rafmacalaba/data-use-mentions-extended huggingface dataset:
or the long-term trend in the location the households now resides? After data cleaning to remove tracked and non-agricultural households, we are left with 3,250 household observations across three survey waves.

The LSMS-ISA data from Niger includes two waves, the first from 2011 and the second from 2014 (NIS, 2014; NIS, 2016). The sample is representative at the national and urban/rural-level. Data cleaning and removal of non-agricultural households gives us 3,913 household observations across two survey waves.

In Nigeria, we use the data from the 2010/11, 2012/13, and 2015/16 rounds of the General Household Survey - Panel, which is representative at the national and urban/rural-level (NBS, 2012; NBS, 2014; NBS, 2019). Data cleaning and removal of non-agricultural households yields 8,384 household observations across three survey waves.

In Tanzania, the data stem from the 2008/09, 2010/11, and 2012/13 rounds of the Tanzania National Panel Survey (TZNPS) (TNBS, 2011; TNBS, 2012; TNBS, 2015). Similar to Malawi, the LSMS-ISA from Tanzania sought to track households the split and moved locations. As a result of this, the sample size expands with each wave. Focusing on rural, crop producing households that do not move, we have 5,669 household observations across three survey waves.

In Uganda, we use the data from the 2009/10, 2010/11, and 2011/12 rounds of the Uganda National Panel Survey (UNPS) (UBOS, 2014a; UBOS, 2014b; UBOS, 2016). As with the other LSMS-ISA data, the Uganda sample was designed to be representative at the national-, urban/ruraland regional-level. We include 5,250 household observations after cleaning and removing nonagricultural households.

For the analysis, we combine data from the six countries and all waves to generate a single cross-country panel data set containing 33,738 household observations. For estimation, we
Burgiss provides aggregated company level PE capital data invested at the industry level based on the Industry Classification Benchmark (ICB). So, a typical unit of observation would be the US$ amount of buyout capital invested in India in the technology sector in 2015. We use both industrylevel and aggregated investment data at the country-level in our study. It is important to note that this is the first data set which has actual dollar amounts of buyout capital invested at this level of detail globally.

Most of our other country-level data are obtained from the World Bank’s Development Indicators; the institutional quality variables come from World Bank’s Worldwide Governance Indicators and the reforms data come from World Bank’s Doing Business Data. These data and the buyout investment data are matched at the country level using country codes. The industrylevel growth data are obtained from DataStream and are matched to Burgiss data using industry codes from Industry Classification Benchmark. After matching data from all the different sources, we have a panel of 61 countries across 19 industries over 29 years. Variable definitions and data sources are provided in Appendix Table A1.

Table 3 presents descriptive statistics on the main variables used in the study. The average amount of PE investment in the sample is $805 million per country-year and $42 million per country-industry-year.<sup>12</sup> As these raw investment figures are hard to compare across countries of different sizes, we measure private equity investments as a percentage of GDP in the analysis. On average, new buyout investments are 0.036% of GDP at the country-level and 0.002% of GDP at the industry-level. If we exclude country- (industry-) years with zero investment, the average buyout to GDP measure goes up to 0.07% (0.01%) at the country- (industry-) level. To compare

> 12 These averages are in 2017 dollars.

12
e<br>|mploye<br>|d populatio<br> <br>|n|||
||1996|1999|2004|2007|2008|<br>2009||2010|2011|
|Male|0.27|0.29|0.89|0.51|0.11|<br>0.99||0.73|0.46|
|Female|0.99|0.28|2.07|1.4|0.37|<br>1.32||1.3|1.08|
|Agriculture|0.5|0.35|4.42|2.92|0.68|<br>2.35||2.44|2.12|
|T&G|1.91|0.18|0.34|0.55|0.48|<br>0.87||0.78|0.89|
|All sectors|0.54|0.29|1.38|0.87|0.21|<br>1.13||0.97|0.74|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011. _Note:_ T&G = textiles and garments.

Table 13 shows that on average in Sri Lanka a smaller percentage of people were working more than 40 hours a week than in Cambodia—47 and 76 percent in 2008, respectively. In Cambodia this share remained roughly the same from 2008 to 2012, while in Sri Lanka it went up to 59 percent by 2012. There was a big difference between the share of men and women working overtime in Sri Lanka—67 and 44 percent in 2012, respectively. Similar to Cambodia, people who worked overtime worked on average 55 hours a week
yet—any evidence that SC political reservations affected the meaning of being low caste for most Indians. The evidence so far available shows that the presence of a reserved SC _pradhan_ induced a backlash from the high castes in North India. Priyanka Pandey (2005) visited public schools in North India at random times. In villages with an SC-reserved _pradhan,_ she found that high-caste public school teachers stole more of the scholarship money from SC students and were more often absent. The mean level of student achievement decreased. A possible interpretation is that the law triggered a backlash by the high-caste teachers—they tried to put SCs “in their place.” A question on India’s Rural Economic and Development Survey of 2006 is: “Have you, or any member of your family, been prevented from entering any street within the village because of your caste now?” Victoire Girard (2018) studied the impact of five years of SC reservation on discrimination, as measured by the responses to this question. In 2006, 45 percent of all SC respondents said Yes to this question. The survey results show that during their tenure as village leaders, SC reservations reduced caste-based discrimination against SCs by 10 percentage points. However, the effect did not persist after the SC reservation ended.

SCs in India are deeply stigmatized. For many centuries and even in many villages today, the isolation between SCs and upper castes has been extreme. Starting from an extreme position, it is perhaps not surprising that, at least based on the information we have now, political reservations for SCs did not create meaningful change in the cultural category SC. We have argued that two kinds of measures (two “punches”) may be needed for law to bring about social change. But there is no assurance that a second punch, by providing new kinds of exemplars, 28
with rules relating to transparency and temporariness of measures. One reason why we have stressed that the data set distinguished between open-ended and time-bound measures is that such information is needed to assess performance considering international disciplines. The data set permits analysis of compliance with – and the effects of – applicable trade agreements. An example of such research is Hoekman et al. (2020), who investigate the relationship between the use of trade policy observed in the first 6 months of the pandemic and characteristics of national public procurement regimes, including membership of the WTO Agreement on Government Procurement and deep trade agreements that include provisions on public procurement. They find statistically strong associations between procurement regimes and use of trade policy.

A related research question concerns the potential impact of cooperation aimed at enhancing transparency. Several observers have pointed to the example of the Agricultural Monitoring and Information System (AMIS) as a possible explanation for the limited recourse to export restrictions on food – and a potential model for cooperation to generate information on production capacity and trends in markets for critical medical supplies (Hoekman, Fiorini and Yildirim, 2020). Does the way AMIS was designed and executed—maybe in terms of crops covered and information collected and made available—account for the smaller number of export controls observed in the food and agri-food sector? Here it would be important to understand where this transparency mechanism has teeth and where it shows up in the data.

# **5. Concluding remarks**

This paper presents new, high-frequency data on trade policy changes in two sectors that are critical during the COVID-19 pandemic: medical goods and medicines, and agricultural and food products. The data were collected on a weekly basis, and span the period from January 2 to mid-October 2020.<sup>16</sup> The data record the jurisdiction implementing the policy change, the direction of the measure (i.e. trade liberalizing or restrictive), the type of measure (e.g. export ban, tariff reduction), the timeline of the
As the information in Figures 6 and 7 collectively show, while Greater Accra dominates other regions in terms of the numbers of enterprises and workers (both informal and formal), its dominance is stronger with respect to workers than enterprises.

C. _Estimates of regional intersectoral productivity gaps_ Figure 8 plots average productivity per worker by formality status and by region (panel a) and the gap in average productivity per worker between the informal and formal sectors by region

> 8 Phases I and II of the IBES surveyed 2,831 and 572 farm establishments, respectively (or 0.4 percent or 2.4 percent of all establishments surveyed under those phases, respectively).

> 9 See Figure A1 for the share of enterprises that is informal in each region and at the national level.

> 10 The pattern also holds on a per capita basis when we account for regional population numbers based on the 2010 Ghana Population and Household Census (GSS 2012).

> 11 See Figure A1 for the share of informal employment in each region and at the national level.

9
is needed to test whether existing findings from developed countries replicate in developing countries.

Fourth, some studies provide evidence on potential unintended consequences of gender progressive legislation in developing economies, especially in the _getting married_ , _having children_ and _managing assets_ categories. This suggests that caution should be exercised regarding “piece-meal” reform attempts to tackle gender discrimination, and the focus should be more on holistic approaches to address this deep-rooted issue.

The paper is organized as follows. Section 2 reviews the existing literature on the existence and impact of gender discriminatory laws on women’s outcomes across the different WBL categories. Section 3 discusses the various theories relating women’s legal rights to their outcomes. Section 4 concludes.

# **2. Literature Review**

## **2.1 Going places**

Restrictions on female mobility are often observed in many developing countries (Jayachandran 2015). Commonly cited reasons for such restrictions include concern for women’s safety and “female purity” issues linked to their family honor (Chen 1995).

Existing evidence points to a negative association between laws restricting female mobility and female outcomes. These restrictions mostly relate to choosing where to live and traveling outside home. Using data from 98 developing countries, Demirguc-Kunt et al. (2013) find that in countries where women face legal restrictions in their ability to choose where to live, they are less likely to own an account, save and borrow relative to men. Using data for approximately 60,000 firms across 94 economies, Islam et al. (2018) find that travel restrictions on women is negative correlated with female business ownership. Htun et al. (2018) find similar cross-country evidence for female labor force participation as well. HallwardDriemeier and Gajigo (2015) find that in Ethiopia, reforms that removed restrictions on female work outside home increased the proportion of women working in occupations involving work outside home, paid work and with higher educational requirements.

3
# **Appendix 5.  Comparison of Mobile Phone Ownership in November 2017 WFP Survey to 2014 Household Budget Survey**

||2014 Hou|sehold Budget|Survey|Novemb|er 2017 WFP Sur|vey|CI<br>Overlap|
|---|---|---|---|---|---|---|---|
|Region|_________<br>Lower<br>Bound-<br>95% CI|___________<br>Upper<br>Bound-<br>95% CI|_______<br>Mean|__________<br>Lower<br>Bound- 95%<br>CI|_____________<br>Upper<br>Bound- 95%<br>CI|_____<br>Mean|_______|
|National|2.17|2.72|2.45|2.08|2.44|2.26|Y|
|Abyan|1.43|2.02|1.72|2.03|2.73|2.38|N|
|Aden|2.58|3.03|2.81|2.14|2.91|2.52|Y|
|Al_Bayda|2.40|2.95|2.67|1.70|2.22|1.96|N|
|Al_Hudayda|1.73|2.15|1.94|1.60|2.10|1.85|Y|
|Al_Jawf|2.00|3.13|2.56|2.21|3.00|2.60|Y|
|Al_Mahrah|2.44|3.72|3.08|2.38|3.98|3.18|Y|
|Al_Mahweet|1.66|2.38|2.02|
- **Coordination** issues, including wait-and-see behavior relative to others in the network, and either weak or lack of interoperability and equal access across markets (linked to standards and regulation).

- **Trust** issues, linked to possible shortcomings at the government legal and regulatory levels (e.g., lack of appropriate liability laws, online consumer protection, e-signature, personal data protection, cybersecurity), and at the platform operator level (e.g., third party certifications, simple dispute resolution procedures).

# **3. Data**

This section presents the data and main patterns of use of DTs by microenterprises. The first subsection presents general characteristics of the data. The second sub-section presents differential uses of DTs by microenterprises owned by men and women, disaggregated further by younger versus older owners.

# **3.1 General characteristics**

The Research ICT Africa (RIA) After Access business survey is focused on micro-size enterprises. The RIA survey was implemented during 2017-18. The data set analyzed in this paper includes 3,325 observations covering seven Sub-Saharan African countries, namely Ghana, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Tanzania.<sup>6</sup> The RIA survey also covers Rwanda and Uganda, but sufficient missing data on variables of interest have precluded its inclusion in this paper.<sup>7</sup> There is considerable heterogeneity across enterprises according to their sector(s) of operation (Table 1).<sup>8</sup> Almost two-thirds (63 percent) of surveyed microenterprises are active in trading activities and almost one-quarter (24 percent) are active in other services.<sup>9</sup> Almost one in ten firms (9 percent) is active in producing agricultural products,<sup>10</sup> while less than one in twenty (4 percent) is manufacturing products.

Formal enterprises are defined as legally registered either at the national
existing PSNP beneficiaries. In particular, the PSNP successfully scaled up during the Horn of Africa drought in 2011, supporting an additional 3.1 m beneficiaries for 3 months, and extending the duration of transfers for 6.5<sup>11</sup> million of the existing 7.6 million beneficiaries<sup>12</sup> . The PSNP’s response to the drought occurred within two months, contrasting with a time lag between the availability of early warning information and a response based on HRD appeals of up to eight months<sup>13</sup> . The PSNP’s response to the 2011 drought was widely credited with preventing the worst impacts of the drought, leading to comparatively less severe drought impacts within Ethiopia relative to its neighboring countries<sup>14</sup> .

Due to the high frequency of droughts in Ethiopia and concomitantly large number of food insecure households, drought response also relies on the issuance of humanitarian appeals to cover those needs that are not met through the PSNP. The Humanitarian Requirements Document (HRD) is developed by the GoE on the basis of bi-annual needs assessments following the main harvest seasons _meher_ and _belg_<sup>_15_</sup> _._ The Government regularly issues emergency appeals requesting emergency assistance to secure the consumption needs of vulnerable people due to the continued negative effects of the failure of _meher_ and _belg_ rains. PSNP contingency budgets are typically already used earlier in the year to provide a rapid response. While Ethiopia’s 2016 US$1.4 billion appeal has received over US$758 million from the Ethiopian government and the international community, leading to a coverage of the appeal by 54%, significant life-saving gaps remain across all sectors<sup>16</sup> .

Responding to drought effectively has been a priority within the GoE, and has been advanced at the policy and institutional level as well as through the establishment of the current drought response framework. Progress is promoted, in particular, through
Figure A.6: Share of Firms Registering VAT Rebates for Consumers Paying by Card <!-- Start of picture text --> VAT rebates expanded<br>2013m1 2014m1 2015m1 2016m1<br>Time<br>Wholesale Retail<br>50<br>15<br>40<br>10<br>30<br>Retail<br>Wholesale 20<br>5<br>10<br>0 0<br><!-- End of picture text --> Notes: This figure shows that the VAT rebates were indeed implemented starting on August 2014, as stipulated by the Financial Inclusion Reform. The figure plots the percentage of firms registering VAT rebates for consumers paying by credit/debit card, as captured in the card transaction data. Wholesale firms are on the left y-axis and retail firms on the right y-axis. The share of firms receiving VAT rebates prior to the reform is not zero, as card purchases at hotels, restaurants and tourism businesses have been subject to a 9 ppt VAT rebate since 2006. These firms should not be part of the retail or wholesale sectors in the ISIC classification, but there is some measurement error in firms’ sector classifications. This figure is discussed in Section 3.2.

40
_3.1. Data sources_

The data that we use to apply the four approaches to delineating metro areas to Indonesia come from a variety of sources. For Duranton’s algorithm, we use data on O-D commuting flows between Indonesian districts that we derive from the August rounds of Indonesia’s national labor

> 10 See Doll (2008) for a description of the underlying causes of the overglow problem with DMSP-OLS NTL data.

> 11 This product is available for download from: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html.

6
139|
|**Uruguay**|UY|ENIGH|2005|9079|7043|77|

This table lists alphabetically the 31 countries in the core sample, the survey names and years. GDP per capita is in PPP USD in the year of the survey, obtained from the World Bank Development Indicators. Code refers to the country acronym which we use in the figures. The sample size refers to the number of households in the survey, and the number of items reported is the number of expenditure items reported on average across all households in the survey.

46
CV(%)**|20.70|23.99|19.57|23.28|
|**Quantile 10%**|160.2|160.2|159.8|160.3|
|**Quantile 90%**|258.8|269.2|251.6|272.3|
|**India demand**|||||
|**Mean**|85.77|86.39|85.41|86.33|
|**CV(%)**|3.98|3.11|2.58|0.84|
|**Mean Indiaproduction**|87.13|86.66|87.37|86.62|
|**Mean India storage **|||||
|**Private**|0.10|0.02|0.00|0.00|
|**Public**|||4.21|10.38|
|**Mean RoWprivate storage **|4.05|4.78|3.69|4.75|
|**Mean exports**|1.38|0.27|1.96|0.18|
|**Mean imports**|0.02|0.00|0.01|0.00|

The policies have little effect on India’s wheat production. Since producers are assumed to be risk neutral in their behavior and their marginal cost linear in the production level, their production decisions depend not on price volatility but on the expectations of prices times the yield shock (equation (2)), which vary little between the policies considered, as evidenced by the mean price changes.<sup>11</sup> Under all scenarios, the model allows for the possibility of competitive domestic storage in India. In
# **A**

as in the Community Innovation Survey, outlined below.

- Product Innovation A product innovation is the introduction of a good or service that is new or significantly improved with respect to its characteristics or intended uses. This includes significant improvements in technical specifications, components and materials, incorporated software, user friendliness or other functional characteristics. Product innovations can utilize new knowledge or technologies, or can be based on new uses or combinations of existing knowledge or technologies.

- proved production or delivery method. This includes significant changes in techniques, equipment and/or software. Process innovations can be intended to decrease unit costs of production or delivery, to increase quality, or to produce or deliver new or significantly improved products. In the questionnaire, process innovation is defined as one of the following:

1. Automate manual processes, partially or fully

2. Adapt a technology or method previously used by this establishment?

3. Introduce a new technology or method 4.

- Organizational Innovation

1. Outsourcing tasks & changes in relations

2. Changes in management structures, integrating departments/units 3. Acquiring management systems for information & knowledge

# **B**

We product and process innovations based on their descriptions provided in the questionnaire in order to correct for missclassification. In some cases there was not enough information to validate an innovation, and in other cases innovations were misidentified between product and process innovations or process and marketing innovations. Below are some examples of how the re-classification was implemented.

## 1. Delivery

- improved delivery process (additional, superior vehicles) = process innovation

- introduce delivery as new offering (not core business) = product innovation

- introduce delivery -direct sales (same core business) = marketing

37
food insecurity and trust-related behavior. Consequently, the estimates may be biased and inconsistent. Following recent research (Ruyssen and Salomone, 2018; Smith and Floro, 2020), we address potential endogeneity issues using a matched sample of food secure and food insecure respondents with identical variable distributions. Entropy matching methods enable us to compare individuals such that, after matching, the only difference between the two subsamples is their food insecurity status. Given the strong correlation between observable and unobservable characteristics, matching on observable characteristics implies at least some matching on unobservable characteristics (Stuart et al., 2010; Ferraro and Miranda, 2014; Ruyssen and Salomone, 2018). Matching produces an unbiased measure of the influence of food insecurity on trust if the entropy algorithm captures all relevant differences between individuals who are food insecure and those who are food secure (see Appendix A for more details).

# **_2.2. Data_**

The data for the study draws from the 2014-17 waves of the Gallup World Poll, including FAO’s FIES. The GWP collects information on individuals’ labor force participation, income, educational attainment, future aspirations, subjective well-being, demographic characteristics, and countryidentifiers. In most countries, the GWP interviews 1,000 individuals and is nationally representative. Researchers use a random route procedure to select sample households within each country and select the respondent randomly within each household using a Kish grid method (Gallup, 2016). Observations for respondents without valid food insecurity responses or who failed to provide valid information on one or more questions used to construct the control variables were dropped from the sample. The final sample is 387,385 individuals aged 15 years and older in 134 countries.

# **_2.3. Measures of Vertical and Horizontal Trust_**

Trust is defined as holding a positive perception about the actions of an individual or an organization (OECD, 2013). Generally, trust has two components: 1) vertical (political) trust, citizens’ faith in government and its institutions