rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3 Results Based on ACLED Data**

We triangulate our results based on interaction between post-war-onset dummies and the waraffected region dummy by running the same set of outcome variables on exposure to battles that took place within 20 km and 30 km of the household’s’ residence. More specifically, we compute the cumulative number of battles that have taken place within 20 km and 30 km radius of the residence of households prior to each survey round.<sup>31</sup> We chose a buffer of 20 km and 30 km distance to ensure that battles are close enough to have impacts on our outcome variables. The coefficients on these variables measure the effect of exposure to an additional battle event on household food security and labor market outcomes.

Table 9 presents the impact of battles that occurred within 20 and 30 km distance from households’ residence on food insecurity. For all three measures, exposure to battles leads to an increase in food insecurity. Exposure to an additional battle within 20 km of residence increases moderate or severe food insecurity and probability of food insecurity by 1 percentage point each. Likewise, battles within 30 km of residence have similar negative impact on food insecurity, though the impacts are slightly smaller. We experimented with varying battle distances from residence, including 10, 20, 30, 40 and 50 km radius. We find that the impact of exposure to battles increase the closer the battles get to the household residence.<sup>32</sup>

**<u>Table 9: Number of battles and food security</u>**

||(1)<br>Moderate or<br>severe food<br>insecurity|(2)<br>Moderate or<br>severe food<br>insecurity|(3)<br>Food<br>insecurity|(4)<br>Food<br>insecurity|(5)<br>Raw score|(6)<br>Raw<br>score|
|---|---|---|---|---|---|---|
|Battles within 20km|0.010<sup>***</sup><br>(0.001)||0.007<sup>***</sup><br>(0.001)||0.040<sup>***</sup><br>(0.007)||
|Battles within 30km||0.008<sup>***</sup>||0.005<sup>***</sup>||0.031<sup>***</sup>|
|||(0.001)||(0.001)||(0.005)|
|Household fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.020|0.021|0.009|0.010|0.021|0.022|
|Mean dep. var (pre-war)|0.361|0.361|0.557|0.557|2.054|2.054|
|No. observations|14374|14374|14374|14374|14374|14374|

Notes: The outcome variable in the first two columns stands for a binary indicator assuming a value of 1 for those households classified as moderate or severe food insecure based on the severity of food insecurity constructed from

> 31 We have also computed battle exposure at 10 km, 40 km, and 50 km distance radius. As expected, the effect of exposure to battles in close distances is greater than those further from the residence. Results based on these buffers are available upon request.

> 32 These results are available upon request.

34
**Figure 3: RER Volatility and Surges in Exports of Traditional and Modern Services**

<!-- Start of picture text --> RER Volatility RER Volatility<br>Modern Services, Developed Countries Modern Services, Developing Countries<br>RER Volatility RER Volatility<br>Traditional Services, Developed Countries Traditional Services, Developing Countries<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 -5 -4 -3 -2 -1 0 1 2 3 4 5<br>surge<br>Median Mean<br>1<br>.8<br>.6<br>.4<br>.2<br>1<br>.8<br>.6<br>.4<br>.2<br><!-- End of picture text --> Note: 0 refers to the first three years of the surge, negative values refer to the years immediately before the surge, and positive values to the years immediately after the surge.

**Figure 4: FDI and Surges in Exports of Traditional and Modern Services**

<!-- Start of picture text --> Net FDI as % of GDP Net FDI as % of GDP<br>Modern Services, Developed Countries Modern Services, Developing Countries<br>Net FDI as % of GDP Net FDI as % of GDP<br>Traditional Services, Developed Countries Traditional Services, Developing Countries<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 -5 -4 -3 -2 -1 0 1 2 3 4 5<br>surge<br>Median Mean<br>6<br>4<br>2<br>0<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Note: 0 refers to the first three years of the surge; negative values refer to the years immediately before the surge, and positive values to the years immediately after the surge.

15
**Table 10.  Secondary Market Remodeled Expected Excess Return vs Risk Measures**

|**ILS and ILW 0**<br>|**6Q2 (148 observati**<br>|**on)**<br>||
|---|---|---|---|
|_Coefficients_|_Standard Error_|_t Stat_|_P-value_|
|Std Dev<br>0.448869|0.025233|17.78891|1.76E-38|
|Multiple R = 0.8263||||
|TVaR90<br>0.17059|0.010945|15.58562|6.03E-33|
|Multiple R = 0.7893||||
|TVaR99<br>0.099455|0.005626|17.67798|3.3E-38|
|Multiple R = 0.8247<br>**ILS and ILW 0**<br>|**8Q1 (213 observati**<br>|**on)**<br>||
|_Coefficients_|_Standard Error_|_t Stat_|_P-value_|
|Std Dev<br>0.247743|0.025202|9.830178|4.98E-19|
|Multiple R = 0.5596||||
|TVaR90<br>0.091548|0.010599|8.637379|1.39E-15|
|Multiple R = 0.5102||||
|TVaR99<br>0.054198|0.005134|10.55756|3.28E-21|
|Multiple R = 0.5870||||

First, consider the standard deviation model. In Q2 2006 individual ILS , or for that matter individual ILWs, appear to have been priced on the basis of expected loss plus 44.88% of the standard deviation embedded in that particular ILS (see Table 10). For example, an ILS with an expected loss of 2% and a standard deviation of 5% would be priced at 4.2445%. That is with a load of 2.2445%, or equivalently an expected profit of 2.2445%. By Q1 2008, however, the estimated load for the standard deviation had dropped to 24.77% of the standard deviation. So the same ILS re-priced in 2008 would have a premium of 3.2385%. The near fifty percent drop of the estimated load has led in this example to a premium drop of some 25%.

Similar stories can be derived from the TVaR regressions. The TVaR90 coefficient drops from 0.1706 to 0.0915. Similarly, the TVaR99 coefficient drops from 0.0995 to 0.0542. In both cases they are cut in half. To give some intuition to the TVaR measures consider an ILS with an expected loss of 2% and a TVaR90 of 40%. In other words, in the worst 10% of cases the investor would expect to lose 40% of principal. The models would suggest premium shift for each of the periods from 8.824% to 5.366%, a drop of some 40%. The TVaR and the standard deviation models demonstrate dramatically the effect of a shift from a hard market to a soft market.

# Concluding Remarks

Theory suggests that an important driver of reinsurance pricing is the amount of capital that is necessary to protect against extreme risk (see, for example, Froot (2007)). In practice, catastrophic risk pricing fluctuates quite dramatically following extreme events. However, identifying or quantifying an exact relationship is more elusive. Some of the principal practitioners of reinsurance are private companies located offshore where disclosure requirements are not as stringent as for onshore primary insurers. Even if required to report to regulators, however, it is unlikely they would reveal either their pricing practice or their capital allocation rules. Moreover, many of the companies that write catastrophe reinsurance do it as part of a multi-line business. There are only a few catastrophe reinsurance specialists. The industry has traditionally been quite opaque.

18
<!-- Start of picture text --> Greater Rangpur Rest of the country<br>:<br>nePs<br>8 —— a Since _ el aw<br>— cod poverty ° Food poverty —e<br>6<br>—<br>_ a -<br>.4 .°———, ——.<br>“Extreme poverty<br>—$ _Extreme— 9 poverty F__ —_we<br>2<br>a<br>1 2 4 4 1 2 a 4<br>1, Bore: Ma-May 2. Aus: Tun-Aug 3. Monga:Sep-Nov 4. Aman: Dec-Feb<br><!-- End of picture text -->
  0 ). In our data, the normalized bid appears to decrease with the number of participants in both treatment plant and network cases (Figure 3).

<!-- Start of picture text --> Figure 3. Correlation between Competition and Normalized Bids<br>(Treatment plant contracts) (Water distribution works)<br>3.5 3.5<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 18<br>Number of bidders Number of bidders<br>Source: Author’s calculation.<br>Bid amount / Engineering cost estimate Bid amount / Engineering cost estimate<br><!-- End of picture text --> Our data are composed of 301 bids on 69 procurement auctions in 12 developing countries (Table 2). The dependent variable is the winning and losing evaluated (not read-out) bids in constant 2005 U.S. dollar terms.<sup>12</sup> Table 3 shows the summary statistics. The contract value varies from less than one million to 270 million U.S. dollars. The physical specifications of contracted work also widely differ. The treatment plant daily capacity could be 2,000 m<sup>3</sup> in some cases and 600,000 m<sup>3</sup> in other cases. The total length of erected pipes ranges from 5 km to 370 km, depending on contracts.

> 12 Both winning and losing bids are considered equally informative in the first-price sealed-bid format, which is normally used in procurement auctions for development projects.
<!-- Start of picture text --> Kampala<br>—— Pioneer Bus<br>—— Minibus Taxis<br>Percentage Access:<br>High :70 .<br>Li<br>X | oly<br>P| too LB.\Lan\ ,NyAYra rata7ia :PPyF jhmy (/ ‘<br>. Nae i Ae }<br>! £ 7~ \ nt ar7£2<br>7 1 rinky:<br>Po, SE<br>Fr v\<br><!-- End of picture text -->

<!-- Start of picture text --> ya<br>Lf f a<br>y .<br>Cl .<br>N 1 a<br>Lo<br>|<br>s<br>(0) 25 5 10<br>eeKilometerser | )<br>Esri, HERE, Garmin, © OpenStreetMap contributors, and the GIS user community<br><!-- End of picture text -->
Fukase, Emiko (2013) Export Liberalization, Job Creation, and the Skill Premium: Evidence from the US–Vietnam Bilateral Trade Agreement (BTA), _World Development_ , Vol. 41, pp 317-337 Hoang, Trung X. & Pham, Cong S. & Ulubaşoğlu, Mehmet A., (2014) "Non-Farm Activity, Household Expenditure, and Poverty Reduction in Rural Vietnam: 2002–2008," _World Development_ , 64(C), pages 554-568.

McCaig, Brian (2011). Exporting out of poverty: Provincial poverty in Vietnam and US market access. _Journal of International Economics_ , 85(1), 102–113.

McCaig, Brian & Nina Pavcnik (2014) "Export Markets and Labor Allocation in a Lowincome Country," _NBER Working Papers 20455_ Nguyen, Minh, Brian McCaig and <mark>Robert Kaestner</mark> (2017) “Trade liberalization and investment in children’s human capital: evidence from the U.S-Vietnam Bilateral Trade Agreement”, _mimeo_ Menon, Nidhiya & van der Meulen Rodgers, Yana & Nguyen, Huong (2014) "Women’s Land Rights and Children’s Human Capital in Vietnam," _World Development_ , Elsevier, vol. 54(C), pages 18-31 Kovak Brian, (2013) "Regional Effects of Trade Reform: What Is the Correct Measure of Liberalization?" _American Economic Review_ , vol. 103(5), pages 1960-1976, August.

<mark>Nguyen, Hai, Steve Wood, Neil Wrigley, (2013) "The emerging food retail structure of Vietnam: Phases of expansion in a post‐socialist environment",</mark> _<mark>International Journal of Retail & Distribution Management</mark>_ <mark>, Vol. 41 Issue: 8, pp.596-626</mark> <mark>Pierce, Justin R., and Peter K. Schott (2016) "The Surprisingly Swift Decline of US Manufacturing Employment."</mark> _<mark>American Economic Review</mark>_ <mark>, 106(7): 1632-62.</mark> <mark>Schank, T., Schnabel, C., Wagner, J., (2007) “Do exporters really pay higher wages? First evidence from German linked employer–employee data”,</mark> _<mark>Journal of International Economics</mark>_ <mark>72 (1), 52–74</mark> 37
Benefits in Russia 4 them. It would be the objective of a well-designed tax system that the costs of supporting local public services would be borne by non-distorting taxes (Wallich et al., 1992).

(ii) If firms pay the costs of social services for their workars, they may become reluctant to hire or to employ workers in "high risk" groups, e.g. women with young children.

(iii) If changing jobs means the loss of access to social benefits, or the disrttption of access, firm-linked benefits will hinder labour mobility.

(iv) There are arguments for managerial specialisation. Firms, it is argued, should have a set of objectives dominated by considerations of productive efficiency and profit maximization. There-may also be arguments for having provision of a service such as education, health or housing under a single management rather than these act:vities scattered around among many providers.

(v) In a company town, or a town dominated by a single industry, the closure of the company or industry will have the additional consequence of disrupting the provision of local social services.

Prior to discussing in more detail the arguments for specific benefit programmes and their methods of implementation, Tie attempt to give a short overview of the type of benefits currently provided by firms before attaching costs and/or income flows to those benefits. In t'is way, we hope to provide some measure of the share of benefits costs in total labour charges from the cost perspective of the firm while also picking up the effective distribution of labour income. In addition, we provide a more extended discuss:Lon of one crucial component of benefits -- the housing programmes run by a significant minority of firms. The data are generated from the two rounds of the World Bank firm survey carried out in the Moscow region in November 1992 and April 1993 2. The information relates for the most part to 1991 and 1992, but also in part to the first quarter of 1993. Because the sa7ple is both small and localized, obvious caveats concerning the represertative nature of our results arise. Further, while the conclusions do not seem inconsistent with information generated by larger samples and more aggregate data, bv concentrating on the Moscow region we

- The survey covered 41 firms in the Moscow area. For a more extended discussion of the results see, Commander et al (1993).
# **Appendix 2: Timeline**

Aug 14, 2015: Applications launched August 2015: Roadshows, advertising Sept 6, 2015: Applications closed Sept 10, 2015: Random assignment done by computer Oct 2, 2015: Registration in AEA RCT registry Sept 10‐Nov 13, 2015: Investment Readiness program implemented, master classes, mentoring, etc.

November 12‐14, 2015: Semi‐finals and pitch event in Belgrade December 2‐4, 2015: Finals with the top 54 firms from the semi‐finals pitching in front of the investors VC fund managers and Business Angels.

April‐August 2016: First follow‐up survey (approximately 6 months post‐program) August 2017‐March 2018: Second follow‐up survey (approximately 2 years post‐program).

# **Appendix 3: Scoring and Data Appendix**

The key variables are measured and defined as follows:

# **_Baseline Investment Readiness_**

The applications were scored by a team from Pioneers Ventures, a seed‐stage venture capital investment unit. Two professional investment managers reviewed each eligible application independently and assigned a score, based on for sub‐scores using an agreed scoring metric as detailed below in Appendix Table 3.1. Where the independent scores differed by more than one unit, they discussed the cases to arrive at a consensus score, otherwise the scores were averaged. Each business was scored on four sub‐ components as follows:

# **Appendix Table 3.1: Description of the Investment Readiness Scoring Scale**

|_Category_|_Weight_|_Points_|_Threshold description_|
|---|---|---|---|
|**Market attractiveness**|**10%**|1<br>2|Market does not exist/ no market need<br>Small market well served by competitors or equally<br>good substitutes|
|||3|Large market well served by competitors or equally<br>good substitutes|
|||4|Attractive niche in small market with unique solution/<br>positioning|
|||5|Attractive niche in large market with unique solution/<br>positioning|
|||6|Very large and mostly untapped/ underserved market<br>with right offering|

45
**Table 6: Individual‐level regression**

|**Independent variables**|**Category**|**Coefficie**|**nts**|
|---|---|---|---|
|||Without country FE|With country<br>FE|
|Residence|Urban|||
||Rural|‐0.30|‐0.30|
|Demographic structure<br>||||
|Two or less adults|0 kids|||
||1‐2 kids|‐0.4|‐0.36|
||More than two kids|‐0.87|‐0.73|
|Two or more adults|0 kids|‐0.28|‐0.27|
||1‐2 kids|‐0.53|‐0.49|
||More than two kids|‐0.87|‐0.73|
|Marital Status of adults|Married|||
||Never married|‐0.02|‐0.02|
||Divorced|‐0.06|‐0.13|
||Living together|‐0.03|‐0.07|
||Widowed|0|0.02|
|Gender of household head|Man|||
||Woman|0.09|0.01|
|Characteristics of individual||||
|Age group|0‐14|||
||15‐24|‐0.22|‐0.20|
||25‐34|‐0.12|‐0.14|
||35‐44|‐0.05|‐0.08|
||45‐54|‐0.03|‐0.07|
||55‐64|‐0.02|‐0.08|
||65 and up|‐0.05|‐0.13|
|Gender|Man|||
||Woman|0.02|0.02|
|Educational attainment|No education|||
||Incomplete primary<br>|0.15|0.09|
||Complete primary or<br>some secondary|0.31|0.24|
||Complete secondary|0.52|0.43|
||Tertiary|0.86|0.68|
|Constant||2.07|2.19|
|Adjusted R2||0.43|0.50|
|Number of countries||78|78|
|Number of observations||5,971,267|5,971,267|
|Weighted number of observations (|‘000s)|4,734,522|4,464,869|

Note: Standard errors and significance is not reported because of the absence of PSU identifiers for many counties. In a subsample of 38 countries, all coefficients are statistically significant. Grey shading indicates that the category is excluded from the regression specification.

24
Table4. Part I. The Logit Model of Services <u>Utilization</u>

||Acute<br>Surgeries/inpatient|planned surgeries|Delivery|
|---|---|---|---|
|Welfare (reference= quintile 5)||||
|Quintile 1|1.47 (1.38 , 1.57)**|1.13 (1.02 , 1.26)*|1.04 (0.87 , 1.24)|
|Quintile 2|1.36 (1.28 , 1.45)**|1.04 (0.93 , 1.15)|1.03 (0.87 , 1.22)|
|Quintile 3|1.21 (1.13 , 1.29)**|1.14 (1.03 , 1.27)*|1.08 (0.92 , 1.27)|
|Quintile 4|1.08 (1.01 , 1.16)*|1 (0.89 , 1.11)|0.96 (0.82 , 1.13)|
|Age (reference=30-40 years old)||||
|18-30|0.82 (0.75 , 0.89)**|0.83 (0.72 , 0.95)**|3.4 (2.99 , 3.88)**|
|40-50|1.27 (1.18 , 1.38)**|1.24 (1.09 , 1.41)**|0.14 (0.11 , 0.20)**|
|50-60|1.83 (1.70 , 1.98)**|1.34 (1.17 , 1.53)**||
|60-70|1.83 (1.69 , 1.98)**|1.55 (1.36 , 1.77)**||
|70-80|1.76 (1.62 , 1.91)**|1.67 (1.46 , 1.91)**||
|Female (=1 if female)|0.88 (0.85 , 0.92)**|0.85 (0.79 , 0.91)**||
|Education (reference: no formal ed|ucation)|||
|Primary|1.28 (1.04 , 1.57)*|1.17 (0.78 , 1.75)|2.62 (1.23 , 5.58)*|
|Secondary|1.28 (1.04 , 1.57)*|1.36 (0.92 , 2.02)|2.56 (1.21 , 5.42)*|
|high school or equivalent|1.03 (0.80 , 1.33)|1.46 (0.94 , 2.26)|1.89 (0.87 , 4.11)|
|Above high school|1.23 (0.99 , 1.53)|1.35 (0.90 , 2.03)|2.84 (1.32 , 6.09)**|
|Education of head (reference: no f|ormal education)|||
|Primary|1.18 (0.95 , 1.47)|1.49 (0.94 , 2.35)|1.06 (0.66 , 1.69)|
|Secondary|1.16 (0.94 , 1.45)|1.58 (1.01 , 2.49)*|0.83 (0.52 , 1.33)|
|high school or equivalent|1.55 (1.14 , 2.11)**|1.66 (0.94 , 2.94)|0.76 (0.36 , 1.60)|
|Above high school|1.1 (0.88 , 1.39)|1.8 (1.13 , 2.87)*|0.65 (0.39 , 1.07)|
|Employment (=1 if employed)|0.74 (0.71 , 0.77)**|0.72 (0.66 , 0.77)**|0.69 (0.61 , 0.77)**|
|Household size|0.97 (0.96 , 0.98)**|0.98 (0.96 , 1.00)*|1.03 (1.00 , 1.06)|
|Relationship to head (reference=h|ead)|||
|Wife|0.96 (0.91 , 1.02)|0.99 (0.90 , 1.09)|1.95 (1.41 , 2.69)**|
|Children|0.74 (0.69 , 0.80)**|0.89 (0.80 , 1.00)*|1.36 (0.99 , 1.88)|
|Other|0.78 (0.71 , 0.86)**|0.8 (0.69 , 0.93)**|0.67 (0.46 , 0.98)*|
|Urban (=1 if urban)|1.34 (1.28 , 1.40)**|1.37 (1.26 , 1.50)**|0.85 (0.74 , 0.98)*|
|Region (reference = Imereti )||||
|Tbilisi|1.2 (1.11 , 1.29)**|2.3 (2.07 , 2.55)**|0.98 (0.77 , 1.25)|
|Guria<br>|0.92 (0.82 , 1.02)|1.25 (1.07 , 1.46)**|1.1 (0.83 , 1.45)|
|Racha-Lechkhumi,||||
|<br>qvemosvaneti|1.62 (1.45 , 1.80)**|0.41 (0.30 , 0.56)**|0.78 (0.48 , 1.24)|
|<br>Kakheti|<br>1.21 (1.13 , 1.30)**|<br>0.81 (0.71 , 0.92)**|<br>0.82 (0.66 , 1.02)|
|Mckheta-Mtianeti|1.43 (1.30 , 1.57)**|1.4 (1.19 , 1.63)**|1.1 (0.83 , 1.47)|
|Samegrelo|<br>1.25 (1.17 , 1.33)**|<br>0.7 (0.61 , 0.79)**|<br>0.97 (0.79 , 1.18)|
|Qvemo Qartli|0.8 (0.73 , 0.87)**|0.48 (0.41 , 0.57)**|1.43 (1.18 , 1.72)**|
|Sida Qartli|1.41 (1.31 , 1.51)**|0.59 (0.51 , 0.70)**|1.08 (0.87 , 1.34)|
|Achara|1.66 (1.54 , 1.78)**|0.8 (0.69 , 0.92)**|1.43 (1.19 , 1.71)**|
|Observations|701,307|701,307|193,517|

95% confidence intervals in parentheses

* significant at 5%; ** significant at 1%

25
<!-- Start of picture text --> Figure 3a. Histogram of built-up: cells with roads in 1976<br>omitting unbuilt areas<br>20%<br>1975 (actual) 2014 (actual) 2053 (forecast) 2092 (forecast)<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70% 71-80% 81-90% 91-100%<br>Figure 3b. Histogram of built-up: cells w/out road in 1976<br>omitting unbuilt areas<br>5%<br>1975 (actual) 2014 (actual) 2053 (forecast) 2092 (forecast)<br>4%<br>4%<br>3%<br>3%<br>2%<br>2%<br>1%<br>1%<br>0%<br>1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70% 71-80% 81-90% 91-100%<br><!-- End of picture text --> Notes for Figures 3a and 3b: The Figures show a stacked histogram of the frequency distribution of builtup proportions divided into 1975 deciles. In Figure 3a only those cells with a road in 1976 are used in the histograms. Figure 3b does the same thing for cells without a road in 1976. In both Figures, fully built cells are included in the top decile. Since an overwhelming proportion of cells are unbuilt in all years, they are omitted from the Figures for the sake of clarity. The 1975 and 2014 values are taken directly from the data. The 2053 and 2092 forecasts are obtained as follows. We first form the decile transition matrix using data from 1975 and 2014, only using cells with a road in 1976 (for Figure 3a) or those without a road in 1976 (for Figure 3b). We then separately project the built-up frequency distribution from 2014 to 2053 and 2092 as two iterations of the transition matrix. The resulting forecasts should be understood as predictions of what the distribution of buildup will look like in 1976 if the transition matrices remain unchanged. Similar forecasts are obtained if we use transition matrices constructed using different data years. The forecast do not specify which cells will be built.
Policy Research Working Paper 6810

# **Abstract**

This paper examines the relationship between entry regulation and the business interests of former President Ben Ali’s family using firm-level data from Tunisia. Connected firms account for a disproportionate share of aggregate employment, output and profits, especially in sectors subject to authorization and restrictions on FDI. Quantile regressions show that profit and market share premia from being connected increase along the firm-size distribution, especially in highly regulated sectors. These patterns are partly explained by Ben Ali’s relatives sorting into the most profitable sectors. The market shares of connected firms are positively correlated with exit and concentration rates in highly regulated sectors. Although causality is difficult to establish, the results are consistent with the hypothesis that the Ben Ali clan abused entry regulation for private gain at the expense of reduced competition.

This paper is a joint product of the Poverty Reduction and Economic Management Unit and Office of the Chief Econmist, Middle East and Northern Africa Region; and Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at brijkers@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