rafmacalaba/datause-encoder-data huggingface dataset:
Table 11: Trade, Legal Origins <u>and Bureaucratic Quality, Robustness</u>

|Dep. Var.: log of average tra|de flow|||||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Different Legal Origin|-0.10|-0.07|-0.14|-0.08|-0.10|-0.05|-0.17|
||(-2.19)|(-1.45)|(-3.13)|(-1.74)|(-2.21)|(-1.06)|(-3.92)|
|Bureaucratic Quality|0.38|0.37|0.33|0.21|0.27|0.17|0.18|
||(26.20)|(25.95)|(22.45)|(13.08)|(15.43)|(10.41)|(11.47)|
|log distance|-1.09|-1.09|-1.09|-1.09|-1.03|-1.09|-0.96|
||(-36.97)|(-37.00)|(-36.57)|(-37.57)|(-34.45)|(-37.16)|(-34.06)|
|log product of GDP|1.07|1.06|1.05|0.98|1.09|0.97|0.96|
||(95.80)|(94.34)|(93.16)|(87.18)|(93.3)|(85.01)|(88.36)|
|log product of land area|-0.20|-0.20|-0.20|-0.11|-0.21|-0.10|-0.11|
||(-21.75)|(-21.30)|(-21.20)|(-11.28)|(-20.79)|(-9.98)|(-11.64)|
|no. of landlocked|-0.33|-0.33|-0.34|-0.29|-0.40|-0.32|-0.36|
||(-7.39)|(-7.43)|(-7.92)|(-6.54)|(-9.18)|(-7.40)|(-8.70)|
|no. of islands|-0.16|-0.14|-0.13|-0.18|-0.22|-0.17|-0.11|
||(-3.43)|(-3.17)|(-2.77)|(-4.03)|(-4.60)|(-3.83)|(-2.63)|
|common border|0.98|0.97|1.05|0.97|1.05|1.02|0.75|
||(6.77)|(6.58)|(7.40)|(6.84)|(7.31)|(7.21)|(5.35)|
|common language|0.51|0.46|0.53|0.59|0.61|0.47|0.44|
||(9.07)|(7.94)|(9.49)|(10.75)|(10.35)|(8.46)|(8.74)|
|currency union|1.90|1.96|1.92|2.38|2.11|2.12|1.36|
||(7.85)|(7.83)|(7.91)|(9.51)|(8.78)|(8.29)|(4.78)|
|common colonizer||-0.01||||0.45|0.33|
|||(-0.14)||||(4.54)|(2.69)|
|colony-colonizer||<br>0.96||||<br>0.90|<br>0.85|
|||(6.90)||||(6.59)|(6.98)|
|both in GATT/WTO|||-0.25|||-0.03|-0.13|
||||(-2.32)|||(-0.25)|(-0.75)|
|only one in GATT/WTO|||-0.16|||-0.06|-0.12|
||||(-1.49)|||(-0.56)|(-0.67)|
|generalized system of<br>f GSP|||<br>0.64|||<br>0.54|<br>0.26|
|preerences ()|||(16.25)|||(14.09)|(6.87)|
|regional trade agreement|||0.53|||0.36|1.77|
||||(3.60)|||(2.47)|(10.28)|
|log of product of phone||||0.23||<br>0.22|<br>0.16|
|lines per 1,000 people||||(18.37)||(17.64)|(13.17)|
|log of product of road|||||011|||
|<br>length per 1,000 people|||||.<br>(7.31)|||
|average weighted tariff|||||||-0.01|
||||||||(-9.62)|
|Observations|77594|77594|77594|74576|46074|74576|<br>32466|
|R^2|0.67|0.67|0.68|0.69|0.71|0.69|0.76|
|% increase in trade due to<br>1s.d. institution|88%|87%|74%|41%|57%|32%|36%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant, year and country dummies are not reported. The sum of bureaucratic quality ranges from 0 to 8 with mean 4.672 and standard deviation 1.667. The percent increase in trade due to 1s.d. institution (bureaucratic quality) is calculated as follows: exp(coef*sd)-1.

46
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sample included randomly chosen households from randomly chosen villages within and outside factory reserve areas for nine randomly chosen factories. The number of villages sampled varied with factory crushing capacity. During 2007–8, 37 sugar factories operated in the 14 districts of AP, including 11 cooperatives, 25 privately owned factories, and one factory owned jointly by the state government and private investors (figure 1). The daily cane crushing capacity per factory ranged from 1000 to 8000 metric tons, with an average of 2700 metric tons. Ten households were chosen randomly in each village using the list of households in the village’s Public Distribution System records. There were 205 sugarcane farmers in the survey dataset.

Gur prices and factory-specific floor prices for cane were obtained from the National Federation of Cooperative Sugar Factories Limited (NFCSF) Year Books. The factory-specific pol% of cane was obtained from NFCSF and Sugar Technologists Association of India (STAI) annual publications for the 1992–3 through 2006–7 seasons (NFCSF various years; STAI various years).

The nature of sugar cane production and the organization of the sugar processing industry obviate concern about two potential selection issues: self-selection into factory reserve or nonreserve areas and self-selection into cooperative membership. Cane is highly perishable and bulky, so farmers’ access to processing outlets is limited geographically. Factories are spatially separated; through the allocation of reserve areas, the government limits competition among processing facilities for raw product. Farmers do not move from their villages to change buyers and/or to locate into a reserve or non-reserve area.

The nature of the cooperative formation process combined with the central government’s regulation of entry and location of processing facilities also prevents individual producers’ selfselection into processing cooperatives. Cooperative formation was undertaken by local elites 16
Rwanda, 0.25%; Uganda, 0.5%; China. 1%; and SADC, 6.35%. We take the weighted average of the identified owners and add the publicly traded shares allocation.

**This gives us the following shares for the regions in our model: SADC, 49.35%; EU, 27.5%; Rest of World, 12.5%; United States, 5%; Kenya, 2%; COMESA, 1%, Tanzania, 1%; Rwanda, 0.25%; Uganda, 0.5%; China. 1%.**

# **COMESA**

Clearly SADC and the Rest of the World are represented well in the communications sector of COMESA countries. Airtel (of India) and MTN (of South Africa) are companies that are well represented in COMESA countries. Among COMESA countries, Airtel is located in the Democratic Republic of Congo, Madagascar, Malawi, Seychelles and Zambia<sup>54</sup> , while MTN is located in Democratic Republic of Congo, Sudan, Swaziland and Zambia.<sup>55</sup> Vodacom (of South Africa) provides telephone service in the Democratic Republic of Congo as well as several SADC countries. We take an average of the Tanzanian and Ugandan market shares as representative of COMESA countries. This yields the following market shares:

**COMESA, 14.65%; SADC, 39.5%; EU, 19.35%; USA, 0.1%; Rest of the World, 26.35%; Kenya, 0.05%.**

> 54 http://en.wikipedia.org/wiki/Airtel_Africa.

> 55 <u>http://en.wikipedia.org/wiki/MTN_Group.</u>

109
an average 84 percent fall in earnings of the owner when the firm dies. Columns 2 and 3 show that the firms which die are, on average, 17 percent less productive and 19 percent less profitable in the baseline period than those that go on to survive.

These results are not in line with the occupational choice theory in which firm owners are voluntarily closing down to pursue better opportunities. They are more in line with the firm competition and firm shocks theory, or non-separability theory. However, Figure 6 shows that this average hides considerable overlap in the productivity distributions of firms that die with those that survive (Figure A.3. shows this same pattern within surveys). There is not a sharp productivity cutoff below which death occurs, but rather many deaths from high productivity firms as well as many low productivity firms surviving. Similarly, while Figure 7 shows considerable mass in the distribution of labor income near zero for owners whose firms die, there are also firm owners who earn relatively high incomes after firm death. These results suggest that there may be some firms that die for reasons related to these alternate theories, even if the average is due to firm competition and firm shocks.

# **6.3 Cause of Death Data to Examine Heterogeneity in Reasons for Firm Death**

To further explore the heterogeneity in reasons why firms die, and to see whether each of the theories seems applicable for different subsets of firms, we use cause of death data. This comes from direct questions asked of firm owners whose businesses have closed, and is asked in 9 of our 16 surveys. Table 5 summarizes what the owners report as the main cause of death of their firm, and what their main activity is after the firm dies. We see that the most common cause of death is in line with the first theory of being driven out by competition and firm shocks: 41 percent say they closed the firm because it was making a loss. The second most common cause is in line with the non-separability theory, with 26 percent closing for sickness or family reasons, while 11 percent give responses in line with occupational mobility theory, closing to pursue better wage opportunities or another idea for a firm.<sup>11</sup> Appendix Table A.6 shows that the cause of death varies by gender. Among male enterprise owners, 49 percent say that they closed their firm because it was making a loss, while among women 35.5 percent give this reason. For women, sickness (16 percent) or family reasons (18 percent) taken together are almost as common as making a loss, and only 9 percent of female owners give reasons related to the occupational mobility theory. We observe a different pattern for male enterprise owners,

> 11 22 percent give “other” as cause of death. This includes some firms in Sri Lanka, where the data for what “other” refers to was never entered, as well as reasons like the government shutting down the firm, disasters causing assets to be destroyed, and international migration.

21
Jaffe, D. and Perlow, M. (2008), “Investment Banking Regulation After Bear Stearns,” _Economist’s Voice_ , Vol 5 (5) Available at: http://www.bepress.com/ev/vol5/iss5/art1/.

Joint Center for Housing Studies (2008), _The State of the Nation’s Housing, 2008_ , Cambridge MA: Harvard University, November. Available at: www.jchs.harvard.edu/publications/markets/son2008/index.htm.

Johnson, S. (2009), “The Quiet Coup,” _The Atlantic_ , May.

Kane, E. (1985), _The Gathering Crisis in Federal Deposit Insurance_ , Cambridge MA: MIT Press.

Kane, E. (2009), Incentive Roots of the Securitization Crisis and its Early Mismangement,” _Yale Journal of Regulation_ , Vol. 26, No. 2, July.

Kroszner, R. S. and Strahan, P. E. (2000), “Obstacles to Optimal Policy: The Interplay of Politics and Economics in Shaping Bank Supervision and Regulation Reforms,” in _Prudential Supervision: What Works and What Doesn’t_ ,” F. Mishkin (ed.), Cambridge MA: National Bureau of Economic Research. Available at: http://www.nber.org/chapters/c10762.pdf.

Leibowitz, S. (2008), “Anatomy of a Train Wreck: Causes of the Mortgage Meltdown,” in _Housing America, Building out of a Crisis_ , edited by R.G. Holcombe and B.W. Powell. Available at: http://www.independent.org/pdf/policy_reports/2008-10-03-trainwreck.pdf Mason, J. R. (2008), “A National Homeownership Strategy for the New Millenium,” mimeo, February 26.

Olson, M. (1965), _The Logic of Collective Action_ , Cambridge MA: Harvard University Press.

Peltzman, S. (1976). “Toward a More General Theory of Regulation,” _Journal of Law and Economics_ , Vol 19, No. 2, pp. 211-240.

Pinto, E. (2008), “Statement before the Committee on Oversight and Government Reform, US House of Representatives, December 9. Available at:: <u>http://oversight.house.gov/documents/20081209145847.pdf</u> Richardson, M. and White, L. (2009), “Fixing the Ratings Agencies: Is Regulation the Answer,” Voxeu, February 2. Available at: http://www.voxeu.org/index.php?q=node/2958.

Senior Supervisors Group (2009), “Observations on Management of Recent Credit Default Swap Events,” March 9. Available at: http://www.sec.gov/news/press/2009/report030909.pdf Stigler, G., “The Theory of Economic Regulation,” _Bell Journal of Economics and Management Science_ ,” Vol. 2, No. 1, pp. 3-21.

Tarr, D. G. (2009), “Bailouts and Deficits or Haircuts: How to Restore U.S. Financial Market Stability,: Social Science Research Network Working Paper 1401555, May. Available at: `http://ssrn.com/abstract=1401555` Tarr, D. G. (2010). “Why Too Big to Fail is a Myth,” Social Science Research Network Working Paper 1533522, January. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1533522
emerges. In the cross-section, controlling for ability is clearly important for outcomes that are closely linked to ability, such as the human capital measure in this study. Ability, traditionally linked to academic tests, may not be nearly as useful a control for other outcomes, at least for the young Kenyan adults in this study.

This study also highlights a possible avenue for researchers interested in the consequences of education throughout sub-Saharan Africa: many countries have examinations much like the KCPE, with analogous cutoff rules for secondary school admission. An important caveat is that while some questions in survey data show a very high degree of reliability, this cannot be said for KCPE scores. Combining administrative test data with a rich follow-up survey overcomes this obstacle, and may yield novel findings establishing causal links between education, fertility, and labor markets throughout the developing world.

34
Table 11: Aid & bilateral OIL – 2<sup>nd</sup> stage - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.060***|-0.057***|-0.066***|-0.062***|
||(0.006)|(0.019)|(0.017)|(0.017)|
|_Ln(Affected)_|0.006|0.008|-0.012|-0.008|
||(0.007)|(0.008)|(0.008)|(0.008)|
|_Ln(GDP p.c.)_|-0.067|-0.053|-0.128*|-0.113|
||(0.064)|(0.078)|(0.069)|(0.070)|
|_Fuel exports (% of_|0.009***|0.011***|0.007***|-0.004|
|_merchandise exports)_|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_|0.540***|0.504***|||
||(0.119)|(0.157)|||
|_Fuel exports_×||0.002|||
|_Regulatory quality_||(0.005)|||
|_Corruption control_|||0.550***<br>(0.134)|0.652***<br>(0.140)|
|_Fuel exports_×||||-0.017**|
|_Corruption control_||||(0.004)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.088**|-0.100**|-0.107**|-0.102**|
||(0.046)|(0.051)|(0.046)|(0.044)|
|_Ln(Affected)_|0.033*|0.022|-0.002|0.001|
||(0.017)|(0.024)|(0.018)|(0.019)|
|_Ln(GDP p.c.)_|-0.064|-0.157|-0.337|-0.290|
||(0.161)|(0.089)|(0.208)|(0.197)|
|_Fuel exports (%_|-0.000|-0.007|-0.003|-0.026**|
|_of merchandise exports)_|(0.004)|(0.010)|(0.003)|(0.011)|
|_Regulatory quality_|0.631**|0.886*|||
||(0.305)|(0.512)|||
|_Fuel exports_×||-0.009|||
|_Regulatory quality_||(0.014)|||
|_Corruption control_|||0.901**|1.424***|
||||(0.372)|(0.396)|
|_Fuel exports_×||||-0.035**|
|_Corruption control_||||(0.017)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

25
strategy estimating the causal effects of training. Some registrants were able to attend and complete the course while others who registered for the course were not allowed to attend due to class oversubscription. The IV estimates suggest those completing the course following receipt of a course offer had an eight-percentage-point higher employment rate than those who did not receive a course offer. This effect is statistically distinguishable from the concurrent technical skills training program without demand-driven features, which had a negligible effect on employment. The demand-driven program is more effective in nearly all subgroups based on course and trainees characteristics. We then explore differences in the composition of courses offered in the two programs, finding that business input improves the matching of course supply to future growth in private sector skill demand.

The current study builds on the literature in a number of ways. First, and most importantly, we evaluate a skills training program that explicitly elicited demands from businesses for the skills and volume of traineeships required in a given area to serve their needs. Because of this, we refer to our focal program, _Pronatec-MDIC_ , as being _demand-driven_ . Second, the institutional setting allows us to compare the program’s effects across types of trainees within _Pronatec-MDIC_ courses, and to similar courses provided outside the demand-driven segment. That is, we investigate differential employment effects for firm-supplied trainees compared to broader sets of registrants concurrently attending the same business-requested courses, including unemployment beneficiaries and those who self-registered. Third, we compare employment effects of the business-requested courses to those from a national training program in which businesses did not provide input into the choice of program course offerings. We provide credible evidence in making these comparisons by using the same empirical strategy that addresses endogenous course completion in both programs. We also eschew common concerns over post-program sample attrition through the use of comprehensive administrative social security data containing monthly employment and earnings information available for all formal sector workers in Brazil.

2
spurious and driven by our methodology. In order to do so we take data for all countries that never experienced a financial crisis and we randomly assign 23 crisis episodes. We repeat this 200 times and then we test our model using each of these “placebo crises” interacted with the external dependence as in our baseline regression. Our results speak against the spuriousness of our findings. The coefficient on the interaction of external dependence and financial crisis turns out negative and significant at the critical 5 percent degree of confidence in less than 3 percent of cases, but also turns out positive at the same degree of confidence in 3 percent of the estimations. In case our results were spurious we would expect the negative and significant result to occur much more frequently. We do a similar experiment with the tangibility measure and in this case the coefficient is positive and significant at 5 percent level in only 3 cases out of 200. However, in 13 more cases it is negative and statistically significant. This suggests that once again our results do not seem to suffer by an apparent bias, if anything it seems such bias would work against us by affecting our estimates downward.

# **5 Conclusions**

In the context of the current financial crisis and the sharp drop in trade volumes this paper relied on evidence from past banking crises to analyze the impact of banking crises on export growth. With this purpose in mind we used a difference-in-difference identification strategy originally suggested by Rajan and Zingales (1998) which allowed us to tackle various endogeneity concerns and identify more precisely the impact of a domestic financial breakdown on exports.

Our results reinforce the evidence about the importance of a healthy financial system for exports growth, or as suggested by previous studies the idea that finance may be a source of comparative advantage and shape exports patterns (Manova 2008a, Manova 2008b). In fact, we find a negative and significant effect of banking crises on exports growth with sectors more dependent on finance growing by 4 percentage points less than sectors less relying on external finance during a crisis.

Second, we find that when analyzing the relationship between finance and exports it is important to distinguish among different financial channels as we show that not all financial channels dry up at times of crisis. In fact, the banking crisis does not appear to adversely affect exporters particularly dependent on inter-firm finance in any significant manner.

Third, the finding that sectors characterized by higher shares of tangible assets are significantly less affected by the crisis, arguably because of their ability to provide collateral, points toward an important policy message. Guarantee schemes can be an important policy device in the context of crisis to reduce credit constraints and support exporting firms to gain access to markets.

21
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# **5 Calibration/Numerical simulation**

To illustrate the operation of the model in the portion of the parameter space that is most relevant for operational risk management reforms, we calibrate the model to Serbian data for 2016 and conduct counterfactual analysis. Because the model is calibrated to data from a system that uses modern risk management methods, our counterfactual is a _reverse_ reform; we increase both ambiguity and search. In order to better understand the quantitative contribution of each change to the system, we conduct the counterfactual in two steps. We first simulate the removal of advanced IT systems by substantially increasing ambiguity while holding fixed the aggregate number of searches. Increased ambiguity magnifies the misallocation of search activity and reduces aggregate compliance. In the second step, we raise the level of total search activity to the level that is necessary to restore aggregate compliance.

Although we calibrate to actual data on inspection activity, our calibration remains a stylized example. In an actual risk management reform, especially one occurring in a customs – – agency, there would be enormous heterogeneity across countries, products and firms in model parameters, and in observed outcomes.<sup>46</sup> In this calibration we shut down much of this variation. Shipment types differ only in their ambiguity parameters, which are determined solely as a function of the lagged shipment frequency of each type. The model is calibrated such that the agency inspects more frequently shipment types about which there is greater ambiguity. Variation in inspection rates generates endogenous variation across bins in the compliance rate.

In the computational model the agency solves the minimization problem in equation (7). Instead of two shipment types _l_ as in the theory, the computational model has seven, each corresponding to one of the lagged shipment bins in Table 1. The compliance cost function is _c_ ( _y_ ) = _y_ . Since _c_<sup>_′_</sup> ( _y_ ) = _y_ in this case, the compliance rate in each bin can be represented as _pl_ ( _sl_ ) _≡ Fl_ (( _slL_ )). We choose a Pareto distribution for _Fl_ and parameterize the distribution 46The non-customs agencies – for example those responsible for sanitary and phytosanitary standards – oversee a smaller range of products and, in some cases, one might expect less variation in their implied model parameters.

27