rafmacalaba/datause-encoder-data huggingface dataset:
competition, labor, the business environment, and performance. The main respondents are business owners and top managers of firms. The ES universe excludes extractive industries, agriculture, informal firms (i.e., firms that are not registered), fully government-owned enterprises and micro firms (i.e., firms with fewer than five full time employees). However, in addition to the standard ES, the World Bank also occasionally collects data on micro firms (registered firms with fewer than 5 employees, which are not part of the universe of inference of the standard ES. In the case of the DRC, a survey of micro firms was carried out in parallel to the standard ES.

The DRC survey was stratified according to three standard ES levels – industry, establishment size, and within country location. The stratification of industry divided the universe of firms into manufacturing, retail and other services sectors, while stratification by size divided firms into micro (1-4 employees), small (5-19), medium (20-99) and large (100+) enterprises. Finally, the within country location stratification divided the DRC into four regions: Center (Kananga and Mbuji Mayi), East (Bukavu, Butembo, Goma, Kisangani), South (Likasi, Lubumbashi), and West (Boma, Kikwit, Kinshasa, Matadi). Survey weights were calculated and are applied to all statistics discussed below. Weights ensure that estimates are inferences with a pre-determined level of precision to the universe of the ES: the non-agricultural, nonextractive formal private sector in the DRC.

The summary statistics presented in Table 2 show that the mean firm operating in the DRC has been in business for nine years and employs approximately six people. Around 4 percent of establishments in the DRC are part of a multi-establishment firm, 2 percent engage in exporting activity and 3 percent are foreign owned. Other firm characteristics of note include total female employment, as well as female participation in management and ownership: the average firm employs only one woman (although the standard deviation is high), while approximately 23 percent of firms have a top female manager and 27 percent have some degree of female participation in ownership. The data show that women have greater representation in the capital city (Kinshasa), where the average firm employs two women, 28 percent of firms have a female top manager and 32 percent have female participation in ownership.<sup>13</sup> The DRC survey contains the standard ES questions on firm characteristics, performance, and business environment. The survey also contains several country-specific questions included to ascertain women’s autonomy, equality of economic opportunity, and gender discrimination in the DRC. These specific questions were built based on the legal gender discriminations identified by the WBL data and discussed in the previous section.<sup>14</sup>

> 13 A breakdown of Table 3 by firm location is not provided here but is available upon request.

> 14 The specific questions are presented in Table A1 in the Annex; in each case the question was only posed where relevant, as explained in the second column of Table A1.

8
Closely related to our paper is the work by Love (2000) who studies the relationship between financial development and financing constraints by estimating Euler equations on a firm level for a sample of 40 countries. Love (2000) finds a strong negative relationship between the sensitivity of investment to the availability of internal funds and an indicator of financial market development, and concludes that financial development reduces the effect of financing constraints on investment. This result provides evidence for the hypothesis that financial development reduces informational asymmetries in financial markets which leads to an improvement in the allocation of capital and ultimately to a higher level of growth.

Section 2 reviews the literature on financing constraints. Section 3 presents the structural model of firm investment that we use to estimate the impact of financial liberalization on financing constraints of firms. Section 4 describes the econometric techniques we employ to estimate our structural model of firm investment. Section 5 presents the firm-level data used in our empirical work. Section 6 presents the results of our empirical work. Section 7 assesses the robustness of our results. Section 8 concludes.

# **2 Literature Review**

Following the work of Fazzari, Hubbard and Petersen (1988) a large body of literature has emerged to provide evidence of such financing constraints. This literature relies on the assumption that external finance is more costly than internal finance due to asymmetric information and agency problems, and that the "premium" on external finance is an inverse function of a borrower's net worth. It has been found that financial variables such as cash flow are important explanatory variables for investment. These findings are usually attributed to capital market imperfections as described above (see the surveys by Schiantarelli (1995), Blundell, Bond and Meghir (1996) and Hubbard (1998)). Following Fazzari, Hubbard and Petersen (1988) it is usually assumed that there are cross-sectional differences in effects of internal funds on firms' investment, so that the investment equation should hold across adjacent periods for _a priori_ unconstrained firms but be violated for constrained firms. This has led to different _a priori_ classifications of firms that have tried to distinguish financially constrained and not-constrained firms. From a theoretical point of view such sorting criteria should focus on a firm's 4
42 These results are broadly compatible with Barro's findings. A decline in the fertility rate from 2.5% to 0.2% is associated with an extension of education of 0.21, or 5.2 years. With a 10-year schooling program this is approximately equivalent to an increase in the proportion of the school-age cohort attending school of 0.52. Converting the simulated figures from rates of fertility to numbers of children per adult, the decline in fertility is from 1.85 to 10.5 children. Doubling these indicates the change in the number of children per woman. The fall in number of children per woman is 1.6 associated with an increase in participation at both types of school of 0.52. Barro's results imply, adding together the coefficients for primary and secondary school attendance, that this would reduce the number of children per woman by 2.4, not very different from the simulation figure of 1.6. This suggests that the simulation exercise is reasonably consistent with the facts. In particular it lends support to the idea that fertility is, at least in part, an economic decision and therefore that fertility is likely to be affected by any change, such as the availability of education, which alters the cost of having children.

> _5._ Conclusions: The External Benefits of Education in Developing Countries

There is a reasonable amount of evidence for three types of externality arising from higher education. First of all, education seems to be associated with an improvement in public health. In developing countries the evidence relates to infant mortality, but other effects, which have been identified in developed countries, may also be present.

Secondly, there is ample evidence for a link between education and fertility in developing countries. This can be explained in economic terms by the idea that education raises the effective labor time parents have to devote to looking after their children, because it deprives them of the availability of child labor. Microeconomic studies indeed identify links between fertility, education and child wages but the elasticities are generally lower than those found by cross-country comparisons of fertility rates. Whatever one's view on the desirability of lower fertility, a reduction in fertility should be seen as a consequence of the widening of the availability of education.

Thirdly, it is also suggested that there is a link between education and 'endogenous' growth or technical progress. This is harder to determine because a rising level of educational attainment leads to output growth even in the absence of such an externality. However, crude calculations performed in this paper suggest that the observed link between school enrolment and economic growth is roughly twice what would be expected to accrue simply from the rising level of attainment. These figures are, however, based on the assumption that education raises earning power by between 5% and 8% p.a. Many of the estimates of the return to education in developing oountries are higher than this and, if such figures are accepted the gap between these and the results of cross-country comparisons to be bridged by such an externality is much reduced.
- 22 -

Table 5: Coverage of GSP and AGOA

|Category/import<br>program|Number**oftarifflines**<br>(8-digit**HTS)**|**AverageMFNad-**<br>valoremrate<sup>2</sup>"|**SSAExportsin**<br>2000(millionUS**S)**|MainProducts|
|---|---|---|---|---|
|||Current**GSP:Extended**<br>|**UnderAGOA**<br>||
|Energf,y|10|1.5%|3149|Energyandrelatedproducts|
|Non-Energy|2,458=A<br>1,071= A<br>1,630=A+<br>Total=**5,15922**|5.0%|776|Agriculturalproducts,machine<br>tools,minerals,metals,yarns<br>and fabric,and chemicals.|
|_SubtotalGSP_|_6,159(48%)_||_3,925(17%°)_||
|||**Alreadyduty-fre**|**eitems**<br>||
|Duty-Free|3,404|0%|2,386|Fish,cocoa,fruits,juices,liquor<br>products, tobacco, minerals<br>(uranium,aluminum,zinc),oils,<br>rubber,wood,wool,stonesand<br>machine parts|
|||**AGOA:**Incremental|**Coverage**||
|Energy|36|1.5%|15,569|Energyand related products|
|Apparel|622|12.8%2'|816|Apparelproducts|
|Non-EnergyNon-<br>Apparel<br>_SubtbtalAGOA_|1978<br>_2632'(21%Ol)_|9.4%|424<br>_16809(73%)_|Agriculturalproducts;minerals,<br>plasticsand metalproducts,<br>articlesofwood,watches,and<br>accessories,paperproducts,<br>footwear|
|||**Exclusionsfrom**|**AGOA**||
|DutyBetween0and<br>5perecent|174|**2.5%**|14.5|Yarns, fabrics, agricultural<br>products,textilefootwear<br>components,andglassfibers|
|DutyGreaterthan<br>5percent|893|10.9%|25.4|Fibers, yarns, andothertextile<br>products|
|_Subtotal_<br>_exclusions_|_1,067(8%)_||_40(0.2Y)_||
|**Total**|**12,750**||**23,160**||
|Source:Authors'c|alculations.||||

> 1/ Includes ad valorem tariffs or the ad valorem equivalent of specific tariffs wherever applicable.

2/ "A" refers to GSP for all developing countries and "A+" for least developed<sup>countries;"A*"refers to GSP</sup> for all countries except those designated as ineligible under that product category.

**3/** This understates the true measure of protection because of the quotas on<sup>textile exportersunder the MFA.</sup>
defecation even among households without access to toilets at baseline, and also had a relatively larger impact on expanding the coverage of safely managed facilities as well as access.

The program had a positive impact on raising awareness of the importance of handwashing among adults and school-going children. The program’s impact on raising awareness of handwashing among adults appears to be strongest among households with access to toilets at the baseline. GPs with intensive awareness campaigns in schools also had a relatively larger impact on raising awareness of handwashing among school-going children and promoting the use of soap before eating. Otherwise, the program’s impact in promoting the use of soap among children and adults was mostly limited. We also found no significant impact on motivating households to treat drinking water, or use safe practices for the disposal of child feces.

At the time of the midline survey, the DWSS was planning to continue the BCC campaigns in the treatment GPs until October 2019, the target end date of the SBMG. Since the program has already shown promise in raising hygiene awareness and discouraging open defecation, more intensive followups are likely to result in better outcomes. The positive impacts found at the midline evaluation of the program promise that positive health and nutritional outcomes associated with better sanitation practices are very likely to materialize over the long term. A follow-up survey and evaluation planned for 2021 will be able to capture these impacts.

# References

- Borja-Vega, Christian. 2014. “The Effects of the Total Sanitation and Sanitation Marketing Programme on Gender and Ethnic Groups in Indonesia.” _Waterlines_ 33 (1): 55–70.

- Briend, André. 1990. “Is Diarrhoea a Major Cause of Malnutrition among the Under-Fives in Developing Countries? A Review of Available Evidence.” _European Journal of Clinical Nutrition_ 44 (9): 611–28.

- Crocker, Jonny, Elvis Abodoo, Daniel Asamani, William Domapielle, Benedict Gyapong, and Jamie Bartram. 2016. “Impact Evaluation of Training Natural Leaders during a Community-Led Total Sanitation Intervention: A Cluster-Randomized Field Trial in Ghana.” _Environmental Science & Technology_ 50 (16): 8867–75. https://doi.org/10.1021/acs.est.6b01557.

- De Boer, Michiel R., Wilma E. Waterlander, Lothar D. Kuijper, Ingrid H. Steenhuis, and Jos W. Twisk. 2015. “Testing for Baseline Differences in Randomized Controlled Trials: An Unhealthy Research Behavior That Is Hard to Eradicate.” _International Journal of Behavioral Nutrition and Physical Activity_ 12 (4). http://doi.org/10.1186/s12966-015-0162-z.

- Dickinson, Katherine, Sumeet Patil, Subhrendu Pattanayak, Christine Poulos, and Jui-Hen Yang. 2015. “Nature’s Call: Impacts of Sanitation Choices in Orissa, India.” _Economic Development and Cultural Change_ 64 (1): 1–29.

- Frison, Lars, and Stuart Pocock. 1992. “Repeated Measures in Clinical Trials: Analysis Using Mean Summary Statistics and Its Implications for Design.” _Statistics in Medicine_ 11 (13): 1685–704.

37
Policy Research Working Paper 7294

# **Abstract**

This paper proposes a new measure of growth in shared prosperity, based on shifts in population shares of different income groups over time. This measure complements the definition of shared prosperity recently proposed by the World Bank in which income growth of the bottom 40 percent is examined. The new measure’s strengths arise from its close ties to countries’ national poverty lines and poverty measures, its focus on inclusion of the vulnerable population, and its identification of a population segment that is neither poor nor at significant risk of falling into poverty. The paper also offers a typology of scenarios for tracking shared prosperity under this measure. It provides illustrative examples using survey data from India, the United States, and Vietnam for the mid-to-late 2000s. Estimation results comparing the two approaches with measuring the evolution of shared prosperity are qualitatively consistent, and suggest that during this period, Vietnam enjoyed the greatest expansion in shared prosperity, followed by India and then the United States.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure 12. LAC: Potential output growth

A. Contributions to subregional potential growth B. Duration of school closures <!-- Start of picture text --> Percent TFP Capital Labor Potential growth Number of days<br>6 420<br>360<br>4<br>300<br>2<br>240<br>0 180<br>120<br>60<br>0<br>South America Mexico and C. Caribbean<br>LAC EMDEs<br>America<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Hale et al. (2021); Haver Analytics; Penn World Tables; UN Population Prospects; World Bank. Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-19.

A. Period averages of annual GDP-weighted averages. Potential growth estimates based on production function approach. South America includes nine economies (Argentina, Brazil, Bolivia, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay), Mexico and Central America includes five economies (Costa Rica, Guatemala, Honduras, Mexico, and Nicaragua), and Caribbean includes two economies (Dominican Republic and Jamaica).

B. Simple averages. Orange whiskers are interquartile range. Sample includes 137 EMDEs (33 from LAC).
**Policy, Research, and External Affluirs|**

# **Education and Employment**

WPS 535

- This paper a product of the Education and Employment Division, Population and Human Resources - Department is part of a larger effort in PRE to develop policies to improve private and public skills training in developing countries. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Valerie Charles, room S6-049, extension 33651 (28 pages).

Katz and Ziderman take issue with the prediction - now standardthat firms will be unwilling to finance training in transferable skills, given the absence of property rights over these investments and the possibility of workers being recruited by nontraining firms. Gary Becker has argued that for such training to take place, workers must themselves bear the burden of financing: without freely working capital markets, market failure results in too little training being demanded and provided.

Extending an approach presented in PRE Working Paper 170, Katz and Ziderman argue that potential recruiting firms possess only limited information about the type and level of general training that workers will have received in other firms. The informational asymmetry between a training and a recruiting firm reduces the net benefits a worker can obtain by moving to another (recruiting) firm - which increases the (training) firm's incentive to finance general training.

Katz and Ziderman discuss the role of the options value of general training in raising information asymmetry. They show Becker's training model to be a special case of zero asymmetrical infornation - rather than a general model of training finance.

This finding has important policy implications. Asymmetrical information counters the deleterious effects on general training of such market imperfections as minimum wage legislation (which makes it difficult for a training firm to recover training costs) and a restricted capital market.

Katz and Ziderman suggest that training certification - by facilitating interfirm mobility - discourages on-the-job training. Certification, by awarding workers property rights over their general training, limits company-financed training and places a heavier financing burden on workers. Certification makes the workers' - training visible but decreases asymmetric information between firms.

The cost to the recruiting firn of discovering a trained worker's potential productivity is high.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal Affairs Complex. An objective of the series is to get thesc findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in **thcsc** papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
|**Table 6: C**|**orrelates o**<br>OL|**f "Being a c**<br>S COMBINED|**rossover"**|O|LS MATCHED||
|---|---|---|---|---|---|---|
||A|B|C|D|E|F|
|**1. Personal characteristics**|||||||
|Age|‐0.003|‐0.001|‐0.003|‐0.004|‐0.001|‐0.003|
|Father owned or managed family enterprise|‐0.113|‐0.093|‐0.155|‐0.200*|‐0.206*|‐0.227*|
|Mother owned or managed family enterprise|‐0.224**|‐0.165**|‐0.190**|‐0.203**|‐0.125|‐0.178*|
|Proportion of male siblings|0.030|0.047|0.032|0.097|0.119|0.072|
|Boy ratio, father owned enterprise interaction|‐0.037|‐0.038|‐0.028|‐0.059|‐0.087|‐0.052|
|**_Marital status: reference ‐ married / living with a_**|**_partner_**||||||
|Divorced|‐0.128|‐0.176*|‐0.086|‐0.163|‐0.198*|‐0.117|
|Widowed|‐0.140|‐0.138|‐0.112|‐0.208*|‐0.215*|‐0.193|
|Never married|0.253*|0.196|0.291**|0.453**|0.451**|0.382*|
|**_Religion: reference ‐ catholic_**|||||||
|Protestant|‐0.162|‐0.188*|‐0.158|‐0.131|‐0.221*|‐0.083|
|Muslim|‐0.088|‐0.077|‐0.074|‐0.078|‐0.069|‐0.024|
|Pentacostal|‐0.060|‐0.099|‐0.029|‐0.014|‐0.085|0.071|
|**_Region of birth: reference ‐ Kampala_**|||||||
|Eastern|0.04|0.063|0.068|0.024|0.095|0.061|
|Central|‐0.004|0.051|‐0.013|0.011|0.076|0.026|
|Western|0.009|0.064|0.051|0.062|0.087|0.118|
|Northern|0.152|0.248|0.302|0.019|0.187|0.180|
|Foreign country|‐0.092|‐0.024|‐0.106||||
|**2. Skills and abilities**|||||||
|Raven test score|‐0.034**|‐0.032**|‐0.034**|‐0.035**|‐0.033**|‐0.028*|
|Digit test score|0.030|0.039|0.052|0.037|0.026|0.046|
|Locus of control aggregate score|0.009|‐0.069|‐0.250|‐0.130|‐0.143|‐0.598|
|Agreeability score|‐0.129|‐0.050|‐0.118|‐0.274|‐0.106|‐0.450|
|Emotional stability score|‐1.371**|‐0.976**|‐1.384**|‐0.77|‐0.471|‐0.797*|
|Conscientious|0.531|0.268|0.622|0.32|0.555|‐0.325|
|Self‐efficacy|0.984*|1.106*|0.898|0.333|0.548|0.533|
|**3. Psychosocial factors**|||||||
|Male role model|0.220**|0.214**|0.188*|0.204*|0.124|0.167|
|First job was in non‐male dominated sector|‐0.461***|‐0.434***|‐0.398***|‐0.619***|‐0.644***|‐0.532***|
|**_Exposure origination: Reference ‐ self idea_**|||||||
|Someone's suggestion||0.216**|||0.232*||
|Was offered job in sector by family or friend||0.315**|||0.21||
|Observed others in sector||0.149|||0.104||
|Worked for a wage in sector – qualitative recode||0.028|||‐0.025||
|**_Exposure enabling factor: Reference ‐ self idea_**|||||||
|Suggested to me|||0.334*|||0.241|
|Offered job by a family member or friend|||0.175|||0.158|
|Observed others|||0.104|||0.118|
|Worked for stranger|||‐0.158|||‐0.169|
|Was trained|||0.057|||0.115|
|**4. Capital**|||||||
|Years of education|‐0.005|0.008|‐0.008|‐0.012|0.039|‐0.022|
|Years of education squared|0.000|‐0.001|0.000|0.000|‐0.004|0.001|
|Owned an enterprise in another sector|0.151*|0.148*|0.141*|0.193**|0.173*|0.175*|

<!-- Start of picture text --> Owned an enterprise in another sector 0.151* 0.148* 0.141* 0.193** 0.173* 0.175*<br><!-- End of picture text --> 17