rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure** **<u>3.3 Rural Nonfarm Business Constraint, Male vs. Female Managers</u>**

<!-- Start of picture text --> male<br>Licenses and permits female<br>Financing Exports and Imports<br>Electricity Court<br>Market Tax system<br>Labor issue Postal/Delivery Services<br>Security/Safety Land-use policy<br>Transportation Business services<br>Technology and IT Telecommunication<br>Water Corruption<br><!-- End of picture text --> _Source:_ Yemen RIC Survey 2010

# **3.2 Assessment by RIC Indicators**

The constraints that entrepreneurs perceive may be largely or entirely subjective. RIC indicators are compiled to assess each component of the investment climate more objectively. Most of the main obstacles that are assessed using RIC indicators are not perceived by rural entrepreneurs themselves. Business and agricultural extension services, improvements and innovations in management, technology, labor, and telecommunications for instance were identified as major obstacles using RIC indicators, while they were not listed as important obstacles by entrepreneurs (figure 3.4). Owing to the prevailing informality of rural business in Yemen, government-related constraints such as those associated with licensing, and tax rates and administration were likewise not perceived as major constraints by rural entrepreneurs. Yet the results compiled through application of RIC indicators suggested that government-related issues such as corruption, permits, regulations, tax rates and administration, and land policy (among others) represented relatively major constraints and an important area for improvement. Because rural entrepreneurs are expected to improve their performance and become taxpayers in the near future, the government would be well-advised to play a more active and constructive role in assisting them.

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maximum at a certain level of income and decreasing from then on. The existence of this relationship as pointed by Cole (2003) has been criticized on grounds of the endogeneity problems afflicting some estimations, the unexplored impact of trade openness, the restriction of some samples to OECD countries, non stationarity of some series and heteroscedasticity on cross-section data. Cole (2003) takes into accounts these considerations and he still finds the existence of U-inverted Kuznets curve for some pollutants. However, Cole’s approach has been strongly criticized by Stern (2004) subtracting validity to his conclusions. Moreover, there is important evidence mostly based on time series that rejects the existence of the Kuznets curve.<sup>16</sup> In our approach we are sympathetic of Stern (2004) and Copeland and Taylor (2004) skepticism about the existence an environmental Kuznets curve, however if there is Kuznets curve its effect should be captured by the regional interacted dummies we use, given that this dummies are highly correlated with levels of income.<sup>17</sup> Beside these interacted terms our analyses encompasses the study of the impact of the productive structure on the environmental outcomes, in particular those of agricultural and non agricultural sectors.

The data on sectorial output, C02 emissions, and water withdrawals were obtained from the World Development Indicators 2004. The forests areas were obtained from FAO. The data on air pollution covers the 1970-2000 period, the fresh water data is from the year 2000, and the forest coverage data is from two years, namely 1990 and 2000.<sup>18</sup> Table 5 (a) reports results from country fixed-effects (FE) estimations of sectorial determinants of CO2 emissions, thus assuming that sectorial outputs are exogenous to air pollution. The probability values of the F-test of the significance of the coefficients associated with LAC for the five economic sectors are reported at the bottom of the table. Regression (1)

> 16 See Stern (2004) for a review of the literature
variable e.g., country GDP (Lee 1993; Rodrik, Subramanian, and Trebbi 2004), growth rates (Kohli and Singh 1989; Levine and Renelt 1992), or various measures of GDP-inducing productivity gains (Bodman 1996; Kunst and Marin 1989; Marin 1992; Nishimizu and Robinson 1984). Trade economics dating back to Ricardo (1817) imply that, in the case of increasing supply and/or demand, given certain circumstances, a country would export and/or import more with its trading partners. This study’s IV Strategy intuits that the increasing GDP of a country’s trading partner can influence that country’s GDP through trade. Our study tests this hypothesis by moving GDP to the right side of the equation, that is, a country’s trading partners’ GDP becomes an exogenous instrument to our “dependent variable country’s” GDP. The main assumption is that the growth of trade partners is external to domestic growth. However, as an instrument “trading partner GDP” may not fully satisfy the exclusion restriction given the potential for investment capital flows

> 25 Air freight is the volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled. Data are obtained from the World Bank World Development Indicators. 26 Fixed effects are essential to isolate growth determinants related to relatively constant country characteristics, such as climate or institutional quality.  Such effects may affect growth but are unresponsive to changes in trade.  They minimize omitted variable bias by eliminating the relevance of the non-trade channels whose effects on GDP are successfully restricted to time varying bilateral relationships.

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The Statistical Capacity Building Indicator (established by the World Bank in 2004) increased by about 2 percent between 2004 to 2017 (World Bank 2020b). The Open Data Inventory Index, which assesses the coverage and openness of official statistics, increased by 9.4 points between 2016 and 2020 (ODW 2020). Total funding commitments to statistics more than tripled between 2014 and 2017, rising from $214 million (0.14 percent official development assistance) to $689 million (0.34%) (PARIS21 2019). There is also some anecdotal evidence that domestic resources in low-income countries increased in recent years (GPSDD 2019).

5
We present four main findings. First, in contrast with the literature, we find that adolescent girls and boys in Uganda compete at similar levels. Second, we also do not find evidence that the empowerment effects of the program on girls were accompanied by increased competitiveness. Third, in line with previous evidence we document that having sisters, relative to brothers, is associated with a lower willingness to compete for boys in control communities. Fourth, strikingly, this pattern is reversed in treatment communities. There, we find that boys surrounded by (empowered) sisters are more willing to engage in competitive behavior.

The paper is organized as follows. Section 2 outlines the program and its impacts in order to sketch some of the theoretical background motivating the analysis. Section 3 describes the data and the lab‐in‐ the field experimental design. Section 4 presents the results. Section 5 concludes.

# **2. Background and Theoretical Context**

The Empowerment and Livelihood for Adolescents (ELA) program in Uganda, implemented by the NGO BRAC, provides adolescent girls with training on ‘hard’ vocational skills to help them successfully transition into the labor market, and on ‘soft’ life skills to enable girls to make informed choices about sex, reproduction and marriage. The program is delivered from development adolescent clubs, which are open five afternoons per week after school, and are led by a local female mentor. Participation is voluntary for girls aged between 14 and 20. To date BRAC has started 1,505 clubs in Uganda which have reached 70,000 girls.

Bandiera _et al._ [2015] evaluate the ELA program in Uganda using a randomized control trial methodology. The study took place in the urban or semi‐urban regions of Kampala and Mukono, and the mostly rural region around Iganga and Jinja. The research design delivered 100 treatment and 50 control communities. Each treatment community hosted an ELA club. A baseline survey was administered just before the establishment of the clubs to about 6,000 adolescent
# **3. Data**

Data from the 2020 ENVIF-VCM was used to conduct the analysis. The ENVIF-VCM survey is carried out by the Undersecretary for Crime Prevention of the Ministry of the Interior and Public Safety and collects information on different forms of intrafamily violence such as psychological, physical, sexual, and economic violence, and on forms of violence experienced in other contexts, such as violence in public spaces, the workplace, and schools. It also gathers information on sexual crimes occurring during childhood and violence against older adults (women aged 66 and over). Additionally, the questionnaire includes questions about the sociodemographic characteristics of the women surveyed, the head of the household, and the current and/or former partner. Questions also elicit information on previous aggressive behavior of the current and/or former partner; economic dependence and the ability to dispose of one's own or the partner's financial resources; attitudes towards gender roles; and support from the extended family and participation in networks.

The survey is nationally and regionally representative of the female population aged 15 and over in urban areas. The 2020 ENVIF-VCM surveyed 7,735 women; 6,775 of this sample were aged 15 to 65 while 960 were over age 66.<sup>11</sup> Since this study focuses on violence inflicted by a partner or ex-partner during the past 12 months,<sup>**12**</sup> the data sample was restricted to the subgroup of women aged 15 to 65 who reported that they are or have been in a relationship. This subgroup corresponds to 89 percent of the sample of women in that age group. Nearly 20 percent of the women in this subsample are between the ages of 15 and 25, 65 percent are between 25 and 55, and 15 percent are between 55 and 65. Moreover, 6 percent are foreigners, 10 percent are indigenous women, 6 percent have a disability, 30 percent have a tertiary education, and 51 percent are employed.
OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

# **Table 6: Estimation of trade gravity regression: IV Results**

IV: VAT rate in the next potential destination

||(1)<br>Control 1|(2)<br>Control 2|(3)<br>Control 3|
|---|---|---|---|
|**Ln (value of trade)**||||
|Ln (Freight rate)|-3.904***|-3.677***|-3.936***|
||(1.160)|(0.888)|(1.169)|
|**Ln (Volume of trade)**||||
|Ln (Freight rate)|-4.445***|-4.295***|-4.479***|
||(1.142)|(0.884)|(1.152)|
|Kleibergen-Paap F statistic|15.65|28.81|15.79|
|Origin Fixed Effects|Yes|Yes|Yes|
|Destination Fixed Effects|Yes|Yes|Yes|
|No. Absorbed FEs|1080|1003|1080|
|No. OD pairs|23288|13678|23288|

Note: Dependent variables are in bold. Each cell represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations
the profession. Further, many employers reported that the skill set of the recently hired graduates had improved compared to those in 1990s, in particular for so-called soft skills.

Academy for Education Development (AED) carried out an employer survey in Egypt, with the objective of providing recommendations to the Ministry of Higher Education on how to improve the quality of Middle Technical Colleges graduates (AED, 2008). A sample of 240 companies was selected based on stratified sampling technique (by size: small, medium, and large). AED sent surveyors to companies for personal interviews. 92 companies fully completed the survey questionnaire (38% response rate). The survey found that the level of skills demanded by employers had increased, mainly because: (i) higher levels of technology, (ii) increasing competition, and (iii) increasing concerns about quality of products. The survey also indicated that companies prioritize ―soft skills‖ including personality (honesty, punctuality, etc) and what they labeled ―basic skills‖ (literacy, problem solving, management, etc). The AED‘s employer survey was especially helpful for our employer satisfaction survey to determine relevant questions of employer characteristics. Data availability on employers‘ characteristics enabled us to scrutinize the survey results disaggregated by employers‘ characteristics.

In addition to the survey instruments above, we refer to several papers applying different analytical tools on data from employer surveys. Paranto and Kelker (1999) analyzed employers‘ satisfaction with job skills of business college graduates in a regional university in the US. They examined which skills employers perceived important when hiring business graduates. 346 employers were identified for the survey by the university‘s placement office. They are mostly in rural areas in the upper Midwest of the US, and hired business graduates during the 1990-94 period. 136 employers responded (39% response rates). By using factor analysis, 18 variables (skills) were reduced to four major factors, namely specific skills, core skills, personal characteristics, and communication skills (
tition intensity across market segments).<sup>14</sup> We used the aggregate score but also investigated the contribution of each regulatory sub-index and indicator in more detail.

# **3.3 Control variables**

Control variables are grouped into two broad categories: variables related to the quality of connectivity and those related to the market size.

## **3.3.1 Quality and availability of connectivity**

We measure the quality of connectivity by the amount of middle and last-mile infrastructure using the number of Internet exchange points (IXPs) which are crucial for the storage and processing of digital data across various networks. Availability of connectivity is measured by the following indicators:

- The number of fixed telephony subscriptions per inhabitant, reflecting the potential availability of fixed broadband which generally rely on or follow existing fixed telephony networks. That variable serves as a control when investigating the price of the fixed BB.

- Percentage of population covered by a mobile broadband network (3G or above).

## **3.3.2 Market size variables**

Potential market size is proxied by several indicators, including:

- Population, expressed in logarithm.

- The average income per inhabitant, proxied by the logarithm of GDP per capita.

- Electricity access, proxied by the share of the population with access to electricity.

# **3.4 Descriptive statistics**

A simple cross-country correlation between changes in international Internet bandwidth and Internet prices is plotted in Figure 5. It suggests that countries with a fast increase in submarine cable capacity experience a fast drop in both fixed and mobile broadband prices.

> 14See the Methodology note of the ICT regulatory tracker: https://app.gen5.digital/tracker/about.

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and Kordufan were economically active in 2014. Compared to those living in Khartoum, women who live in Kordufan and Darfur are more likely than men to participate in the labor force. Living in rural areas increases the odds for women to be in the labor market and to be employed by 42 percent and 87 percent, respectively.<sup>6</sup> Yet despite the much lower participation rates, working-age women were about as likely as men to be unemployed. Conversely, youth labor force participation rates increased by varying degrees in all regions and in both urban and rural areas. For youth in rural areas, the odds to be employed and to be in the labor force increased by nearly 80 percent and by 32 percent, respectively. The labor force participation rate increased by 12 percentage points in the Northern region and only by 1.3 percentage points in Kordufan (Figure 4).

**Figure 4. Labor force participation rate by locality and region**

<!-- Start of picture text --> (a) Youth labor force participation (b) Women labor force participation<br>60% 70%<br>50% 60%<br>40% 50%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>0%<br>by rural/urban by region<br>by rural/urban by region<br>2009 2014<br>2009 2014<br>Sudan Rural Urban Northern Eastern Khartoum Central Kordofan Darfur Sudan Rural Urban Eastern Central Darfur<br>Northern Khartoum Kordofan<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

Many women do not even enter the labor force: women generally, and female youth in particular, represent the highest share of the population out
it would imply that the purpose of the general question is to sum up. In this case assimilation is more likely than contrast effects.

Context or anchoring effects have been uncovered in the larger behavioral and experimental literature. The early work by Kahneman and Tversky (1981) reported that support of a policy can vary if it was presented as “lives lost” versus “lives gained.” Ariely et al., (2003) show that social security numbers of individuals create substantial anchoring effects on their willingness to pay for products that has no relation to their social security number. The effects of an uninformative anchor on a subject’s willingness to pay was also confirmed by Bergman et al., (2010). For a literature review of the robustness of anchoring effects across studies, see Furnham and Boo (2011). In contrast, studies have uncovered no influence of control questions in experimental games (Roux and Thoni, 2015). Using data on self-employed households in coastal India, Gine et al., (2014) find that the order of recall variables in the survey has no effect on self-reported earnings. Furthermore, some studies have indicated that a number of factors can weaken anchors. Implausible anchors may weaken anchoring effects (Sudgen et al., 2013). Anchoring effects may also be minimized by opposing anchors (Sitzia and Zizzo, 2011). Finally, there is some indication that anchoring effects are weaker than those reported in early studies (Maniadis et al., 2014).

To elucidate the likely channels that may influence the stability of perceptions, it is important to delve into the general structure of the WBES questionnaire. The WBES questionnaire evaluates 15 elements of the business environment (list provided in next section). Typically, the perception-based ratings of these elements appear at the end of each section. For instance, after exploring the incidence, frequency and losses due to crime experienced by firms, respondents are asked to rate to what extent crime is an obstacle to operations. Note that for
Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition.

# 3.2 Which firms benefit?

Examining the heterogeneity of the spillover effects can provide us with some additional insights into the mechanisms behind our previous results and some related policy considerations. We explore this avenue in three ways. First, we study the difference between these relationships for manufacturing and services firms (Table 5). We find evidence of a positive relationship between productivity growth and intra‐industry HGF presence for both groups of firms. Vertical linkages, however, are more important in manufacturing, where all three spillover variables are positively associated with subsequent productivity, employment and sales growth. In services, it is still mainly intra‐industry linkages that matter, with a higher share of HGFs being associated with stronger subsequent productivity growth and higher profitability. These distinctions, however, may capture the different breadths of industries in manufacturing and services, rather than substantive differences between the two groups.

## **Table 5 Main results for Manufacturing and Services**

||**Labor prod**|**Productivity (**<br>**uctivity**|**3‐year change)**<br>**TFP**||
|---|---|---|---|---|
||**Manufacturing**|**Services**|**Manufacturing**|
unscheduled tariff revisions organized in the context of contract renegotiations have managed to do so effectively. This is well documented for Latin America (Estache, Foster and Wodon (2001) or Ugaz and Waddams-Price (2003)) and there is increasing evidence that this was also an issue in the context of reforms in Africa (Estache (2004)) and to some extent in Eastern Europe (Estache and Gassner (2004)). The adjustment adopted by many countries has been the decision to continue or scale up the subsidies to large segments of the population. This in turn feeds back to the fiscal impact of the reforms. This is a clear major problem. From a more social viewpoint, the major issue is that many of the subsidies are still not that progressive. As mentioned earlier, historically subsidies have tended to favor the middle and upper income classes simply because they were connected and the real poor were not. In the poorest regions of the world, including countries which have reformed, this continues to be the case. Increases in access rates hardly keep up with population growth rates.

The last promise of the reforms was the improvement in the governance structure of the sectors. This implied that countries would increase the independence of their regulators and that this, in turn, would allow a more effective regulation of the sector (i.e. it would speed up increases in access rates, reduce costs, improve cost recovery and quality) and reduce the corruption levels of the sector. On the first promise, there is cross sectoral information available. However, Table 2 shows that, for electricity and telecoms, many government have indeed created autonomous regulatory agencies, including governments which have, willingly or unwillingly, not opened up to private capital. In 53% and 60% of the countries, respectively for electricity distribution and telecoms, autonomous agencies have been created. There are 10 times as many countries with agencies in 2002 than there were in 1990. Of course, this does not mean that agencies are really independent