rafmacalaba/datause-encoder-data huggingface dataset:
Table A9: Effect of pre-trial speed on parties’ average log revenues after pre-trial start (ANCOVA specification)

||(1)<br>OLS|(2)<br>IV|(3)<br>RF|(4)<br>FS|
|---|---|---|---|---|
|Fast pre-trial|0.078**|0.031|||
||(0.034)|(0.214)|||
|Entered after decree|||0.004|0.119***|
|application|||(0.026)|(0.032)|
|Log revenue, minus 1|0.293***|0.292***|0.291***|-0.024|
||(0.032)|(0.033)|(0.032)|(0.021)|
|Log revenue, minus 2|0.271***|0.271***|0.271***|0.012|
||(0.045)|(0.045)|(0.046)|(0.025)|
|Log revenue, minus 3|0.155***|0.157***|0.158***|0.029*|
||(0.022)|(0.023)|(0.022)|(0.017)|
|Log revenue, minus 4|0.038|0.038|0.038|-0.007|
||(0.030)|(0.030)|(0.030)|(0.019)|
|Log revenue, minus 5|0.093***|0.093***|0.092***|-0.013|
||(0.032)|(0.031)|(0.032)|(0.015)|
|Log revenue, minus 6|0.146***|0.145***|0.145***|-0.009|
||(0.026)|(0.026)|(0.026)|(0.014)|
|FEs|chamber|chamber|chamber|chamber|
|Time FEs|calmth|calmth|calmth|calmth|
|Pre-mean|18.737||||
|Pre-sd|2.696||||
|Fstat excl inst||||14.2|
|Observations|1622|1622|1622|1622|

<u>Notes:</u> The dependant variable in cols 1-3 is average monthly log revenues after the start of the pre-trial; that in col 5 is the Fast pre-trial dummy. RF is the reduced form (col 3), FS the first stage (col 4). Fast pre-trial is a dummy that takes value 1 when a case party’s pre-trial was completed in four months or less. Entered after decree application is a dummy that takes value 1 when a party’s case started its pre-trial after the application of the decree. All models include chamber and calendar month fixed effects, control for six baseline values of log monthly revenues (the six months before the start of the pre-trial), and include a constant. All models are estimated by OLS; they include one observations for each of the 1622 case parties. Standard errors in parentheses, clustered at the (chamber x hearing of entry) level. Significance levels are denoted as follows: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Pre-mean and pre-sd refer to sample average and standard deviation respectively of case parties’ log revenues in the month before pre-trial start.

63
|**Table 4: Characteristics as**|**sociated with**<br>M|**being a wor**<br>ale|**ker in the for**<br>Fe|**mal sector**<br>male|
|---|---|---|---|---|
||Youth|Adults|Youth|Adults|
|Age|1.11|1.18|0.94|1.16|
||[0.72]|[13.34]***|[0.14]|[3.18]***|
|Age Squared|1|1|1|1|
||[0.30]|[13.54]***|[0.18]|[3.01]***|
|Married|1.01|1.06|0.93|0.9|
||[0.09]|[1.22]|[0.33]|[0.85]|
|Had Training|1.71|1.55|1.18|1.29|
||[2.92]***|[4.86]***|[0.46]|[0.74]|
|_Education (no formal education_|_as compariso_|_n)_|||
|Below Primary Education|1.02|1.28|0.36|1.16|
||[0.14]|[3.17]***|[1.63]|[0.42]|
|Primary Education|<br>1.02|<br>1.26|<br>0.6|<br>0.71|
||[0.22]|[4.87]***|[1.70]*|[1.55]|
|Middle|0.93|1.83|1.08|1.01|
||[0.88]|[12.45]***|[0.26]|[0.04]|
|Matric|1.24|2.64|4.16|6.44|
||[2.76]***|[22.31]***|[6.36]***|[10.81]***|
|Inter|<br>1.84|<br>3.75|<br>7.37|<br>9.1|
||[5.28]***|[23.73]***|[7.59]***|[10.16]***|
|Bachelor’s or Higher|2.38|6.04|12.61|17.3|
||[5.91]***|[35.62]***|[8.69]***|[15.60]***|
|In Agricultural Sector|0.13|0.1|0.02|0.01|
||[20.85]***|[39.19]***|[7.30]***|[13.73]***|
|Number of other family member|s are||||
|Employer|0.63|0.55|1.15|0.29|
||[2.50]**|[4.56]***|[0.31]|[3.74]***|
|Own-account workers|<br>0.74|<br>0.87|<br>0.63|<br>0.61|
||[7.45]***|[5.21]***|[4.20]***|[5.26]***|
|Unpaid family workers|<br>0.83|<br>0.63|<br>0.69|<br>0.69|
||[5.48]***|[17.63]***|[3.77]***|[4.50]***|
|Salaried workers|<br>2.67|<br>2|<br>1.53|<br>1.59|
||[28.00]***|[29.38]***|[4.63]***|[6.42]***|
|Casual wage workers|<br>0.67|<br>0.84|<br>0.58|<br>0.68|
||[10.89]***|[6.84]***|[5.42]***|[4.33]***|
|Migration within past 10 years|<br>1.5|<br>1.4|<br>1.73|<br>1.56|
||[3.62]***|[5.86]***|[1.80]*|[2.16]**|
|Household size|<br>0.96|<br>0.97|<br>1.05|<br>1|
||[4.15]***|[5.06]***|[1.50]|[0.23]|
|Urban|<br>1.15|<br>0.76|<br>1.04|<br>1.06|
||[2.51]**|[8.63]***|[0.24]|[0.48]|
|_Provinces (Punjab as compariso_|_n group)_||||
|Sindh|089|129|191|121|
||.<br>[197]**|.<br>[721]***|.<br>[291]***|.<br>[121]|
|NWFP|.<br>0.68|.<br>1.22|.<br>2.25|.<br>1.64|
||[4.86]***|[4.50]***|[3.49]***|[2.93]***|
|Balochistan|<br>0.6|<br>1.65|<br>2.25|<br>2.68|
||[5.02]***|[10.54]***|[1.91]*|[3.17]***|
|Number of Observations|14579|34403|3087|6805|

Note: 1. Absolute value of z statistics in brackets; 2. * significant at 10%; ** significant at 5%; *** significant at 1% 25
is borne out by firm entry rates that doubled between 2004 and 2012.<sup>18</sup> Yet while mature firms account for four out of ten firms, they also constitute two-thirds of employment, an indication of grouping of larger firms among older firms. In fact, mature firms are on average four times larger than young, startup counterparts.

In terms of the distribution by sector, there is some evidence of sectoral concentration. While industry makes up to nearly 50 percent of establishments, it accounts for more than 70 percent of employment, an indication of much higher relative size in the sector (Table 2). This scale of establishments is particularly remarkable in the areas of chemicals, rubber, plastics manufacturing, and construction. For instance, while only accounting for roughly 5 percent of firms, those in chemicals, plastics and rubber manufacturing, likewise as construction, account for a fifth of employment each (that is, four out of ten workers combined). By contrast, firms in retail and wholesale trade account for more than half of all firms put together; yet these establishments are disproportionately small accounting for only a quarter of the employment stock (at ten and five percent, respectively). In other words, the latter groups are themselves bellwethers of concentration of employment in larger-firm sectors; while retailers and wholesalers are disproportionately small. Figure 4 shows that larger firms are operating indeed in the manufacturing sector, export more than 10 percent of their sales to foreign markets and that larger firms in Tema are larger than elsewhere.

More than half of the workers in formal firms are concentrated in the Accra region, in Accra Metropolitan Area (AMA) and Tema. The distribution of jobs reflects the geographical distribution of formal firms: 54 percent of firms are in the AMA, and they employ 60 percent of the formal private sector workforce; 14 percent of firms are in Tema; 22 percent are in Tamale (Northern region); and 10 percent are in Takoradi (Western region). The location of establishments is related to agglomeration effects (urban centers). Thus the employment density is highest in Accra and decreases with the size of the firm locality (Table 1).

# **_Employment Growth: Which Firms Are Adding Jobs?_**

Figure 5 shows the aggregate net growth employment rate, which was an estimated 8 percent per annum — that is the stock of employment was estimated to grow at nearly ten percent a year over the period 2010 to 2012. Underlying this net rate was a job creation rate of nearly twelve percent and a contraction rate of four percent. Put differently, then, the growing firms over this period added jobs at nearly a fourfold rate compared to the firms that shed jobs over the same timeframe.

> 18 Based on the World Bank’s Entrepreneurship database, which includes information on the density of new businesses registered per 1,000 people. By this metric, in 2004, the first available data point, this rate was 0.49; by 2012 the rate was 0.90 (and was 1.09 in 2011).

12
the death and survival of the siblings of the adult female, not of current household members.

Despite the shortcomings, the mortality estimates - derived from the siblings’ data from a large representative sample – offer useful insights. Even if they might underestimate the number of deaths, they provide an accurate measure of the impact of mortality on the current population structure of Rwanda. Such data also allow the analysis to address questions about the timing and the socio-economic distribution of excess mortality that cannot be answered with other approaches like the reconstruction approach that compares population counts and distributions from one census to the other (see for the case of Cambodia, Heuveline, 2001a and 2001b).

In the siblings’ data collected in a nationally representative Demographic and Health Survey, there is no direct information on the socio-economic status of the siblings. However, by using information about the respondents, it is possible to proxy the socioeconomic status of the family. For example, the schooling levels of the female respondents can be considered a good indicator of the social and educational status of their family. For the type of residence, one variable indicates whether the respondent is living in a rural or an urban area. Combined with variables about migration history, this allows us, in most cases, to determine whether the family was living in a rural or an urban area in 1994. For individuals who never moved, or who moved for the last time before 1994, the current residence is the residence in 1994. For individuals who moved after 1994, the survey only records the type of residence immediately prior to the last move. If there was only one move between 1994 and 2000, this does not constitute a problem. If there have been several moves between 1994 and 2000, we cannot be certain of the type of residence in 1994, but we make the assumption that the type of residence (urban vs.

9
informal sector employment as a whole (B, C and D), the evaders account for 13.0% of workers, the avoiders account for at most 1.7%, and the outsiders account for the balance of 85.2%.

|**Table 1: Compliers, Evaders, Avoiders and Outsid**|**ers under the Facto**|**Firm Cate**<br>**ries Act, 1948, in 2**|**gories**<br>**009-10***||
|---|---|---|---|---|
|**Firm Characteristics**|**A**<br>**ASI Firms**|**B**<br>**NSSO Firms with**<br>**10 or employees**<br>|**C**<br>**NSSO Firms with**<br>**9 employees**|**D**<br>**NSSO Rest**|
|Number of Firms|143,452|256,993|67,249|16,900,000|
|Share of Firms|0.8%|1.5%|0.4%|97.3%|
|Total Employment**|11,500,000|4,543,668<br>|605,245|29,700,000|
|Share of Employment|24.8%|9.8%|1.3%|64.1%|
|Mean Employment|79.9|17.7<br>|9.0|1.8|
|Median Employment|21|13|9|1|
|Median Labor Productivtiy (Rupees)****|135,626|59,820<br>|74,000|23,400|

* Usage of power is ignored and only the criteria of 10 or more employees is considered for registration under the Factories Act,1948. Excludes firms with missng or zero employment. **Includes unpaid family membes/helpers working in the firm.

***Capital here is defined as the value (market value for NSSO and book value for ASI) of assets at the end of the year ****Labor productivity is total gross value added divided by total employment

From the 2010-11 NSSO survey, 256,993 firms had 10 workers or more but were not registered. This compares with 143,452 firms registered under the Act, as shown by the ASI for 2009-10. Thus more firms appear to be noncompliant than compliant with the law. Noncompliant firms formed a staggering 64.2% of compliant and noncompliant enterprises. However, noncompliant enterprises are much smaller—a mean size of 17.7 workers compared to 79.9 workers for compliant enterprises. The total number of workers employed by these noncompliant enterprises was 4,543,668. This compares with 11,500,000 workers employed by all registered firms as shown in the ASI. In other words, unregistered enterprises accounted for 28.3% of the labor force of compliant and noncompliant enterprises.

How does noncompliance compare to the magnitude of adjustment out of the regulation? In 2010-11 the number of enterprises in the 9 workers category was 67,249, with total employment of 605,245. Thus, even if we attribute _all_ of these enterprises to the adjusted out category, their number is less than the number of noncompliant firms, and total employment in them is less than a third of that in noncompliant firms. But these are of course overestimates of adjustment out of regulation since it includes firms who are naturally of size 9. It would seem, therefore, that noncompliance with regulation dominates adjustment out of regulation by a large margin.

8
7 Before turning to the data, an obvious question concerns the accuracy of the responses. The survey was sent to the principal contacts in each country of the Basel Committee on Bank Supervision. Even though these contacts should know the regulatory environment, the survey’s scope is such that for any country a number of people usually are involved in its completion, and some or all of the members of this group might change over time, raising the issue of differences in the interpretation of questions over time (in addition to changes in the wording noted above). In the first three surveys, we ourselves went back to or directed correspondence with authorities for clarification, where there were notable changes, as well as posting the survey responses on the web, so that the data could be challenged and inconsistencies resolved. As none of us were with the World Bank, we were not involved with this cleaning process for Survey IV, and in this paper use the data posted on the World Bank website. As always, there are instances of unclear responses from the authorities and we accordingly recommend ongoing efforts to clean (and update) the data. There are also some notable gaps at present, such as the absence of responses from Japan and Saudi Arabia in the latest survey. It might also be noted that some countries chose not to respond to any surveys, not to respond to some surveys but to others, and not to answer some questions but others, which raises the question as to whether this was a strategic decision or simply survey fatigue.

# **III. Bank Regulation and Supervision around the World** : **What the Data Say**

With four surveys over more than a decade, one can ask to what extent have there been changes in the regulatory environment in countries around the world. As Survey IV is just becoming available, analysis of these changes understandably is in an early stage, and we hope that with the data available on the web, more people will investigate the impact of variations in supervisors, one could instead include this variable in an index of supervision.
**Figure 1a: Log Differences of Billionaire Net Worth and Commodity Shocks**

<!-- Start of picture text --> Global<br>0.4 0.015<br>0.01<br>0.2<br>0.005<br>0 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>-0.2 -0.005<br>-0.01<br>-0.4<br>-0.015<br>-0.6 -0.02<br>Years<br>Log difference of Billionaire Net Worth<br>Log difference of commodity Net Export Price Index (historic rolling weights)<br>Worth<br>Export Price Index<br>Log difference of Billionaire Net Log difference of commodity Net<br><!-- End of picture text -->

**Figure 1b: Log Differences of Billionaire Net Worth and Commodity Shocks – Developing vs Developed Economies**

<!-- Start of picture text --> Developed Economies Developing Economies<br>0.4 0.015 0.6 0.015<br>0.3 0.01 0.4 0.01<br>0.2<br>0.005<br>0.1 0.005 0.2<br>0 0<br>0<br>-0.1 0 -0.005<br>-0.005<br>-0.2 -0.01<br>-0.2<br>-0.3 -0.01 -0.015<br>-0.4 -0.4<br>-0.015 -0.02<br>-0.5<br>-0.6 -0.02 -0.6 -0.025<br>Log difference of Billionaire Net Worth Log difference of Billionaire Net Worth<br>Log difference of commodity Net Export Price Index (historic rolling weights) Log difference of commodity Net Export Price Index (historic rolling weights)<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Log Difference of Billionaire Net Worth Log Difference of Billionaire Net Worth<br>Log Difference of Commodity Net Export Price Index Log Difference of Commodity Net Export Price Index<br><!-- End of picture text --> Sources: Forbes Magazine (2001 to 2018); Gruss et al. (2019).

Globalization has led to a significant decrease in the cost of international capital mobility. In turn, this has fueled intense tax competition, which offers multiple opportunities to shift profits to wealth in tax accommodating countries or tax havens. Any tax coordination at the international level is rendered difficult or nearly impossible (see Rota-Graziosi, 2019). This may explain why taxation appears less efficient than regulation to tame top wealth inequalities as in this paper. Alstadsæter et al. (2018) find that 10 percent of world wealth is held in tax havens and that this masks important heterogeneity. Andersen et al. (2017) find that around 15% of the windfall gains accruing to petroleum-producing countries with autocratic rulers is diverted to secret accounts. The emerging debate on curbing top incomes has centered around the wealth tax (Saez and Zucman, 2019). There is indeed a strong theoretical case for a wealth tax especially after calamities such as wars and pandemics, yet its implementation and effectiveness have been challenged. In this paper, we find empirically that both resource and non-resource taxation do not moderate the effect of commodity booms on top incomes.

4
- [32] Dudley, N., Groves, C., Redford, K. H. & Stolton, S. Where now for protected areas? Setting the stage for the 2014 World Parks Congress. _Oryx_ **48** , 496–503 (2014).

- [33] Doumenge, C., Palla, F. & Itsoua Madzous, G.-L. (eds) _State of Protected Areas in Central Africa 2020_ (OFAC-COMIFAC, Yaounde, Cameroon & IUCN, Gland, Switzerland, 2021).

- [34] Hatchwell, M. Public–private partnerships as a management option for protected areas. _Animal Conservation_ **17** , 3–4 (2014).

- [35] Duffy, R. Waging a war to save biodiversity: the rise of militarized conservation. _International Affairs_ **90** , 819–834 (2014).

- [36] Duffy, R. _et al._ Why we must question the militarisation of conservation. _Biological Conservation_ **232** , 66–73 (2019).

- [37] Macura, B., Secco, L. & Pullin, A. S. What evidence exists on the impact of governance type on the conservation effectiveness of forest protected areas? Knowledge base and evidence gaps. _Environmental Evidence_ **4** , 1–29 (2015).

- [38] African Parks. Anchoring Africa’s Resilience: African Parks Annual Report 2022 (2023). URL https://www.africanparks.org/sites/default/files/uploads/ resources/2023-05/0148-AP ~~2~~ 022 ~~A~~ nnual ~~R~~ eport-Full ~~F~~ inal-Medium.pdf.

- [39] Estes, J. A. _et al._ Trophic downgrading of planet Earth. _Science_ **333** , 301–306 (2011).

- [40] Malhi, Y. _et al._ Megafauna and ecosystem function from the Pleistocene to the Anthropocene. _Proceedings of the National Academy of Sciences_ **113** , 838–846 (2016).

- [41] Guyton, J. A. _et al._ Trophic rewilding revives biotic resistance to shrub invasion. _Nature Ecology & Evolution_ **4** , 712–724 (2020).

- [42] Stichting African Parks Foundation. Annual Report 2003 (2004). URL https://www.africanparks.org/sites/default/files/uploads/resources/2017-05/ APN ~~A~~ nnualReport ~~2~~ 003.pdf.

- [43] African Parks. “Park Protection”. Accessed: January 9, 2024 (2024). URL https://www.africanparks.org/our-work/park-protection.

- [44] UNESCO. Garamba National Park (DRC) attacked by LRA rebels (2009). URL https://whc.unesco.org/en/news/479.

- [45] African Parks. Three Killed and Two Wounded By Elephant Poachers In Garamba National Park, DRC (2016). URL https://www.africanparks.org/newsroom/press-releases/ three-killed-and-two-wounded-elephant-poachers-garamba-national-park-drc.

- [46] Gilbert, Tiffany. Peace, Security, and Conservation on Central Africa’s Radios (2019). URL https://www.usaid.gov/biodiversity/stories/central-africa-radios.

- [47] African Parks. Annual Report 2007 (2008). URL https://www.africanparks.org/ sites/default/files/uploads/resources/2017-05/APN ~~A~~ nnualReport ~~2~~ 007.pdf.

- [48] African Parks. Annual Report 2009 (2010). URL https://www.africanparks.org/ sites/default/files/uploads/resources/2017-05/APN ~~A~~ nnualReport ~~2~~ 009.pdf.

- [49] Sun, L. & Abraham, S. Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. _Journal of Econometrics_ **225** , 175–199 (2021).

19
# **Table 2: Effect of the information intervention on use of SEBRAE services**

||U|sed any SEBRA|E service withi|n|
|---|---|---|---|---|
||3 months|6 months|12 months|18 months|
||(1)|(2)|(3)|(4)|
|_Panel A: Pooled treatment effect_|||||
|Any information sheet|0.077**†|0.073**|0.017|-0.009|
||(0.031)|(0.033)|(0.040)|(0.043)|
|R2|0.284|0.250|0.197|0.165|
|Control mean|0.167|0.208|0.357|0.494|
|_Panel B: Effects for each treatment group_|||||
|(1) No motiv, no pre-enroll|0.050|0.074*|0.030|-0.027|
||(0.039)|(0.043)|(0.050)|(0.054)|
|(2) No motiv, pre-enroll|0.060|0.056|-0.002|-0.023|
||(0.040)|(0.043)|(0.051)|(0.055)|
|(3) Motiv, no pre-enroll|0.056|0.049|-0.003|-0.030|
||(0.039)|(0.042)|(0.050)|(0.054)|
|(4) Motiv, pre-enroll|0.140***††|0.114**†|0.042|0.043|
||(0.042)|(0.044)|(0.050)|(0.053)|
|R2|0.290|0.253|0.198|0.168|
|F-test p-value (2=1)|0.799|0.683|0.532|0.945|
|F-test p-value (3=1)|0.884|0.580|0.518|0.962|
|F-test p-value (4=1)|0.036|0.382|0.816|0.181|
|F-test p-value (1=2=3=4)|0.137|0.490|0.750|0.427|
|<br>_Panel C: Heterogenous treatment effects by_|_baseline busin_|_ess practices (B_|_Ps)_||
|Any information sheet*Low BPs|0.128**††|0.107**|0.093*|0.042|
||(0.040)|(0.045)|(0.055)|(0.061)|
|Any information sheet*High BPs|0.016|0.029|-0.074|-0.081|
||(0.051)|(0.052)|(0.062)|(0.064)|
|R2|0.291|0.256|0.205|0.176|
|F-test p-value (Low BPs=High BPs)|0.092|0.275|0.048|0.172|
|<br>Control mean low BPs|0.093|0.151|0.267|0.419|
|Control mean high BPs|0.253|0.278|0.468|0.595|
|N|866|866|866|866|

Notes: This table shows OLS estimates for the effect of receiving the information sheet on use of SEBRAE services. The outcome variables are dummy variables equal to one if the firm used any type of SEBRAE service within the stated number of months after the intervention and zero otherwise. Panel A pools all treatment groups. Panel B shows separate treatment effects for each of the four different versions of the information sheet. Panel C shows heterogenous treatment effects for firms using below and above the median percentage of baseline business practices. BP stands for business practices. Low BP refers to below the median (50%), while high BP refers to above the median. All regressions control for the fraction of firms that used a SEBRAE service within 12 months before the intervention and strata fixed effects. The regressions in Panel C additionally control for a dummy variable equal to one if the firm had low BPs at baseline. ***, **, and * indicate significance at the 1, 5, and 10 percent level. To deal with multiple hypothesis testing, †††, ††, and † indicate sharpened q-values statistically significant at the 1, 5, and 10 percent level.

18