rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Introduction**

Gender inequality represents a significant barrier for human development, and consequently a hinder to the full economic growth and development of any country. Gender inequality entails a different degree of access to similar opportunities based on the single circumstance of gender, placing women in a disadvantageous situation. Its importance has been emphasized in recent years by including it as a key dimension in the context of the MDGs and SDGs. Therefore, a comprehensive assessment of gender disparities should come from several fronts, as suggested by the different measures that have been developed to evaluate the extent of it. For instance, the United Nations Gender Inequality Index (GII) measures gender inequalities present in three human development aspects, which include reproductive health, empowerment, and economic status. On the other hand, the Global Gender Gap Index analyzes gender inequalities in four different areas, which are health, education, economy, and politics. The advantages of achieving gender equality are numerous, however in this process it is of primary importance to identify the underlying factors and understand how they drive the disparities across genders in several fronts.

This paper focuses on the economic front of gender equality, as its primary focus is the gender wage gap in Georgia. In the global context, Georgia ranked 76 of 159 countries in the 2015 UN GII, placing it just above the lower half. However, in a more recent ranking, the country ranks 94 of 144 in the 2017 Global Gender Gap Index, placing it below the global average. In the context of the Eastern Europe and Central Asia region, the country ranks as the fifth lowest performer in terms of the Global Gender Gap Index. Of the four sub-indexes that compose the global index, Georgia performs best at economic participation and opportunity, ranking 75 globally. Nevertheless, similar to other countries in the region, Georgia performs worst at health and survival, ranking 124 of 144. The main underlying factor for the low health performance is the high sex ratio at birth of boys to girls
on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 25The weights from the SDID approach are used in all survey data-based analyses at the individual level.

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**|0.637***|0.650***|0.766***|
|flows from the United States|(0.112)|(0.107)|(0.063)|(0.044)|(0.144)|(0.102)|(0.118)|(0.105)|(0.120)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
271.4<br> <br>|3,462.8<br>|<br>7.84<br>|
|Cement<br>Bricks<br>|3,107.7<br>4,408.5<br>|13,194.5<br> <br>2,884.2<br>|23.55<br>152.85<br>|<br>313.9<br> <br>1,217.2<br>|1,332.9<br> <br>715.7<br>|23.55<br>170.07<br>|
|Iron and Steel<br>|18,079.<br>|141,222.<br>|5 12.80<br>|2,487.0<br> <br>|15,783.<br>|2 15.76<br>|
|Radio and TV Receivers|3,636.9<br>|11,902.2<br>|30.56<br>|<br>475.8<br>|1,542.5<br>|30.84<br>|
|Motor Vehicles|14,411.<br>-----------------------------|4 139,258.<br>-----------------------------|3 10.35<br>|2,279.4|21,465.|9 10.62|
|Total in our sample|101,404.|1 788,550.|7 12.86|15
services reported not being able to access such services at some point since the start of the pandemic, with the most cited reasons being mobility restriction and increased cost of public transportation during the lockdowns (Balogun et al., 2021). In a study of 18 lowand middle-income countries, Ahmed et al. (2021) find a correlation between monthly reductions in health service utilization and stringency of mobility restrictions, even controlling for the monthly COVID-19 burden. If lockdowns reduce coverage of life-saving health services, then we would be under-estimating the ratio between excess child deaths and COVID-19 mortality averted by a given lockdown. As for reduced demand for services out of fear of contracting the disease in health facilities, it may happen independent of whether lockdowns are imposed. In sum, it is not clear whether non-COVID-19 mortality due to reduced health service utilization (or reduced service quality) would be higher with lockdowns or with health system congestion.

**State capacity** In our framework, the lockdown is successfully implemented by the government. One may be worried that if a particular country does not have the state capacity to enforce a lockdown, then our quantification is of limited policy relevance to that country. This is a general critique of policy analysis in low-state-capacity environments. Nonetheless, our conclusions are still relevant for countries with sufficient state capacity to enforce lockdowns. Judging from the widespread use of lockdowns in countries across all levels of development, governments at least believed that their policies would have some effect. There are also journalistic accounts that low-income country governments deployed police to enforce lockdowns.<sup>8</sup>

The substantial GDP contractions in 2020 are suggestive evidence that lockdowns had consequences in even the poorest countries. While real GDP data are annual and thus will have imperfect overlap with lockdowns, more high-frequency data sources also point to large contractions in economic activity during lockdowns. Aminjonov et al. (2021) analyze mobility data from Android smartphones to document widespread mobility declines in every

> 8See, e.g., India: https
|---|---|
|Direct questioning method|Face-to-face (status quo)|ACASI|
|Indirect questioning method|List experiment||

## **2.1 More Privacy: Audio Computer-Assisted Self-Interview**

ACASI is a method of data collection where respondents listen to pre-recorded questions through headphones and respond by selecting their answers (for example, the corresponding colored square) on a touchscreen or keypad.<sup>7</sup> After demonstrating how to use the technology, enumerators stand a small distance from the respondent and ensure that privacy is maintained.

ACASI eliminates the embarrassment that respondents may feel about disclosing a sensitive answer face-to-face with an enumerator, potentially minimizing underreporting (Falb et al., 2016; Jarlais et al., 1999; Jaya, Hindin and Ahmed, 2008; Langhaug, Sherr and Cowan, 2010). If shame is a key driver of underreporting, then reporting under ACASI should be greater than face-to-face. ACASI may also reduce enumerator-associated variance by providing a more uniform approach to data collection.

> 5Tourangeau and Yan (2007) define sensitive questions as those a respondent deems intrusive; where they fear the threat of disclosure and the possible consequences of giving a truthful response; or where there are social norms that imply there are socially desirable and undesirable responses.

> 6Palermo, Bleck and Peterman (2014) analyzed data from 24 developing countries DHSs and found that only 7 percent of women who report experiencing IPV made a report to a formal source that would then be captured in administrative data.

> 7Figure A1 in the appendix shows ACASI being used.

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administrative data, type of economic activity, and the number of workers, which is the crucial element for our purpose.

For this analysis, we obtained establishment censuses from the statistical agencies of some selected Sub-Saharan African countries, namely Burkina Faso, Cameroon, Ghana, and Rwanda. The selection of these countries is primarily because their census datasets feature a number of characteristics that make them closer to the true size distribution. On the whole, these data are consistent with Lewis (1954) widely held view of developing economies as characterized by the coexistence of a few capital-intensive industries and a mass of inefficient businesses (”tiny islands of capitalist employment surrounded by a vast sea of subsistence workers”). Specifically, small establishments (fewer than ten workers) represent the overwhelming majority of the manufacturing sector in our datasets; for comparison, small businesses account for less than half the manufacturing establishments in

> 6 To fix ideas, idiosyncratic distortions are a class of distortions causing allocative inefficiency. Financial frictions are a specific distortion within this class. Similarly, business registration costs are a specific example within the class of entry barriers.

> 7 Alvarez and Ruane (2019) also consider idiosyncratic distortions alongside other specific frictions in the context of Mexico. In our model, however, these distortions are strongly increasing with the physical productivity of the firms, an elasticity which we estimate from the firm-level Ghanaian data. Moreover, the productivity elasticity of distortions interacts with the endogenous innovation decisions of firms, absent in their work. We show that endowing firms with an endogenous life-cycle constitutes an important mechanism through which distortions generate a missing middle in the distribution.

6
India is the second fastest growing economy in the world, but it also has a very large informal sector. In the manufacturing sector, nearly 99% of enterprises and 81% of employment are in the unorganized sector. In the services sector, the estimated size of the unorganized sector ranges from 74% to 90% of employment, depending on the definition used.. The Indian economy has undergone amazing structural transformation during the last two decades, but the share of the unorganized sector has remained high (Ghani et al., 2011a, b). The employment share in the unorganized sector for manufacturing in 2005 is almost exactly the same as it was in 1989 at 81%. So the unorganized sector is also exceptionally persistent. India provides a good case study to examine how urbanization and formalization have interacted and evolved over time? Are formal sectors moving out of urban areas? Is informal sector moving into cities? Do cities generate agglomeration economies in the informal sector? Has the spatial allocation of plants improved across urban and rural locations?

Ghani et al (2012) examine the trends in the spatial location of organized and unorganized enterprises in India. They combine the enterprise data from the Annual Survey of Industries for the organized (formal) sector and from the National Sample Survey for the unorganized (informal). The organized manufacturing sector is surveyed by the Central Statistical Organization every year through the Annual Survey of Industries (ASI), while unorganized manufacturing establishments are separately surveyed by the National Sample Survey Organization (NSSO) at approximately five-year intervals. These surveys identify for each establishment whether or not it is in an urban or rural location. Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The authors note: “On the whole, India’s manufacturing sector became more urbanized, with the share of workers in urban areas rising from 33% of employees in 1989 to 41% in 2005 (Figure 3a). Urbanization growth was most dramatic from 1989 to 1994, but slowed down
Maps (Figure 1b). The number of vehicles on the road at this time drops to close to 0; therefore, the measured decrease in speed may be a function of how Google calculates driving time in the absence of vehicles. To confirm the data, we compare the percent change in speeds from Google with the percent change in speeds from Uber. While the Uber and Google data are fully aligned for the daytime hours and the hours right before the curfew, there is a deviation in the percent change between Uber and the Google speed data for the curfew hours for the period where Uber is still reporting this statistic. Since we do not have data from Uber after March 2020, we do not consider the curfew hours when conducting any analysis related to speed (Appendix Figure A.1).

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out a troubled bank with the average cost of a bank bailout depending on the applied bailout instrument. Specifically, we predict “annual” fiscal contingent liabilities from bank bailouts using the financial statement data for each bank from October 2022 published by the National Bank of Kazakhstan (NBK). We follow these steps:

- a) Obtain the October 2022 bank-level financial statement data,

- b) Compute the likelihood of a bailout for each bank by type of bailout instrument using estimated equation (2).

- c) Compute the average bailout expenditure for each bank using estimated equation (3).

- d) Use the bank financial data and the predicted bailout probabilities and expenditures obtained in the b), and c) steps. The expected annual contingent liability per bank is obtained by multiplying the predictions from (2) and (3) for each bank, and adjusting for the historical frequency with which each bailout instrument was used (equity 30%, debt 26% and NPE purchase 44% of the time on average during the studied sample). We then

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Retrieved from <u>http://www.un.org/millenniumgoals</u>

United Nations. (2015). Transforming Our World: The 2030 Agenda for Sustainable Development. Retrieved from http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E Voluntary Organization for Social Action and Rural Development (VOSARD), Conservation of Natural Resources, Drinking Water Management, Jalanidhi Project, Video online at: <u>http://www.vosard.org/conservation%20of%20natural%20resources.html</u> World Bank 2013 A -“World Development Indicators”, Data, Rural population (% of total population), 2010-2014, online at: <u>http://data.worldbank.org/indicator/SP.RUR.TOTL.ZS</u> World Bank 2013 B- World Bank, India: Getting Water on Tap in Rural Kerala, August, 2013, Online at: <u>http://www.worldbank.org/en/news/feature/2013/08/12/india-getting-water-on-tap-in-ruralkerala</u>

- World Bank 2013 C- Project Performance Assessment Report, Republic of India, Kerala Rural Water Supply and Environmental Sanitation Project “Jalanidhi”, June 27, 2013, Independent Evaluation Group Public Sector.

- World Bank 2009- Implementation Completion and Results Report, Republic of India for the Kerala Rural Water Supply and Environmental Sanitation Project, March 9, 2009, South Asia Sustainable Development Unit, The World Bank Group.

- World Bank 2006 - Background Paper, Rural Water Supply and Sanitation, India Water Supply and Sanitation, Bridging the Gap between Infrastructure and Service, January 2006, the World Bank Group.

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5.7|13.4|4.4|
|memo. item||||||||
|GDP growth|-11.7|-7.3|-1.5|1.8|2.1|-10.9|-7.4|

_Source: Hassan and Bogetic (1996); World Bank (1997)._ Before discussing the revenue potential, it is necessary to outline the actual tax structure and the associated actual revenue performance. The 1992 personal income tax exemplified a steeply graduated tax-rate schedule (see Table 2). There were seven tax brackets including the lowest tax-free bracket. The exemption level for the poor was low, comprising individuals earning up to 9,000 Lev, or about 50 percent of average household per capita income as reported in the 1992 household budget survey (nearly 88 percent of the minimum annual wage). There was a progressive scale, with marginal tax rates ranging from 20 percent to a maximum of 40 percent at annual income over 240,000 Lev.
Table 1: Company and Job Descriptive Statistics (Percentage)

||||Deg<br>|ree<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Account.|Business|Comp. Sci.|Elec. Eng.|Mec. Eng.|Total|
|**Location**|||||||
|Kuala Lumpur|66.3|65.9|70.6|39.2|39.6|63.2|
|Rest of Malaysia|33.7|34.1|29.4|60.8|60.4|36.8|
|**Company Size**|||||||
|Up to 50 employees|44.1|46.5|45.8|39.2|33.7|44.5|
|51 employees or more|55.9|53.5|54.2|60.8|66.3|55.5|
|**Area of Job Specializa**|**tion**||||||
|Accounting/Finance|100.0|0.0|0.0|0.0|0.0|24.5|
|Admin/Human Res.|0.0|29.3|0.0|0.0|0.0|14.5|
|Information Tech.|0.0|0.0|89.6|0.0|0.0|11.8|
|Construc./Manufact.|0.0|0.0|0.0|2.7|12
**Box 2. Applying CDR-Based Poverty Models to Fill Data Gaps**

Much of the discussion surrounding using CDRs for socioeconomic research ignores the practical conditions in which they will be applied. CDR data are almost always available in addition to conventional data, such as household surveys or censuses. There is little to be gained in showing that CDR data can be used to predict this existing survey data; instead, the fitted model must be applied to new, unseen data to answer new questions.

There are at least three different ways in which CDR data can complement conventional data sources:

**1. Spatial infill: generating small-area statistics.** Given the relatively high spatial resolution of CDRs, spatial infill likely offers the most added value as a socioeconomic research method. A particular household survey with a limited sample size may only support estimates at a relatively coarse spatial resolution, such as the _departamento_ level. The high-resolution behavioral signals in CDRs can enhance the statistical strength of survey data, enabling more detailed and accurate estimates. Ideally, the CDR collection period should coincide with the period in which the survey was conducted. This approach entails the risk of ignoring the role that space and geography might play in the prediction of poverty rates.

**2. Time interpolation/extrapolation.** The potentially high frequency of CDRs can also complement conventional data sources. Household surveys are generally updated at 2-to-5-year intervals. CDRs can be used to update these estimates, providing officials with current information on policy-specific subjects. A predictive model can be constructed for a survey year using contemporaneous CDR data. This model can then be applied to more recent CDR data for which corresponding survey data are not available. There is a risk, however, that the relationship between CDR behavioral signals and the target variable, in this case the poverty rate, could change over time.

**3. Spatial extrapolation.** This is the most ambitious potential application of CDR data. In countries or regions in which no recent survey data are available
data from World Bank internal sources as follows (for the years 2003-2023): 4.9%, 5.1%, 5.5%, 11.2%, 7.8%, 10.3%, 12.2%, 8.5%, 9.0%, 9.0%, 10.6%, 10.7%, 8.1%, 7.4%, 7.6%, 7.1%, 5.1%, 3.7%, 4.0%, 4.4%, 4.4%.

~ 5 ~
53,110 lives over the 15-year period, at a financial cost

> 9 Calculations for 2016, based on the WDI database. This share has dropped significantly from 72.7 percent in 2010. 10 The policies considered are: (1) Increasing taxes on tobacco to reduce affordability (FCTC Art. 6), (2) Banning indoor smoking in public places (FCTC Art. 8), (3) WHO-FCTC recommended labeling and packing standards (FCTC Art. 11), and (4) Banning tobacco advertising, sponsorship and promotion (FCTC Art. 13).

> 11 The authors approximate the tobacco-related medical expenses by applying a Smoking Attributable Fraction (SAF) of 8 percent on the total health care expenditures in the country (NCDCPH et al. 2016).

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# **Conclusion and implications for policy**

Analyses of the political economy of development generally stop short of probing the organizational obstacles to collective action by citizens and, when they investigate them, tend to give short shrift to the role and characteristics of political parties. Policy makers encourage reforms that give the poor greater voice (community-based development, social funds, national elections, reserved seats for lower caste citizens or for women), but pay less attention to the ability of the disenfranchised to act collectively, or to be organized to act collectively, in support of their interests. Unobserved heterogeneity across communities or countries in the degree to which citizens can act collectively likely explains the weak evidence supportive of the development impact of community-led development strategies or competitive elections: simply enfranchising citizens at the community or national levels is insufficient to ensure that politicians have strong incentives to pursue citizens‟ interests.

The literature discusses a wide array of factors that can disrupt collective action by citizens, ranging from lack of information to social polarization. The analysis here argues that organizations are key to the solution of collective action problems and that one key type of organization is the political party. Organizations that support collective action should receive greater attention in these discussions. For example, ethnic appeals are often, and perhaps correctly, viewed as a precursor to ethnic conflict. However, political parties that solve the collective action problems of a society‟s ethnic groups could leave a society better off than if it had no political parties at all to mitigate collective action problems. Keefer (2010) uses Afrobarometer data to argue that political parties in Africa, though they rely on ethnic appeals to mobilize support, do not solve the collective action problems of ethnic groups. Instead, the sources of partisan preference suggest that citizens are unorganized and
|_4518_|

_Notes:_ This table presents results for the impact of trade on different sources of taxation, estimated using the IV. Across columns, the outcome is the revenue collected by each tax as a percentage of NDP: total taxes, then indirect taxes, corporate income tax, property and wealth taxes, personal income tax, and social security taxes. The regression coefficients for _Trade_ as well as the interaction with a dummy for high-income countries, _Trade ∗_ 1(High-income) are presented. The bottom row reports the coefficient for the linear combination of _Trade_ and the interaction term. Trade is measured as the sum of export and imports divided by GDP. We instrument for trade using the oil-price and the gravity-equation instruments from Egger, Nigai, and Strecker (2019). All estimates include country and year fixed effects and observations are weighted by net domestic product in constant 2019 USD at PPP. We use dummy variable controls for significantly interpolated revenue data (rare) or imputed factor share data (frequent) in all columns. * p<0.10 ** p<0.05 *** p<0.01. Standard errors in parentheses are clustered at the country level.

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