rafmacalaba/datause-encoder-data huggingface dataset:
able to raise capital). Because we have limited information on firm-level characteristics before the IPOs, we include in our data set only firm-level balance sheet information for the post-IPO years. Our final matched data set comprises 2,458 firms from China and 4,305 firms from India. Of these firms, 1,915 Chinese firms and 3,428 Indian firms did not have any capital raising issue through equities or bonds in domestic or foreign markets between 2003 and 2011.<sup>15</sup> The number of firms with capital raising activity in our final matched data set is smaller than the number of firms included in the SDC Platinum database alone because several firms that raised capital through security issues do not have balance sheet data available from Orbis.

We focus the analysis on some key performance indicators (described in Appendix Table 1).<sup>16</sup> Specifically, we focus on the level and growth rate of total assets, sales, and the number of employees to shed light on the relation between firm size, growth, and their use of domestic and international markets.<sup>17</sup> We also examine firm profitability and financial indicators such as return on assets (ROA), leverage (including bank and other types of financing), the maturity profile of the liabilities, and retained earnings. These indicators allow us to shed light on how healthy firms are and to what extent access to capital markets might affect firm mismatches and their dependence on the more expensive internal financing.

To obtain information on firm investments, which is not available in the Orbis database, we match the SDC Platinum database on the use of capital markets with the Thomson Reuters’ Worldscope database, which also contains balance sheet information for listed firms. The data

> 15 Our merged data set comprises both financial and non-financial firms. The results in this paper are quantitatively and qualitatively robust to the exclusion of financial firms.

> 16 We deflate the nominal variables (measured in US dollars) by the US Consumer Price Index (CPI).

> 17 Given the focus of this paper on firm size and growth, we explore three variables for robustness purposes. Each of these measures captures different conceptual aspects of the firm dynamics and can be influenced by different factors. For example, sales and total assets are affected by inflation and exchange rate dynamics, whereas the number of employees is not. Another example is an increase in productivity, which can lead to sales growth without parallel increase in employees or assets. Total assets are arguably important for the capital-intensive firms, the number of employees might be relevant for labor-intensive ones, while sales might depend on the value of intermediate inputs.

9
**Table VI:** Program impacts on child outcomes (children of beneficiaries)

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

||Height-for-Age<br>z-score|Neonatal<br>Mortality|Postneonatal<br>Mortality|Parenting<br>Practices<br>Percentage<br>Score|Exclusively<br>Breastfed for<br>First 6<br>Months|Malawi<br>Developmental<br>Assessment Tool<br>(3-4 year-olds)<br>(Standardized)|<br>Reported<br>Child<br>Difficulties<br>(3-4 year-olds)<br>(Standardized)|<br> <br> <br>Reported Pro-<br>Social<br>Behaviors (3-4<br>year-olds)<br>(Standardized)|
|---|---|---|---|---|---|---|---|---|
|||||Two Yea|rs After Program||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|=1 if Treatment Dropout|-0.013|0.013|-0.009|-0.003|0.030|-0.086|0.104|0.123|
||(0.091)|(0.011)|(0.013)|(0.018)|(0.026)|(0.112)|(0.190)|(0.157)|
|Mean in Control Group|-1.351|0.015|0.026|0.496|0.804|0.000|0.000|0.000|
|Sample Size|742|958|707|861|971|213|223|223|
|**Panel A: Baseline Schoolgirls**|||||||||
|=1 if Conditional Schoolgirl|0.096|-0.014|0.005|0.012|0.029|-0.294*|-0.011|-0.357|
||(0.109)|(0.009)|(0.012)|(0.018)|(0.033)|(0.176)|(0.180)|(0.282)|
|=1 if Unconditional Schoolgirl|0.065|-0.012|0.001|0.050*|0.126***|0.213|0.035|-0.132|
||(0.176)|(0.012)|(0.010)|(0.029)|(0.039)|(0.376)|(0.173)|(0.309)|
|p-value UCT vs. CCT|0.872|0.901|0.734|0.229|0.014|0.172|0.835|0.568|
|p-value Treatment|0.666|0.302|0.912|0.215|0.006|0.145|0.974|0.434|
|Mean in Control Group|-1.410|0.028|0.013|0.484|0.771|0.000|0.000|0.000|
|Sample Size|1,032|1,167|756|1,090|1,169|185|196|196|

_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. The height-for-age z-score is calculated using the 2006 WHO child growth standards. The parenting practices score is the percentage score on a set of parenting practices. The Malawi Developmental Assessment Tool is a test of fine motor skills, language, and hearing administered directly to the child. The reported child difficulties and reported pro-social behaviors are created using the Strengths and Difficulties Questionnaire (http://www.sdqinfo.com/c3.html). Additional details on the outcome variables can be found in Appendix A and Appendix B. Baseline values of the following variables are included as controls in the regression analyses: gender of the child, 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). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
<!-- Start of picture text --> Deposit dollarization 1995-2004<br>Balanced panel 76 countries<br>80<br>70<br>60<br>Top decile<br>Top quartile<br>50<br>Mean<br>Median<br>40<br>30<br>20<br>1995 1998 2001 2004<br>%<br><!-- End of picture text --> Figure 3: _Trends in deposit dollarization 1995-2004_ . Data relates to balanced panel comprising all 76 countries for which data is available for all years. Source: Based on data in De Nicoló, Honohan and Ize, (2005); Levy-Yeyati (2006) “for table 1 06.xls” <!-- Start of picture text --> Estimated time effects<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>Fixed time effect<br>3<br>Quadratic<br>2 Cubic<br>1<br>0<br>percentage points<br>1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Figure 4: _Time effects: estimated coefficients on linear, quadratic or annual dummies._ Based on balanced panel of 76 countries. (See note to Figure 3) 5
# **Figure 7: Cost of Living by Region (100%=National Average)**

<!-- Start of picture text --> 700%<br>600%<br>500%<br>400%<br>300%<br>200%<br>100%<br>0%<br>Food Rent + Food Paid Quality Adjusted<br><!-- End of picture text --> _Notes: Cost of Living estimates are derived from survey responses in the L2CU Baseline. For detailed methods, see appendix C. Author’s Calculations._ For either method of assessing the differences in the cost of housing, the differences are stark. In 2018, imputed rent was on average 2.5 times higher in urban areas than in rural areas.<sup>19</sup> But the costof-living is most dramatically higher in the city of Tashkent. Imputed rents in regions with the lowest average costs (such as Syrdaryia) were as low as 5 percent of imputed rent in the capital. After adjusting for housing characteristics, the cost-of-living in Andijon was 181 percent, Samarkand was 226 percent, and the city of Tashkent was 550 percent of the national average. These results are quite similar if one uses an alternative approach based on the reported value of the home, instead of imputed rents.

Absent legal and other restrictions,<sup>20</sup> such a large disparity housing prices would be expected to drive new construction in the most in-demand areas (Glaeser & Gyourko (2018) provide a detailed discussion). In the long-run, these additional housing units would be expected to put downward pressure on housing prices, making housing comparatively more affordable for a larger number of people. However, the pace of housing construction in the City of Tashkent, and in the regions of Samarkand and Andijon are either lower than or only slightly above the national average (Figure 8).

> 19 Please see appendix C for average imputed rental values and hedonic rent estimates for housing by region.

> 20 For a relevant example instance, China’s deregulation experience is described in detail Chow & Niu, 2015.

24
may have been sufficient. Now, consumers must be able to differentiate between a wide range of financial products and services, and providers of those products and services.”<sup>8</sup> While consumers may have the information, they often do not read or understand the material provided or choose to ignore it, especially when induced by the latest fads or fashion. And mis-information and herding effects are becoming easier in an internet and mass-communication world.

⎯ This is not an issue of consumer protection in a narrow sense as the products offered may satisfy all fair, equal opportunity, etc. requirements, but more of getting greater welfare benefits from financial services provision and financial liberalization. As noted, it is especially the less well-educated which stand to suffer the most with the increased complexity. As such, increasing financial literacy is an obvious policy prescription to this issue. But this has proven to be difficult. Although governments have stepped up efforts to increase financial education, it has proven hard to teach individuals on the details of financial services. A study produced by the OECD’s Financial Education Project (2005) concludes that given the (increased) complexity of financial services, education about finance, and how it works, must be incorporated into basic education systems, a long-term undertaking. It also found that specific financial education programs can work, but might not always be the most effective approach.<sup>9</sup> _2.4 Evaluating the costs of regulation._ As in other areas of economic activity, a perennial issue is assuring the right balance between the private (and public) costs and the public (and private) benefits of regulation and supervision. While there has been much financial deregulation, there has also been much re-regulation to make markets function more effectively, efficiently and sound. With the many new regulations and regulatory changes in recent decades arising from global, regional or national initiatives, new costs have been imposed on financial services industries. These costs have come in the form of direct expenditures on compliance, increased reporting, further internal system

> 8 As recited in the Keynote Address  “Importance of Financial Literacy in the Global Economy” by The Hon. Donald J. Johnston, Secretary-General of the OECD,  to the Financial Education Summit, Kuala Lumpur, 12 December 2005.

> 9 The OECD has published a set of principles and good practices for financial education and awareness OECD (2005).

23
<!-- Start of picture text --> Investor clout (12) Investor clout (1) Investor clout (/2)<br>dy?<br>_<br>do [a _ ><br>do ~ | av" =0 | 7<br>dyM faye 9 >? | dé<br>do do?<br>weyD_wyM : yD=yS faye H| :<br>: —>0: dy© i<br>M i dé ; 77 >0 i<br>i yr _=YyC | v5 —ye : y’>YD Cc :<br>© i i i © i<br>ww i RN & i 4 i<br>i/a 1 i/a 1 i/a 1<br>Government effectiveness (@) Government effectiveness (0) Government effectiveness (0)<br><!-- End of picture text -->
# **Patterns of Business Creation, Survival and Growth: Evidence from Africa**<sup>*****</sup>

Leora Klapper<sup>**</sup> Christine Richmond<sup>***</sup>

# _JEL Classification_ : G18, G38, L51, M13

_Keywords_ : Business Incorporation; Net Employment; Regulatory Barriers; Economic Growth <u>* We are grateful to Francois Bourguignon for providing us the data and Daniel Dias, Sebastian Edwards, Phil</u> English, Vesela Grozeva, Ed Leamer, Phil Kim, Javier Miranda, Nico Voigtlander, Mark Wright, Romain Wacziarg, and seminar participants at the World Bank, 2009 COST Workshop on Firm-level Data Analysis in Transition and Developing Economies, George Mason University-Kauffman Foundation Business Creation Symposium, 2009 Northeast Universities Development Consortium, World Bank-Kauffman Foundation Conference on Entrepreneurship and Growth, PACDEV 2010, 5th IZA/World Bank Conference on Employment and Development, UCLA, and ICN Nancy for helpful comments. We also thank Markus Frölich and two anonymous referees for excellent comments. Financial assistance is gratefully acknowledged from UCLA CIBER by Christine Richmond and from the Knowledge for Change (KCP) program by Leora Klapper. The opinions expressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent, the IMF, or IMF policy.

** World Bank, Development Research Group. The World Bank. 1818 H Street N.W., Washington, DC 20433.  Tel: +1.202.473.8738; Email: lklapper@worldbank.org.

*** International Monetary Fund. 1900 Pennsylvania Avenue N.W., Washington, DC 20431. Tel: +1.202.623.4864; Email: crichmond@imf.org
It should be noted that, by construction, this definition considers only those who reported a crop output and not necessarily all those involved in agriculture. In particular these numbers do not include agricultural wage workers or unpaid family workers.

Paddy is the main staple crop in Sri Lanka and a vast amount of land area is devoted to paddy cultivation. Nearly half of all farm households grow paddy, mostly for home consumption or domestic sales (World Bank 2021b). Major cash crops include tea, rubber, spices (e.g., cinnamon) and coconut, and a large share of their production is aimed toward export markets (World Bank 2021b). In particular, Sri Lanka is among the world’s leading tea producers as the climate in the central highlands and in some low-elevation areas is favorable for the production of high-quality tea. Today about 70% of tea production is accounted for by smallholders and the rest by large plantations (ILO 2018).

Comparing the log of gross output value per acre by individual crop, our measure of productivity, we find that paddy stands out for its low productivity while export-oriented crops such as tea appear to be significantly more productive (Figure 1). Consistent with the low productivity of paddy, poor farmers rely more on paddy cultivation than other crops for their income (World Bank 2021b).<sup>4</sup> Figure 1. Kernel density estimates of the log of gross output value per acre by crop <!-- Start of picture text --> 1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>6 8 10 12 14<br>Paddy Chilies Onions Yams Vegetables<br>Tea Rubber Coconut C,P,B Banana/fruits<br>Density<br><!-- End of picture text --> 2021b, Box 4). A final caveat is that the sample includes those individuals whose main activity does not involve farming as long as they report a positive crop output.

4 The poorest farmers, those who belong to the bottom 20% of the household income distribution, are most reliant on paddy farming, with over 40% of their farm output value coming from paddy, while the contribution of cash crops to their income is low. In contrast, farmers in the top 20% receive only about 26% of their farm output value from paddy (World Bank 2021b).

5
**Figure 13. Marginal contribution of direct transfers to poverty reduction, 2016**

<!-- Start of picture text -->
-0.01 Birth support for newborn<br>-0.07 Home Care Benefit<br>-0.07 Scholarship Transfers<br>-0.10 Sickness Transfers<br>-0.12 Unemployment Transfers<br>-0.13 Disability Contributory Benefit<br>-0.14 Agricultural Support Transfers<br>-0.17 Family Transfers<br>-0.23 Disability Benefit<br>-0.26 Near Cash Transfers<br>-0.38 Widow transfer social assistance<br>-0.42 Old Age Transfers<br>-0.62 MSAAGI<br>-2.95 All direct transfers excl contributory pension<br>-6.49 All contributory pensions<br>-9.83 All direct transfers incl contributory pensions<br>-12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0<br>Change in poverty rate, percentage points<br><!-- End of picture text -->

Source: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to poverty is the difference between the poverty rate without the particular fiscal intervention and the poverty rate of all income components together.

# **7. Conclusions**

This paper contributes the most comprehensive analysis of the poverty and inequality impacts of fiscal policy in Turkey to date, by considering both the combined and individual incidence of direct and indirect taxes and social spending. While fiscal interventions can be pivotal instruments in the policy toolkit to foster inclusive growth, an integrated analysis of the distributional implications of fiscal policy was missing.

The paper finds that Turkey’s overall tax and social spending policy significantly reduces income inequality in the population. The observed inequality-reducing impact is driven primarily by the strong equalizing effect of social spending on education and health. Direct taxes and transfer schemes are also equalizing and help mitigate the inequality-increasing impact of indirect taxes. In contrast, with regards to poverty, the system of direct transfers and direct taxes cannot counterweight the poverty-increasing impact of indirect taxes, and therefore net increases are observed in poverty indicators.

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# **Table 25** : **Test scores before and after by intervention group**

|||WS|D|||Cont|rol||
|---|---|---|---|---|---|---|---|---|
||3rd G|rade|5th G|rade|3rd G|rade|5th G|rade|
||2008|2010|<br>2008|2010|2008|2010|2008|2010|
|Math (0-100)|32|36|59|56|35|36|59|58|
||(22)|(23)|(25)|(24)|(22)|(23)|(25)|(24)|
|English (0-100)|35|35|48|48|34|35|47|49|
||(11)|(12)|(18)|(18)|(10)|(12)|(17)|(18)|
|14 - 8 (% correct)|45|45|65|66|47|47|64|66|
|11 + 5 (% correct)|65|67|89|84|72|71|88|88|
|2×33 (% correct)|9|11|46|38|12|11|45|41|
|Observations|1484|144|1359|142|143|151|136|142|
|||5||4|1|9|7|1|

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. The test of comparison of mean is between years.

41
their expected salary. Discrimination decreases when the majority of applicants have a low expected salary. Malay and Indian sounding names discrimination gap for the rejection outcome decreases in 58 and 49%, respectively. Profile views discrimination gap for Malay names drops by 43% and for Indian names by 34%. Finally, contact discrimination gap for Malay-names drops by 45% and 33% for Indian-sounding names. Interestingly, results are stronger for Malay-sounding name candidates.

The model of statistical discrimination predicts that companies would discriminate and rely on stereotypes when facing an overload of applications. Our analysis shows that there is not a differential effect with a high number of applicants. A potential explanation of our results is that companies that receive more applications have a greater capacity to process them. Importantly, we find that when the pool of applicants has a low perceived quality, measured either by education level or expected salary in the pool of applicants, narrows the discrimination gap against Malay and Indian profiles.

Table 14: Ethnic Discrimination: Majority of Supply with Low Expected Salary

||(1)<br>Rejected|(2)<br>Profle<br>viewed|(3)<br>N of profle<br>views|(4)<br>Contacted|(5)<br>Interview<br>requested|
|---|---|---|---|---|---|
|Malay|0.153***<br>(0.033)|-0.334***<br>(0.035)|-0.952***<br>(0.117)|-0.178***<br>(0.024)|-0.097***<br>(0.021)|
|Indian|0.196***|-0.370***|-1.026***|-0.191***|-0.115***|
||(0.033)|(0.034)|(0.112)|(0.023)|(0.019)|
|Low Salary Supply|0.084***|-0.113***|-0.205*|-0.064**|-0.035*|
||(0.028)|(0.033)|(0.124)|(0.026)|(0.021)|
|Malay _×_ LSS|-0.089**|0.145***|0.232|0.081***|0.034|
||(0.042)|(0.044)|(0.146)|(0.029)|(0.025)|
|Indian _×_ LSS|-0.096**|0.124***|0.173|0.064**|0.040*|
||(0.043)|(0.043)|(0.140)|(0.028)|(0.023)|
|Constant|0.197***|0.576***|1.407***|0.216***|0.132***|
||(0.021)|(0.026)|(0.098)|(0.022)|(0.018)|
|_R_<sup>2</sup>|0.017|0.078|0.082|0.060|0.032|
|N|2,957|2,957|2,957|2,957|2,957|
|_Note:_ This table present<br>with low expected salar<br>1 Rejected takes the val<br>2 Profle viewed takes th<br>3 is the number of visi<br>the Company contacted <br>the value of 1 if the Co<br>observations is smaller t<br>observe information of <br>p_<_0.1|s the results <br>y and its inter<br>ue of 1 if the <br>e value of 1 if<br>ts to the web<br> the candidat<br>mpany conta<br>han the rest o<br>the pool of a|of specifcation<br>action with et<br> company disre<br> the profle wa<br>site profle.<br><br>e through em<br>cted the cand<br>f the specifca<br>pplicants. Ro|1 adding a dum<br>hnicity variables<br>garded the appli<br>s visited by the <br>In column 4 Co<br>ail. Finally, colu<br>idate requesting <br>tions because the<br>bust Standard er|my for majority<br>. The outcomes<br>cation as not-s<br>company at leas<br>ntacted takes t<br>mn 5 Interview <br> an interview.<br>re are jobs for <br>rors. * p_<_0.1,|of job positions<br> are: In column<br>uitable. Column<br>t once. Column<br>he value of 1 if<br> requested takes<br>The number of<br>which we cannot<br> ** p_<_0.05, ***|

34
- 17 -

price at the EEX doubled during the past three years.<sup>15</sup> Albania is modestly adjusting domestic electricity prices in recent years, though the existing price may already be high with the country’s income level taken into account.

The implemented price adjustments seem to have successfully motivated consumers to use electricity more wisely than before, but perhaps the response may not be sufficient. Per capita consumption of electricity in SEE countries continues increasing and remains at high levels by global standards (Figure 12). The figure does not mean that there was no effect of price adjustments; rather, it implies that the demand for energy or electricity may continue to be strong in this region for other reasons, for instance, the region’s relatively robust economic growth (until recently).<sup>16</sup> This reminds us of the difficulty in governing the demand for energy, while balancing various policy objectives, including energy security, economic growth and fiscal consolidation.

Figure 11. Quasi-fiscal deficits in electricity sector <!-- Start of picture text --> 25<br>22.5<br>2000<br>2001<br>20 2002<br>2003<br>15<br>10.5<br>10 9.5 8.7<br>5.4 5.6<br>5.0<br>5 4.2 3.8 3.8<br>1.4 2.1 0.9 1.3<br>0<br>Source: World Bank (2006b).<br>Quasi-fiscal deficits in the power sector (% of GDP) Albania Bosnia Bosnia and Herzegovina Croatia Macedonia Romania Serbia & Montenegro<br><!-- End of picture text --> 15 There is normally a time lag between an increase in market prices and the associated administered price adjustment. Moreover, high international energy prices should only partially translate into domestic retail prices, when some fraction of the domestic energy is purchased from abroad.

16 In addition, this figure includes the residential demand for electricity, which is considered less price-elastic and expected to increase along with economic development.