Africa, such as high and unsustainable fiscal deficits, worsening terms of trade, and dwindling foreign aid, have meant that the ability of Sub-Saharan African countries to invest in agricuitural supply and therefore partially mitigate the potential negative consequence of such distortions on agriculture, is substantially reduced. With regard to indirect taxation of agriculture, the analysis of Sub-Saharan Africa wide real exchange rate and its fundamentals shows sustained real appreciation over most of the 1970's and the first half of the 1980's while major fundamentals such as the terms of trade and capital flows call for equilibrium real depreciation. It is likely, therefore, that real exchange rates in SSA have been overvalued over the period. This
33 Czech Republic 68 Kazakhstan 103 Poland 34 Côte d'Ivoire 69 Kenya 104 Portugal 35 Denmark 70 Korea, Rep. 105 Romania

**Table 2 Regional and Income Distribution of Countries in 2005**

|Region|High Income:<br>OECD|High Income:<br>nonOECD|Low<br>Income|Lower<br>Middle|Upper<br>Middle|Total|
|---|---|---|---|---|---|---|
|East Asia & Pacific|4|3|3|5|1|16|
|Europe & Central Asia|21|4|3|7|13|48|
|Latin America & Carib.|2|1|-|8|12|23|
|Middle East & North Afr.|-|-|-|8|2|10|
|South Asia|-|-|1|2|-|3|
|Sub-Saharan Africa|-|-|24|6|6|36|
|Total|27|8|31|36|34|136|

**Table 3 Variables Used in the Analysis**

|Variable|Description|Years Used|Source|Expected<br>Relation|
|---|---|---|---|---|
|Real Export|Log of export in constant 2005 $|2006-2008|WDI||
|RealGDP(PPP)|Log of GDP, PPP (constant 2005 international $)|2005-2007|WDI|+|
|Finance|Log of the ratio of M2 (money and quasi money) to GDP|2005-2007|WDI|+|
|FDI|Foreign direct investment, net inflows (% of GDP)|2005-2007|WDI|+|
|log(MATTRI)|Log of MA-TTRI (applied tariff incl. prefs.) - All goods|2005-2007|WTI|-|
|log(TTRI)|Log of TTRI (MFN† applied tariff) - All goods|2005-2007|WTI|-|
|log(MAOTRI)|Log of MA-OTRI (applied tariff incl. prefs.+NTMs) - All<br>goods|2005-2007|WTI|-|
|log(OTRI)|Log of OTRI (MFN applied tariff+NTMs) - All goods|2005-2007|WTI|-|
|log(# of FTAs)|No. of FTAs / CUs - goods and services|2006-2007|WTI|+|
|Time to Export|Log of time to export (days) trading across borders|2005-2007|DB|-|
|Entry|Number of procedures required to start a business|2005-2007|DB|-|
|Size of Trade|Size of the trade sector relative to expected|2005-2007|EFW|+|
|Property|Index measuring protection of property rights|2005-2007|EFW|+|
|Infrastructure|Log of overall quality of infrastructure|2008|GCR|+|
|Log(Area)|Log of area in square kilometers|-|CEPII|-|
|Remoteness|Weighted measure of remoteness of the country|2008|Author|-|
|Regulatory Quality|Overallqualityof the regulatorysystem|2005-2007|WGI|+|

Note: WDI: World Bank Development Indicators, WTI: World Trade Indicators, DB: Doing Business, GCR: Global Competitiveness Report, EFW: Economic Freedom of the World, WGI: World Governance Indicators.

15
of electricity provision is a major problem for more than 50 percent of firms in more than half of the countries in our sample. In Burundi, Cameroon, Benin, Burkina Faso, and Cape Verde, the percentage of firms considering electricity as a severe or very severe obstacle exceeds 80 percent; on the other hand, only 20 percent of firms in Morocco, South Africa, Botswana, and Namibia consider electricity a severe obstacle. Customs clearance is considered an acute problem in Benin, Kenya, Madagascar, Senegal, and Algeria. Finally, transportation is considered a severe obstacle by more than 70 percent of firms in Burkina Faso and Benin.

**Figure 1.4 Percentage of firms that consider telecommunications, electricity, customs, and**

**transport as severe or very severe constraints on economic performance (by country)**

<!-- Start of picture text --> A. Telecommunications B. Electricity<br>Benin Burundi<br>Kenya Cameroon<br>Zambia Benin<br>Niger Burkina Faso<br>Ethiopia Cape Verde<br>Lesotho Tanzania<br>Madagascar Malawi<br>Burkina Faso Kenya<br>Algeria Madagascar<br>Malawi Uganda<br>Cameroon Ethiopia<br>Tanzania Mauritania<br>Swaziland Zambia<br>Mali Eritrea<br>Cape Verde Lesotho<br>Eritrea Senegal<br>Botswana Mali<br>Burundi Swaziland<br>Mauritania Niger<br>Namibia Egypt<br>Uganda Algeria<br>Mauritius Mauritius<br>South Africa Morocco<br>Senegal South Africa<br>Egypt Botswana<br>Morocco Namibia<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Percentage of firms Percentage of firms<br>C. Customs clearance D. Transport<br>Benin Burkina<br>Kenya Benin<br>Madagas Cameroon<br>Senegal Kenya<br>Algeria Senegal<br>Cameroon Zambia<br>Tanzania Malawi<br>Ethiopia Niger<br>Zambia Burundi<br>Uganda Madagascar<br>Niger Tanzania<br>Malawi Uganda<br>Mauritius Cape Verde<br>Mauritania Lesotho<br>Mali Mali<br>Egypt Mauritania<br>South Namibia<br>Lesotho Swaziland<br>Cape Ethiopia<br>Burundi Mauritius<br>Burkina South Africa<br>Morocco Botswana<br>Namibia Eritrea<br>Swaziland Morocco<br>Botswana Egypt<br>Eritrea Algeria<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Percentage of firms Percentage of firms<br><!-- End of picture text --> _Source:_ Authors’ calculations based on IC data. _Note:_ No data are available for perceptions of transport in Algeria .

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|**Plot level data**<br>Percent of output sold|Follow-up|Plot level data. Total quantity harvested and sold as share of total quantity<br>harvested in the long rainy season of 2010/11.|
|---|---|---|
|Maize yield (Kgs/acre) in 2009/10|Follow-up|Plot level data. Maize yield (KG/acre) for any type of maize seed during the long<br>rainy season of 2009/10.|
|Kg of Basal Fert. per acre|Follow-up|Plot level data. KG of basal fertilizer used per acre of area planted in the long<br>rainy season of 2010/11.|
|Kg of Fertilizer per acre|Follow-up|Plot level data. KG of top-dressing fertilizer used per acre of area planted in the<br>long rainy season of 2010/11.|
|Crop Failure|Follow-up|Plot level data. Dummy variable. Equals to 1 if yield is zero. in the long rainy<br>season of 2010/11.|
|Log Total Yield|Follow-up|Plot level data. Log of the total yield (KG/acre) in the long rainy season of<br>2010/11. Include zeros (crop failure) and uses planted area. Yields over 4000<br>KGs/acre were removed from the sample.|
|Standardized index of agriculture outcomes<br>**Link level data**|Follow-up|Plot level data. Standardized sum of 5 agricultural outcome variables, also<br>standardized before the sum: (i) area planted, (ii) kilograms of basal fertilier per<br>acre, (iii) kilograms of total fertilizer per acre, (iv) dummy that takes value of 1 if<br>yield is positive (1 - crop failure) and (v) log total yield.|
|Gives inputs to others|Follow-up|Link level data. Based on self-reported data on network of neighboring farmers in<br>the village that the household head would go for advice of that would come to<br>him/her for advice. Variable contains one observation for each link reported.<br>Dummy variable. Equals to one if the household head reports giving any fertilizer<br>and/or seed to a member of his/her network during 2010/11 planting season.|
to trace demand in the ICT sector in the pre- and COVID-19 periods. To measure demand for delivery services, we use the term "delivery," which can effectively capture demand for takeout and delivery of food, groceries, and other items.

# **2.3 Temporal Evolution of Demand for Services**

The Google search intensity data are expected to exhibit some form of seasonality. For instance, demand for transport and tourism services have seasonal patterns, with peaks during the summer and holiday months. To disentangle the effects of seasonality, we use three years of data (20182020) and compare search trends in the COVID-19 period (January–April 2020) with the same time window in the previous two years (2018 and 2019). Once seasonality is removed, structural breaks in Google search trends during the COVID-19 period would reflect the impacts of the pandemic.

Figures 1 and 2 present the global temporal evolution of demand for various services in the last five years, as captured by global Google search data. The search trends demonstrate an interesting mix of secular trend and seasonality in the pre-COVID-19 period. Figure 1 shows that demand for hotel services has remained stable, but with significant seasonal variation, whereas demand for delivery services has been steadily rising with little seasonality. The other services exhibit varying degrees of the two dimensions.

Since the first confirmed COVID-19 cases in China in late December 2019, demand for some services has dipped while demand for others has gone up. Demand for hotels and restaurants, transport and tourism has sharply dived to their lowest levels. On the other hand, demand for ICT and delivery services have peaked following the outbreak. The scale of the fall or rise in demand for these services is much greater during the pandemic than seasonal variations in demand. For instance, while demand for hotels and restaurants peaks in the summer months (July-August), air travel peaks around December-January. In the five pre-COVID-19 years, Google search queries for hotel and restaurant, transport, and tourism key terms dropped 15-20 percentage points on average between peak and trough. This is significantly lower than the over 60 percentage points fall during COVID-19. Likewise, the popularity of search queries for ICT and delivery services rose by over 75 percentage points during COVID-19 compared to the pre-COVID-19 averages. These facts demonstrate the scale of changes in demand for these services due to COVID-19, and the need to account for seasonality in our analysis.

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# 1. Introduction and overview

This paper explores the sovereign ‐ bank nexus in emerging markets and developing economies (EMDEs), which reflects the interconnectedness between the health of the sovereign and the banking system, whereby stress in one sector may create and amplify stress in the other. We identify two _direct_ channels (bank’s direct exposures to the sovereign and government support to failing banks) and two _indirect_ channels (fiscal and bank interactions with the real sector).

Our analysis of data from 140 EMDEs seem to suggest that the sovereign‐bank nexus is rising, but deeper country analysis is required given country idiosyncrasies, including the structure of sovereign debt and the composition of the investor base and data lags and opacities. First, banks have increased their exposures to their sovereign in the last decade, both relative to their balance sheets and country GDP – this tightens the exposure channel. The median banking system has seen its total exposures to government and non‐financial public entities more than double since 2007 (current median: 8% of GDP). Regressions for 36 EMDEs suggest this factor is also positively associated with the probability of default of financial institutions. Further, banks’ sovereign securities holdings as a fraction of banking system assets have doubled as well (current median: 10% of assets). Second, government debt has grown, and fiscal positions have deteriorated, raising the specter of sovereign stress, particularly in an environment of tightening global financial conditions ‐‐ this tightens all direct and indirect channels and may activate the nexus. Third, banking systems assets and bank credit to the private sector have steadily increased which may restrict the sovereign’s capacity to contain a banking crisis – this tightens the government support channel. However, bank buffers have improved during this period as well. Yet, this may be the result of larger exposures to the sovereign which do not carry a risk weight. Capital buffers may thus be overstated. Fourth, empirical evidence documents the existence of the nexus in EMDEs and suggests that it has increased recently. Using a novel quantile regression framework, we estimate that historically, the nexus increases sharply as sovereign stress increases in EDMEs. Also, regressions suggest that banks’ exposures to the public sector are positively associated with the estimated probability of default of financial institutions. Lastly, the estimated nexus for the median EMDE has tripled since 2013: a one‐percentage point increase in the probability of 2