rafmacalaba/datause-encoder-data huggingface dataset:
The gender gap in business ownership is observed in every income group. However, women in low-income economies are proportionately less likely to start businesses at the level of either LLCs or sole proprietorships than those in middle- and high-income economies. The significant gender gaps observed in low-income economies suggest that few women are able to start new businesses.

One of the biggest gaps can be observed in the Middle East and North Africa region, where only one out of four women participates in the labor force (Gatti et al. 2013:10). Despite a marginal increase in women’s participation in economic activities in recent years, it is estimated that it would take 150 years for the economies of the Middle East and North Africa to reach the current world average for female labor force participation (Gatti et al. 2013, 10). The Entrepreneurship Database provides further evidence of the substantial gender gap in female entrepreneurship in the Middle East and North Africa region. Over the past five years, however, the number of women-owned businesses has risen steadily in some economies, including Morocco, Jordan and the United Arab Emirates. Figure 4 depicts the proportion of new female business owners in 2016 in the five economies in the Middle East and North Africa region included in this study. Morocco had the largest proportion of new female LLC owners (17.7 percent), followed by Jordan (15.5 percent) and Oman (15 percent).

**Figure 4: The level of female business ownership in select economies in the Middle East and North Africa in 2016**

<!-- Start of picture text --> 20 30<br>18<br>16 25<br>14 20<br>12<br>10 15<br>8<br>6 10<br>4 5<br>2<br>0 0<br>(%)<br>one female owner (%)<br>Share of new female LLC owners Share of new LLCs with at least<br><!-- End of picture text --> _Source:_ Entrepreneurship Database.

Levels of female business ownership are relatively low in the Middle East and North Africa region compared to those in the OECD high-income group. Women entrepreneurs in the Middle East and North Africa face barriers including financial constraints and a lack of female-friendly entrepreneurship policies. For example, women in the Middle East are half as likely as men to have a formal bank account (DemirgüçKunt et al. 2015). Furthermore, women business owners across the region identify similar challenges to doing business, such as accessing financial management skills training, finding and keeping good employees and securing capital (CAWTAR and IFC 2007, 7). Inequalities surrounding legal rights also hamper the participation rate of female entrepreneurs. Gender legal disparities are higher than the estimated world average in the Middle East and North Africa (Iqbal et al. 2016, 11). Of the regional economies included in the Entrepreneurship Database, Morocco has the lowest measure of legal rights disparities; Saudi Arabia has the highest (Iqbal et al. 2016, 13). Indeed, legal rights disparities correspond to differences in female business ownership in these economies.

9
**Table 1: Number of enterprises per sector in the Kassida data set**

||**Female‐**|**owned**|**Male‐owned**|
|---|---|---|---|
|**Sector**|**Crossovers**|**Non‐crossovers**||
|Barbershop / Hair Salon|0|63|7|
|**Carpentry**|**4**|**0**|**44**|
|Catering|0|107|2|
|**Electricals**|**1**|**0**|**11**|
|Fitting and machinery|0|0|11|
|**Foundry & forgery**<sup>**8**</sup>|**15**|**0**|**48**|
|**Metal fabrication**|**7**|**0**|**185**|
|**Shoe making & repair**|**3**|**0**|**33**|
|Tailoring / knitting|0|126|68|
|**Total**|**30**|**296**|**409**|

This paper also uses the outcomes of a mixed methods questionnaire collected uniquely for this paper (hereafter the mixed methods data set). This questionnaire was administered in July/August 2012 to 187 female entrepreneurs – 67 working in male‐dominated sectors such as those identified in Table 1, and 120 working in traditionally female sectors (catering, hair dressing, and tailoring).

**Table 2: Number of enterprises per sector in the mixed methods data set**

||**Female‐**<br>|**owned**<br>|
|---|---|---|
|**Sector**|**Crossovers**|**Non‐crossovers**|
|**Carpentry**|**26**|**0**|
|Catering|0|41|
|Clay molding|0|1|
|**Electricals**|**1**|**0**|
|**Fitting and machinery**|**7**|**0**|
|**Foundry & forgery**|**15**|**0**|
|Hair salon|0|23|
|**Metal fabrication**|**10**|**0**|
|**Shoe making & repair**|**8**|**0**|
|Tailoring / knitting|0|55|
|**Total**|**67**|**120**|

As the study was designed to include both formal and informal businesses, it is not representative of all enterprises in Kampala.<sup>9</sup> Instead, an initial sample of respondents was drawn from the KASSIDA data

> 8 Forgery could be integrated in metal fabrication under a different definition of sectors. The definition used follows the list of sectors identified by KASSIDA to organize their work.

> 9  The inclusion of informal businesses results in a lack of an accessible sample frame from which to draw.

6
school (not shown). On average, villages have 1 agro-dealer and the NAIVS subsidy as a share of the total cost of inputs is 48.6 percent.

The villager survey included a couple of questions regarding the social and/or political connections of respondents. In particular, respondents were asked whether they were related to or socialized with government or NAIVS officials such as the Ward Executive Officer (WEO), the VEO, the District Agriculture and Livestock Development Officer (DALDO), or any VVC member or village influential such as the hamlet, village or ward chairs.<sup>6</sup> Column 2 of Panel C in Table 1 reports that 22 percent of villagers were related to government officials or influential, and 21 percent socialized with them regularly. In addition, 68 percent of villagers had used improved seeds during the 2009/10 season and 48 percent had used inorganic fertilizer (mostly urea).

We find no differences across treatment arms in any of the panels in Table 1. We report the p-value of a joint F-test that all the characteristics reported in a given panel do not predict being assigned to a lottery (column 5) or an open meeting village (column 8). None of the p-values reported is significant at conventional levels (the lowest p-value is 0.385). The different treatment arms thus appear to be balanced.

# **4. Results on Targeting and Impacts**

# _4.1 Targeting_

We use the villager survey to first study whether the eligibility criteria successfully identified the poor. While only the eligible household survey instrument collected food security, we use landholdings as a proxy for poverty as they were collected across all surveys. Using the eligible household survey, we find that the correlation between landholdings and food security is 0.13 and highly significant (p-value is 0.000). Figure 2 shows the distribution of landholdings and the average landholdings among eligible households, that is, those that cultivated less than 1 hectare, and not-eligible households. We find that the NAIVS eligibility criteria successfully targeted farmers at the bottom of the distribution of

> 6 The DALDO, WEO and VEO are appointed by the district government. In contrast, the village or ward chairman are hereditary titles. We also asked at baseline the political party supported by the head of the household. Although there are no differences between the different treatment groups, 90 percent of the respondents reported supporting the same party, Chama Cha Mapinduzi (CCM), the ruling party in Tanzania at the time of the study. Only around 7 percent reported supporting the main opposition party (Chadema) and 2 percent chose not to answer.

12
Finally, consider sorting individuals by increasing order of income their actual income with no attention paid to group membership. People are now ranked for the overall poorest to the overall richest. To the extent that there is overlapping between subgroup distributions, some people will shift ranks relative to their positions in the lexicographic parade. The extent of this overlapping is measured by subtracting the concentration coefficient of the lexicographic distribution (which embeds both the between and within group components) from the overall Gini coefficient.

Table 2.6. A Threefold Decomposition of the Gini Measure of Inequality for 2001&2007

||Level(inp|ercentage)|Relative(in||percentage)|
|---|---|---|---|---|---|
||2001|2007|2001||2007|
|Between-Group|17.46|19.38|43.21||49.75|
|<br>Within-Group|8.26|1.25|20.45||3.21|
|<br>Overlapping|14.69|18.33|36.35||47.05|
|Overall|40.41|38.96|100||100|

Source:Authors’ Calculations <u>Figure 2.2. Relative Contribution of Gini Components, 2001& 2007</u> <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Between Within Overlap<br>Y_2001 Y_2007<br>Percentage<br><!-- End of picture text --> Source: Authors’ Calculations 18
Figure 2. The Distribution of Severity: Cumulative Severity of COVID-19 and Proportionate Mortality from Top Causes <!-- Start of picture text --> Cumulative severity ratio: COVID−19 Proportionate mortality ratios: Top cause Second cause Third cause<br>60<br>40<br>20<br>0<br>Ratios (percent)<br>Belgium Spain United Kingdom Ireland Sweden Italy France Ecuador Netherlands United States Luxembourg Switzerland Canada Peru Kuwait United Arab Emirates Brazil Iran Panama Portugal Mexico Denmark Turkey Germany Austria Dominican Rep Antigua and Barbuda Chile Israel Qatar Finland North Macedonia Bahamas, The Norway Moldova Slovenia Honduras Iceland Romania Estonia Bahrain Bolivia Oman Bosnia and Herzegovina Saudi Arabia Hungary Armenia Czech Republic Barbados Algeria Colombia Malta Poland Serbia Cyprus Russian Federation Djibouti Equatorial Guinea Croatia Belarus<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

Note: The cumulative severity ratio is the ratio of COVID-19 deaths to all-cause deaths in 2016 base year averaged over the country-specific length of the pandemic thus far. Proportionate mortality ratio is proportion of cause-specific deaths to all-cause deaths in. Cut-off date for all data is May 23.

8
Figure 3

# Real Interest

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# Rates on Loans and Deposits

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**Table 7: Labor tax wedge for single earners with no children by level of average wage 2008**

||**Leve**<br>|**l of average wage**<br>||
|---|---|---|---|
||**33%**|**50%**|**100%**|
|Sweden|41.8%|44.6%|47.9%|
|**Hungary**|**39.5%**|**43.4%**|**54.1%**|
|**Romania**|**37.9%**|**39.9%**|**42.4%**|
|Bosnia – Federation(1)|37.8%|39.5%|41.8%|
|Serbia(1)|36.7%|38.0%|39.3%|
|Germany|36.3%|43.0%|51.5%|
|Belgium|36.0%|48.5%|55.7%|
|**Lithuania**|**36.0%**|**38.9%**|**41.6%**|
|Finland|35.5%|38.0%|44.9%|
|**Czech Republic**|**35.2%**|**36.8%**|**43.5%**|
|**Bulgaria**|**35.1%**|**35.1%**|**35.1%**|
|**Poland**|**35.0%**|**37.4%**|**39.6%**|
|**Latvia**|**34.7%**|**38.2%**|**41.6%**|
|Greece|34.4%|34.4%|41.5%|
|**Estonia**|**34.0%**|**36.8%**|**39.5%**|
|Austria|33.5%|39.8%|48.5%|
|**Slovenia**|**32.9%**|**35.0%**|**42.9%**|
|France|32.5%|35.0%|49.3%|
|Netherlands|32.1%|37.5%|45.1%|
|Italy|31.5%|36.7%|45.8%|
|**Slovak Republic**|**31.4%**|**33.2%**|**38.8%**|
|Bosnia - Republika Srpska(1)|31.1%|32.8%|34.5%|
|Macedonia(1)|28.5%|30.9%|33.2%|
|Portugal|28.1%|30.3%|37.3%|
|Spain|28.0%|29.3%|38.0%|
|Norway|27.5%|31.1%|37.5%|
|Japan|26.0%|27.3%|29.5%|
|United States|22.6%|25.7%|30.1%|
|United Kingdom|19.9%|26.6%|32.8%|
|Switzerland|15.9%|26.9%|31.7%|
|Ireland|7.8%|14.0%|27.0%|

1. Values refer to 2009. _Source:_ OECD Tax and Benefit model.

19
Page 22 Source: Sanctioned Development Plan 1965, Drawing Branch, Ahmedabad Municipal Corporation Interventionist planning policies continued in the subsequent plan for Ahmedabad sanctioned in 1976. But by the mid-1990s, it was clear that this approach had proved a failure. There was substantial illegal growth at the periphery, FSI and zoning regulations were widely ignored, and artificial scarcities created by these restrictions led to rapid price increases. At the periphery, infrastructure development moved slowly. The main tool for urban expansion, the Town Planning Schemes, a form of land readjustment, took years to negotiate and execute, and landowners gave up their lands without benefitting from infrastructure investments, oftentimes for ten to twenty years. Funds mobilized under the schemes, by the time they were negotiated and approved, were inadequate to build the necessary infrastructure.

In 2001, subsequent to the Bhuj earthquake, the Town Planning Schemes were revamped to move more quickly through modest shifts that had a large impact.

- The Revenue Department allowed AUDA to build the infrastructure as soon as the draft plan was approved, but before all the tenure issues in each plot were addressed.

- The share of land appropriated was increased modestly so that excess lands could be auctioned to cover infrastructure costs.

- Private Contractors could be hired to prepare the schemes, thus avoiding potential bottlenecks due to shortages of skilled government town planners.

Together these changes made dramatic improvements in the speed of execution. Between 2000 and 2009, Town Planning Schemes, vigorously pursued by AUDA and the AMC, developed over 200 sq km on the urban periphery vs. 100 sq km in the preceding 25 years.

Table IV.1: Town Planning Schemes in Ahmedabad

|**T P Schemes**|**AUDA**|**AMC**|
|---|---|---|
|Up to 1999|29|58|
|1999-2010|74|43|
|Total|103|101|
|Total Area Covered (sq km)|153|148|

Source: Urban Planning Cell, Ahmedabad Urban Development Authority, Ahmedabad Urban Planning Cell, Ahmedabad Municipal Corporation The Development Plan for 2001 also changed course. Over 100 sq. kms of land in the periphery were zoned for new residential growth. Rigid reservations, such as the green belt designation, were lifted. Appropriation of private land for public purposes was abandoned, replaced by more proactive use of the Town Planning Schemes.<sup>12</sup> 50 percent of reservations not yet acquired were de-reserved. The FSI limit in the city was also raised—albeit quite modestly.

> 12 Balleney, Shirley and Bimal Patel. 2009.  “Using the ‘Development Plan—Town Planning Scheme’ Mechanism to Appropriate Land and Build Urban Infrastructure” Chapter 24 in India Infrastructure Report 2009: Land: A Critical Input for Infrastructure. New Delhi: Oxford University Press
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# level of employment, are distinguished here:

|**Range of**<br>**employment**|**Number of plants**<br>in**sample**|
|---|---|
|Small<br>1-20|2346|
|Medium 21-100|2143|
|Large<br>101+|1310|

Research on industrial emissions must consider some subset of pollutants, because factories discharge hundreds of waslte products which are potentially harmful to human health or ecosystems. In this paper, we focus on emissions of airborne suspended particulates from the Mexico data base. Environmental scientists generally agree that air pollution from fine particles (those with diameters less than 10 microns) has the greatest impact on morbidity and mortality. Although fine particulate emissions are not yet commonly measured in developing countries, they are known to be highly correlated with total particulate emissions on which the present study focuses.

# **3.2 Brazil**

Although the Mexican data are very rich, no information on plant location is available. Thus it is not possible with these data to explore hypotheses relating to local levels of development. The Mexican data are therefore supplemented by a database of 156,000 factories in 5,000 Brazilian municipios, made available b! the Instituto Brasileiro de Geografia e Estatistica (LBGE). The factories in this database are categorized by 266 4-digit Classificacao Nacional de Atividades Economicas (CNAE) codes, employment size, and location. Of the 5000 municipios, 3455 report some industrial employment and these form the data base for the Brazilian analysis.

Information on total population for each municipio is drawn from the 1991 demographi **c** ensus. To separate Brazilian municipios into ten income classes, median wage of the head-of-household from the same source is used for each municipio. It should, however, be noted that the deciles are constructed from a ranking of municipios by this measure, regardless of population. As a result, the two highest deciles contain half the sample population. No wage data are available for 271 of the 7
margins. Simple averages of the average number of OECD suppliers by SITC4 product category are rising monotonically over time.<sup>4</sup> <!-- Start of picture text --> Figure 2<br>Average number of exporters to OECD, 1963-2006<br>Extra-OECD suppliers only 5<br>(a) Simple average (b) Import-weighted average<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>120<br>100<br>80<br>60<br>40<br>60<br>50<br>40<br>30<br>20<br><!-- End of picture text --> Note: a/ Simple averages of number of exporters to OECD at the product (SITC4) level. b/ Import-weighted averages (weights = shares of each SITC4 product in OECD imports in given year) Import-weighted averages are leveling out after 2000, but this is not very surprising. The numbers on the vertical axis show that on a trade-weighted basis, the average number of suppliers per product was over 100. For many products, this is likely to exhaust the pool of potential exporters, so a leveling off is to be expected.

Table 3 reports the results of pooled and fixed-effects regressions of the number of exporters to the OECD on time, its square, and a specific time trend for the post2000 period.

In the latter period, as expected from Figure 2, there is a decline in the rate of increase in the number of exporters to the OECD. This is reflected by the negative coefficient on Post 2000. This inflexion is however not strong enough to reverse the

> 4 Bas and Strauss-Kahn (2010) provide evidence of this increased number of exporters serving the OECD market. Focusing on French firms, they found that the number of imported inputs varieties from developing countries increase in average by 48% over the 1995-2005 period.

> 5 Figures including all suppliers are very similar to the one presented here and are available upon request.

9
Table A.13: Savings: Using median dowry to proxy for expected dowry

|**Dependent variable:**||**Per Capita **|**Household **|**Saving in **|**2007**||
|---|---|---|---|---|---|---|
||Saving in fn|ancial institutions|Plus cas|h saving|Plus inter|est earned|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Expected dowry_|507.76**|524.60**|500.14**|488.13*|627.44**|648.63|
||[174.49]|[240.32]|[188.95]|[264.50]|[266.96]|[393.25]|
|_Expected dowry_|42.75||-229.43||-218.03||
||[217.73]||[258.55]||[271.38]||
|_Firstborn girl_|-152.45|270.08|-869.37|-460.25|-1,666.66|-1,420.38|
||[1,500.31]|[2,914.30]|[1,520.13]|[2,909.32]|[1,836.77]|[3,376.16]|
|N|||2,840||||
|**X**_i_|x|x|x|x|x|x|
|Caste FE|x|x|x|x|x|x|
|YOB FE|x|x|x|x|x|x|
|State FE|x|x|x|x|x|x|
|State*YOB FE|x|x|x|x|x|x|
|Caste*YOB FE|x|x|x|x|x|x|
|Caste*State FE|x|x|x|x|x|x|
|Firstborn girl*YOB FE|x|x|x|x|x|x|
|Firstborn girl*State FE|x|x|x|x|x|x|
|Firstborn girl*Caste FE|x|x|x|x|x|x|
|Caste*State*YOB FE||x||x||x|

NOTES: This table reports the coefficients corresponding to specifications (1) and (2) estimated for households where all children have the same mother. Each column is a separate regression. The dependent variable is per capita household saving in financial institutions in columns (1) and (2), plus per capita cash saving in columns (3) and (4), and plus per capita interest earning in the last two columns. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the _median_ net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Caste refers to indicators for SC, ST, OBC, and upper castes. YOB refers to the year of birth of the firstborn child. State refers to the state of residence at the time of survey. **X** _i_ controls for parents’ age, schooling, religion, and month of survey. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.