rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1 Introduction

This paper investigates the link between inequality and demand for redistribution by looking at how individuals form their perceptions of inequality and how these determine demand for redistribution. Most of the literature focused on explaining demand for redistribution – or, more broadly, on political support for redistributive institutions such as the welfare state – identifies inequality as a key determinant. And, more importantly, it assumes that this inequality –usually represented by an inequality index calculated from a distribution of income of a household survey, which one could call _objective_ inequality – is common knowledge for all individuals, both in terms of what it exactly represents and its levels (or changes). This literature, in other words, ignores the issue of how subjective perceptions of inequality are formed. This is clearly a problem if there is a gap between subjective perceptions of inequality, which influence actions and choices of individuals, and objective inequality, which is used to explain those same actions by the literature. Some authors dismiss this issue, in part because of a widespread wariness towards subjective data,<sup>1</sup> and tend to characterize subjective assessment of inequality as individuals’ _mis_ perceptions rather than as something we need to understand. Mismeasurement, misconception, or simple mistakes are likely a part of the reason behind the gap between objective and subjective inequality but, in this paper, we show that they are not the whole story.

By presenting a simple model of the formation of perceptions we show its importance for demand for redistribution as well as the fact that perceptions are shaped not only by the objectively defined inequality measure (Gini) or by misperceptions, but they are also systematically correlated with other context (aggregate) and individual variables. We show that individuals’ perceptions on inequality encompass a broader definition of inequality that correlates, for example, with poverty or unemployment, as well as with fairness or social mobility, own situation and ideology. In other words, perceptions depend on the objective situation of both, the country and the individual
since it reduces precautionary savings in both cases. This is in line with empirical findings in Forbes, Fratzscher and Straub(2015), where the authors documented that the effects of outflow controls on exchange rate and capital flows are both statistically and economically more significant than these of inflow controls. Second, market participants in countries with on-and-off control measures (Gate countries) may find it easier to learn about fundamentals from repeated experiences than market participants in countries where there is no change to control measures for a long time (Wall countries). Hence, inflow controls should work better in Wall countries than in Gate countries, since learning reduces precautionary savings less in the former. The main empirical findings in Klein (2012) is supportive of this view: among inflow measures, persistent controls are more efficient than episodic measures in reducing financial fragility and moderating exchange rate movements.

# **5 Measuring the information content of capital controls**

My theoretical model assumes that private agent learns about economic fundamentals from capital controls. In the empirical section, I present two complementary sets of empirical evidence on the relationship between capital controls and expectations of fundamentals to justify this assumption. The first set of evidence comes from Brazilian survey data of expectations, a direct measure of market participant’s expectations of fundamentals, where the daily frequency of the data allows for relatively clean identification. However, the survey data is an average of expectations of many forecasters using different methodologies, and hence hard to interpret in more details. Therefore, my second set of empirical evidence takes a stand on the methodology used to form expectations. I use a standard econometric model to construct nowcasts for real GDP growth and examines whether capital control announcements help improve nowcast of Brazilian real GDP. Empirical evidence coming from both exercises show that capital controls convey valuable information about economic fundamentals agent uses in their forecasts. The empirical section starts with a brief description of Brazil’s experience with capital controls between 2006 and 2013 and proceeds to the two sets of empirical evidence.

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in median household post-tax income and individual post-tax income is identical. Then we take the 2019 welfare vector and change each quintile’s income share to match the published change. Subsequently, we grow the entire distribution such that the change in median welfare equals the published change.

For Vietnam, we rely on growth rates in consumption per capita by decile between the 2018 and 2020 Vietnam Household Living Standards Survey (VHLSS). We apply those growth rates to the 2018 distribution we have for Vietnam in PIP, which is from to the 2018 VHLSS.

# **A.2 Estimates from the literature**

For 21 countries, we use Eurostat’s flash estimates of income inequality and poverty, which are based on a microsimulation building on the work of Rastrigina et al. (2016). These flash estimates contain lower and upper limits of five points on a growth incidence curve. We take the midpoint of those two limits and linearly interpolate between them to generate a full growth incidence curve. Though these estimates are based on adult equivalent income, we apply them to our 2019 welfare vector which is in per capita terms.

For South Africa, we use the results from Barnes et al. (2021). This study contains decile growth rates comparing April-June 2020 with March 2020 and uses a measure of disposable income. To use these results, we assume that disposable incomes did not change after June and apply three-fourth of the April-June growth rates to the 2019 welfare vector (essentially assuming no change to welfare from 2019 to March 2020). We also ignore the fact that the 2019 welfare vector is based on consumption rather than disposable income.

For the Islamic Republic of Iran, we have decile specific growth rates reported in per capita terms that simulate the impact of the pandemic from the 2018/19 survey from Rodriguez and Atamanov (2021). We apply these growth rates to our distribution for the Islamic Republic of Iran for 2019. For <mark>Türkiye,</mark> we have percentile specific simulated
more to household chores and featuring men proudly showing their domestic contributions. More broadly, as Krafft, Selwaness, and Sieverding (2022) argue with respect to the MENA region as a whole, “the pandemic has underscored gender inequality in care work, but also offers an opportunity to rethink care responsibilities and ultimately recognize, reduce, and redistribute care work” (3).

In prior work (Barnett 2022, 2023), I collected the first evidence available on the relationship between personal attitudes and perceived norms on a range of gender issues in Morocco. One striking, unexpected finding was a large gap between what Moroccans say they value and what they believe others value when it comes to men’s contributions to unpaid domestic and care work. In a nationally representative survey, 75.6% of Moroccans agreed or strongly agreed that men are just as responsible for taking care of the household and children as women are, but only 54.7% believed most of their neighbors similarly agreed. To the extent that behavior aligns more with perceived norms than personal attitudes, such misperceptions may represent an unnecessary constraint on more equal household distributions of labor and ultimately on female labor force participation.

Previous research in OECD contexts has highlighted gender gaps in perceptions of how others value unpaid labor and the extent to which they see them as fair (see e.g. Cerrato and Cifre (2018)). We know little, however, about individuals’ perceptions of others’ attitudes toward and time spent on unpaid labor and care work in the MENA region, with some notable recent exceptions in studies of Egypt (Zeitoun et al. 2023) and Jordan (Gauri, Rahman, and Sen 2019). At the same time, recent research has shown that people in the MENA region and South Asia, especially men, generally tend to overestimate others’ embrace of patriarchal attitudes (Bursztyn, González, and YanagizawaDrott 2020; Gulzar, Sonnet, and Khan 2020; Minardi, Akmal, Crawfurd, and Hares 2021).
In summary, the distance measure is an absolute measure of the difference between the soil properties on a given plot and a specific suitability class, while the membership grade is a relative score, ranging from zero to one, indicating the relative fit of a plot into each suitability class. The membership grades for S1, S2, S3, and N, therefore, sum to one for each plot. Two separate suitability class assignments are constructed for each agricultural plot: one derived from MAPS plot-level soil sample results and one from AFSIS geospatially-derived soil properties.

# **4.2.Econometric Modeling of Production Frontiers**

Aigner, Lovell, and Schmidt (1977) lay out the potential problems in minimizing the sum of squares of a simple production function, such as Cobb Douglas, in estimating the maximum output for a given level of inputs. The authors argue that this method of estimation inadequately explains observed deviations from the maximum output for given levels of inputs. In their proposed stochastic frontier model, they explain the variation in deviations from the modeled maximum output, or the production frontier, and predict an observation-level measure of technical inefficiency.

Much of the literature on stochastic frontier models assumes a translog production function, in which inputs into the production function are also interacted (see Greene (2008), Sherlund et al. (2002), and Ekbom and Sterner (2008)). This can, however, result in an explosion of parameters to be estimated in the case of many inputs, such as in agricultural models. Rather than the translog function, we assume a log-linear Cobb Douglas model, following the seminal work of Aigner, Lovell, and Schmidt (1977) and the agricultural examples set forth by Deininger et al. (2007), Kilic et al. (2009), and others. The estimated stochastic frontier model is as follows: KK ii kk iikk ii (3) ln(YY ) = αα+ � ββ ln(XX ) + εε kk=1 ii ii ii (
25+|Avail.|Educ. attainment as the rest of OECD|
|**France**|Census|Count. Birth|Nat. Inst. of Stat. and Econ. studies|25+|Avail.|-|
|**Germany**|Census|Citizenship|European Council|25+|Avail.|Citizenship estimated on EC. Educ. attainment & share of 25+ on LFS|
|**Greece**|Census|Citizenship|European Council|Estim.|Estim.|-|
|**Hungary**|European Council|Citizenship|European Council|Not Avail.|Not Avail.|Citizenship estimated on EC|
|**Iceland**|Admin. register|Count. Birth|Stat. Iceland|Total|Not Avail.|-|
|**Ireland**|Census|Count. Birth|Central Stat. Office|25+|Avail.|Count. birth estimated on Irish Census. Educ. attainment & share of 25+ on LFS|
|**Italy**|Census|Citizenship|National Statistical Institute|Not Avail.|Not Avail.|Citizenship estimated on EC. Educ. attainment as the rest of OECD|
|**Japan**|Admin. register|Citizenship|Ministry of Justice.|Total|Not Avail.|-|
|**Korea**|Population register|Citizenship|National Statistical Office|Total|Not Avail.|-|
|**Luxemburg**|Census|Count. Birth|Nat. Inst. of Stat. and Econ. studies|25+|Avail.|-|
|**Mexico**|Census|Not Avail.|None|Total|Not Avail.|-|
|**Netherlands**|Census|Count. Birth|Statistic Netherlands|15+|Estim.|Citizenship estimated on EC. Educ. attainment as the rest of OECD|
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stronger effect on children's health than does the education of fathers.<sup>4</sup> Better-educated mothers know more about the benefits of proper diet and hygiene; they are also more likely to seek medical care, ensure that their children are immunized, be better informed about their children's nutritional requirements, and adopt improved sanitation practices. In addition, these women may have better access to information about health care, and to be better able to process and act on the information that they acquire. As a result, infants and children of women with some formal education have higher survival rates and tend to be healthier and better nourished. A mother with a few years of formal education is also more likely to send her children to school—which in turn may improve their health, by allowing them to learn better hygiene practices.

The evidence for Sub-Saharan Africa for instance shows that children of mothers who receive five years of primary education are 40 percent more likely to live beyond age five, and educated mothers are about 50 percent more likely to immunize their children than uneducated mothers (see Morrison, Raju, and Sinha (2007)). McGuire (2006) found that the average number of schooling years for women has a statistically significant impact on the mortality rate of children under five. Kiros and Hogan (2001) for Ethiopia, Castro et al. (2006) for Guatemala, and Oloo (2005) for a larger group of developing countries, also found that female literacy tends to lower child mortality. Similarly, in a study of 47 Sub-Saharan African countries over the period 1999-2004, Anyanwu and Erhijakpor (2009) found that female literacy is significantly and negatively related to both infant and under-five mortality rates. A study that followed women for over 35 years in Guatemala showed that the benefits of mothers’ schooling for their children’s health are even greater than previously estimated from studies conducted at one point in time (Maluccio et al. (2009)). More generally, the
practices of establishments; the 2017 data for LAC represent some of the first available results from these newly added questions.

# 3.2 Description of the sample

As Table 1 shows, establishments differ across the seven countries in many respects. The sample sizes are notably larger in the three largest economies—Argentina, Colombia and Peru, where approximately 1,000 establishments were surveyed. In Argentina, most establishments in the sample are engaged in manufacturing, whereas, in all other economies, the highest proportion of establishments surveyed operate in the other services sector. Establishments are largest (as measured by the number of employees) in Peru, where the average establishment employs 90 workers, and the median establishment employs 20, and smallest in Bolivia (average number of employees is 31, and median is nine). Multinationals have the strongest presence in Ecuador, where 12 percent of establishments are foreign owned, and the weakest presence in Argentina, where only 2.8 percent of establishments are. The most open economy, measured as the proportion of establishments engaged in exporting activity, is Peru where 12 percent of establishments are exporters, whereas the least open economy is Ecuador, where only 3.2 percent of establishments are exporters. Establishments that are part of a larger multi‐establishment firm are most prevalent in Colombia (23 percent) and least prevalent in Paraguay (7.5 percent).

The data show heterogeneous levels of female empowerment in the private sector (as measured by the proportion of establishments with a female top manager). In Bolivia, just over 26 percent of establishments are managed by women, whereas only 8 percent of establishments in Argentina are. In Bolivia, establishments are also characterized by highly concentrated ownership structures—here, 45 percent of establishments are sole proprietorships. In Ecuador, on the other hand, in only 1.6 percent of establishments is the ownership concentrated in the hands of a single owner. In all countries, the average establishment employs experienced top managers, with the average number of years
Mwale, M. L., Kamninga, T. M., & Cassim, L. (2022). The effects of the Malawi Farm Input Subsidy Program on household per-capita consumption convergence. _Developement in Practice_ , _32_ (336–348). https://doi.org/10.1080/09614524.2021.1937552 Nisbett, N., van den Bold, M., Gillespie, S., Menon, P., Davis, P., Roopnaraine, T., ... Warren, A. (2017). Community-level perceptions of drivers of change in nutrition: Evidence from South Asia and sub-Saharan Africa. _Global Food Security_ , _13_ (September 2016), 74–82. https://doi.org/10.1016/j.gfs.2017.01.006

- Nkhata, R., Jumbe, C., & Mwabumba, M. (2014). _Does irrigation have an impact on food security and poverty: Evidence from Bwanje Valley Irrigation Scheme in Malawi_ (IFPRI Working paper No. 04). Lilongwe, Malawi. Retrieved from

http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/128180/filename/128391.pdf Nonvide, G. M. A. (2019). A re-examination of the impact of irrigation on rice production in benin: An application of the endogenous switching model. _Kasetsart Journal of Social Sciences_ , _40_ (3), 657–662. https://doi.org/10.1016/j.kjss.2017.12.020 Pace, N., Daidone, S., Davis, B., Handa, S., Knowles, M., & Pickmans, R. (2017). One Plus One can be Greater than Two: Evaluating Synergies of Development Programmes in Malawi. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2017.1380794
– the authors predict the extent of poverty reduction in developing countries. Of course, this depends on the poverty line. It also depends on the baseline poverty projections, which decline considerably between 2001 and 20015. For $1/day poverty, the estimated reduction in 2015, in the absence of additional productivity gains, is 2.5 million for Doha and 31.9 million for Full-Lib. When applied to current (2001) poverty levels, the authors’ calculations result in poverty reductions of 9.7 million and 80.5 million under Doha and Full-Lib, respectively. The 2015 poverty reductions are increased to 4.3 million and 43.5 million, for Doha and Full-Lib respectively, when productivity gains are factored in. For $2/day poverty, the reduction in number of poor is larger, but the percentage reduction is smaller (see Table 1).<sup>8</sup> Based on the Doha/Full-Lib comparison, it is clear that the (rather ambitious) Doha scenarios capture only a relatively small portion of the total poverty reduction possible under trade reforms. When the authors consider the Doha-All scenario, they find that implementing deeper cuts in the developing countries enhances the poverty outcome, nearly doubling the poverty reduction obtained under the central Doha scenario. This finding reinforces Ivanic’s (2005) conclusions with respect to the beneficial poverty impacts of developing country tariff cuts under the Doha Development Agenda. It is also hardly surprising in light of the increasing importance of South-South trade and the relatively high level of developing country tariffs as reported by Anderson and Martin (2005).

> 8 These estimates of poverty reduction are considerably smaller than earlier predictions using the World Bank’s Linkage model. The difference is due to the fact that these estimates are based on the most recent (version 6) GTAP data base, which is further updated to account for EU enlargement as well as the WTO accession of China and others. These recent trade reforms have reduced the overall level of
form appropriate groups of firms to compare their performance. The Governments of El Salvador and Georgia also tried to target the neediest, but they used pre-pandemic fixed characteristics such as firm size and sector. In selecting the set of countries, we were also mindful of the number of firms that received government support in the COV-ES data. A lower bound on the number of observations and the nature of eligibility criteria for government support narrowed down the set of countries we could study to just El Salvador and Georgia.

> 2 Both ES and COV-ES data sets are publicly available on data portal: <u>https://login.enterprisesurveys.org/. As of June</u> 2021, COV-ES has covered 44 countries, some of which have three rounds of surveys available. For further information on COV-ES, please visit https://www.enterprisesurveys.org/en/covid-19.

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# I.   The policy case for improved data on rural women’s employment

Across developing countries, rural women are heavily engaged in a range of employment activities — including smallholder farming, contributing work in the family farm or business, as well as in off-farm jobs that are typically seasonal and informal (ILO, 2018a, 2018b). Employment surveys typically have difficulties in collecting data on these types of jobs, as well as on social and economic barriers that women face in achieving better-quality employment outcomes. These include time burdens in unpaid household and care work, transport and safety issues, and other local norms and institutional barriers around marriage, work and having children (Jayachandran, 2015). Rural women often find themselves in particularly vulnerable employment situations, given limited access to resources, infrastructure and markets, as well as diversified sources of income.

A better understanding of rural women’s work, paid and unpaid,<sup>1</sup> and the constraints they face is critical for policy design as rural economies evolve with economic growth. Depending on country context, growth (particularly in the nonfarm sector) has led to rapid out-migration to urban areas, as well as increased diversification of rural incomes, although the quality and expected earnings of off-farm opportunities are often low (Davis et al 2017, Mellor, 2017). A growing urban middle class in many developing countries has led to increased demand for food and other resources, renewing policy efforts to improve incomes and productivity in agriculture, including investments in inputs and new technology. At the same time, however, rural land ownings are increasingly fragmented across countries, leading to a rising global base of less-productive, smallholder farmers, as well as growing casual labor on farms among the landless (Lowder et al, 2017). Since rural women are often concentrated in these activities, they are increasingly targeted by country programs aiming to raise rural productivity and economic growth (Kochhar et al, 2017). But these programs need to be informed by data that more