rafmacalaba/datause-encoder-data huggingface dataset:
United States, also has a preferential trading agreement with Sub-Saharan Africa, signed into law in 2000 as AGOA (African Growth and Opportunity Act). The Act offers many tangible incentives to African countries in an effort to “continue their efforts to open up their economies and build free markets.”<sup>4</sup> Many countries that have seen rapid growth over the last fifteen years (especially in Asia) have done so by making the transition from labor-intensive, low-valueadded production (e.g. agricultural products, primary commodities) into higher stages of the value chain, i.e. capital-intensive, high-value-added products (e.g. manufactured products, automotive parts). Sub-Saharan Africa hardly figures in exports of apparel to the US and EU markets (Figures 3 and 4). There are no African countries represented in the top 25 exporters to the U.S. market (Egypt is 27<sup>th</sup> , Lesotho is 30<sup>th</sup> and Kenya 32<sup>nd</sup> – all with less than 0.4% of market share). The same is true for the European market.<sup>5</sup> Both in Europe and America, African producers have seen growing competition from the emerging economic giants in Asia (e.g. China, India, but also Bangladesh) – even after taking into account their respective preferential trading agreements, Cotonou and AGOA.

The same picture emerges if we look at the world’s main exporters of automotive parts. The only Sub-Saharan African country represented in the top-50 exporters is South Africa.

<u>Figures 3 & 4</u> <!-- Start of picture text --> Figrue 3: U.S. Apparel Imports by Origin (2005) Figure 4: E.U. Apparel Imports by Origin (2005)<br>30.0% 35.0%<br>25.0% 30.0%<br>25.0%<br>20.0%<br>20.0%<br>15.0%<br>15.0%<br>10.0%<br>10.0%<br>5.0%<br>5.0%<br>0.0% 0.0%<br><!-- End of picture text --> <u>Source: U.S. Department of Commerce,</u> _Office of Textiles and Apparel_ and European Union, _Eurostat_ .

Oil still dominates exports from Sub-Saharan Africa, together with primary commodities such as platinum, diamonds, and cocoa. The only exception is woven and knit apparel. If Sub-Saharan Africa wants to move up in the value

> 4 <u>http://www.agoa.gov/</u>

> 5 With the exception of North Africa: Tunisia, Morocco, and Egypt

3
Robinson, Jonathan and Ethan Yeh. 2011. “Transactional Sex as a Response to Risk in Western Kenya.” _American Economic Journal: Applied Economics_ 3(1): 35-64.

Rosenzweig, Mark R. and Oded Stark. 1989. “Consumption Smoothing, Migration, and Marriage: Evidence from Rural India.” _Journal of Political Economy_ 97(4): 905-926. Rosenzweig, Mark and Kenneth Wolpin. 1993. “Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investments in Bullocks in India.” _Journal of Political Economy_ 101(2): 22344.

Schoepf, Brooke Grundfest. 2004. “AIDS in Africa: Structure, Agency, and Risk.” In _HIV and AIDS in Africa: Beyond Epidemiology_ , eds. Ezekiel Kalipeni, Susan Craddock, Joseph R. Oppong, and Jayati Ghosh, 121-132. Malden, MA: Blackwell Publishers.

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36
31 In aggregate, Table _5_ suggests that NAFTA should have only a relatively minor influence on East Asian exporters--a finding that closely confonns to other investigatiqns which focussed on different regions (see Section II). For the East Asia region as a whole, trade losses of about $384 million are projected with China, Hong Kong, and Taiwan (China) accounting for almost 70 percenL of the total.' Ihe projected losses represent less than one-half of one percent of total 1990 East Asian exp orts to the United States although this ratio rises to about three-quarters of one percent in the case of some individual economies like Macau and Hong Kong?23 Sectoral analysis of the projected losses indicates fhat approximately one half of the East Asian trade diversion occurs- in the textiles and clothing sector (SITC 65 and 84), with clothing alone accounting for roughly 40 percent ($151 million) of the total. Aside from the manufactring sector, only about $6 million in East Asian trade losses are projected with about half this total occurring in the crude materials (SITC 2) sector -particularly in component products like vegetable fibers, and synthetic and regenerated fiblers.

Although the East Asian trade losses look modest, there are several reasons to 2 There are two main reasons why the projected trade losses are relatively small. First, Canada is simply not a viable competitor for many of the labor intensive manufacures that East Asian countries specialize in with the result that Canada's capacity for trade diversion is limited. As an example, in 1991 US imports of clothing and footwear from Canada were only $306 million and $31 million, respectively, thile those from East Asia were $18.4 and $6.7 billion. Scownd, Mexico still plays a relatively small role in US trade-i 1991 only about 6 percent of all US imports came from Mexico--and does not have either the size or the capacity to divert a large share of nonNAFTA countries' trade in the short-mn.

_D_ The relatively high losses for Macau and Hong Kong are, as previously noted, expected due to concentration by exporters in textiles and clothing (see Appendix I, Table A2). The results for Taiwan (China) are somewhat different in that trade losses of about $15 million are projected for miscellaneous manufactures (SITC 89), a total for this group that is more than five times higher than that for any other East Asian exporter. The underlying tariff line level data shows that Taiwan's losses are concentrated in about four product lines like stuffed toys and dolls, Christmas decorations, and children's toys where it has a rela4vely high market share and US tariffs avcrage over 10 percent.
- 21 -

The most striking impression from Figure 2<sup>is that the exports of the</sup> highest income suppliers -- Singapore,<sup>Hong Kong, Macau, Taiwan (China),</sup> and Korea -- were only moderately capital-intensive. On the other hand, the exports of the EE countries, which have considerably<sup>lower income levels,6 were among the most</sup> capital-intensive. Czechoslovakia was the only exception in this respect. A more systematic analysis of the average<sup>capital intensity of MFA</sup> exports to the EC revealed only an insignificant<sup>(positive)</sup> relationship with the per capita GNP of the suppliers. The<sup>correlatiun coefficient</sup> between the two **was** 0.05 (and the Spearman rank order<sup>correlation coefficient was 0.09).</sup> In the case of the East Asian suppliers,<sup>the prime cause of their</sup> non-comformity with the theory is probably the quota rents<sup>which enable them to</sup> export otherwise non-competitive, excessively labor-intensive products. This would occur under binding quotas. In fact,<sup>the correlation coefficient between</sup> the overall capital intensity of exports<sup>and the average quota utilization rate</sup> of the suppliers was -0.60, significant<sup>at the 1 percent level. Thus, the more</sup> binding the quota. the greater is the<sup>likelihood that labor-intensive</sup> exports become feasible.

To analyze the combined effects of<sup>relative capital abundance,</sup> proxied by GNP per capita (GNP/CAP),<sup>and average quota utilization rate (AV.</sup> QUOTA UTIL.)2' on the overall capital<sup>intensity of exports (K/L), we estimated</sup> the following equation: _K/L_ = a + a GNP/CAP + 0 AV.QUOTA UTIL.<sup>+</sup><sup>**e**</sup> X Obviously, this analysis is extremely<sup>sensitive to the GNP per capita</sup> estimates for EE.

" 1985-89 average quota utilization of<sup>each exporter in the SC market.</sup>
strategic changes.

# 3.2.2. Firm-Level Characteristics

To evaluate how firm characteristics affect firm closure and growth during the pandemic, we include the following set of firm-level variables drawing on prior studies (Beck et al., 2005; Beck et al., 2008; Cheng et al., 2020; D'Souza et al., 2017; Liu et al., 2020; Ullah and Wei, 2017). We include firm size, firm age, three ownership variables, manager’s experiences and labor productivity. All these firm-level variables are obtained from the standard WBES implemented before the outbreak of the COVID-19 pandemic.

The indicator for small firms, _Sizable_ , is a dummy variable indicating a firm’s number of permanent employees being more than 20. Firm age, _Log Age_ , is the natural logarithm of firm age plus one. Ownership and group affiliation are measured in three ways. (1) State ownership, _SOEs_ , is a dummy variable that equals one for firms with government/state ownership, and zero otherwise. (2) Foreign ownership, _Foreign_ , is a dummy variable that equals one for firms with foreign ownership, and zero otherwise. (3) Subsidiary status, _Subsidiary,_ is a dummy variable that equals one if a firm is a subsidiary of another firm, and zero otherwise. Manager’s experience, _Log Experience_ , is the natural logarithm of a manager's years of experience by 2020. Labor productivity, _Log LP_ , is the Log natural logarithm of LP which equals to sales over the number of employees. Employees include both permanent workers (with a weight of one) and temporary workers (with a weight of 0.5).

# 3.2.3. Country Characteristics

We use two country-level variables. The first is a country’s economic development proxied by _Log GDPPC_ , which is the natural logarithm of GDP per capita in constant 2010 US dollars. The second is the index of government COVID policy stringency ( _Policy stringency_ ), which is a comprehensive measure that takes into account nine response indicators, including school and workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home orders, public publicity campaigns, restrictions on internal activities 11
- 16 -

We can use the postal index score (letters per capita performance allowing for income) for 2000 to see what correlates with “better than expected” postal performance. Looking across the first row of the correlation matrix, it is possible to see how closely our index of postal performance is related to the other indicators in our database. In short, the postal index appears to be strongly and positively correlated with our measure of trust in the postal system and the number of letters per employee, and strongly negatively correlated with total postal expenditure divided by the number of letters delivered.<sup>21</sup> Both postal expenses per letter and letters delivered per postal staff member are strongly correlated with postal performance—lower costs and fewer staff per letter are correlated with more letters sent at a given income (see Figure 3).

# **Figure 3.**

<!-- Start of picture text --> 4<br>3<br>2<br>y = -1.2492x + 0.8064<br>1<br>R 2 = 0.4544<br>0<br>-1<br>-2<br>-3<br>-4<br>0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50<br>Expenses/Letter<br>(tw o outliers removed)<br>Postal Performance<br><!-- End of picture text --> Causation runs in both directions. High fixed-costs mean that factors that increase volumes will improve efficiency, while improved efficiency and quality will also increase the demand for mail services. Trust in the postal network is also a significant correlate with postal performance— countries with low trust in the postal system (a score of below 4 out of 7) average postal performance of -0.59, compared to countries with high trust (above 4) that average 0.51.

> 21 It is important to remember that this is a measure of postal performance _given income—_ if the measure used was letter per capita, it would be strongly correlated with any indicator that was also related to income per capita (including, for example, urbanization).  It should also be noted that letter/capita and expenditures/letter are all arithmetically linked by the inclusion of letter volume.
# **Table 5: Absolute convergence of real per capita GRDP growth**

||**Geometric**|**average growth**|**in real per cap**|**ita GDP**|||||
|---|---|---|---|---|---|---|---|---|
||**1993-97**|**1999-2001**|**2001-05**|**1993-2005**|**1993-97**|**1999-2001**|**2001-05**|**1993-2005**|
|Ln per capita real GDP, 1993|0.006<br>-0.004|||-0.009<br>(0.003)**|0.004<br>-0.005|||0.011<br>(0.005)*|
|Weighted<br>average<br>growth|of||||||||
|neighboring districts 93-97|0.059<br>(0.023)*||||0.051<br>(0.022)*||||
|Ln per capita real GDP, 1999||-0.025<br>(0.012)*||||-0.027<br>-0.015|||
|Weighted<br>average<br>growth<br>neighboring districts 99-01|of|0.092<br>-0.047||||0.032<br>-0.047|||
|Ln per capita real GDP, 2001|||-0.006<br>(0.003)*||||-0.008<br>(0.003)**||
|Weighted<br>average<br>growth<br>neighboring districts 01-05|of||-0.002<br>-0.026||||-0.008<br>-0.027||
|Weighted<br>average<br>growth<br>neighboring districts 93-05|of|||0.021<br>(0.007)**||||0.017<br>(0.008)*|
|Dummy Sumatra Island|||||-0.008|0.051|0.005|0.007|
||||||-0.005|(0.017)**|(0.003)*|-0.005|
|Dummy Kalimantan Island|||||-0.004|0.055|0.004|0.001|
||||||-0.006|(0.021)**|-0.007|-0.005|
|Dummy Sulawesi Island|||||-0.012|0.037|0.000|0.005|
||||||(0.006)*|(0.014)**|-0.005|-0.003|
|Dummy Nusa Tenggara & Malu|ku||||||||
|<br>Island|||||-0.016|0.046|-0.010|0.011|
||||||-0.010|-0.039|(0.005)*|-0.009|
|Dummy Papua Island|||||-0.003|0.033|0.006|-0.008|
||||||-0.012|-0.030|-0.010|-0.008|
|Constant|-0.043|0.390|0.117|0.156|0.001|0.403|0.154|-0.143|
||-0.058|(0.170)*|(0.039)**|(0.050)**|-0.066|-0.207|(0.044)**|-0.076|
|Observations|244|268|277|243|244|268|277|243|
|R-squared|0.05|0.04|0.03|0.09|0.08|0.09|0.06|0.12|

Note: all variables are for the base year of the regression shown unless otherwise indicated. 1993-97 and 1999-2001 growth rates are in constant 1993 prices; 2001-2005 and 19932005 growth rates are in constant 2000 price.

18
Wholesale and retail trade, Financial and real state, Industry, and Services. In addition to sectoral differences across levels of economic development, we expect variation across regulatory and governance regimes. For instance, we might expect that capital- and labor-intensive industries would be under-represented in countries with weak financial development and burdensome labor regulations (Rajan and Zingales, 1998). We should also find that sectors that have greater needs for formal sector registration documents are over-represented in countries with challenging business environments.

**Figure 4: Total Business Distribution, by Sector, 2003-2005**

<!-- Start of picture text --> Industrialized Countries Developing Countries<br>50% 47.16%<br>40.77%<br>40%<br>30%<br>25.05% 24.43%<br>19.05%<br>20% 16.79% 17.01%<br>9.74%<br>10%<br>0%<br>Wholesale & Retail Trade Finance Insurance & Real Industry Services<br>Estate<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

Indeed, an initial analysis of the data shows an almost perfect asymmetry in the business distribution in developing and industrialized countries (Figure 4). While in developing countries the retail and finance sectors are twice as big as in the industrialized countries, the industry and services sectors are half their size. Approximately the same distribution was found among new business created in 2005 (not shown). An in-depth study would be necessary to better understand why entrepreneurs focus so disproportionably on certain sectors in developing countries. Nevertheless, a preliminary analysis might suggest the relative lower requirements of investment, human resources, knowledge, and capital as among the reasons that entrepreneurs in developing countries focus on the retail sector. In addition, in developing countries with costly and timely barriers to starting a business, only firms in wholesale and retail trade might have the 17
Using village level data from Nepal, we analyze two dimensions of structural change in agriculture: the pattern of product diversi fi cation (crop specialization), and the degree of market participation (i.e., commercialization). The pattern of crop specialization is measured by the Her fi ndahl index of concentration of cropland use, and by the share of land devoted to non-cereal (non-rice) crops. Sales of non-rice crops and all crops as a percentage of total production are taken as measures of commercialization. As opposed to the standard practice of de fi ning market as the nearest urban center, we introduce a broader measure of the extent of market which incorporates the possibility that villagers may trade at multiple urban locations. The empirical analysis also corrects for the possible endogeneity of the extent of the market.

The parametric regression results indicatea statistically signi fi cant and positive e ff ect of the extent of market on the share of land devoted to non-cereal crops and sales of non-rice and all crops. The e ff ect of market size is negative, albeit statistically signi fi cant only at 10 percent level, in the case of Her fi ndahl index. The semi-parametric regression analysis suggests that the relationship between the extent of market and Her fi ndahl index of specialization is U- shaped. No such non-linearity is uncovered in the case of crop sales and share of land devoted to non-cereal crops.The results imply that when the farmers do not have access to large urban markets, crop production is dominated by subsistence considerations with villages specializing in the production of staple cereal crops. With an increase in the extent of market, crop production fi rst becomes diversi fi ed with farmers producing both cereal and non-cereal crops. As the extent of market crosses a critical threshold, villages begin to specialize again— this time in the production of non-cereal crops. The evidence on the stages of agricultural diversi fi cation is thus similar to the stages identi fi ed earlier in the literature for the economy as a whole and for the manufacturing sector (Imbs and Wacziarg, 2003).

The paper demonstrates the importance of market size in inducing agricultural specialization and commercialization. This evidence con fi rms the crucial role of improving farmers’ access to markets, through investment in transport infrastructure and removal of barriers to trading both at the domestic and international level.

22
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The low level of access to electricity is a major factor constraining telecommunications in the CAR. Only 1 percent of households had an electrical connection in 2009 (10 percent in Bangui) and less than 1 percent of the population owned a computer (Institut Centrafricain des Statistiques, et des Etudes Economiques et Sociale 2009).

Internet costs remain high, pending completion of fiber-optic links to submarine cables. Being landlocked, the CAR is reliant on costly satellite VSAT<sup>14</sup> connectivity for international Internet access. While there has been significant growth in the Internet market, international Internet connectivity, and the number of Internet users (around 3.5 bits per person,<sup>15</sup> or 1.4 users per 100 people; see figure 15a), the CAR’s Internet market is one of the least developed in the CEMAC region (figure 15b). The poor condition of SOCATEL’s network makes the launch of DSL<sup>16</sup> Internet services unlikely.

# **Figure 15. Internet market, 2008**

<!-- Start of picture text --> a Internet ervice trends, Central African Republic b. Internet service trends, select peers<br>1.4 4.0 25 1600<br>1.2 3.5 1400<br>20<br>1.0 3.0 1200<br>0.8 2.5 15 1000<br>2.0<br>800<br>0.6<br>1.5 10 600<br>0.4<br>1.0<br>400<br>0.2 0.5 5<br>200<br>0.0 0.0<br>0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source:_ World Bank, including AICD analysis.

> 14 Very Small Aperture Terminal.

> 15 International bandwidth was estimated at between 14 to 16 megabits per second (Mbps) in 2007 (World Bank 2009e).

> 16 Digital subscriber line.

32
# **I. Introduction**

Political violence kills human beings, destroys natural resources and wild life, and wipes out productive physical capital. Although such violence is itself endogenous to a range of complex factors, once ignited, it can become the direct cause of untold human suffering, loss of life as well as massive economic decline and political instability. There is a large body of literature documenting the direct impact of political violence, most notably civil wars. For example, out of 30 major conflicts recorded in 2000, there were 23 civil wars, of which ten were in Sub-Saharan Africa (SSA). These wars are estimated to have resulted in over four million deaths and have cost the countries in question more than $138 billion (in 1995 prices).<sup>1</sup> Battle-related deaths declined precipitously between 1942 and 2002, where nearly 700,000 people were killed in the wars of 1950 compared to 20,000 in 2002<sup>2</sup> . Still, the toll in terms of loss of human life is high, especially if we account for the multiple lives lost by the indirect causes of wars, such as disease and starvation. Moreover, between 1980 and 1992 the total number of displaced people as a result of wars was estimated to have risen from 16 million to more than 40 million. In terms of the regional distribution of political violence, the largest number of battle deaths were registered in East Asia, Southeast Asia and Oceania during the period up to the 1970s; then in the Middle East and North Africa, Central and South Asia and Sub-Saharan Africa in the 1980s; and by the turn of the 21<sup>st</sup> century, Sub-Saharan Africa has become the most violent region, accounting for more battle-related deaths than all other regions combined (Human Security Centre, 2005).

The evidence on the direct destructive impact of political violence on human, natural and physical capital is abundant. However, the overall, general equilibrium, development costs of political violence are, in fact, much larger. This is particularly true for long-drawnout violence, such as civil wars; or short-duration but highly intense violence, such as riots or uprisings. In addition to its direct destructive effect, political violence undermines the micro-security of the individual as well as the macro-security of communities, nations and countries. Therefore, it can change behavior, preferences as well as institutions and public policy. For example, it has been argued that the increased mortality associated with violence tends to shorten agents’ planning horizon, leading to much heavier than normal discounting

> 1 Reported in Willett (2001), and based on estimates taken from the International Institute for Strategic Studies’ report for 2000/2001: IISS (2000).

> 2 This decline in the number of casualties is due to changing face of wars, from inter-state wars, fought by major armies, to intrastate insurgency-type wars.

2