rafmacalaba/datause-encoder-data huggingface dataset:
particularly in developing countries, and uncertainty surrounding these political relationships can negatively impact firms‟ performances (Faccio, 2006, 2007; Fisman, 2001).

Column (3) presents the results of the 50% nominal devaluation and subsequent proprivate sector reforms on firm survival. Contrary to the idea that a revaluation of the exchange rate can help restore international competitiveness, we find that, on average, the reform had no significant impact on firm survival. However, we do find that the tradables sector responded favorably to the episode, reducing the likelihood of exit by about 30% relative to the service sector firms. Regarding firm size, we find that the reform lessened the disadvantage middle-size firms faced relative to large firms by 50%.

# **6. Conclusion**

We study firm dynamics using a database of all formally registered firms in Cote d'Ivoire from 1977 to 1997, which account for about 60% of GDP. Our analysis focuses on private sector dynamics over the business cycle. We also examine separate episodes of trade, fiscal, and monetary reforms and the relationship of these interventions with private sector dynamics.

First, we examine entry and exit patterns over this period and the role of new and exiting firms versus incumbents in job creation and destruction. We find that while the rate of job creation at new firms is quiet high at 8% on average, the absolute numbers of jobs added by new firms is small in absolute terms. Next, we examine survival rates and find that the probability of survival increases monotonically with firm size, but that manufacturing and foreign-owned firms face higher likelihoods of exit compared to service oriented and domestically-owned firms, respectively. We find that higher GDP growth increases the probability of firm survival, but this is a broad impact with no firm size disproportionately affected. In robustness checks we found that post-1987, size is no longer a significant determinant of firm survival for new entrants, suggesting that the operating environment for firms changed. Finally, we find that the trade and fiscal reform episodes raised the probability of firm exit particularly for large firms, but the exchange rate revaluation and pro-private sector reforms did not significantly lower the probability of exit.

22
countries 49% of the firms have a bank loan, while 26% of recent investment is through banks. Surprisingly, the use of external finance is higher in the candidate countries, where 60% of the firms have a bank loan. Firm leverage seems to be the lowest in the EU neighborhood countries where only 39 percent of the firms have a bank loan.

Is the leverage of firms in Emerging Europe particularly low compared to firms in more advanced European economies? Recent evidence suggests that the use of bank finance is actually quite similar in emerging and advanced Europe. Using data from the 2005 wave of BEEPS, Brown et al. (2011a) show that the share of firms which have a loan in Emerging Europe is only slightly lower than in a selection of Western European countries (Ireland, Portugal, Spain, Greece, Germany).

**Table 4.1. Enterprise Credit by Country**

||Bank loan(%)|Overdraft(%)|Leverage|
|---|---|---|---|
|_EU members_|_49_|_42_|_26_|
|Bulgaria|39|28|28|
|Czech Rep|48|58|17|
|Estonia|50|41|23|
|Hungary|42|47|33|
|Latvia|48|19|28|
|Lithuania|52|13|31|
|Poland|50|52|23|
|Romania|43|44|21|
|Slovak Rep|43|53|21|
|Slovenia|74|64|34|
|_EU (potential ) candidates_|_61_|_60_|_28_|
|Albania|49|84|15|
|Bosnia|65|51|27|
|Croatia|66|67|33|
|Macedonia|62|21|29|
|Serbia|67|66|29|
|Turkey|56|68|38|
|_EU neighborhood_|_39_|_37_|_22_|
|Armenia|43|44|21|
|Georgia|41|35|26|
|Moldova|40|26|21|
|Ukraine|32|44|18|
|**Total**|**50**|**46**|**25**|

Source: BEEPS survey and authors‟ calculation. Note: This table reports means for each indicator of credit use by country. Observations are weighted to account for stratification of the sample with respect to industry (manufacturing, retail and other services) as well as frim size. Bank loan is 1 if the firm currently has a loan or a credit line from a bank and 0 otherwise. Overdraft is 1 if the firm currently has an overdraft facility from a bank and 0 otherwise. Leverage is the share of firm investment in 2007 which was financed by bank credit.

Which types of firms in Emerging Europe are indebted to banks? Panel A of Table 4.2 shows that the **incidence of bank credit** is related to firm size, financial transparency and economic activity. In line with existing evidence on information asymmetries and credit access (see e.g. Brown et al. 2009), small firms (those which have less than 50 employees), young firms (less than 15 years old) and firms which are not financially transparent (i.e. do not have their accounts audited) are less likely to have bank credit. Firms with export activities are also much more likely to have bank credit than firms which sell purely to their domestic market. The effects of firm size, financial transparency and export orientation are also economically important: Roughly 60% of large, audited and exporting firms have bank credit, while only 40% of small, non-audited or non-exporting firms do so. By contrast, firm age, sector of activity 24
occupations by their skill intensity and then use information on the occupational distribution of labor over time. In this case, skilled labor and less skilled labor have to be weighted by their respective relative labor productivity to account for differences in skills. For the case of Guatemala, similar to equation (14), this can be done by weighting labor inputs of different industries with the share that each type of labor occupies in total labor compensation.

However, it should be kept in mind that this kind of implicit differentiation of labor is a rather incomplete substitute for labor quality. It can only take into account some of the quality changes of labor input and does not allow the sources of the change to be identified. Moreover, the eight industry categories available from Banco de Guatemala (2003) statistics only apply to the _formal_ sector. As such, they ignore approximately 75 percent of the population working in the informal and rural economy. Placing less emphasis on educational improvements in _primary_ schooling—the working population of the IGSS is typically better educated than the population in the informal and rural sectors—the implicit labor quality index is biased downwards.<sup>19</sup>

**Figure 4. Guatemala: Comparison of Labor Quality Indices, 1970-2002** (in percent, relative to base year 1970)

<!-- Start of picture text --> 160<br>1970 = 100<br>140<br>120<br>100<br>80<br>60<br>70 75 80 85 90 95 00<br>Implicit labor quality index (formal sector) Labor quality index (hq)<br><!-- End of picture text --> _Source:_ Author’s calculations based on Banco de Guatemala (2003) data (implicit labor quality index), and human capital stock estimate for labor quality index ( _hq_ ).

> 19 This is because primary school enrollment has increased substantially over time (see Table 3).

-27-
||_Tables continued..._|
|---|---|
|C.16|Testing for Diferences Across Reform Instruments|
|Nati|onal Estimates with Combined Instrument: Accounting for FPE and MTI|
||C.17<br>Efect of Reforms on Years of Schooling and Number of Births (DHS)|
||C.18<br>Efect of Years of Schooling on Knowledge and Health|
||C.19<br>Efect of Years of Schooling on Labor Market Outcomes and Fertility Preference|
||C.20<br>Efect of Schooling on Husband’s Characteristics|
||C.21<br>Efect of Reforms on Likelihood of First Birth, Marriage, and Intercourse, by Age|
|D.1|Placebo Estimates of Misplaced Timing of Reform Prior to Actual Implementation|
|D.2|Additional Language(s) in Boothe and Walker (1997) Defnition|
|D.3|Additional Language(s) in Zenebe Gebre (2014) Defnition|
|D.4|Efect of FPE and MTI Reforms on Years of School|
|D.5|Efect of Reforms on Years of Schooling: Joint Intensity Measure|
|D.6|Efect of Years of Schooling on Knowledge and Health|
|D.7|Efect of Years of Schooling on Labor Market Outcomes and Fertility Preference|

A.3
**Table S5:** Program impacts on sexual behavior (beneficiaries: **intensive** margin)

**<u>Panel A:  Baseline Dropouts</u>**

||A|ge at First|T Y<br>Sex|=|1 if Older P|artner|=1 if Us|T Y<br>e a Condom|
|---|---|---|---|---|---|---|---|---|
||During<br>Program|End of<br>Program|wo ears<br>After<br>Program|During<br>Program|End of<br>Program|Two Years<br>After Program|End of<br>Program|wo ears<br>After<br>Program|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|=1 if Conditional Schoolgirl|-0.064|-0.061|0.110|0.018|-0.005|0.015|0.046|0.030|
||(0.137)|(0.144)|(0.133)|(0.054)|(0.045)|(0.037)|(0.037)|(0.030)|
|Mean in Control Group|16.250|16.578|16.790|0.230|0.300|0.309|0.159|0.156|
|Sample Size|525|625|723|303|427|578|446|600|
|**Panel B: Baseline Schoolgirls**|||||||||
|=1 if Conditional Schoolgirl|0.220|0.136|0.147|-0.074|-0.006|-0.041|-0.006|0.015|
||(0.146)|(0.130)|(0.146)|(0.050)|(0.044)|(0.038)|(0.055)|(0.041)|
|=1 if Unconditional Schoolgirl|-0.152|-0.039|-0.207|0.022|-0.081|0.018|0.102|0.057|
||(0.179)|(0.189)|(0.127)|(0.103)|(0.057)|(0.049)|(0.086)|(0.048)|
|p-value UCT vs. CCT|0.064|0.404|0.052|0.351|0.258|0.248|0.268|0.482|
|p-value Treatment|0.143|0.536|0.128|0.291|0.367|0.422|0.483|0.479|
|Mean in Control Group|15.731|16.393|17.199|0.193|0.274|0.304|0.247|0.268|
|Sample Size|522|893|1,494|376|661|1,162|672|1,183|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). We correct for inconsistencies in 'ever had sex' across rounds. 'Age at First Sex' is defined for those that had ever had sex. 'Older Partner' is defined as having a partner who is 5 years older or more in the past 12 months. 'Condom Use' is defined as using a condom at last sex with most recent sexual partner. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
# 4. Results and Discussion

Table 1 has annual estimates of flaring from 2012 to 2018 (covering timing of the DHS), comparing satellite-detected estimates with the official estimates reported by the Department of Petroleum Resources [DPR] (2018). The satellite-detected estimates are broken down by location, while the DPR figures are just totals. We examine both offshore and onshore flaring and aggregate onshore flaring to the state level (the second administrative level). Flaring volumes show that around half of all gas flared happens offshore. About 56% of flaring in 2013 was offshore, reducing to 48% in 2018. With respect to onshore flaring, Delta State has the highest flaring by volume with around a quarter of all gas flared in 2018 in this state. Two states (Ondo and Anambra) had gas flared in 2013 but not at the time of our second DHS in 2018. The gap between satellite estimates and the official data vary by year. In 2013, official estimates are two percent higher than satellite estimates while in 2018 official estimates are around 32 percent less than satellite estimates. The official estimates rely on self-reporting by oil companies, so it is likely that recent gaps between official estimates and satellite estimates is picking up under-reporting by gas companies in anticipation of regulatory changes.10 _Table 1: Gas volume flared in million standard cubic feet (MMSCF) by location in Nigeria_

|Flare location by<br>satellite|2012|2013|2014|2015|2016|2017|2018|Share<br>2013|Share<br>2018|
|---|---|---|---|---|---|---|---|---|---|
|Abia|1,027|2,255|2,601|1,032|1,096|1,604|1,556|1%|0%|
|Akwa Ibom|3,715|6,070|4,992|5,206|5,059|5,528|5,350|1%|1%|
|Anambra|91|610|533|414|-|-|-|0%|0%|
|Bayelsa|39,400|38,600|45,100|32,500|42,000|42,600|50,000|9%|11%|
|Delta|46,900|59,000|54,500|59,600|45,500|66,800|107,000|14%|23%|
|Edo|12,200|18,100|18,200|18,800|7,324|13,800|23,200|4%|5%|
|Imo|15,100|14,200|8,608|6,901|7,961|6,816|6,053|3%|1%|
|Ondo|2,434|2,635|2,447|-|-|-|-|1%|0%|
|Rivers|45,600|42,500|47,900|41,800|53,400|53,300|52,200|10%|11%|
|Offshore|194,000|236,000|204,000|181,000|181,000|182,000|226,000|56%|48%|
|Total flaring by|480,623<sup>11</sup>|419,970|388,880|347,254|343,340|372,448|471,359|100%|100%|
|satellite estimates||||||||||
|Official flaring<br>estimates from DPR<sup>12</sup>|465,257|427,971|393,840|330,933|288,917|324,192|321,290|||
|Gap (as % of satellite<br>total)|3%|2%|1%|-5%|-16%|-13%|-32%|||

> 10 For example, from 2017, the government had approved the National Gas Policy and the Gas flaring prohibition bill was deliberated upon by the Senate in March 2017 with public hearing for stakeholders in the industry and the public in May 2017.

> 11  Satellite estimates for 2012 are from the month of March. Annualising the estimates from March leads to a full-year estimate of 480,623MMSCF

> 12 Official estimates are from the Department of Petroleum Resources 2018 Annual report.  The report comes with a disclaimer which states that figures presented are subject to ongoing review based on continuous reconciliation with various stakeholders

9
_- CMEAEX,_ the pre-transition share of sector I production exported to countries belonging to the CMEA<sup>18</sup> ; _- CMEAIM, :_ the pre-transition share of inputs used by sector I imported from countries belonging to the CMEA.

These variables attempt to proxy the disorganization shock by exploiting different dirnensions along which economic activities may have been coordinated during the Soviet regime. The intuition is the following. The Soviet system coordinated activities and exchanges though the use of government agencies and ministries. The transition from plan to market implied the dismantling of the old Soviet coordinating institutions with a subsequent increase in transaction costs for individual fisrm. This rise in transaction costs varied however across enterprises depending on the pre-existing institutional arrangements existing between firns and the government, their production links, and their spatial location.

CGonsider, first, the dummy _AU,,_ which exploits the institutional heterogeneity in terms of coordination existing across firms before the transition. During the Soviet period, firns' activities were monitored and coordinated by the Soviet government. In particular, each sector of the economy was controlled by a separate Ministry. The degree of control and coordination was however different across sectors. The activities and exchanges of sectors under the so-called All Union Ministries were highly monitored and coordinated. Sectors instead under the Union Republic or the Republic Ministries were given greater degree of freedom."<sup>9</sup> The dismantling of the Soviet institutions halted this coordinating activities and unevenly increased trnsaction costs across s 'The Council for Mutual Economic Assistance, or CQIEA, formerly coordinated trade between Soviet and East European countries. It was dismantled in 1991.

14
_Figure 1: The Regressiveness of Indirect taxes on Consumption in 2001_ <!-- Start of picture text --> Consumption Tax Payments per Household as a Percentage<br>of Income<br>6<br>5<br>4<br>VAT<br>3<br>2 NON-VAT<br>1<br>0<br>Source: SA SAM 2003<br>Figure 2: The progressiveness of the complete tax system in 2001<br>Total Tax Payments per Household as a Percentage of Income<br>25<br>20<br>15<br>10<br>5<br>0<br>Household Deciles<br>2001<br>h_d0 h_d1 h_d2 h_d3 h_d4 h_d5 h_d6 h_d7 h_d8 h_d91 h_d921 h_d922 h_d923 h_d924<br>h_d0h_d1h_d2h_d3h_d4h_d5h_d6h_d7h_d8h_d91h_d921h_d922h_d923h_d924<br>percent<br><!-- End of picture text --> Source: SA SAM 2003 _Figure 2: The progressiveness of the complete tax system in 2001_ Source: SA SAM 2003 Devarajan and Panagariya (2000) show that a uniform value-added tax would closely mimic a lump-sum tax. However, when commodities are left out of the tax base, there is an efficiency loss. This is certainly the case for South Africa as the South African Value-Added Tax Act No.89 of 1991 makes allowances for exemptions, exceptions, deductions and adjustments that effectively lower the VAT liability. At inception various basic food items such as brown bread, maize meal, samp, mealie rice, dried mealies, dried beans, lentils, pilchards, milk powder, milk, rice, unprocessed vegetables and fruit, vegetable oil, and eggs were exempted from VAT (SA Tax, 2001:Schedule 2 Part B). To reduce the regressiveness of VAT even further, paraffin, an energy source used by most poor households, was exempted in 2001. However, suppliers absorbed most of the benefits and did not pass it on to the consumers as intended. Various other exclusions exist: Small firms supplying less than R300 000 per year are not required to register 6
models “the interaction effect (...) cannot be evaluated simply by looking at the sign, magnitude, or statistical significance of the coefficient of the interaction term.” Instead, the interpretation of the interaction term requires computing the cross derivative of the expected value of the dependent variable, which depends on all the covariates in the model and their values. Testing the significance of interaction terms in non-linear models with two or more continuous variables is thus an extremely complex task.

Instead, we interact dummy variables for our main variables of interest with the continuous earthquake propensity variable. This is easiest done for our measure of regime type, which is already captured by a dummy variable for democracy. We thus interact dummy variables for democracies and autocracies with earthquake propensity. These estimated coefficients are not subject to the difficulty identified by Ai and Norton (2003). They indicate how mortality changes with increases in earthquake propensity in democracies on the one hand versus increases in earthquake propensity in autocracies on the other. This formulation is equivalent to, but easier to interpret than interacting a single democracy dummy variable, equal to one for democratic and zero for non-democratic countries, with propensity.<sup>10</sup> We similarly interact, in separate estimations, the continuous propensity variable with dummy variables for high-income versus low-income (or, to be precise, “not high-income”) countries, using the World Bank (2009) classification, to estimate different earthquake 10 To see this, imagine an estimation model of the form y = b1x+b2z+b3xz, with x a continuous and z a dummy variable. The marginal effect of x on y is given by b1 for z=0 and by (b1+b3) for z=1. Now consider instead an estimation model of the form y = b4x (for z=0) + b5x (for z=1) + b6z. b4 is the same as b1, while b5 is the same as (b1+b3). In the first model, the interaction effect is statistically significant if b3 is statistically significantly different from zero. In the second model, it is significant if b4 and b5 are statistically significantly different from each other.

24
Overall, we find that the intervention is effective at increasing the rate of registration among treated firms. The effects are small in levels but extremely large in percent terms, relative to the control group. Up to three months from the intervention only up to 17 percent of the firms declare to be registered in the treatment group versus 13 percent in the control group (post intervention) (column (2)), an even smaller share can give us their registration number, 3.5 percent versus 1.1 percent (column (3)), and even fewer firms are able to show us a valid tax ID card 3.1 percent versus 0.9 percent in the control group (ex-post) (column (4)). We have a similar result if we look at our intensity measure (column (5)) although the interpretation of the coefficient is not very meaningful. We find it reassuring that the more stringent definition of registration is the outcome where we see the larger and more significant effects. However, there is a clear issue which comes to mind looking at the above results: despite the positive effect of the treatment and the threat of penalty associated with it, the vast majority of firms decide not to register with the tax authority. Even if we were to use the softest definition for registration (self-reported registration as in column (2)) we find that only below 17 percent of the treatment firms are registered by the time of our follow-up survey, and there are therefore still 83 percent of the firms that remain informal. There could be several reasons why this might be the case with the obvious explanations being: our treatment and associated threat wasn’t credible enough or just negligible, or even that firms simply find it optimal not to register even if they think of the threat as credible. We tested for the following: i. too short a span between the treatment and the follow-up survey (we tested for this in unreported regressions and found no noticeable increase over the available time since the treatment. The treatment ranged from 47 to 84 days before the follow-up survey with a median time since treatment of 59 days.); ii. whether the size of the expected penalty is actually too small to be a factor in the firms’ decisions (we looked for heterogeneous responses in terms of the size of the penalty over the baseline revenues (or profits) and we did not find evidence for this and in fact we will see later how this is not the determinant of registration.

# **6.2 Heterogeneous Direct Effects: Baseline Revenues**

As noted in the narrative of registration (Section 2) and on the testable hypothesis ( _HP −_ 2), we expect firms with larger baseline revenues to be the ones responding to the treatment. The reasoning is as follows: if revenues are correlated in the non-registration and registration 12