rafmacalaba/datause-encoder-data huggingface dataset:
working may not get paid, and in other cases, it is difficult to identify those with other source of income or new employment: some laid-off workers with income continue to receive compensation, while many without an income source do not receive payments.58 All these issues have to be addressed through a concerted, well coordinated effort across the government agencies (such as ministries of labor, personnel, finance, civil affairs, and State Development and Reform Commission), public/private sectors, and urban/rural areas. An urgent need is to increase the funding for the minimum living guarantee system. Although the fiscal budget for minimum living guarantee system reached nearly 11.5 billion yuan in 2002, it only accounted for tiny 0.1% of its total GDP or less than 0.5% of the total government fiscal expenditure (table 12). This is very low compared with developed countries. Figures of developed countries in mid 1990s show that, on average, they spent about 1-2% of GDP for their social relief system, about 10 times that of China. Considering the huge needs and massive unemployment in China, the governments at all levels have to substantially increase their funding for the social security system.

**Table 12.  Scale and Magnitude of the Urban Minimum Living Guarantee Fund (1996-** **<u>2002)</u>**

|Year|People<br>covered|Minimum|Living Guarante<br>million yuan)|e Fund (100|Share of<br>GDP (%)|Share of total<br>fiscal|
|---|---|---|---|---|---|---|
||(million)|Total|Central|Local||expenditure (%)|
||||government|government|||
|1996|0.85|3.0|0.0|3.0|0.004|0.04|
|1997|0.88|11.5|0.0|11.5|0.004|0.03|
|1998|1.84|12.0|0.0|12.0|0.015|0.11|
|1999|2.81|19.7|4.0|15.7|0.024|0.15|
|2000|3.82|29.6|8.0|21.6|0.034|0.19|
|2001|11.32|54.2|23.0|31.2|0.055|0.28|
|2002|19.30|114.7|46.0|68.7|0.112|0.52|

Source: China National Bureau of Statistics, _China Statistic Yearbook_ , 2002, 2003. Hong Dayong (2003), “Latest Development of Urban Minimum Living Guarantee System”, _China Social Development Research Report 2002_ , Publishing House of People’s University, p124.

Meanwhile, while the fund size is increased, the scope of the minimum living guarantee fund should gradually be expanded to cover the long-term rural migrant workers in the urban areas, who have become part of urban dwellers, and also the most vulnerable group in the cities. Once they are unemployed, their risk of falling into poverty is higher than the local urban dwellers. Table 13 shows the current composition of the minimum guarantee recipients, with no rural migrants covered at all.

**-** **<u>Table 13.  Composition of the Urban Minimum Living Guarantee Recipients (2001 2002)</u>**

||June20<br>|01<br>|June2<br>|002<br>|
|---|---|---|---|---|
|Category|Number (10,000)|Share (%)|Number (10,000)|Share (%)|
|On-post workers<br>a<br>|58|12.66|191|9.89|
|Xiagang workers<br>b|110|24.02|255|13.21|
|Off-post workers<br>c|0|0.00|442|22.89|
|Retired workers|26|5.68|93|4.82|

> 58 Cook, Sarah and S. Jolly, _Unemployment, Poverty and Gender in Urban China: Perceptions and Experiences of Laid Off Workers in Three Chinese Cities_ , Institute of Development Studies, Sussex, England, August 2000.

25
difference between the vocational training beneficiaries and those of the basic CCT package was hence that vocational training beneficiaries had participated in a number of meetings with other beneficiaries of the same package with the objective to select the courses they were going to take. They might also have had, off course, other expectations about future skills, about related future income and/or expectations about compensation for the time spend in training. The beneficiaries of the productive investment package, on the other hand, had received the largest amount of benefits: 2-3 months before being surveyed they had received $175 to invest in a nonagricultural activity.<sup>10</sup> In addition they had received technical assistance to select the activity and develop a business plan, help which they were still receiving during the follow-up survey. Given these insights and the enthusiasm observed about the productive investment package during qualitative fieldwork, we focus on whether social interactions with leaders that received the productive investment package affected investments and attitudes of other beneficiaries.

# _2.2. Attitudes about the future and social interactions in the context of the program_

The main economic activity of most of the beneficiaries of the program is the cultivation of corn and beans, mainly for subsistence purposes. With the frequent occurrence of droughts, this livelihood is quite precarious as harvests are often completely lost. Many households attempt to cope with these shocks through seasonal migration. Despite the frequent re-occurrence of weather shocks, few households seem to rely on ex-ante risk management strategies. Data from control communities show that 38 percent of households planned to invest more in agriculture, which - given that they reside in a drought-prone region - arguably increases their exposure to future shocks. And 20 percent of control households report that they would do nothing or only pray to God to prevent negative impacts of future shocks.

In qualitative interviews, informants often said that households do not really plan ahead and instead live from “day to day”. But they also testified that they had started to think more about the future because of the program, and in particular the productive investment package. Beneficiaries also described the importance of learning about other’s experiences in changing these attitudes.<sup>11</sup> This is interesting as the productive interventions of the program aimed at

> 10 The remaining $25 was to be paid on the next payment day (after survey completion).

> 11 A beneficiary, who received the productive investment package, noted: _“Before the program, I just thought about working in order to eat from day to day. Now I think about working in order to move forward through my business._

8
contactless payments up to 2020:12 (the control group).<sup>10</sup> To ensure that the merchants in my sample are actively in business and have not been directly affected by the measures, I require them to have positive card sales for 24 months (all months from 2019:01 to 2020:12) and to be an essential sector including supermarkets, groceries, bakeries, pharmacies, health, tobacco stores, and fuel. Finally, I exclude all online stores from my sample because the increase of contactless payment limit should only affect offline sales. After this step, I am left with 94,199 unique merchants in total during the two-year period. My data set covers 75,668 merchants in the treatment group and 18,531 merchants in the control group.

Although the COVID-19 crisis is exogenous to merchants and allows me to compare both groups using the difference-in-differences approach, merchants who accept contactless payments may not be directly comparable to those who do not. Indeed, merchants may decide when to accept the contactless payment technology, thereby making the acceptance of contactless payments a non-random experiment. To avoid possible bias, I use a score matching approach to compare merchants with similar pre-treatment characteristics. These include merchant characteristics and its city characteristics. A merchant may indeed for instance be more willing to accept contactless payments as it is a supermarket and the contactless payments is high in its city. Therefore, in addition to score matching, I use difference-in-differences setting and rely on the untreated merchants to investigate the resilience to shock as well as the effect of the staggered shocks on the substitutions between payment methods. This approach requires the control group to have the same sales patterns as the treatment group in the pre-treatment period so their sales after the shocks constitute a valid counterfactual.

Table 1 provides summary statistics on merchant and its city characteristics for the treatment and control groups in my sample. It highlights that the control group (non-NFC equipped merchants) is not directly comparable with the treatment group (NFC equipped merchants) along several dimensions (Column (5)). The control group on average has, for instance, a older than the treatment group and is much more likely to live in big city. This suggests that the treatment group may have a sales pattern inherently different from that of the control group. For my purposes and to reliably identify the effect, I construct a matched sample of merchants (with and without contactless) that are observationally similar. Specifically, I compute propensity scores based on a logistic regression<sup>11</sup> using a rich set of merchant characteristics including its age, sector activity, average transaction value, card-sales growth, a dummy variable indicating whether the firm has employees, the num-

> 10Additionally, I only include the card sales within France metropolitan and drop all in overseas territories.

> 11For further information on propensity score method, please refer for example to Bounie and Camara (2020).

11
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- Kaplow, L. (2001). A Framework for Assessing Estate and Gift Taxation. In W. Gale, J. H. and Slemrod, J., editors, _Rethinking Estate and Gift Taxation_ , number January. Brookings Institution Press, Washington, D. C.

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- Kopczuk, W. (2013b). Taxation of intergenerational transfers and wealth. In _Handbook of public economics_ , volume 5, pages 329–390. Elsevier.

- Lockwood, B. B. and Weinzierl, M. (2016). Positive and normative judgments implicit in U . S . tax policy , and the costs of unequal growth and recessions $. _Journal of Monetary Economics_ , 77:30–47.

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# **5.1 Main Specification**

We first estimate the following equation: where _Yist_ is the outcome of interest such as age at marriage, an indicator for marriage before age 18, age at cohabitation, years of education, a measure of autonomy or domestic violence for woman _i_ in state _s_ married in the year _t_ ; _Postt_ equals 1 if the woman was married after the year 2002 and 0 otherwise; _Riots_ is an indicator for residence in the state of Gujarat;<sup>2</sup> and _Xist_ is a vector of controls that includes indicators for wealth, religion, caste, residence in a rural area, the gender of the household head, and household’s wealth. We also include marriage year fixed effects and district fixed effects. The coefficient of interest, _β_ , measures, depending on the specification, the changes in women’s marital outcomes in Gujarat relative to the rest of India/ neighboring states/ synthetic Gujarat post the 2002 riots.

# **5.2**

We study the effects of communal violence on women’s marital and socioeconomic outcomes using the 2002 Gujarat riots as an event study. The Gujarat riots of 2002 erupted after a carriage carrying Hindu activists was set on fire and the violence lasted for three days. Even though there have been sporadic instances of communal violence throughout history in India, the particular timing and location of the violence was entirely unexpected. While the violence was widespread but confined within the state of Gujarat, it varied in intensity, with some towns and cities even very close to the epicenter not witnessing any violence (Jha, 2014; Varshney, 2004). As pointed out by (Varshney, 2004): It was also argued that the outbreaks of communal violence tend to be highly locally concentrated: many towns, only a few miles away from the worst cities, have insulated themselves from communal riots, entirely or substantially. In contrast to Ahmedabad, Surats old city (not the part where its shantytowns are) was argued to be such an example: yet again in March and April 2002, the violence in Surat was minimal, even as Vadodara and Ahmedabad, neither too far away from Surat, experienced carnage.

> 2Since the survey only provides information on the state of residence, we are unable to account for women who migrated to another state due to marriage. However, this should not significantly affect our results because interstate migration in India is very low (Biswas, 2017). According to the 2011 census, the share of intra-state migration was about 88% and the share of inter-state migration was about 12%.

8
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|---|---|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|EmergencyReconstruction and Rehabilitation Project|Subramaniam Vishwanathan Iyer|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Irrigation Systems Rehabilitation Project(02)|Tetsuo Yaguchi|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Punjab Urban Development Project|AnthonyGraeme Lee|
|Technical Assistance Credit Project(03)|Argun Ceyhan|
|Technical Assistance Credit Project(03)|Argun Ceyhan|
|Technical Assistance Credit Project(03)|Argun Ceyhan|
|Technical Assistance Credit Project(03)|Argun Ceyhan|
|Technical Assistance Credit Project(03)|Argun Ceyhan|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Health and Population Project|Frances T. Plunkett|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
|Smallholder Rubber Rehabilitation Project(02)|Douglas W. Lister|
# **2 Data and Sample**

## **2.1 The Gender Equality at Home Survey**

Our analysis uses the individual level data from the Gender Equality at Home survey that was administered on the Facebook platform in July 2020.<sup>10</sup> The Gender Equality at Home survey is a collaboration between Facebook, the World Bank and other development partners to survey individuals on Facebook on issues related to gender equality and women’s empowerment. The partners developed a short survey questionnaire to collect data from Facebook’s general population of users. The questionnaire was designed to measure employment, beliefs and norms on gender, plus a number of key demographic questions (for example, gender, age, and marital status of the respondent) as well as time spent on work and domestic and care responsibilities, decision making and resource allocation across household members.

Since the survey was administered in July 2020, additional questions on the impact of the coronavirus (COVID-19) pandemic were also included. Since the survey was still early in the coronavirus pandemic it is less of a concern that survey responses were directly affected by the pandemic.<sup>11</sup> Certain survey modules were randomized across participants in order to mitigate survey fatigue among respondents such that no more than 30 questions were asked in total.

The survey was administered on the Facebook platform across 208 countries, territories, and islands. The sampling frame for this survey is the individual database of Facebook and it was administered to 461,748 respondents sampled across the globe from the target population. While 208 economies were surveyed, the sample considered in this paper is the 111 countries with sufficient sample size to conduct gender-disaggregated analysis as described further in section 2.2. The dataset is designed to reflect the Facebook user base rather than any specific national population, focusing on countries where internet access is widely available.<sup>12</sup>

## **2.2 Sample**

In this paper, we report responses from the primary individual targeted by Facebook from a sample of general Facebook users. The analysis in this paper restricts the sample to the 111 countries with sufficient sample size to conduct gender-disaggregated analysis. Sample

> 10See https://dataforgood.fb.com/tools/gendersurvey/. The survey was collected over 3 weeks in July 2020.

> 11The hours worked variable that has a short recall period of “in the past 7 days” might be more affected by the pandemic.

> 12 To account for the sampling design, non-response, and online presence, advanced statistical weights were used. These weights reflect the number of persons each person in the sample represents and help calibrate the results to the target population which is in this case is the online population within each country.

6
et al. (2015) use the same self‐reported survey measure on propensity to take risks and find migrants are more risk‐seeking, while Hao et al. (2014) use incentivized lotteries and find no difference in risk attitudes between migrants and stayers.

# **3.2 Measurement**

We use two survey approaches and one incentivized lottery task to measure risk aversion. The first survey measure is the self‐reported propensity to take risks used by Jaeger et al. (2010) and a number of other studies referenced above. This question asks individuals for their attitude toward risk in general, allowing respondents to indicate their willingness to take risks on an eleven‐point scale, with 0 indicating complete unwillingness to take risks and 10 indicating complete willingness to take risks. The second survey measure is based on Barsky et al. (1997) and asks “Suppose you are the only income earner in your family, and you have a good job guaranteed to give you your current (family) income for life. You are given the opportunity to take a new job, with a 50:50 chance it will double your current income, and a 50:50 chance that it will cut your income by 20 percent. Would you take the new job?”. Appendix 1 sets out the exact variable definitions used as outcomes.

Our incentivized lottery measure is based on Eckel and Grossman (2002) and is designed to be a simple way to elicit risk preferences through asking individuals to make only a single choice from a menu of options. Aside from only requiring one choice, another key advantage of this method reported by Charness et al. (2013) is that the measure is relatively easy for individuals to understand compared to more complicated lottery designs, and as a result can produce less noisy estimates of risk preferences when individuals have lower math abilities. Individuals chose between competing lotteries with payoffs that ranged from 0 to 40 pa’anga (US$0 to US$22). Each participant was paid according to his or her choices, with the expected value equivalent to approximately 2 to 4 hours of average earnings in Tonga. Based on these choices, we construct three variables to indicate whether they made an inefficient choice (that was dominated by other lotteries they could have chosen), a choice consistent with severe risk aversion, or a choice consistent with risk‐seeking.

To account for multiple hypothesis testing in the risk domain and to examine an overall measure of risk attitudes, we construct an aggregate index which signs each risk measure so that a higher score indicates more risk‐seeking and then takes the average of the standardized z‐scores.

Time preferences were assessed using standard hypothetical questions which asked whether individuals would prefer an amount of money today compared to an amount one year from today, and also whether 9