rafmacalaba/datause-encoder-data huggingface dataset:
||||||||Gene<br>|ral Obstacles<br>|||Anti-<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Nation|Firm<br>Growth|Number<br>of firms|Financing|Policy<br>Instability|Inflation|Exchange<br>Rate|Judicial<br>Efficiency|Street<br>Crime|Corruption|Taxes and<br>Regulation|competitive<br>Behavior|Infrastructure|
|Romania|0.07|96|3.26|3.44|3.75|3.19|2.59|2.45|2.88|3.57|2.52|2.44|
|Russian Federation|0.29|384|3.2|3.49|3.53|3.15|2.17|2.65|2.62|3.58|2.67|2.12|
|Senegal|0.15|38|3|2.21|2.56|2||2.61|3.04|2.97||2.88|
|Singapore|0.12|74|1.97|1.5|1.61|1.88|1.32|1.22|1.28|1.55|1.58|1.42|
|Slovak Republic|0.14|91|3.34|1.53|3.13|2.43|2.13|2.49|2.47|3.25|2.26|1.98|
|Slovenia|0.29|101|2.3|2.6|2.23|2.21|2.29|1.68|1.64|2.91|2.43|1.74|
|South Africa|0.26|87|2.34|1.97|2.45|2.39||3.58|2.58|2.64||1.83|
|Spain|0.25|66|2.21|2.17|2.27|1.93|1.97|1.92|2.08|2.65|2.25|1.94|
|Sweden|0.23|73|1.83|2.46|1.66|1.78|1.46|1.54|1.18|2.67|1.97|1.52|
|Tanzania|0.25|40|2.85|2.48|2.65|2.07||1.96|2.88|2.7||3.21|
|Thailand|-0.02|337|3.1|3.49|3.4|3.62|2.13|3.48|3.47|3.54|3.6|2.76|
|Trinidad and Tobago|0.18|80|3.03|1.81|2.49|2.41|1.45|2.18|1.68|2.78|1.79|2.1|
|Tunisia|0.14|41|1.79|1.94|1.7|1.94||1.55|2.11|2.12||2.1|
|Turkey|0.1|115|3.12|3.55|3.61|2.83|2.3|2.09|2.89|3.16|2.79|2.22|
|Uganda|0.18|67|3.17|2.47|2.68|1.78||2.27|2.93|2.48||2.81|
|Ukraine|0.03|170|3.45|3.22|3.43|3.05|2.16|2.49|2.51|3.7|2.86|2.22|
|United Kingdom|0.27|62|2.33|2.19|2.16|2.28|1.5|1.95|1.24|2.87|1.72|1.69|
|United States|0.16|66|2.38|2.05|2.12|1.71|1.84|2.14|1.88|2.39|1.7|1.83|
|Uruguay|0|72|2.73|2.61|2.03|2.39|1.91|2.07|2|3.21|1.71|1.9|
|Uzbekistan|0.64|94|2.77|2.03|3.04|2.6|1.68|1.77|2.22|2.66|2.28|1.95|
|Venezuela|-0.02|78|2.62|3.64|3.48|3.12|2.65|3.18|3|3.1|2.63|2.31|
|Zambia|0.18|46|2.95|2.57|3.45|1.88||3.18|2.78|2.39||3.07|
|Zimbabwe|0.47|91|3.05|2.73|3.83|2.93||2.57|2.87|2.87||2.53|
|**_Average _**|_0.15_|_86.73_|2.8|2.72|2.76|2.49|2.15|2.51|2.56|2.9|2.37|2.34|

Note: The variables are described as follows: Firm growth is the percentage change in firm sales over the past three years (1996-99). Financing, Policy Instability, Inflation, Exchange Rate, Judicial Efficiency, Street Crime, Corruption, Taxes and Regulation, Anti-competitive Behavior and Infrastructure are general obstacles as indicated in the firm questionnaire. They take values 1 to 4, with higher values indicating greater obstacles. Firm obstacles are averaged over all firms in each country. The number of firms reported are the firms with non-missing firm growth rates. Source: Authors’ analysis based on data described in the text.

31
It is useful to complement the baseline specification through an event study design that allows both to visualize the dynamic impact of changes to product regulation and to check for possible pre-reform trends. The event study estimates in Figure 1 show a significant impact of the NIL, which increases markdowns by nearly 50% relative to the year before the reform and by around 35% relatively to the preceding years. The effect appears to be persistent over two years following the reform.

The event study does not indicate any significant pre-reform trends, which provides further reassurance vis-à-vis endogeneity bias. In our framework, the bias would be particularly problematic if positive, because it would make it more likely to reject the hypothesis that _γ_ 1 = 0. This might happen, for instance, if plants with market power and high markdowns find it easier to obtain protection. However, in the context of trade policy Grossman and Helpman (1994) suggest that the bias is more likely to be negative: protection is more valuable in markets with a large number of entrants and low market power.<sup>31</sup> In this case, the negative bias would results in estimates of _γ_ 1 constituting a lower bound for the actual effect.

We test the robustness of the result by using a regency-based identification, which is less exposed to such bias as argued in Section 5.1. The results of the estimation are presented in column 2. Reassuringly, the coefficient remains negative and statistically significant. However, the absolute magnitude of the coefficient is slightly smaller and the coefficient is less precisely estimated than in column 1. This is likely a reflection of the nature of the identification that—unlike the baseline specification—assumes fully homogeneous labor supply across product markets within a commuting zone. To the extent that some skill-specificity exists at the product level, this effectively “dilutes” the impact of the regency-level Bartik instrument relative to the baseline specification.<sup>32</sup>

> Given the Bartik-type identifying variable, we follow Goldsmith-Pinkham et al. (2020) and compute the “Rotemberg weights” to assess the relative importance of each product markets in the overall power of the shift-share variable. We find that virtually all the

> 31 For instance, an increase in entry—either actual or perspective—in a given product market, could trigger lobbying efforts to increase entry costs and reduce competition.

> 32 This is even more likely if the bias is negative, as suggested by Grossman and Helpman (1994), and so the difference cannot be due to the endogeneity of reforming activity.

20
minimum road distances between all cities for which we have data in those two countries. This is the same approach as in Buys et al. (2006).<sup>1</sup> As a check on the reliability of our international distance measure, we compare it with the CEPII distance database (Mayer and Zignago 2006). The most commonly used CEPII measure expresses the distance between two countries as the great circle distance between their respective largest cities. Over the full sample, we find a correlation coefficient ρ equal to 0.93. However, at long distances (greater than 3000km) the relationship between the two series is substantially weaker (ρ = 0.66). Visual evidence (Figure 2) suggests that the CEPII measure is systematically lower than ours over long bilateral distances. We take this as indicating that while the great circle approximation is reasonable for short overland distances, it loses much of its relevance as those distances grow. A detailed mapping is therefore particularly important for long international routes, such as those we are dealing with in the ECA context.

One potential drawback with our approach—which might also explain part of the difference between our measures and CEPII’s—stems from the considerable cross-country variation that our network map displays in terms of detail. The minimum population threshold that we have chosen results in 16 out of 27 countries being represented by a single city, while the largest country (Russia) is represented by 63 cities (Table 5). At first glance, it seems plausible that Russia might therefore exert an undue influence on our results. However, even with Russia excluded from the sample, the pattern identified above with respect to the CEPII distance measure still stands (full sample ρ = 0.94; for distances greater than 3000km, ρ = 0.62). Further, we find below that exclusion of Russia from the estimation sample in fact has little bearing on

> 1 Weighting average distances by city population, or using the median rather than the mean, does not change our results.

- 6 -
Although there is some CRB activity in non-GMAs, it is more intense in the GMAs. Proportionately, participation in non-GMAs (6 percent) is only about half that of the households in GMAs (13 percent). A similar pattern can be observed with respect to the number of households participating in the CRB. The proportion of households participating in CRBs that have been funded through community funds from ZAWA in GMAs is almost three times as high as that in non-GMAs.

# _3.1 Impacts of being in GMA and participation in VAGs and CRBs_

Tables 2 and 3 show, respectively, the average impacts on household welfare of living in GMAs and, for those in GMAs, the impact of participation in CRB/VAGs, comparing PSM and TRE estimates. In the areas where the treatment regression estimates are statistically significant, negative selection bias from unobservable factors could not be rejected. As expected, the PSM estimates of the impact of GMA and participation are significantly smaller than the TREestimates. As a result, we focus on the treatment regression estimates for the rest of the paper.

**Table 2. Comparison of propensity score matched (PSM) and treatment regression** **<u>(TRE) of average GMA effect on the households in GMA</u>**

||PSM<br>||T<br>|reatment <br>|Regressio|n|
|---|---|---|---|---|---|---|
|Outcome:per capita consumption|Estimates||Estimate|s|||
||(1)||(2)||(3)||
|Bangweulu|-0.151|**|0.729|***|-0.76|***|
|Kafue|-0.029||-0.444||0.51||
|Lower Zambezi|-0.118||-0.362||0.26||
|Luangwa|0.260|**|0.744|***|-0.39|***|
|Overall|-0.005||0.665|***|-0.58|***|

Significance: *=Significance at 10%; **=Significance at 5%; ***=Significance at 1% ATT: Average treatment effect on the treated Source: Authors’ calculations, Data from the IGMAW survey, 2006 Substantial differences between the actual and the counterfactual average per capita consumption expenditures of households in GMAs near Bangweulu and Luangwa park systems, as well as for the overall sample show significant welfare gains to the households associated with the GMA institutions (Table 2). Unconditional comparisons of the welfare of households in GMA and non-GMA areas in Table 1 hide these relatively large benefits as other household and community characteristics of GMA households make them worse off relative to households in non-GMA areas (see Column 2 in Table 2).

For the overall sample, 66 percent of the average per capita consumption expenditure may be associated with the GMA institution. However, this impact of GMA is not evenly distributed across all park systems. Households in GMAs near Bangweulu and Luangwa park systems show significant welfare gains while those near Kafue and Lower Zambezi park systems appear not to benefit at all.

8
a line of $31.96 per month ($1.05 per day). (This was estimated by Chen and Ravallion, 2001, using a regression of the log of the poverty line on a quadratic function of the level of mean consumption per person at PPP.) The simplest way one might imagine updating the old “$1 a day” line for 1993 to 2005 prices would be to apply the US rate of inflation over that period. Updating the $32.74/month 1993 PPP for inflation over 1993-2005 (using the Consumer Price Index for the US) gives $44.25 ($1.45/day). However, this calculation makes two strong assumptions: (1) that the 1993 PPPs on which the old “$1 a day” line was based are correct and (2) that the principle of purchasing power parity holds, whereby the PPP for a given country evolves over time according to differences in that country’s rate of inflation and that for the US. We have already noted the problems with the 1993 ICP and that there are a number of comparability problems between the 1993 and 2005 ICP data. Ravallion, Chen and Sangraula (RCS) (2008) provide an econometric test using a new set of national poverty lines (which we return to below). Their test firmly rejects the joint implications of the PPP principle and comparability of the 1993 and 2005 PPP’s. RCS also show that if one compares fixed national poverty lines (fixed in local currency at a given date) valued at the 1993 versus 2005, the estimated poverty line at the 2005 PPP corresponding to the 1993 line of $32.74 is $35.69 (with a standard error of $4.13). This is significantly lower than the figure of $44.25 for 2005 obtained by only adjusting for inflation in the US.

These observations echo those made in Chen and Ravallion (2001, 2004) concerning the 1993 round of the ICP. Then it was also noted that there was a serious comparability problem, in that case between the 1985 PPPs based on Penn World Tables (PWT) and the Bank’s PPPs at base 1993; the comparability problems related to both the primary data and the methods used. So Chen and Ravallion (2001) also argued that it is wrong to simply adjust for inflation in the US between 1985 and 1993 to update the poverty line.<sup>13</sup> Given these concerns, a better approach is to return to the basic idea behind the “$1 a day” poverty line. From the outset, this was designed to be a representative poverty line for lowincome countries. The original $1 per day poverty line was chosen as being representative of the

> 13 In the light of these observations, we clearly do not accept the claim by Reddy and Pogge (2002) and Wade (2004) that we lowered the real value of the poverty line in Chen and Ravallion (2001) (as compared to Ravallion et al., 1991) because its real value in the US had fallen.  This would only be correct if the principle of purchasing power parity holds and the PPP methods are comparable over time. Neither condition holds, either in the switch from 1985 to 1993 PPPs or the switch from 1993 to 2005. For further discussion see Ravallion (2008b).

7
|Indonesia Social Assistance Reform Program|ChangqingSun|
|---|---|
|Indonesia Social Assistance Reform Program|ChangqingSun|
|FIRST IMPROVED GOVERNANCE OF PUBLIC RESOURCES AND NUTRI|TCristina Savescu|
|FIRST IMPROVED GOVERNANCE OF PUBLIC RESOURCES AND NUTRI|TCristina Savescu|
|FIRST IMPROVED GOVERNANCE OF PUBLIC RESOURCES AND NUTRI|TCristina Savescu|
|Lowlands Water Development Project - Phase II|Ruth Jane Kennedy-Walker|
|Lowlands Water Development Project - Phase II|Ruth Jane Kennedy-Walker|
|Lowlands Water Development Project - Phase II|Ruth Jane Kennedy-Walker|
|Lowlands Water Development Project - Phase II|Ruth Jane Kennedy-Walker|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Social Protection Enhancement Project|Samira Ahmed Hillis|
|Guinea Bissau: TheQualityEducation for All Project|EmilyElaine Gardner|
|Guinea Bissau: TheQualityEducation for All Project|EmilyElaine Gardner|
|Guinea Bissau: TheQualityEducation for All Project|EmilyElaine Gardner|
|Guinea Bissau: TheQualityEducation for All Project|EmilyElaine Gardner|
|Sustainable Low-Carbon Development in Orinoquia region Project|Luz Berania Diaz Rios|
|Sustainable Low-Carbon Development in Orinoquia region Project<br>|Luz Berania Diaz Rios<br>|
|Sustainable Low-Carbon Development in Orinoquia region Project|Luz Berania Diaz Rios|
|Sustainable Low-Carbon Development in Orinoquia region Project|Luz Berania Diaz Rios|
|Sustainable Low-Carbon Development in Orinoquia region Project|Luz Berania Diaz Rios|
|Youth Employment Transformation Initiative Project|Jasmine Rajbhandary|
|Youth Employment Transformation Initiative Project|Jasmine Rajbhandary|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|Renewable EnergyFund|Joern Thorsten Huenteler|
|CLEAR Center for Francophone Africa Phase 2|R Jason Thomas Mayfield|
|Regional Off Grid Electrification Project|Raihan Elahi|
|Regional Off Grid Electrification Project|Raihan Elahi|
|Data for Decision Making|RoyShuji Katayama|
|Data for Decision Making|RoyShuji Katayama|
|Data for Decision Making|RoyShuji Katayama|
|Data for Decision Making|RoyShuji Katayama|
|Data for Decision Making|RoyShuji Katayama|
|Data for Decision Making|RoyShuji Katayama|
|Extractive Industries Technical Assistance Project Phase 2|Daniele La Porta Arrobas|
|Extractive Industries Technical Assistance Project Phase 2|Daniele La Porta Arrobas|
|Extractive Industries Technical Assistance Project Phase 2|Daniele La Porta Arrobas|
4. Single capital for both entities will facilitate the registration of a company in multiple entities.

5. <u>It is too expensive. Lowering the investment will result in more people starting</u> businesses. Additionally, lowering the cost of the minimum stake (share amount or price) will result in enabling small business to start with more partners (e.g. a small group could reach the proposed threshold of 2000 KM by having some individuals bring contributions as small as 100 KM to start the business).

# **Bulldozing solution:**

Make the minimal statutory capital requirement 2000 KM in all cases (single proprietor or multiple partners) and in all instances, and lower the minimum share price to 100 KM.

# **Action item:**

Modify: Art. 314 in the Law on Business Companies (Federation); Art. 331 in the Law on Enterprises (R.S.), and Art. 343 in the Law on Enterprises (Brcko). (see Annex)

# **FEDERATION OF BOSNIA AND HERZEGOVINA**

# **Law on Business Companies**

# **(Official Gazette of the Federation of Bosnia and Herzegovina, No. 23/99, 45/00 and 2/02)**

# **<u>Current Text:</u>**

# **Article 314**

- (1) Statutory capital of limited liability company with several founders shall be no less than 10.000 (ten thousand) KM, and if there is only one founder 2.000 (two thousand) KM, unless otherwise provided by other laws.

- (2) Value of an individual share may not be less than 2.000 (two thousand) KM.

- (3) Contributions in money may not in total be less than the amount provided by paragraph 1. of this article.

- (4) Contributions in items and rights shall be in total be invested in company until the day of submission of application for entry of company establishment into the court register, so that the company may permanently and freely dispose of them.

- (5) Until the day of submission of application for entry of company establishment into the court registry at least half of the contribution in money shall be paid in, and it shall be no less than the amount provided by paragraph 1 of this article.

# **<u>Suggested Text:</u>**

# **Article 314**

- (1) Statutory capital of a limited liability company **_with one or several founders shall be no less than 2.000 (two thousand) KM, unless otherwise provided by other laws._**

- (2) **_Value of an individual share may not be less than 100 (one hundred) KM._**

- (3) Contributions in money may not in total be less than the amount provided by paragraph 1. of this article.

- (4) Contributions in items and rights shall be in total be invested in company until the day of submission of application for entry of company establishment into the court register, so that the company may permanently and freely dispose of them.

- (5) Until the day of submission of application for entry of company establishment into the court registry at least half of the contribution in money shall be paid in, and it shall be no less than the amount provided by paragraph 1 of this article.

Page 34 of 51
less corruption, etc. (see for example, Acemoglu et al. 2001, Dollar et al. 2005, Hall and Jones 1999, Schmitz Jr. 2005, Syverson 2011). Following the broader literature on institutions and governance quality, we use two separate proxy measures to capture their impact on firm productivity. For the quality of governance, we use the _Corruption_ measure as defined above. The data source for the variable is Transparency International. The variable ranges between 0 and 100, with higher values implying less corruption. For the quality of institutions, we use the _Law and Order_ Index from ICRG. The variable ranges between 1 and 7 with higher values implying better rule of law (from the firm’s point of view).

Regression results in both Table 3 for the pooled sample and Table 4 for UMICs and HICs separately, uncover several factors highly correlated with labor productivity. In the pooled sample and irrespective of the controls in place, labor productivity is lower in UMICs than in the HICs, and the difference is significant at the 1 percent level (columns 1-7, Table 3). This productivity gap is quantitatively large, although it reduces when we include the various determinants of labor productivity described above in the regressions. Specifically, the labor productivity (in USD terms) is approximately 57.5% lower in UMICs when we do not add any additional controls (column 1, Table 3) compared with 32%, our most conservative estimate so far, when all the controls are included in the regressions (column 7, Table 3).

Second, there is a negative and significant relationship between labor productivity and firm-size for the pooled sample, and for the sub-samples of UMICs and HICs. This result holds irrespective of the controls in place (see Table 3 and 4). For instance, a 1 percent increase in the number of workers at the firm is associated with a decrease in labor productivity by 0.29 percent (table 3, column 7). These results support the view that larger firm-size results in decreasing returns to scale overall or due to diminishing returns to labor hence leading to diminishing labor 15
reflects a weaker positive selection into higher education among younger cohorts, we should observe that the effect of education is lower for younger women than for older women in a given year. Furthermore, we should find that the effect declines over time for a given age group (e.g., as 30-year old women in 2011 went to school more recently than 30-year old women in 1987) but not for a given birth-year cohort (e.g., those born in 1970).

For this analysis we use five 6-year age groups, which allows us to trace birth cohorts between the surveys in 1987, 1999, and 2011. The age group specific effects of education are estimated by including interaction effects of age group and education level in the probit specification (Eqn. 1) and calculating marginal effects of education for each age group. Figure 7 summarizes the results graphically, plotting the marginal effects of graduate education across survey years, by age group in panel (a) and by birth-year cohort in panel (b).<sup>26</sup> Full results are available in Table A1.6 in the appendix.

# **Figure 7: Marginal effect of graduate education by age group and birth-year cohort**

> 26 Marginal effects in nonlinear models vary with the value of other explanatory variables. For interaction terms, this variation is typically large. Effects can range from significantly negative to significantly positive, so the average marginal effect does not necessarily convey anything meaningful (see Ai and Norton 2004). We find that the marginal effects of graduate education are always positive. Effects are statistically significant only for the oldest two age groups (i.e., the effect of education is significantly larger for the two oldest groups than for the youngest group). For reasons of space we report only the averages, but detailed results are available from the authors.

35
**Table 2: Fertilizer and hired-labor demand in Uganda, tobit results**

||Fertili<br>|zer use|Hired lab<br>|or demand|
|---|---|---|---|---|
|Market indicators|Coef.|z-score|Coef.|z-score|
|Share of agricultural output sold, by value|4.51<sup>2</sup>|2.28|5.03<sup>1</sup>|4.17|
|Population density (people/sq. meter)|0.02<sup>1</sup>|2.85|-0.01<sup>1</sup>|-2.97|
|Liquidity<br>Wage or business income ($US 1000)|0.16|0.59|0.37<sup>1</sup>|2.55|
|Wealth Index (Principal Component)|0.22|0.40|2.56<sup>1</sup>|7.84|
|Knowledge/Social norms<br>Extension officer visited|6.58<sup>1</sup>|4.19|3.03<sup>1</sup>|3.53|
|Female head of household|-4.23<sup>1</sup>|-2.67|0.84|1.04|
|Labor assets|||||
|Family members, ages 14-60, per ha.|-2.66<sup>1</sup>|-3.28|-1.19<sup>1</sup>|-4.28|
|Constant|-32.30<sup>1</sup>|-7.48|2.15|1.52|

Note: The superscripts 1, 2 and 3 signify significance at the .01, .05 and .10 thresholds. Enumerator-area dummies were included as random effects .

17