rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 11041

# **Abstract**

This paper provides evidence on the nature of financial constraints faced by women entrepreneurs, especially in contexts of stringent social norms. Using micro-data from the World Bank Enterprise Surveys for 61 countries, the analysis shows that formal firms managed by women do not face credit constraints on the extensive margin. They are equally likely to apply for credit as their male counterparts and experience lower rates of credit rejection, with a higher likelihood of opening credit lines. However, on the intensive margin, firms managed by women receive lower credit amounts, indicating signs of credit constraints. This disparity in access to credit cannot be explained by gender differences in risk profiles, profitability, or productivity.

However, firms managed by women have lower sales per worker, suggesting challenges in accessing product and labor markets. The paper finds suggestive evidence of capital misallocation based on gender, particularly in countries with more restrictive gender and cultural norms. Firms managed by women demonstrate a 15 percent higher average return on capital compared to firms managed by men, indicating the potential benefits of increased access to credit for women-led businesses. These findings emphasize the importance of addressing gender-specific constraints to accessing finance and promoting gender-inclusive policies to enhance firm growth and reduce capital misallocation.

This paper is a product of the International Finance Corporation. 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://www.worldbank.org/prwp. The authors may be contacted at agrover1@ifc.org and mviollaz@cedlas.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
portion of shared taxes, the sub-national governmental revenue share steadily declined from 78% in 1993 to 45% in 2002. The tax assignment system has cut sharply into sub-national governments’ own revenues and their share of fiscal revenue distribution.

On the expenditure side of budgets, however, the share of sub-national government spending in total fiscal expenditure has kept stable, at roughly 70% (see Figure 2).

Figure 2: National fiscal expenditure and its structure <!-- Start of picture text --> 30000<br>25000<br>national fiscal<br>20000<br>expenditure<br>the central fiscal<br>15000<br>expenditure<br>10000 sbu national fiscal<br>expenditure<br>5000<br>0<br>1978 1985 1990 1992 1994 1996 1998 2000 2002<br>fiscal year<br>million)<br>fiscal expenditure(100<br><!-- End of picture text --> Source : Authors calculations The widening fiscal gap between sub-national expenditure and sub-national fiscal revenue is shown dramatically in Figure 3. As a result of these trends, sub-national governments have grown more and more fiscally dependent on the central government. The tax rebating system has become a type of universal grant, which all sub-national governments need to receive. At the same time, regional disparities in income, fiscal revenue, and self-financing capacity have grown. Figure 4 shows that the sub-national ‘self-support’ rate ranges from almost 90% in Beijing to 14
Notes: Stars indicates levels of significance of t-test on equality of means between the two years: * p<0.1, ** p<0.05, *** p<0.01 Source: LFS 2019-20, 2020-21 In order to better gauge the differential labor market impact of the COVID-19 crisis between comparable men and women, we estimate the following linear OLS model on the pooled sample of individuals in the working age population observed in the 2019-20 and 2020-21 LFS survey waves: i 0 F F C F F ∗C C + F + F where (1) YY is the outcome of interest (labor force participation, employment and unemployment ii = αα + ββ F ii + γγ C CCii + δδ (F ii CCii) + ηη XXii ii ii + εεii<sup>9</sup> ) for individual _i_ in province _p_ and season _s._ The model further includes a dummy variable to identify the gender ii of the respondent ( YY _Female_ ), a dummy identifying the pandemic (corresponding to Persian year 2020-21 from March 2020 to February 2021) and an interaction between the two, which identify the differential impact of the pandemic on female and male individuals. The vector includes individual controls, such as age, age squared, education, marital status as well as a dummy indicating urban residence. Additional ii XX controls include season and province dummies. Moreover, in order to capture how the presence of children might have differentially impacted men and women after controlling for individual characteristics, the sample was partitioned to focus on the following groups: women and men with any child aged 0-5; women and men with any school-aged child (age 6-14) and women and men without any child aged 0-14. Results are presented in Table 6.

_Table 6: Gendered impact of the pandemic - regression results_

||**All**|**Any 0-5 child**|**Any 6-14 child**|**No child 0-14**|
|---|---|---|---|---|
|**Labor force participation**|||||
|female==1|-0.548***|-0.719***|-0.634***|-0.421***|
||(0.002)|(0.002)|(0.002)|(0.002)|
|covid==1|-0.024***|-0.011***|-0.020***|-0.032***|
||(0.002)|(0.002)|(0.002)|(0.003)|
|**_female==1 & covid==1_**|-0.007***|-0.017***|-0.013***|0.003|
||(0.002)|(0.003)|(0.003)|(0.003)|
|**Employment**|||||
|female==1|-0.523***|-0.691***|-0.605***|-0.398***|
||(0.002)|(0.002)|(0.002)|(0.002)|
|covid==1|-0.018***|-0.012***|-0.016***|-0.022***|
||(0.002)|(0.002)|(0.002)|(0.003)|
|**_female==1 & covid==1_**|-0.005**|-0.009**|-0.010***|0.003|
||(0.002)|(0.003)|(0.003)|(0.003)|
|**Unemployment**|||||
|female==1|0.062***|0.072***|0.041***|0.066***|
||(0.003)|(0.005)|(0.003)|(0.004)|
|covid==1|-0.004***|0.003*|-0.001|-0.008***|
||(0.001)|(0.002)|(0.002)|(0.002)|
|**_female==1 & covid==1_**|-0.016***|-0.027***|-0.011**|-0.016***|
||(0.004)|(0.006)|(0.005)|(0.006)|

> 9 For the purpose of this analysis, unemployment is defined over the working age population, not the labor force.

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|Sheep|0.49|0.51|
|---|---|---|
||(0.50)|(0.50)|
|Chicken|0.60|0.59|
||(0.49)|(0.49)|
|Financial access|0.25|0.50|
||(0.43)<br>|(0.50)<br>|
|Distance to town|326.83|378.64|
||(229.93)|(285.74)|
|Dependency ratio|0.49|0.50|
||(0.24)|(0.23)|
|Death shock|0.08|0.02|
||(0.27)|(0.13)|
|Illness shock|0.23|0.08|
||(0.42)|(0.27)|
|Cropdamage shock|0.10|0.03|
||(0.30)|(0.17)|
|Livestock shock|0.09|0.05|
||(0.29)|(0.21)|
|Jobloss shock|0.01|0.00|
||(0.09)|(0.04)|
|Price shock|0.02<br>|0.18<br>|
||(0.14)|(0.38)|
|Psnp beneficiary|0.00<br>|0.15<br>|
||(0.00)|(0.36)|
|Highlands‐drought|0.39<br>|0.37<br>|
||(0.49)|(0.48)|
|Highlandsreliable|0.38|0.34|
||(0.48)|(0.47)|
|Lowlandsreliable|0.03|0.11|
||(0.18)|(0.3)1|
|Lowlandsenset|0.19|0.18|
||(0.40)|(0.39)|
|Goodroof|0.22|0.34|
||(0.42)|(0.47)|
|Electricity|0.00|0.06|
||(0.00)|(0.24)|
|Improved loo|0.21|0.59|
||(0.41)|(0.49)|
|Householdsize|4.91|5.00|
||(2.28)|(2.20<br>|
|Number obs|8431|)<br>8807|

# **b. Regression results**

In this section we discuss the result for the vulnerability relationships tested. We modelled the dependent variable log of consumption per adult equivalent in the household, and included (the same list of ) household and community characteristics in each model. We then estimated a baseline model with simply the LEAP % loss in linear form.

We tested for a non‐linear relationship of LEAP but as results below show, the linear model is the preferred specification. We noted above that in line with the disaster modelling, we explicitly incorporate characteristics of households that may mitigate or exacerbate the impact of the drought shock. The analogy can be drawn with table 2.2 above which shows characteristics of buildings with different wall structures. Table 3.2 below shows the list of mediating characteristics that were included in the regression model. In economics, this would be termed as heterogeneity in the 17
**Table 3a Sources of FDI Liabilities, 2004**

||**China**||**India**|
|---|---|---|---|
|World|100.0|World|100.0|
|Hong Kong (China)|45.0|Mauritius|35.6|
|United States|8.9|United States|16.5|
|Japan|8.7|Japan|6.9|
|Taiwan (China)|7.4|Netherlands|6.9|
|British Virgin Islands|6.9|UK|6.6|
|Korea|4.8|Germany|4.4|
|Singapore|4.8|Singapore|3.1|
|United Kingdom|2.3|France|2.7|
|Germany|1.8|Korea|2.2|
|France|1.3|Switzerland|2.0|
|Other|8.2|Other|13.2|

Distribution by country of origin of China's and India's FDI liabilities. Figures show percent of liabilities from each source country. Source: Authors’ calculations based on data from the Chinese Ministry of Commerce of the People's Republic of China (http://www.fdi.gov.cn) and the Indian Department of Industrial Policy and Promotion (http://dipp.nic.in/).

**Table 3b Sources of Portfolio Liabilities, 2004**

||**Ch**<br>Equity|**ina**<br>Debt||**In**<br>Equity|**dia**<br>Debt|
|---|---|---|---|---|---|
|World|100.0|100.0|World|100.0|100.0|
|Hong Kong (China)|34.3|36.7|United States|41.2|13.4|
|United States|28.6|16.3|Mauritius|31.5|27.8|
|Europe 15|24.7|20.4|Europe 15|24.1|22.8|
|Japan|4.6|10.3|Japan|0.2|11.9|
|Singapore|3.9|10.3|Singapore|0.4|16.6|
|Other|4.0|5.9|Other|2.6|7.6|

Distribution by country of origin of China's and India's portfolio liabilities. Figures show percent of liabilities from each source country. Source: Authors’ calculations based on data from Coordinated Portfolio Investment Survey.
**Figure 3. Households receiving remittances by quintile of the non-remittances income** **<u>distribution</u>**

<!-- Start of picture text --> Bolivia Ecuador<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>El Salvador Guatemala<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Haiti Honduras<br>30 30<br>20 20<br>10 10<br>0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Mexico Nicaragua<br>80 40<br>60 30<br>40 20<br>20 10<br>0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Paraguay Peru<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Dominican Republic<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br><!-- End of picture text --> The figure reports the percentage of households receiving remittances that fall in each of the five quintiles of the income distribution. Source: Own calculations using the last available household survey.

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describe. For example, rice price increases may cause large landholding households to desire more labor. In the presence of labor market imperfections, increased labor may spur the household to import more labor directly into the household. In turn, changes in household labor could affect the allocation of child time. This type of mechanism for changes in child labor supply is not captured in the theoretical framework of section 2. However, it may be an important part of the reduced form effect of rice price changes. Hence, the empirical work in the next section will not attempt to separately identify the various income and substation effects described in the previous section. Rather, they serve as motivation for why rice price increases might affect child labor. Shifts in household composition may be an additional mechanism.

# **3.3 Child Labor**

In general, there are large changes in child labor occurring in Vietnam in the 1990s (Edmonds and Turk 2004). Table 2 summarizes by gender and year the changes in child labor that occur in the sample of rural households in communes that are visited in both rounds of the VLSS. Throughout this study, the focus is on children 6-15. The top panel of table 2 contains participation rates in various activities in the last 7 days. Wage work consists of work for pay (in cash or in kind) outside of the child's household in agricultural or non-agricultural activities. Within the household, the VLSS collects data on participation in the family's farming activities (labeled "agriculture"), the family's non-farm home enterprise, and household production. Participation in household production is asked in a single question that defines household production as domestic duties, home repair and household maintenance, time spent working on agricultural tools, caring for animals, fetching water, and collecting firewood. Participation in each category of activity (wage work, own farm agriculture, home enterprise, household production) is asked directly in the questionnaires and the questions in the survey instrument are 15
unique challenges in these fields. Effective approaches should focus on enhancing women’s skills, providing exposure to better-paying sectors, connecting them with supportive role models, and challenging prevailing gender norms and attitudes.

# **2 Context**

## **2.1 Labor Market In Cˆote d’Ivoire**

Data from the World Bank<sup>4</sup> sheds light on gender dynamics within the labor market of Cˆote d’Ivoire. As of 2023, the labor force participation rate for females stands at 56.5%, compared to 72.2% for males. These disparities also apply to young people aged 15 to 24. Notably, female labor force participation has shown an upward trend since 1990, indicative of a gradual narrowing of the gender gap. On a regional scale, the labor force participation rate for women in Sub-Saharan Africa is reported at 60.7%, marginally higher than Cˆote d’Ivoire’s 56.5%. The incidence of vulnerable employment, characterized by informal work arrangements lacking social protection and safety nets, remains a significant issue. In 2022, 80.8% of employed women and 63.4% of employed men in Cˆote d’Ivoire were engaged in vulnerable employment. Despite high rates, there has been a discernible improvement in reducing vulnerable employment among females since 1991, reflecting ongoing structural changes within the labor market. Business ownership also reveals pronounced gender disparities. In 2022, the proportion of female business owners in Cˆote d’Ivoire was 26%. This underscores the continuing challenges women face in achieving parity in entrepreneurial activities. Women in Cˆote d’Ivoire face significant barriers to entrepreneurship, including limited access to finance and business networks. These barriers are exacerbated by socio-cultural norms that often discourage female entrepreneurship. Additionally, women’s participation in high-growth sectors remains low, further limiting their economic opportunities (The African Development Bank Group, 2021). In summary, while Cˆote d’Ivoire has made strides in increasing female labor force participation and reducing vulnerable employment among women, significant gender disparities persist, particularly in business ownership. Future policy interventions should aim to address these gaps to foster a more inclusive and equitable labor market.

## **2.2 TVET In Cˆote d’Ivoire**

According to the UNESCO Institute for Statistics (UIS) database, only 5% of secondary education students are enrolled in vocational programs. At the same time, individuals with low

> 4World Bank Gender Data Portal.

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note that often policies that show promising results when implemented as pilots at small scale turn out to be disappointing when implemented at scale. They point to the need for continuous evaluation and openness to course corrections as projects are scaled up to avoid such disappointing outcomes. The next group of notes turns to the question of _“how to evaluate policies.”_ One of the most striking trends in economics – and particularly in development economics – in the past decade or two has been the increase in the seriousness with which the profession has taken the identification of causal effects of policies on outcomes. As Xavier Giné and Hanan Jacoby write, this has most visibly come through the mainstreaming of randomized controlled trials as a tool to evaluate policy interventions. This has been accompanied by increasingly creative use of natural experiments to identify causal effects in circumstances where randomization is not possible or desirable, as well as by more carefully combining economic theory with empirical evidence to shed light on underlying mechanisms. The scope for insightful evaluation of policy outcomes has also broadened remarkably over the past decade as economists have been able to draw on nontraditional data sources, ranging from satellite imagery to cell phone records. Robert Cull, Dean Jolliffe, and Vijayendra Rao provide examples of how these new data types, combined with new tools such as machine learning and natural language processing algorithms, have shed new light on old questions.

The last set of four notes covers an assortment of policy domains where recent research has provided new insights on the effects of policies. Sergio Schmukler, Michael Toman, and Adam Wagstaff draw on examples as disparate as early childhood development, financial crises, and environmental degradation to illustrate the general principle that early policy interventions at large enough scale can be highly cost‐ effective. They also discuss why, despite these high returns, opportunities for early intervention often are missed. Susmita Dasgupta and Michael Toman describe the accumulating evidence that investments to improve resilience to climate change can have big payoffs in terms of development outcomes. Leora Klapper discusses how the combination of policy and technological changes that have enabled widespread access to digital financial transactions have improved outcomes for users in a variety of ways. Finally, Erhan Artuc and Bob Rijkers synthesize what we have learned from recent research on the size of the welfare gains from trade – and more importantly about how they are distributed across people, space, and sectors.

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build in order to do that. Additional analysis not undertaken in this paper could further establish sector targets and specific policies required.

# IV. Results

## A. Which capabilities matter most for GVC participation overall?

GVC participation is to a great extent the result of the available mix of capabilities in the economy. Some capabilities may matter more than others. For each traded product, our database of revealed capability intensities (RCI) provides a measure of the extent to which each of the nine chosen capabilities is associated with the export of each GVC product, on a global basis. Taking these RCI estimates, we run a test of statistical significance between GVC products and non‐GVC products on each capability. Table 4 shows the results. Confirming our expectations, nearly all of these capabilities assessed in this study are important for participation in GVC trade. Overall, logistics, proximity to markets and strength of institutions seem to be most important. Even though they are not considered significant, we choose to leave wages and market access in the model because these two capabilities are likely to be important at the sector level even though they do not appear to be significant at the aggregate level.

**Table 4: Revealed Capability Intensity (RCI) of GVC Products versus Non‐GVC Products**

|**Category**|**Capability**|**GVC Products**|**Non‐GVC**<br>**Products**|
|---|---|---|---|
|Fixed|Proximityto markets|0.0085***|0.003|
||Natural capital|0.0061***|0.0024|
|Long‐term|Human capital|0.012***|0.0035|
|Policy<br>|Physical capital|0.0079***|0.0042|
|Variables|Institutional capital|0.0113***|0.0033|
|Short‐term|Logistics/connectivity|0.0157***|0.0046|
|Policy<br>|Wage competitiveness|‐0.0052|‐0.0036*|
|Variables|Market access|0.0048|0.0037|
||Access to inputs|0.0044**|0.0006|

Source: Authors’ calculations In this study, we differentiate between final and intermediate stages of the value chain. Historical experience suggests that only after becoming adept at final assembly using imported parts and components, then generating a local supplier base and acquiring a greater set of capabilities, countries begin to produce and then export intermediate products (Kaminsky and Ng 2005, Pathikonda 2011). Consistent with this understanding, the intermediate GVC products have greater capability requirements: they embody six of the nine capabilities to a greater degree than final products, as shown below in Table 5. As expected, wage competitiveness and access to inputs are more important for the export of final products. There is no significant difference between final and intermediate products on natural capital.

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number of parking tickets issued to individual diplomats to develop a per capita measure of the abuse of parking violations. They divide the number of tickets issued to diplomats of a certain nationality between 1998 and 2000, by the number of diplomats in that country’s UN delegation.

**Coding.** The number of parking tickets per capita (size of the diplomatic delegation) is an interval indicator ranging from 0-249. As Table 6 indicates, there is considerable variation in the number of tickets issued per capita, with the standard error (33.0) larger than the mean (19.7). For many countries, especially high-income OECD countries in Northern Europe, the number of parking tickets issued was 0, while as a region, the Middle East had the highest rate of ticketing (Kuwait had the highest rate of all countries: 249.4).

# _Table 6: Per Capita Issue of Tickets (1998‐2001)_

|**Number of**|**Average per**|**Standard Error**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|**Observations**|**capita Issue of**||||
||**Tickets**||||
|137|19.7|33.0|0.0|249.4|

_Source: Fishman and Miguel, 2007_ While this measure has several weaknesses – it may only be measuring a very narrow form of corruption norm, and diplomats are not necessarily representative of the population – it also has several strengths. Firstly, it does in fact correlate strongly with subjective survey data on corruption (Control of Corruption and the CPI), and secondly, diplomats may not be representative of the average citizen, but are more likely to be similar to the senior policy makers who may undertake large-scale corruption. The data are available for a large number of countries (146), comparable, and exist over a time period (1998-2001) for which corruption indices exists. Because of this, it is used as a robustness check for these subjective results.

# **(C) Which Measure?**

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