rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Estimated number of formal and informal businesses (thousands)

||(1)<br>Formal|(2)<br>Informal|(3)<br>Informal Share|
|---|---|---|---|
|Micro (less than 5)|1,361|6,322|82|
|Small (5-19)|1,377|2,620|66|
|Medium (20-100)|577|356|38|
|Large (100+)|101|26|20|
|Total Firms|3,416|9,324|73|
|Own-account|13,355|218,334|94|
|Total Firms + Own-account|16,771|227,658|93|

Source: Own calculations.

Note: Micro firms exclude own-account businesses, where the proprietor constitutes the sole employee.

Excluding own account, we estimate that firms absorbs 38% of the labor force on the African continent. Labor force, in this case, involves anyone engaged in labor that is not an employer. This definition, thus, is the sum of individuals working as employees and individuals engaged in own-account activities. We estimate that 62% of the labor force is employed in own-account activities, of which the large majority works informally. In addition, 21% is employed by formal firms. The remaining 17% is employed by informal firms. Notably, informal micro firms employ 11.2% of the total labor force alone, which means small, medium and large informal firms together only account for a combined 5.9% of the total labor force.

The labor share across firm size in the formal market is less concentrated; however, it exhibits the well-known regularity of the missing middle found with other data sources as in Abreha et al. (2022). While formal micro-firms absorb 5.1% of the labor force, small formal firms employ 3.5%. Medium-sized formal firms, on the other hand, account for 5.6% of workers. The group of large firms absorbs the largest share of labor within the formal sector, with 6.9% of the total labor force employed by these firms.

Table 4: Labor absortion

|(1)<br>Formal|(2)<br>Informal|
|---|---|
|Micro (less than 5)<br>5.1|11.2|
|Small (5-19)<br>3.5|3.7|
|Medium (20-100)<br>5.6|1.6|
|Large (100+)<br>6.9|0.6|
|Total Firms<br>21.2|17.1|
|Own-account<br>3.6|58.1|
|Total Firms + Own-account<br>24.7|75.3|

Source: Own calculations Note: Micro firms exclude own-account businesses, where the proprietor constitutes the sole employee.

18
Table B11: Summary of relative variance contributions, alternative choices of π, never enrolled children only

|||Ken|ya|Tanz|ania|Uga|nda|
|---|---|---|---|---|---|---|---|
|||�<br>�||�<br>�||�<br>�||
||π|σ.<br>/σ�<br>|(s.e.)|σ.<br>/σ�<br>|(s.e.)|σ.<br>/σ�<br>|(s.e.)|
|Individual|0|0.44|(0.10)|0.26|(0.22)|2.66|(0.31)|
||0.5|0.43|(0.11)|0.30|(0.21)|2.75|(0.30)|
||1|0.41|(0.11)|0.24|(0.22)|2.77|(0.30)|
|Household|0|35.23|(0.90)|46.59|(1.29)|32.30|(1.28)|
||0.5|17.31|(0.63)|26.17|(0.97)|20.52|(1.02)|
||1|10.80|(0.50)|17.18|(0.78)|14.72|(0.87)|
|School|0|1.68|(0.23)|2.19|(0.30)|2.06|(0.37)|
||0.5|8.10|(0.50)|5.96|(0.49)|3.62|(0.49)|
||1|26.80|(0.90)|21.22|(0.92)|12.01|(0.89)|
|Sorting|0|0.47|(0.11)|-1.95|(0.27)|0.10|(0.08)|
||0.5|10.01|(0.51)|9.73|(0.61)|5.84|(0.58)|
||1|4.18|(0.33)|7.33|(0.53)|4.83|(0.53)|
|Residual|.|61.38|(1.56)|54.93|(1.85)|65.28|(2.39)|
|Total|.|100.00|(1.15)|100.00|(1.44)|100.00|(1.71)|

Note: the table sets out the relative variance contribution (in percent) attributable to each component of the test score, where ‘sorting’ is the contribution of the between-factor covariance; different initializations of the partitioned iterative algorithm are indicated by column _π_ ; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003). Source: own calculations.

56
Source: ECB Lending Survey July 2012 Source: ECB Lending Survey July 2012

# **Second, European banks face several Euro-Area-specific regulatory conditions.**

- _EBA capital exercise:_ In addition to adjusting to Basel III, European banks were required by the European Banking Authority (EBA) to raise their Core Tier 1 ratios to 9 percent by June 2012 after accounting for an additional buffer against sovereign risks in their portfolios. Most European banks achieved the target ratio by end-June 2012 and have boosted their capital by over €200 billion since December 2011.<sup>30</sup> The EBA recently announced banks are required to conserve the absolute amount of capital they have accumulated.

- _State-aid conditions:_ In 2009, the European Commission required that “banks benefitting from state aid may be required to divest subsidiaries and branches, portfolios of customers or business units...” to minimize competitive distortions and cost to the tax payer. Several banks have still not finalized this clean-up process of their balance sheets and the shedding of legacy assets.

- _Additional national regulatory measures:_ Banks have also been subject to additional regulatory requirements by national regulators including ring fencing of foreign bank operations. Yet, while these measures aim to protect the domestic financial system, they also contribute to further financial fragmentation of the European financial system. Banks in peripheral countries may also have been compelled to participate in domestic sovereign bond markets to substitute for foreign capital flight crowding out lending activity in the process.

# **_<u>Economic drivers</u>_**

- _Economic slowdown:_ Significant economic slowdown in the Euro Area, in part a result of front-loaded austerity programs, depresses bank asset growth and profitability which undermines the capacity of banks to boost retained earnings. Moreover, going forward the global economic slowdown impedes the expansion and profitability of foreign operations.

- _Sovereign-bank-real economy feedback loop:_ The negative bank-sovereign-real economy feedback loop that became apparent during the European sovereign debt crisis has not yet been broken. In fact, it has intensified due to the domestication of sovereign debt markets in peripheral countries and reinforced by austerity programs. As such, periods of increased sovereign stress and economic slowdown will continue to impede bank’s operations and tighten credit standards. Moreover, European banks have proven to be “international in life and national in death”. As such, the current lack of a banking union with appropriate pan-European backstops will perpetuate this feedback loop.

> 30 Banks mainly accomplished this by raising new capital and reserves (38 percent of total) and adjusting riskweighted assets (RWA) (28 percent of total), of which asset disposals and other deleveraging measures accounted for 9 percentage points. For details, see EBA (October 2012). Note these figures cannot be directly compared to the EBA’s Basel III exercise because the bank sample and definitions of capital differ.

19
Policy Research Working Paper 5168

# **Abstract**

Critical appraisals of the current and potential benefits from developing country engagement in the World Trade Organization (WTO) focus mainly on the Doha Round of negotiations. This paper examines developing country participation in the WTO dispute settlement system to enforce foreign market access rights already negotiated in earlier multilateral rounds. The dispute data from 1995 through 2008 reveal three notable trends: developing countries’ sustained rate of self-enforcement actions despite declining use of the Dispute Settlement Understanding (DSU) by developed countries, developing countries’ increased use of the DSU to selfenforce their access to the markets of developing as well as developed country markets, and the prevalence of disputes targeting highly observable causes of lost foreign market access, such as antidumping, countervailing duties, and safeguards. The paper also examines potential impacts of the Advisory Centre on WTO Law (ACWL) into the WTO system in 2001. A close look at the data reveals evidence on at least three channels through which the ACWL may be enhancing developing countries’ ability to self-enforce foreign market access: increased initiation of sole-complainant cases, more extensive pursuit of the DSU legal process for any given case, and initiation of disputes over smaller values of lost trade.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to evaluate the impact that international institutions have on the market access opportunities of developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Capacity bid TM Capacity built<br>_. 1200<br>=<br>= 1000<br>£ 800<br>oO<br>a<br>S 600<br>gfe) 400<br>Qa<br>z 200<br>Ss 0<br>2003 2004 2005 2006 2007<br>Auctions<br><!-- End of picture text -->
of effects in the even more violent south. It is also important to bear in mind that NSP, although funded by international donors, is executed by the Afghan government and that villages receiving the program are informed that NSP is sponsored by the central government.<sup>23</sup> Thus, the results presented herein cannot be easily extended to projects delivered by foreign military forces (e.g. CERP in Iraq or Afghanistan), which may be perceived differently by the local population and thus have different effects on attitudes to government.

# **VIII. Conclusion**

In this paper we analyze the effect of the National Solidarity Program (NSP) - the largest development program in Afghanistan - on counterinsurgency outcomes. In particular, we test the strategy of “wining hearts and minds” by looking at the effect of the program on economic welfare, attitudes to government, and security. Random assignment of the development program across 500 villages allows us to estimate causal effects. Our results indicate that NSP has a significant positive effect on economic well-being and attitudes toward all levels of government, NGOs, and possibly also to foreign forces. We also identify a positive effect on violence in the long run, although only in regions with moderate levels of violence. In areas with heightened levels of violence, however, no effect on attitudes toward government or security is observed, despite a stronger positive effect on economic outcomes.

Overall, the findings provide general support for the strategy of winning “hearts and minds” through development projects. The provision of projects appears to make non-combatants more inclined to view government actors as working in their best interest, which in turn seems to make them less likely to support the insurgency. That the effect on violent incidents is apparent only in the long run suggests that the effect comes mainly through reducing the number of people willing to join the insurgency, rather than by increasing the population’s willingness to share information with the government. The results, however, indicate that development projects can prevent the spread of violence in relatively secure regions, but they are not effective in reducing violence in regions already experiencing significant security problems. Finally, the results are particularly important in demonstrating that the benefits of development projects are not limited to the provision of direct economic and social benefits, but can also contribute to preventing the spread of violent civil conflicts.

> 23 Results of the follow-up survey indicate that more than 70 percent of respondent indicate central government as the sponsor of the projects, about 22 percent indicating that they are provided by NGOs and about 2 percent of villagers indicating that they are provided by either sub-national governments, local leaders, foreigners or villagers themselves.

22
# Table 6.6 (Con't)

||Based<br>in D|on Value-Ad<br>omestic Price|ded<br>|Base<br>in|d on ValueAd<br>World Prices|ded<br>|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|% Exports|1.978<br>(1350)|1.541+<br>(0.933)|1964<br>(0.853)|1.613*<br>(0.792)|1.179'<br>(0.603)|1.076<br>(0.600)|
|Inputs:|||||||
|In (Senegalese Skilled<br>and Unskilled)|-0.050<br>(0285)|||0388<br>(0.262)|||
|ln (Other African<br>Silled and Unskilled)|-12E-4<br>(3.OE-4)|||_.6.2E4_<br>(23E-4)|||
|In (Non-African Skled<br>and Unskilled)|1.2E-4<br>(3.OE14)|||9.9E-5<br>(2.3E-4)|||
|In (Senegalese Middle<br>and Upper Management)|0.001<br>(5.4E-4)|||32E-4<br>(5.9-4)|||
|In (Other African Middle<br>and Upper Management)|2AE4<br>(32A4)|||2.3E-4<br>(22-4)|||
|In (Non-African Middle<br>and Upper Management)|35B-4<br>(6.OE-4)|||_-5.4E-5_<br>(5.1E-4)|||
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 17 The fourth column in Table 6 and Table 7 includes the block of variables for metropolitan coordination. None of the coordination variables appears significant, which does not provide evidence in favor of the convenience of the regionalist model. It is important to note that these results are conditioned to the variables used to measure the degree of metropolitan coordination. Finally, column five shows that the above conclusions remain the same after including a series of control variables.
The efficiency measures for 1991, which result from this analysis<sup>9</sup> are reported in Table 4 of the next section. This is an assessment of aggregate, or total, efficiency, and includes both technical and scale effects, although these elements will be considered separately next. Since part of the current land reform policy in South Africa is based on the potential productive efficiency of small farms, it is important to measure the effects of farm size. Following Fare, Grosskopf and Lovell (1985), Figure 2 shows the<sup>effect</sup> of this decomposition.

**Figure** 2: **Decomposition of Technical and Scale Efficiency**

<!-- Start of picture text --> Y P<br>D<br>C<br>B<br>A ~~~E<br>x X. X. X--* X<br><!-- End of picture text --> In the Figure, the constant returns to scale (CRTS) technology is denoted by the linear total product curve, OP, from the origin, through the efficient production units B and C. Units A and D, in this example, are inefficient as they are below the CRTS frontier. When non-constant returns to scale are allowed for, the frontier is concave, and the input-output combinations A, B, C and D are all technically efficient.

When technical efficiency is extracted from total efficiency, only the scale effect remains. Thus, farm A is scale inefficient by OX'/OX, due to being too small, but is technically efficient. Farm D is similarly technically efficient, but is too large and is scale inefficient by OX .. /OX". Finally, farm E is _9_ The equations for the programming problem are fully stated in Piesse et al. (forthcoming).

19
**Table 1. FDI inflows and GDP: ranking of 29 African countries, average 1996-1997** _(Millions of dollars)_

|Net FDI Inflows|GDP|
|---|---|
|South Africa<br>2313.5|129094|
|Nigeria<br>1566.0|36540|
|Cote d'Ivoire<br>305.1|10251|
|Angola<br>265.5|7396|
|Tanzania<br>154.0|6707|
|Uganda<br>148.0|6555|
|Namibia<br>109.9|3453|
|Ghana<br>101.3|6762|
|Senegal<br>92.2|4542|
|Mozambique<br>68.3|1944|
|Zimbabwe<br>66.5|8512|
|Zambia<br>64.0|4051|
|Mali<br>61.6|2532|
|Mauritius<br>46.7|4151|
|Cameroon<br>40.0|9115|
|Benin<br>31.5|2137|
|Guinea<br>20.6|3998|
|Chad<br>16.5|1603|
|Kenya<br>16.2|9899|
|Madagascar<br>12.1|3552|
|Congo, Republic<br>8.5|2298|
|Central African Republic<br>5.5|954|
|Ethiopia<br>5.0<br>|6330|
|Rwanda<br>2.4|1771|
|Congo, Democratic Rep. Of.<br>1.5|6904|
|Malawi<br>1.5|2424|
|Burundi<br>1.0|1137|
|Niger<br>1.0|1858|
|Sierra Leone<br>1.0|940|
|Source:**World Development Report, World Bank (1999).**||

Our indicator captures the attraction of African countries for FDI when they can rely on everything except for their natural resources and market size. Therefore, it reflects not only policy and political variables but also a series of structural factors such as infrastructure, transport costs and human capital. By indicating the attraction of the FDI business climate for each country, it complements the data collected in investors' surveys and cross-country ranking such as _The Africa Competitiveness Report_ published by the World Economic Forum. One has to keep in mind, however, that our indicator reflects existing rather than potential data/information and, thus, might be a poor predicator of future FDI flows.

_5_
are classified as an urban resident, while the rest are considered rural. In wave 1, 66 out of 146 (45 percent) of the regions in the sample are considered urban according to this definition. Across all waves, 223 out of 599 (37 percent) are.<sup>14</sup> The LASSO model selection and estimation in each case are carried out only for the relevant subsample of regions, drawing on multiple imputations conducted in the full sample.

Tables 3a-3d generally show much stronger predictive performance of the model in urban regions. In wave 1, the model explains 48 percent of the variation in reported income decreases on average across the imputations in urban regions, as opposed to only 14 percent in rural regions. However, predictions in rural areas are more accurate for the reported declines in farm income in wave 1, explaining 35 percent of the decline in variation, with google search data for assistance, school, and student being statistically significant predictors. When looking at variation within urban regions over time, the model explains 55 percent of the variation in reported declines in total income, with changes in google searches for employment and online school having significant predictive power. The models for predicting variation over time within rural regions are much less accurate, with R2 values ranging from 0.1 for total income to 0.25 for wage income loss. These results suggest that google search data in particular is better suited to predicting changes in welfare in urban areas than rural areas.

# _4.3 Decomposing the Explanatory Power of Predictors_

Turning back to the models estimated on the full sample of areas, we conduct Shapley R<sup>2</sup> decompositions to better understand the role of different types of variables in explaining variation in the model. Table 8 reports the results, which show the share of the R<sup>2</sup> that is accounted for by ten different sets of variables. The sets are the Google mobility data, Google searches related to food (“Milk”, “Bread”, “Cheap food”), Money (“Money”), Jobs (“Jobs”, “Salary”, “Unemployment”, “Vacancy”), assistance (“Assistance”, “Free Food”, “Free Meal”, and “Food Bank”), exercise (“Exercise” and “Gym”), Religion (“Bible”, “Quran”, “Prayer”), and School (“Student”, “Online School”, “Online Education”). The final two sets of variables are the EVI (vegetation), and No2 (a measure of pollution), taken from satellites.

> 14 These figures for both wave 1 and the full sample are obtained from each multiple imputation replication.

21
a number of countries it was possible to identify the superstars by name, we selected three countries from our sample—each from a different region: Jordan, Peru and Tanzania—and contacted their superstars –from the last year in their sample- to learn more about who they are and how they started exporting.<sup>17</sup> Table 10 presents a summary of the findings from this investigation.<sup>18</sup> In the three cases, we learned that the overwhelming majority of the superstars are either a producers or manufacturers—only in Peru we find a few traders.<sup>19</sup> This result is consistent with evidence from Bernard, Jensen, Redding and Schott (2010) and Ahn et al. (2011), who find that that the most productive firms or largest firms export directly.

We also find that superstars are very linked to the presence of foreign capital and this association seems to be stronger in the countries with lower GDP per capita. Finally, while the evidence is somewhat mixed, most of them become exporters almost immediately after they begin operations. The case of successful domestic firms that turn to export markets and become superstars over time is important only in the case of Tanzania; in Jordan and Peru, most are born to be exporters.

To sum up, the group of superstars is a unique group of firms, they explain most of the export growth and diversification observed across countries, they drive comparative advantage, and they are born big or very rapidly become so. The cases of small firms making it to the top are rare.

> 17 The years used in the identification of the superstars were 2009 in the case of Peru and Tanzania and 2010 in the case of Jordan. The total sample of firms that fall under the “superstars” category are 25 in Jordan, 69 in Peru and 16 in Tanzania.

> 18 Table A4 in the Appendix has the percent of superstars whose information was accounted for in each of the issues investigated and presented in Table 9.

> 19 For the purpose of this investigation, we consider a firm a trader if it only acts as an intermediary, without engaging in any activity that implies any type of transformation of the merchandised traded. If a firm engages in packaging or basic processing of goods (for example, sorting or drying) we consider that firm a “transformer” and it is not counted as a trader.

22
In this section we examine decisions that farmers make throughout the planting and harvest season that may contribute to higher repayment among fingerprinted farmers. The dependent variables in the remaining results tables are available from a smaller subset of loan recipients (N=520) who were successfully interviewed in the August 2008 follow-up survey round. To help rule out the possibility that selection into the 520-observation August 2008 follow-up survey sample might bias the regression results for that sample, Column 2 of Online Appendix Table 3 examines selection of loan recipients into the follow-up survey sample. The regressions are analogous in structure to those in the main results tables (Panels A, B, and C), and the dependent variable is a dummy variable for attrition from the baseline (September 2007) to the August 2008 survey. There is no evidence of selective attrition related to treatment status: in no case is fingerprinting or fingerprinting interacted with predicted repayment statistically significantly associated with attrition from the survey.

Online Appendix Table 4 presents regression results for repayment outcomes that are analogous to those in main Table 4, but where the sample is restricted to this 520-observation sample. The results confirm that the repayment results in the 520-observation sample are very similar to those in the overall loan recipient sample, in terms of both magnitudes of effects and statistical significance levels.

# _<u>Land area allocated to various crops</u>_

One of the first decisions that farmers make in any planting season (which typically starts in November and December) is the proportion of land allocated to different crops. Table 5 examines the average and heterogeneous impact of fingerprinting on land allocation; the dependent variables across columns are fraction of land used in maize (column 1), 7 cash crops (columns 2-8), and all cash crops combined (column 9).<sup>23</sup> Why might land allocation to different crops respond to fingerprinting? As discussed in the context of the theoretical model (footnote 22), non-production of paprika is a form of moral hazard, since the lender can only feasibly seize paprika output (in collaboration with the paprika buyer, Cheetah Paprika) and not other types of crop output. By not producing paprika (or producing less), the borrower is better able to avoid repayment on the loan. Therefore, by

> 23 For each farmer, the value of the variables across columns 1-8 add up to 1.

18