rafmacalaba/data-use-mentions-extended huggingface dataset:
_To improve business training interventions for entrepreneurs_

**Identify the specific gaps in business knowledge.** Apart from a few FinScope studies, we have little systematic survey evidence of the business literacy needs of entrepreneurs in developing countries. Although it can be difficult to design an instrument to assess business knowledge, doing so would allows us to better tailor the curriculum of business training programs to fit the needs and abilities of local entrepreneurs.

**Pay attention to the curriculum.** Drexler et al. (2010) demonstrates that teaching ―rule-ofthumb‖ accounting concepts (in this case, the separation of household and business accounts) can be more effective than teaching accounting in a more traditional way. It may be that it is easier to teach certain types of knowledge and practices than others, in which case further investigation is needed. In addition, while this study highlights the low hanging fruit of teaching financial education in simpler terms, the saturation point of such interventions likely arrives quickly, beyond which perhaps a more comprehensive intervention would be necessary. An interesting area of extending this existing study the add-on effects of structured business training among individuals who earlier received rule-of-thumb training. Perhaps they would benefit more from formal training if they have had time to absorb lessons from the basic training and established a higher baseline level of financial knowledge.

# **Keep in mind that female entrepreneurs have often failed to benefit from existing**

**interventions.** This has been widely observed across a number of studies, although there are exceptions. Berge et al. (2011) and Field et al. (2010) suggest that behavioral and social constraints on women may explain why business training programs do not work as well for female entrepreneurs as they do for male entrepreneurs. Relatedly, Giné and Mansuri (forthcoming) show that women in their study in Pakistan tend to allocate far less time to their business, and more to household activities. More work needs to be done on what motivates females to enter entrepreneurship and what external constraints
Policy Research Working Paper 6003

# **Abstract**

In 2005 India introduced an ambitious national antipoverty program, now called the Mahatma Gandhi National Rural Employment Guarantee Scheme. The program offers up to 100 days of unskilled manual labor per year on public works projects for any rural household member who wants such work at the stipulated minimum wage rate. The aim is to dramatically reduce poverty by providing extra earnings for poor families, as well as empowerment and insurance. If the program worked in practice the way it is designed, then anyone who wanted work on the scheme would get it. However, analysis of data from India’s National Sample Survey for 2009/10 reveals considerable un-met demand for work in all states. The authors confirm expectations that poorer families tend to have more demand for work on the scheme, and that (despite the un-met demand) the self-targeting mechanism allows it to reach relatively poor families and backward castes. The extent of the un-met demand is greater in the poorest states—ironically where the scheme is needed most. Labor-market responses to the scheme are likely to be weak. The scheme is attracting poor women into the workforce, although the local-level rationing processes favor men.

This paper is a joint product of the Director’s Office, Development Research Group; South Asia Social Protection Unit; South Asia Economic Policy and Poverty Unit; Social Protection and Labor Unit, Human Development Network. 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 mravallion@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
rower, total value of the deal in USD, maturity of each tranche, the general industry group of the deal and the name of the lending banks. We also have information on deal type, specifically whether the syndicated loan is an investment grade, leveraged or highly leveraged loan.<sup>8</sup> Thanks to the information on the lender, we are able to distinguish the syndicated loans in which there is the participation of at least one MDB. The MDBs participate in the syndicated loans markets at commercial terms through their private sector windows to mobilize financing from domestic and foreign creditors. MDBs operate under a joint financial and development mandate, which differentiates them from commercial banks. In our sample, the largest players are the European Investment Bank (EIB), the European Bank for Reconstruction and Development (EBRD) and the International Finance Corporation (IFC)—see Table A1. Deals that involve MDBs are 9.2 percent of the sample and they are, on average, significantly larger, with longer maturities, and with fewer non-MDB banks involved (Table 1).

We group the information on the general industry of the syndicated loans into 9 sectors: agriculture, construction and real estate, finance, government, infrastructure, manufacturing, mining and metals, oil and gas, and services. The majority of loans belong to the finance, infrastructure, and manufacturing sectors, which are also the sectors with the majority of loans with MDB participation.

To prevent large countries from driving the results, we use nominal GDP in USD from the World Economic Outlook (WEO) as a scaling factor for the total value of the syndicated loan. Scaling by GDP also gives us a better understanding of the relative magnitude of the loan size with respect to the country’s economy. For data cleaning purposes we winsorize the top and bottom 1 percent of the total amount of syndicated loans, scaled by GDP .

With this information we build a balanced panel at the country-sector-year level. In total, we have 26
missionary backgrounds and in one specific direction, which seems unlikely. Furthermore, Dreher et al., 2019 tested and rejected the hypothesis that African presidents channel a disproportionate amount of World Bank aid to their birth regions by using a dataset with approximately the same temporal and geographical coverage as the present study.

# **4.2 The Role of Christian Religion and of Human Capital**

Human capital and religion are potential mediators in the relationship between aid and historical missions, although investigation of this is challenging, as is any mediation analysis. Simply including proxies for the candidate mechanism in the baseline equation (1) is problematic for causal inference; if these variables really are mediators, they are by definition not predetermined with respect to Mission _ik_ , which leads to a bad control problem (Angrist & Pischke, 2008). In the absence of a quasi-experimental strategy like the one in Section 4.1, the analysis has to proceed with caution, as interpretation of results relies on several assumptions. First, an assumption is made on the direction of the selection bias that is introduced by controlling for a “posttreatment” variable _Mik_ (education or religion). Then the equation is estimated with or without _Mik_ . When _Mik_ is included, and assuming that Mission _ik_ positively (respectively, negatively) affects _Mik_ and that _Mik_ positively affects aid allocation, it is possible to interpret the coefficient _β_ as a lower (respectively, an upper) bound on the true effect of Mission _ik_ on EverAid _ik_ .<sup>33</sup> If the estimates of _β_ from the regressions with and without _Mik_ are not significantly different from each other, one can conclude that _Mik_ is not the main mechanism of interest.

Individual-level georeferenced data from the individual recode of the DHS (the same data source as used in Section 3) is used to construct measures of education and Christian religion. The DHS asks respondents (women between the ages of 15 and 49) about their highest level of education achieved: no education,
., Shakya, K., Lawrence, M., 2019. Nepal emission inventory – Part I: Technologies and combustion sources (NEEMI-Tech) for 2001–2016. Atmos. Chem. Phys. 19, 12953-12973.

- Sah, D.P., Chaudhary, S., Shakya, R., Sah, P.K., Mishra, A.K., 2019. Status of Brick Kilns Stack Emission in Kathmandu Valley of Nepal. Journal of Advanced Research in Civil and Environmental Engineering 6, 1-9.

- Sanjel, S., Khanal, S.N., Thygerson, S.M., Carter, W.S., Johnston, J.D., Joshi, S.K., 2017. Respiratory symptoms and illnesses related to the concentration of airborne particulate matter among brick kiln workers in Kathmandu valley, Nepal. Ann. Occup. Environ. Med. 29, 12.

- Seay, B., Adetona, A., Sadoff, N., Sarofim, M.C., Kolian, M., 2021. Impact of South Asian brick kiln emission mitigation strategies on select pollutants and near-term Arctic temperature responses. Environmental Research Communications 3, 061004.

- Shrestha, S., 2019. A case study of brick properties manufacture in Bhaktapur. Journal of Science and Engineering 7, 27-33.

- Shriraam, V., Srihari, R., Gayathri, T., Murali, L., 2020. Active case finding for Tuberculosis among migrant brick kiln workers in South India. Indian Journal of Tuberculosis 67, 38-42.

- Siraz, M.M.M., Rakib, M.D.A., Alam, M.S., Al Mahmud, J., Rashid, M.B., Khandaker, M.U., Islam, M.S., Yeasmin, S., 2023. Assessment of radionuclides from coal-fired brick kilns on the outskirts of Dhaka city and the consequent hazards on
, although average income growth is similar in the two groups.<sup>4</sup> Their explanation is somewhat different from Rodrik’s. They argue that voters are risk-averse, and show that voters reward incumbent governments only modestly for growth, but penalize them severely for volatility. This explanation is consistent with research by Sen and Dreze (1989), attributing India’s ability to avoid major famines (during a period when its average economic performance was weak) to its democratic institutions, including a free media. Mobarak (2005) provides additional supporting evidence, showing that democracy causes reduced volatility rather than the other way around. He argues that if policy makers “unilaterally set policies, the variance of policies and outcomes will generally be higher than if policies are chosen through consensus” by democratic procedures. As noted by Henisz (2004) and others, checks on executive power tend to increase the stability of economic policy.

We therefore use _Political Freedoms_ as a second indicator of institutional development. Following Rodrik (1999), Mobarak (2005), and others, we base this indicator on the Freedom House measures of Civil Liberties and Political Rights. The Civil Liberties index captures freedom of

> 4 Many cross-country time-series studies (for example, Burkhart and Lewis-Beck 1994) conclude that the link between democracy and high per capita income is explained mostly by causation from income to democracy.   Acemoglu, Johnson, Robinson, and Yared (2008) conclude from their analysis that there is no causal link between income and democracy, but that both are determined jointly by longer-run historical factors.

6
)|
|Global bank CDS|-0.014|-0.001|-0.004|0.002|
||(0.01)<sup>_∗∗_</sup>|(0.01)|(0.01)|(0.01)|
|Global bank CDS_×_ crisis||||-0.012<br>(0.01)|
|Asset volatility|-0.062|-0.060|-0.067|-0.068|
||(0.02)<sup>_∗∗_</sup>|(0.02)<sup>_∗∗_</sup>|(0.02)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗_</sup>|
|Asset volatility _×_ crisis||||0.018<br>(0.06)|
|Loan demand|0.009|-0.002|-0.006|-0.017|
||(0.03)|(0.03)|(0.04)|(0.04)|
|Infation|-0.019|-0.022|-0.022|-0.022|
||(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗∗∗_</sup>|(0.17)|
|Growth|-0.131|-0.168|-0.159|-0.173|
||(0.15)|(0.15)|(0.15)|(0.
provision in the donor country, and (3) the trustworthiness of recipient country systems. In measuring the latter, however, Knack (2013) did not employ the CPIA indicator of PFM quality specified in the Paris Declaration. Rather, trustworthiness of country systems was measured primarily using the Control of Corruption indicator from the Worldwide Governance Indicators (WGI) project.

> 1 See www.pefa.org for more information on PEFA assessments and indicators.

5
gross exports ratio (VAX). The complement of this measure (1 _− V AX_ ) has been interpreted in some cases as a measure of the share of trade involved in GVCs. While changes in this indicator align closely with variations in international production fragmentation, especially globally (Johnson and Noguera, 2017), the indicator in levels tends to underestimate the significance of GVCs in trade, a point elaborated in Antr`as and Chor (2022).<sup>2</sup> Building on the definition by Hummels et al. (2001), Borin and Mancini (2015) showed how to calculate GVC-related trade using global input-output tables in a way that aligns with the original concept established by Hummels and co-authors in 2001. The accounting framework proposed by Borin and Mancini was the first to provide a quantitative assessment of trade crossing at least two borders. The method ensured that the measure of GVC-trade corresponds to the sum of two established metrics of cross-border GVC linkages: forward GVC participation (producing and exporting inputs for further re-export by the trading partner) and backward GVC participation (utilizing imported inputs for goods exported abroad). More recently, Borin and Mancini (2023) proposed a comprehensive methodology for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels, aligning different accounting perspectives to the economic questions they address best.

All the above-mentioned concepts and measures are now commonly accepted in academic

> 1In theory, value-added generated in the final stages of an internationally fragmented production process should be regarded as related to Global Value Chains (GVC), even if it only crosses one border. However, in practical terms, this segment cannot be distinguished from conventional trade using standard inter-country input-output (ICIO) tables. Due to this empirical constraint, the study Hummels et al. (2001) and all other macro-level measures (that is, at the country-sector level) of GVC-related trade in the wider literature concentrate on the “minimal” criterion for GVC trade, namely, crossing at least two borders
Cruz, E. Davies, A. Grover, L. Iacovone, et al. 2020. “Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World.” Policy Research Working Paper 9434, World Bank, Washington, DC.

Aravena, C., J. Friedman, and A. Hofman. 2017. “Sources of Productivity and Economic Growth in Latin America and the Caribbean, 1990-2013.” International Productivity Monitor 33 (Fall): 51-76.

Asian Productivity Organization. 2016. APO Productivity Databook. Tokyo: Asian Productivity Organization.

Aslam, A., S. Beidas-Strom, R. Bems, O. Celasun, S. Kilic Celik, and Z. Kóczán. 2016. “Trading on Their Terms? Commodity Exporters in the Aftermath of the Commodity Boom.” IMF Working Paper 16/127, International Monetary Fund, Washington, DC.

Aturupane, H., P. Glewwe, R. Ravina, U. Sonnadara, and S. Wisniewski. 2014. “An Assessment of the Impacts of Sri Lanka’s Programme for School Improvement and School Report Card Programme on Students’ Academic Progress.” The Journal of Development Studies 50 (12): 1647-1669.

Azevedo, J. P., A. Hasan, D. Goldemberg, K. Geven, and S. A. Iqbal. 2021. “Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates.” The World Bank Research Observer 36 (1): 1- 40.
40<br>20<br>0<br>World AEs EMDEs<br>F. Per capita potential growth<br>Percent<br>8 Potential growth<br>Actual growth<br>6 1998-2017 potential growth<br>4<br>2<br>0<br>-2<br>G7 EM7 LICs<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br>2003-07 2013-17 2003-07 2013-17 2003-07 2013-17<br>1998-2017 2003-07 2008-12 2013-17 2018-27 1998-2017 2003-07 2008-12 2013-17 2018-27 1998-2017 2003-07 2008-12 2013-17 2018-27<br><!-- End of picture text --> Sources: Penn World Tables, World Bank. Notes: Based on potential growth derived using production function approach. A. B. C. E. F. GDP-weighted average. A. C. E. Sample includes 30 advanced and 50 emerging market and developing economies. B. F. G7 includes Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. EM7 includes Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. LICs includes 6 countries.

C. Blue bars denote average actual global growth during 2003-07 and 2013-17. Red bars denote the contribution of global potential growth to the change in actual growth between the two five-year periods; orange bars denote contribution of the cyclical component of global growth to change in actual global growth between the two five-year periods. D. Number of economies and their share of global GDP among 30 advanced economies and 50 EMDEs with potential growth in each period below its longer-term average (19982017). Horizontal line indicates 50 percent.

20
entrepreneurship for economic growth. These metrics may also be additionally skewed in the Indian context given the well-known problem of the ̳missing middle‘ in the firm-size distribution, which is often associated with rigid employment laws. A large count of small firms may not represent an entrepreneurial cluster very well in an environment where labor laws are particularly binding for firm size (e.g., Besley and Burgess 2004).

Understandably, many researchers are instead drawn to metrics that are more tightly connected to the dynamic nature of entrepreneurship. The more recent micro-data have both enabled these measurements and stressed their better performance relative to small business counts (e.g., Haltiwanger et al. 2010). One approach focuses on start-ups within a single industry so that finer characterizations and case studies can be made (e.g., Saxenian 1994, Feldman 2003). Others look at new product introductions (e.g., Audretsch and Feldman 1996), venture capital placement (e.g., Samila and Sorenson 2011), or the founding of new firms (e.g., Kerr and Nanda 2009, Rosenthal and Strange 2010). These dynamic measures of entrepreneurship are less available than self-employment rates or small business statistics, but are more tightly connected to entrepreneurship that can generate job growth.

We follow this emerging strand and principally define entrepreneurship as the presence of young establishments. Haltiwanger et al. (2010) emphasize how young firms are more tightly associated with employment growth than small firms (conditioning on age) in the US. While we would also like to employ measures of entering establishments in their first year (e.g., Glaeser and Kerr 2009), the young establishment definition—less than three years old—is the most refined measure feasible for India at this time. Incumbent establishments, which are used to model existing activity in the district-industry and Marshallian spillovers, are defined as firms that are three years old or more. We principally define entry measures through employment in these new establishments
0.04|0.104|0.25|
|# Countries|87|87|87|79|79|79|

Note: The first three columns show panel data regression results for each sector. The last three columns include control variables such as export and import as shares of GDP, human capital, the dependency ratio, the real interest rate and the log of population. Standard errors in brackets; * shows significance at 10% level, ** at 5% and *** at 1%.

# **6. Conclusion**

This paper provides a theoretical framework and empirical evidence to understand and estimate the relationship between economic growth and employment growth. The results show that employment growth resulting from economic growth varies substantially across developing countries and across sectors, reflecting considerable differences in production technologies and informality. The paper 23
Interestingly, the employment rates of immigrants from other origins seem to be substantially lower than for natives, although the difference is not statistically significant. It is also very clear that the quality of employment is much lower for Venezuelan workers than for natives. Their informality rate is 15 percentage points higher and they are 29 percentage-points more likely to have temporary contracts. Additionally, Venezuelan workers were 6 percentage-points more likely to be underpaid by their employers than the average native in their canton of residence.

Turning now to columns 10-12 of Table 3, we learn that Venezuelan migrants work 5.5 hours more than the average native worker. However, their monthly earnings are 45 log points lower. More specifically, the average monthly earnings for Ecuadorans in our sample is $476 but the average Venezuelan worker in the same canton earns 36% less. Obviously, the higher working hours and lower monthly earnings lead to a very large gap in terms of hourly wages. Whereas the average Ecuadoran worker earned $2.84, the average Venezuelan in the same canton earned 56% less, despite the fact that Venezuelan migrants have substantially higher educational attainment.

It will also be useful to compare the main labor market outcomes (employment, informality, temporality, work hours, earnings and hourly wages) between Venezuelan migrants and Ecuadoran workers with the same (nominal) education levels. This comparison also allows us to examine whether the outcomes of highly educated Venezuelans are relatively better or worse than the outcomes for the less educated ones.<sup>13</sup> Table 4 reports this information. As before, we restrict the sample to the workingage population (age 15-70). The first column summarizes the values for Ecuadoran natives, column 2 reports the data for the Venezuelan migrants, and column 3 presents the ratio of the value in column 2 relative to column 1. Several points are worth noting. As noted earlier, Venezuelan workers are much less likely to have low education levels (26 percentage points) and much more likely to have