rafmacalaba/data-use-mentions-extended huggingface dataset:
In the rest of the paper we test how these different factors affect inference about the relation between subjective well-being and income inequality. The list of factors is nonexhaustive and we do not pretend to cover in this paper all possible causes of empirical heterogeneity. However, if the factors listed above contribute to explain such heterogeneity, then any inference from any study on the relation between happiness and inequality is context specific and cannot be generalized to other contexts. On the contrary, if life satisfaction and income inequality are strongly correlated, then the significance of this relation should persist under different specifications of the life satisfaction equation and the sign of this relation should be consistent irrespective of the factors listed.

# **4 Data, model and variables**

The _data set_ adopted has been compiled aggregating all rounds of the European and the World values surveys carried out between 1981 and 2004.<sup>7</sup> These surveys question individuals worldwide on happiness, personal values, social attitudes and individual attributes and include questions on income and inequality. The version of the data set we use is a 2006 version which contains a total of 267,870 individuals, 1,349 regions and 84 countries where each country has been surveyed from a minimum of one to a maximum of four times. Table A2 in the annex provides details on countries, years and number of observations.

We also merged this data set with two other variables: GDP per capita at Purchasing Power Parity (PPP) extracted from the IMF world economic outlook database<sup>8</sup> and the Gini coefficient extracted from the United Nations University, World Institute for Development Economics Research (UNU-WIDER) database on inequality.<sup>9</sup> We use GDP per capita to control for countries wealth and the UNU-WIDER Gini to adopt an alternative measure of income inequality independent of the database we use.

As a benchmark for our analysis, we use what we could call a ‘standard’ model in happiness studies that combines cross-country and longitudinal
of both voice and accountability, as reflected in their perceived ability to participate in selecting their government, along with their freedom of expression, freedom of association, and a free media (figure 12b).

Though no causal relations can be drawn from these correlations, the high level of positive associations indicate that the regulatory framework for data does not exist in isolation from a country’s wider governance framework and tends to do better in country contexts where citizen perceptions indicate relatively high levels of trust in the regulatory environment. One additional caveat is that the World Governance Indicators measure public trust in governance environment in general, rather than direct measurements of public trust in data economy.

# **5. COVID-19-specific provisions**

The ongoing COVID-19 pandemic makes a robust data governance environment more salient than ever, as many governments have been exploring data-driven options to halt the spread of the virus. For instance, “contact tracing” mechanisms hinge upon the collection and processing of personal data by public authorities—and private actors on their behalf such as app developers. Data protection and cybersecurity concerns regarding such initiatives are rapidly rising. This calls for transparent and consistent rules and regulations on enabling data usage for public health purposes while safeguarding individual rights.

As the pandemic unfolded during the implementation of this diagnostic, the team examined the adoption of COVID-19 emergency regulations in the surveyed countries to assess whether emergency regulations led to the implementation of additional data protection safeguards or the suppression of the existing ones. Overall, 15 countries in the diagnostic sample adopted emergency regulations that had data protection implications. Nine of them had a preexisting data protection law (including Armenia, Côte d’Ivoire, Israel, Korea, Madagascar, Mauritius, Morocco, Peru, and the United Kingdom) and the remaining six did not (Brazil, China, Indonesia, the Islamic Republic of Iran, Jordan, and Papua New Guinea).

For countries that had a pre-existing robust regulatory framework for personal data protection, the legislation usually contained provisions specifying legal grounds or conditions enabling public
# **Barriers to Household Risk Management:**

# **Evidence from India**<sup>*****</sup>

Shawn Cole Xavier Giné Jeremy Tobacman Harvard Business School World Bank University of Pennsylvania Petia Topalova Robert Townsend James Vickery IMF MIT Federal Reserve Bank of New York

# JEL: C93, D14, G22, O12, O16.

Key Words: Insurance, Consumer Finance, Liquidity Constraints, Trust, Economic Development.

* This project is a collaborative exercise involving many people. The work in Andhra Pradesh was directed by Giné, Townsend and Vickery. The work in Gujarat was directed by Cole, Tobacman, and Topalova. In Andhra Pradesh, we gratefully acknowledge the financial support of the Switzerland State Secretariat for Economic Affairs (SECO), the Global Association of Risk Professionals (GARP), and the World Bank Commodity Risk Management Group (CRMG), and the Consortium on Financial Systems and Poverty from the Bill and Melinda Gates Foundation. We thank ICRISAT, and particularly K.P.C. Rao, for their efforts in collecting the survey data, and employees of BASIX and ICICI Lombard for their assistance. In Gujarat, we would like to thank SEWA for their tremendous contributions to the research agenda, and in particular Chhayaben Bhavsar; USAID / BASIS for financial support, and the Centre for Microfinance for generous financial and superb administrative and research support, the latter provided in particular by Aparna Krishnan and Monika Singh. Paola de Baldomero Zazo, Fenella Carpena, Nilesh Fernando, Lev Menand and Gillian Welch also provided excellent research assistance. We also thank participants at a number of seminars and conferences for their helpful comments and feedback. The views expressed in this paper are those of the authors, and do not reflect the opinions of the Federal Reserve Bank of New York, the Federal Reserve System, the World Bank or the International Monetary Fund.
section (7.2) suggests that the temporary trade shock caused by the CIV civil war led to significant changes in the employment pattern in the non-Abidjan route when compared to the directly affected Abidjan route. In this section, we analyze whether there are reasons to expect that the changes observed in the employment pattern may be longer-lasting, and may not revert back to the pre-war pattern of resource allocation and sectoral specialization. If the empirical estimates are capturing only the reallocation of mobile factors without any sunk investments, then one would expect the share of Abidjan route in total trade to revert back close to its original level after the end of the CIV-crisis. Empirical evidence in this regard is quite the contrary. For Burkina Faso, we have trade flow data till 2016 which is plotted in Figure 2a. The analysis of trade flow confirms that the share of maritime trade flow through Abidjan route never recovered to its pre-war level even 10 years after the end of the CIV crisis. This alerts us to the 28
-3-

In order to give some sense of the magnitudes involved, Table 1.1 gives Mexican data on real growth, inflation, the fiscal position of the government, and net foreign reserves for the period 1973-85. It is clear that the key variable that is, at least superficially, controlled by the government and that has growth dramaticallv is expenditure.

Two general approaches have been used in examining government expenditures. The first of these is to consider the provision of public goods, and to derive conditions for their optimal production and allocation. The appeal of 'this approach is its theoretical consistency; however, it is very difficult to implement empirically. The second approach, that is frequently used in applied general equilibrium analysis, is to treat government output as useless, and to derive the cost of its production to the rest of the economy. If the output is to be worth producing, its benefits must outweigh its costs. This approach, while being relatively straightforward to apply, overlooks the fact that government production may have a direct positive impact on private production. Government infrastructure, for example, such as roads or education, may contribute to the efficiency of private output. An analysis that only looks at the crowding out effects of government spending ignores these benefits.

We will develop an intertemporal general equilibrium model that will be used to analyze reductions in government spending, and the implications for the exchange rate and the balance of payments of these reductions. We will develop our analysis in the context of an exchange rate regime that is typical of many developing countries: the rate is fixed, but if the foreign reserves of the central bank fall below some
results suggest that the wave of Pan-African foreign bank entry was likely to be healthy for the financial sectors of host countries and for their consumers.

And yet, the best recent evidence does not confirm that foreign banks headquartered in other African countries have performance superior to that of other banks. Pelletier (2018) uses data from financial statements and surveys to compare the performance of three different categories of foreign banks to domestic banks in Sub-Saharan Africa: global banks from developed countries, regional African banks, and banks from non-African emerging economies. Emerging-market banks and global banks consistently outperform domestic banks in terms of profitability and cost efficiency. Those differences are not, however, significant for regional African banks. Better performance of global banks and emerging-market banks stems from greater operational efficiency and lower costs of funding. To this point, regional African banks have struggled to generate interest income in comparison to other bank types, including domestic banks, and there is no evidence of loan market segmentation – which could indicate that regional African banks are finding their niche in credit provision. These findings are reminiscent of those in Claessens and van Horen (2012) that foreign banks from home countries that are geographically close to a host country do not perform significantly better than other banks. However, they also find that foreign banks from home countries that speak the same language and have similar banking regulation as the host country perform better than others, and these conjectures have not yet been directly tested in the African context. Moreover, 2012 is the last year of financial data used in Pelletier (2018) and, since the influx of foreign banks from other African countries is relatively recent, this may not have provided sufficient time for the full benefits of that African regional 17
0.0205|1|0.6478**|
|Number of observations|250|1165|1165|1165|1165|1165|1164|
|Support per AWU|0.0829|-0.0186|0.1467**|0.0863**|0.0185|0.6478**|1|
|Number of observations|250|1166|1166|1166|1166|1164|1166|

Note: (1) Symbol + indicates correlation statistically significant at 0.1 level; * indicates correlation statistically significant at 0.05 level; ** indicates correlation statistically significant at 0.01 level; (2) CAP support are the sum of payments for the period 2008-2013; (3) Unemployment rate data is at NUTS2 level; (4) EU15 are Belgium, Denmark, Greece, Finland, France, Germany, Ireland, Italy, Luxumbourg, Malta, Netherlands, Portugal, Spain, Sweden and the UK

**Table 5.** Pearson correlation coefficients between level of LFA payment (2008-2013) and socio-economic indicators (2011)

|Variables|Unemploym<br>ent rate|Poverty rate|Share of the<br>country<br>poor|GDP per<br>inhabitant|Population<br>change<br>(2008-2013)|Support per<br>hectare<br>UAA|Support per<br>AWU|
|---|---|---|---|---|---|---|---|
|||||**EU15**||||
|Support per hectare UAA|0.0882|0.1219**|0.2913**|-0.1447**|-0.0404|1|0.4097**|
|Number of observations|193|963|963|963|963|963|962|
|
area. Half of SuperVia’s passengers are women,<sup>6</sup> and one in six to eight carriages are reserved for women, depending on the train length.<sup>7</sup> Thus only a fraction of all women riders could ride in the reserved space at times of high congestion. Male compliance with the reservation rule is enforced by platform officers who patrol to ensure the overall safety of the boarding process without having any policing power. However, their presence varies substantially across time and stations. This foreshadows substantial differences in the _de facto_ effectiveness of the reservation rule across space and time.

# **3 Data**

We generate two main types of data.<sup>8</sup> First, we use a crowdsourcing app to task regular women and men commuters to repeatedly report on riding conditions. Second, we administer a platform survey and IATs on a random sample of men and women commuters. Appendix B describes the construction of the variables used in our analyses. Appendix C describes the measures the research team took to ensure the study followed ethics guidelines.

## **3.1 Crowdsourced Rider Experiences**

Women and men riders were recruited and invited to report on their commuting experiences through a smartphone application for a monetary payment. The application allows us to vary the assigned location (reserved vs public space), pay-out, and data collection task across rides. This setup is used to 1) elicit women commuters’ revealed preferences for a reserved space, and 2) introduce exogenous variation in which space to ride. These are described in Sections 5 - 6. We use the same application to task male riders with the collection of data on transit conditions throughout the network. This allows us to capture granular variation in the ride environment, independent of women riders’ choice of space during their commute.

> 6Source: SuperVia administrative data.

> 7The location of the reserved cars is stable for a given train composition.

> 8The analysis
# **Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings: The Case of the Producer Support Grant Program in Ukraine**

# **1. Introduction**

The Russian Federation’s invasion of Ukraine in February 2022 started a war that has to date displaced more than 8 million individuals, destroyed the productive capacity and infrastructure of vast stretches of territory, and resulted in marked decreases in agricultural productivity. To help small and medium-size farmers, who are most likely to be credit constrained, to cope with this shock, Ukraine’s Ministry of Agriculture established a digital registry platform, the State Agrarian Register (SAR) in August 2022 with the goal of implementing support programs, including an EU-supported US$ 50 million Producer Support Grant (PSG) and improve farmers’ ability to access markets. We use the SAR’s link to administrative data from the registry of property rights and the cadaster at parcel-level to assess the targeting and impact of this program, compare results to those from a phone survey, and draw out implications for future research.

At the start of the war, most of the 2021 harvest was still in storage but traditional export routes had been blocked, requiring development of logistics through alternative and more costly overland routes. Similarly, fertilizer imports from Russia were cut off. Once immediate military challenges had been fought off, an immediate challenge was to secure market access and essential support (with reconstruction, generators, essential inputs, grain drying facilities) to many farmers in a setting with restrictions on physical movement, limited capacity, and a tradition of weak governance. While power supplies and cyber-attacks posed challenges and many doubted farmers who traditionally remained in informality to avoid taxes in a cashbased economy would be willing to abandon their anonymity, a digital registry platform emerged as the most viable solution and the SAR, interoperable with registry and cadaster, was launched by August 2022.

The PSG, which provided a cash grant to small farmers (below 120 ha) proportional to the amount
Figure 1: Take-up rates compared to priors <!-- Start of picture text --> Literature Prior<br>Firm Prior<br>Policymaker Prior<br>Academic Prior<br>Actual rate (83-88%)<br>0 .2 .4 .6 .8 1<br>Proportion taking up treatment<br><!-- End of picture text -->

**Notes** : Circles show medians of elicited prior distributions, and lines show the range from the 2.5th to the 97.5th percentiles of these distributions. Actual take-up rate was 88% according to administrative data on starting the program, or 83% according to administrative data on hours of consulting received.

37
11</sup> induce two major shortcomings. First, European Council data must be used for 10 countries. These data are based on the concept of citizenship (rather than country of birth). In all cases, information on the birth country will be available from the national Censuses. Second, the Labor Force Survey list of country of origin is usually less detailed than the Census list. The survey distinguishes 22 countries of origin and 10 groups of countries in 1990, 26 countries of origin and 13 groups of countries in 2000. The most striking example is Africa, which is divided in three groups (South Africa, Northern Africa and Other Africa). We compute the skill structure of emigration for these groups of countries and consider it as homogenous within groups. Despite this limitation, we attempted to a comparison between Labor Force Survey and Census data for France and obtained very accurate estimates for most African countries.

Note that we also use the Labor Force Survey to eliminate immigrants aged under 25. For countries where detailed data are not available, the share of young immigrants is also assumed to be homogenous within groups.

> 11The ...nal database for 1990 and 2000 should be available by December 2004.

14
<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Return period: 1-in-20 1-in-100 1-in-500<br>44%<br>99%<br>94%<br>81%<br>61%<br>90%<br>79% 52% 28%<br>68% 46% 47% 56% 57%<br>Exposed rice production (hectares, thousands) 66% 69% 41% 29% 48% 38%<br>29% 50%<br>60% 55% 70% 13% 10% 13%<br>Quang Ninh Hai Phong City Thai Binh Nam Dinh Ninh Binh Thanh Hoa Nghe An Ha Tinh Quang Binh Quang Tri Thua Thien-Hue Da Nang City Quang Nam Quang Ngai Binh Dinh Phu Yen Khanh Hoa Ninh Thuan Binh Thuan Ba Ria-Vung Tau Ho Chi Minh City Tien Giang Ben Tre Tra Vinh Soc Trang Bac Lieu Ca Mau Kien Giang<br><!-- End of picture text --> Sources: Based on data from JAXA EORC 2018 (land use maps), Braese et al. 2020 (coastal flood data) and Fathom<sup>1</sup> (riverine flood data). Note: Shown percentages are percentages exposed rice area for a 1in500 year flood. A flood threshold of 25cm has been applied **.** The exposure of aquaculture production in the two big deltas is based on the two datasets of aquaculture ponds described in section 3.2, provided in vector format. In order to overlay these with flood maps, the pond datasets are converted into high-resolution raster data. This reduces the accuracy of the data, as ponds are not square and thus do not exactly match the raster grid. To increase robustness of the analysis
# _Table 3_ . Soil Suitability Classification Summary

_Row-wise summary; binary variables, where 1 indicates soil property falls within given suitability class._

|||**MA**|**PS**|||**AF**|**SIS**||
|---|---|---|---|---|---|---|---|---|
||S1|S2|S3|N|S1|S2|S3|N|
|pH|0.35|0.64|0.01|-|0.00|0.99|0.01|-|
|CEC|0.15|0.08|0.35|0.43|0.14|0.08|0.75|0.03|
|Organic Carbon (%)|0.27|0.37|0.34|0.02|0.38|0.51|0.11|0.00|
|Salinity (ECD)|1.00|-|-|-|1.00|-|-|-|
|Slope (%)|0.23|0.29|0.27|0.21|0.23|0.29|0.27|0.21|
|Overall Class|0.13|0.75|0.12|-|0.07|0.88|0.06|-|
|N|106|634|100|0|57|736|47|0|

Benchmarked against the preferred method of plot-level soil testing, there is evidence that the geospatial data fails to adequately distinguish soil suitability levels. AFSIS-based classification results in more intense clustering of observations in the central, moderately suitable class (88 percent), with only 7 percent classified as highly
short distances, nearby firms have similar exposure to the virus. However, around the border of a lockdown, social distancing measures are different: one firm is in lockdown while the other is not. To perform the analysis, we re-estimate Equation (3) by restricting the sample to firms that run along the municipality border. The main results remain unchanged (Table 4, Panel B).

Lastly, we augment Equation (3) by interacting firm risk with the other variables in the equation to study whether risk is correlated with the adoption of the programs within firms under a lockdown.<sup>51</sup> Since the lockdown regressions use CMF financial data rather than the

> 50We estimate this regression using financial data from the Financial Market Commission, rather than the merged dataset with real-sector variables. This is because only the Commission’s records include firm-level geolocation, which is crucial for identifying firms along municipal borders, a key component of our identification strategy, as detailed below. 51 Specifically, we estimate:

> _ProgramUseit_ = _αi_ + _αt_ + _βLockdowni_ + _γPostt_ + _ηRiski_ + _δLockdowni × Postt_

> + _ζLockdowni × Riski_ + _λRiski × Postt_ + _θLockdowni × Postt × Riski_ + _uit._

19
.0 to 8.0 (out of 10) in terms of their baseline business practices<sup>3</sup> . This resulted in an experimental sample of 753 firms. Our initial plan was to continue enrolling induction batches until we reached a sample size of 2,000 firms, but concerns about other components of the overall GEM project led to the government’s program implementors not inducting new batches that we

> 3 This was done based on the induction workshop diagnostic questionnaire. 73% of firms that attended the induction workshops had scores in the 5.0 to 8.0 range. The 24% of firms that received lower scores were not eligible for support (if they scored below 4.0) or were eligible for basic online training only (if they scored in the 4.0 to 5.0 range). The remaining 3% of firms whose business practices were already above 8.0 (out of 10) were instead directed towards personalized consulting services and some grant windows.

6