rafmacalaba/datause-encoder-data huggingface dataset:
firms. The effect we find is large compared to the previous literature. Previous studies have estimated about a 5% increase in new firms due to the introduction of one-stop shops in Colombia and Mexico (see Motta, Oviedo, and Santini, 2010, for a summary of effect sizes) and a 17% increase in Portugal (Branstetter et al. 2013).

Most other papers do not look at the characteristics of new firms, except for Branstetter et al. (2013) who find that the reform in Portugal led to the registration of smaller firms on average, and these firms were also less likely to survive during the first two years. Their interpretation is thus that the one-stop shop in Portugal allowed lower quality firms to register. This interpretation makes sense in a model where registration represents a fixed cost that only firms with the highest returns are willing to pay. When registration costs drop, firms with lower returns, i.e. lower quality firms, also find it profitable to pay the fixed costs of registration.

We do not find evidence that lower quality firms register after the reform in Serbia. In fact, firms seem to be of higher quality as suggested by the increase in the one- and two-year growth rates. The reason could be that registration at courts represented not only an administrative burden and fixed costs, but was also often discretionary and may have prevented some high-quality firms from registering at all. Our findings thus suggest that business registration reforms that move registration away from courts may be more effective at creating new and high-quality businesses than one-stop-shop reforms.

The rest of this paper is organized as follows. Section 2 provides details on the 2005 business registration reform in Serbia. Section 3 describes the data and section 4 the identification strategy. Section 5 presents that results. Section 6 concludes.

# **2. Description of the reform**

For decades, starting a business in Serbia was time consuming and burdened with unnecessary bureaucratic hurdles. The principal problems included:

- (i) There was **no unified registry/database of all business entities** in one place. Sixteen courts handled registration of companies and 131 municipalities handled registration of sole proprietors. Even within the same entity – court registry, data were not unified and allowed for different companies to be registered under the exact same name in any of the court registry locations.

4
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40
Table 7c. Household consumption change in regions under ETS and ETS-cum-RES cases (%).

||Constru|ction|Trans|port|Whole|sale|Real E|state|Othe|rs|
|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|
|Total|-0.52|-0.55|-0.03|-0.05|0.08|0.05|0.05|0.04|-0.11|-0.13|
|BJ|-0.21|-0.26|0.14|0.08|0.25|0.20|0.19|0.15|0.08|0.03|
|TJ|-0.25|-0.24|0.13|0.13|0.26|0.27|0.28|0.29|0.14|0.14|
|HB|-2.93|-3.00|-2.31|-2.39|-2.23|-2.29|-2.25|-2.31|-2.25|-2.33|
|SX|0.00|0.00|2.00|2.08|2.01|2.09|2.04|2.12|1.63|1.72|
|IM|0.27|0.30|0.66|0.69|0.75|0.77|0.79|0.82|0.64|0.66|
|LN|-1.49|-1.57|-0.99|-1.06|-0.78|-0.85|-0.71|-0.78|-0.82|-0.89|
|JL|-0.90|-1.03|-0.52|-0.65|-0.43|-0.56|-0.41|-0.53|-0.39|-0.52|
|HLJ|-1.85|-1.93|-1.32|-1.40|-1.30|-1.39|-1.22|-1.30|-1.22|-1.31|
|SH|0.00|0.00|0.17|0.19|0.27|0.30|0.24|0.28|0.12|0.14|
|JS|0.21|0.23|0.46|0.49|0.55|0.58|0.56|0.59|0.47|0.49|
|ZJ|-0.37|-0.49|0.15|0.04|0.24|0.13|0.24|0.14|0.17|0.06|
|AH|-0.80|-0.76|-0.41|-0.37|-0.30|-0.26|-0.36|-0.31|-0.35|-0.32|
|FJ|0.19|0.21|0.48|0.50|0.54|0.57|0.53|0.56|0.48|0.49|
|JX|-0.62|-0.71|0.07|-0.02|0.04|-0.07|0.11|0.04|0.13|0.05|
|SD|-0.15|-0.17|0.06|0.04|0.04|0.02|0.30|0.29|0.09|0.06|
|HN|-0.62|-0.61|-0.26|-0.24|-0.13|-0.11|-0.28|-0.26|-0.19|-0.19|
|HB|-1.32|-1.28|-0.49|-0.48|-0.41|-0.40|-0.21|-0.20|-0.42|-0.41|
|HN|-1.27|-1.22|-0.81|-0.76|-0.60|-0.56|-0.73|-0.68|-0.66|-0.62|
|GD|0.65|0.62|0.85|0.86|0.91|0.91|0.88|0.90|0.78|0.78|
|GX|-0.62|-0.62|0.02|0.02|0.04|0.04|0.02|0.03|0.01|0.01|
|HAN|-0.32|-0.19|-0.22|-0.10|-0.15|-0.02|-0.06|0.06|-0.24|-0.12|
|CQ|-0.83|-0.80|-0.31|-0.29|-0.25|-0.22|-0.20|-0.18|-0.35|-0.32|
|SC|-1.03|-0.98|-0.50|-0.45|-0.42|-0.38|-0.31|-0.27|-0.41|-0.37|
|GZ|-1.25|-1.99|-0.68|-1.36|-0.71|-1.40|-0.65|-1.30|-0.71|-1.37|
|YN|-0.36|-0.31|0.06|0.11|0.24|0.29|0.27|0.31|0.13|0.17|
|SAX|-0.46|-0.47|-0.11|-0.12|0.00|-0.01|0.14|0.13|0.03|0.02|
|GS|0.68|0.69|0.75|0.76|0.84|0.85|0.96|0.97|0.90|0.90|
|QH|-0.26|-0.26|0.11|0.12|0.13|0.13|0.17|0.17|0.09|0.08|
|NX|0.11|-0.09|0.57|0.38|0.64|0.46|0.64|0.46|0.53|0.35|
|XJ|-1.58|-1.61|-1.44|-1.47|-1.39|-1.41|-1.25|-1.27|-1.28|-1.31|

# **4.4.4 Export and import**

Figure 6 presents the impacts on export and import of total goods and services under ETS and ETS-cum-RES scenarios. Both ETS and ETS-cum-RES scenarios would cause exports of fossil fuels and energy intensive goods to decrease and exports 31
market income is below the PIT threshold (figure 5).<sup>33,</sup> By contrast, Brazil collects almost a similar amount to South Africa in direct taxes as a share of GDP,<sup>34</sup> households in its top decile pay about 11 percent of their market income in direct taxes (or about 5 percent of market income in PIT), while those in its poorest decile pay about 1 percent. The difference in effective rates of market income collected in direct taxes between the two countries reflects both the steeper PIT tax rate structure in South Africa (which peaks at a top rate of 40 percent, compared with a top statutory rate of 27.5 percent in Brazil), and its exemption threshold, which helps to exclude poorer households.

<!-- Start of picture text --> Figure 5. Personal income taxes are progressive<br>A. South Africa. Concentration shares of B. Incidence of personal income taxes and contributions<br>personal income taxes relative to revenue collections<br>(share of taxes paid by each market income (share of taxes by market income deciles; PIT as a share of<br>decile) GDP)<br>100% 20% 9.0%<br>90% 87% 18% 8.0%<br>16% 7.0%<br>80% 14%<br>6.0%<br>70% 12% 5.0%<br>10%<br>60% 4.0%<br>8%<br>50% 6% 3.0%<br>40% 4% 2.0%<br>2% 1.0%<br>30%<br>0% 0.0%<br>20%<br>11%<br>10% 2%<br>0%<br>Poorest decile Richest decile Share of GDP (right axis)<br>Sources: Armenia (Younger and Khachatryan, forthcoming), Brazil (Higgins and Pereira, 2014), Mexico (Scott, 2014), Peru (Jaramillo,<br>2014), Uruguay (Bucheli et al, 2014), and own estimates for South Africa based on IES 2010/11.<br>Although it is clear that Figure 6. Progressivity of South Africa’s direct tax system: the Kakwani coefficient<br>direct taxes are<br>progressive in South Peru (2009) 0.43<br>Africa, how progressive<br>are they relative to other Mexico (2010) 0.30<br>countries? One<br>Ethiopia (2011) 0.28<br>common approach to<br>compare across Brazil (2009) 0.27<br>countries is to measure<br>progressivity using Uruguay (2009) 0.25<br>summary statistics such<br>as the Kakwani index. Armenia (2011) 0.23<br>The Kakwani index is<br>South Africa (2010) 0.13<br>equal to the difference<br>between the 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50<br>concentration<br>Sources: Armenia (Younger and Khachatryan, forthcoming), Bolivia (Paz et al, 2014), Brazil (Higgins<br>coefficient of a<br>and Pereira, 2014), Ethiopia (Woldehanna et al, forthcoming), Indonesia (Jellema et al forthcoming),<br>particular tax minus the Mexico (Scott, 2014), Peru (Jaramillo, 2014), Uruguay (Bucheli et al, 2014), and own estimates for South<br>Gini coefficient of the Africa based on IES 2010/11.<br>Share of GDP<br>Share of market income<br><!-- End of picture text --> 33 When using consumption as the welfare measure instead of market income, the results are nearly identical. The richest decile pays 18 percent relative to their total consumption.

> 34 In Brazil, direct taxes include personal income taxes, payroll taxes and property taxes.

17
_Subjective well-being in five years:_ Which step on the Cantril ladder they think their household will be on in 5 years time.

_Total household income in past year:_ Income from all sources, top coded at the 99<sup>th</sup> percentile of the control group distribution(74,000 Turkish Lira) _Transformed household income:_ The inverse hyperbolic sine of total household income.

_Durable asset index:_ the first principal component of 15 indicators of household durable asset and infrastructure ownership (own a gas or electric oven; own a microwave oven; own a dishwasher; own a DVD/VCD player; own a camera; have Digiturk/Satelite; own an air conditioner; own a CD player or iPod; own a telephone; own a computer; have an internet connection; own a private car; own a taxi, minibus or commercial vehicle; own a bicycle; have 4 or more rooms in their house).

# **_Human capital measures_**

_Is the main decision maker for where they work:_ based on a question which asks them who in the household makes decisions over whether they can work outside the home.

_Raven test score:_ score out of 12 on a Raven Progressive Matrices test _Numerate:_ was able to answer four computational questions involving time, percentages, division and subtraction correctly.

_Work centrality:_ Answer to the question “The most important thing that happens in life involves work”, where 5 = strongly agree, and 1 = strongly disagree _Tenacity:_ this measures the extent to which individuals persist in difficult circumstances and is taken as the sum of responses on two questions taken from Baum and Locke (2004).

# **References**

Abdulkadiroglu, Atila, Joshua Angrist, Susan Dynarski, Thomas Kane and Parag Pathak (2011) “Accountability and Flexibility in Public Schools: Evidence from Boston’s Charters and Pilots.” _Quarterly Journal of Economics_ 126(2): 699-748.

Attanasio, Orazio, Adriana Kugler, Costas Meghir (2011) “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial”, _American Economic Journal: Applied Economics_ 3(3): 188-220.

Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul, and Munshi Sulaiman (2012) “Empowering Adolescent Girls: Evidence from a Randomized Control Trial in Uganda”, Mimeo. LSE.

Baum, J. Robert and Edwin A. Locke (2004) “The Relationship of Entrepreneurial Traits, Skill, and Motivation to Subsequent Venture Growth”, _Journal of Applied Psychology_ 89(4): 587-98.

28
# **Table 3: Cross-regional Average Scores on Legal Rights**

||**East Asia**<br>**& Pacific**|**Europe &**<br>**Central**|**High**<br>**income:**|**Latin America**<br>**& Caribbean**|**Middle East**<br>**& North**|**South**<br>**Asia**|**Sub-**<br>**Saharan**|
|---|---|---|---|---|---|---|---|
|||**Asia**|**OECD**||**Africa**||**Africa**|
|**Legal Right**<br>**Index (0-10)**|**5.7**|**6.6**|**6.8**|**5.5**|**3.3**|**5.3**|**4.6**|
|1|100.0|100.0|100.0|90.5|83.3|85.7|97.4|
|2|66.7|57.1|63.6|28.6|0.0|71.4|41.0|
|3|75.0|57.1|63.6|42.9|38.9|85.7|92.3|
|4|50.0|39.3|72.7|61.9|11.1|71.4|41.0|
|5|83.3|75.0|100.0|81.0|55.6|57.1|87.2|
|6|50.0|67.9|40.9|23.8|22.2|14.3|23.1|
|7|58.3|75.0|59.1|38.1|16.7|14.3|25.6|
|8|33.3|71.4|59.1|4.8|11.1|14.3|15.4|
|9|41.7|35.7|36.4|47.6|38.9|42.9|30.8|
|10|58.3|75.0|77.3|33.3|16.7|85.7|41.0|
|**Average**|**61.7**|**65.4**|**67.3**|**45.2**|**29.4**|**54.3**|**49.5**|

_Source: Doing Business_ 2010

# _2.2 Scope of the Law_

Modern secured transactions systems include a broad scope of what the system should be based on, including (i) the types of parties, particularly debtors, to which the law applies; (ii) the types of movable assets to which the law applies, and; (iii) the types of contractual agreements and obligations that may be secured. UNCITRAL’s Legislative Guide on Secured Transactions includes a very comprehensive and clear recommendation on what the scope of the law should be (see Annex 1).

**Types of parties** _._ All natural (including consumers) and legal persons (businesses) should be allowed to create security interests on movable property. In some jurisdictions, natural persons (including sole proprietorships) and some legal person may not use their movable assets as collateral. In jurisdictions with the UK’s English common law system (India, Bangladesh, Sri Lanka, etc) in which security interests are registered in the Company Registry, non-incorporated companies are excluded from the formal financing channel. Likewise, jurisdictions with a French

12
**Table 4: Probit regression results on the determinants of HIV infection status by region**

_<u>(weights that make distribution by age/gender for women in the sentinel survey reflect that for all</u> women in the DHS)_

|||<br>North||||<br>Centra|l|||<br>Sout|h||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|dF/dx|Std. Err.|P>|z||x-bar|dF/dx|Std. Err.|P>|z||x-bar|dF/dx|Std. Err.|P>|z||x-bar|
|Urban (city) location|0.018|0.003|0.000|0.804|0.094|0.027|0.003|0.816|||||
|Blantyre (largest city)|||||||||0.368|0.079|0.000|0.195|
|Blantyre X Age|||||||||-0.004|0.001|0.000|4.859|
|Age|0.064|0.009|0.000|25.210|0.046|0.014|0.002|27.221|0.061|0.032|0.062|26.034|
|Age squared/100|-0.110|0.017|0.000|6.861|-0.072|0.026|0.006|8.008|-0.107|0.059|0.075|7.309|
|No education|-0.103|0.009|0.000|0.080|-0.085|0.032|0.017|0.294|0.050|0.061|0.399|0.322|
|Primary|-0.112|0.009|0.000|0.785|-0.020|0.019|0.290|0.606|-0.007|0.035|0.843|0.556|
|Secondary + (_reference_)|||||||||||||
|Urban X No education|0.019|0.028|0.464|0.047|0.017|0.045|0.694|0.228|-0.126|0.024|0.000|0.022|
|Literacy rate, age 15+ (TA)|||||||||||||
|Mean population age (TA)|||||-0.028|0.016|0.076|20.939|||||
|% in poverty (TA)|0.001|0.0001|0.000|56.613|||||0.0004|0.0002|0.188|51.142|
|% in ultra-poverty (TA)|||||-0.0001|0.0002|0.705|37.580|||||
|% of orphans (age <=14), (TA)|0.005|0.001|0.000|9.120|0.026|0.011|0.020|8.223|0.054|0.004|0.000|10.311|
|Mean age at first sex (TA)|-0.040|0.002|0.000|15.902|-0.102|0.038|0.008|17.062|||||
|Mean age at first marriage,<br>women (TA)|||||||||-0.085|0.011|0.000|18.742|
|Share of women who report<br>knowledge of HIV prevention|||||||||||||
|methods (TA)|||||-0.524|0.302|0.082|0.938|||||
|obs. P|0.141||||0.170||||0.228||||
|pred. P (at x-bar)|0.126||||0.151||||0.212||||
|Log likelihood|-752.674||||-1111.669||||-1311.518||||
|Pseudo R2|0.057||||0.061||||0.060||||
|N of obs.|1960||||2594||||2600||||

Note: standard errors adjusted for clustering on sentinel site 25
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2

# **I. INTRODUCTION**

Most economists accept that, in the long run, open economies fair better in aggregate than do closed ones, and that relatively open policies contribute significantly to development. Many commentators fear, however, that in the shorter run, one of the steps towards openness – trade liberalization – harms the poorer actors in the economy, and that even in the longer run successful open regimes may leave some people behind in poverty. Africa remains the poorest continent of the world. Yet, at the same time, African countries have experienced significant improvements in trade liberalization. It seems that the large gains expected from opening up to international economic forces have, to date, been limited in Africa, especially for poor people.

While the traditional trade theory predicts welfare gains from openness at the country level through specialization, investment in innovation, productivity improvement, or a better resource allocation, the theoretical impact of trade on the poor remains uncertain. Besides, empirical results do not converge on this point and it seems that developing countries are not equally able to make use of the opportunities arising out of increased access to markets in the developed world.

The contribution of this paper lies in providing new cross-country empirical evidence focused on Africa on how the poverty reduction effect of greater trade openness depends on a variety of structural characteristics, including some that are subject to reform. Using a panel of African countries over the period 1981-2010 and testing for non-linearities in the trade-poverty relationship, this paper explores the empirical link between trade openness and poverty. Its results uncover an interesting pattern of reform complementarity: trade openness tends to reduce poverty in countries as their financial sector grows deeper, their education level higher and their governance stronger.

Our concern is with poverty, not inequality. Since trade liberalization tends to increase the opportunities for economic activity, it can very easily widen income inequality while at the same time reduce poverty. Consequently, statements about its effects on inequality cannot be translated directly into statements about its impact on absolute poverty. There may be sound positive and normative reasons for interest in inequality, but they are not the concerns of this paper.

The rest of the paper is organized as follows. Section II reviews the impact of trade on poverty by focusing on the transmission channels and the conditional variables that can influence the trade-poverty relationship. Section III sets up the empirical analysis of the non-linear trade-poverty relationship. Conclusions are presented in Section IV.
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19
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