rafmacalaba/data-use-mentions-extended huggingface dataset:
contact, followed by peer influence. Their review, however, cautions against expecting large impacts. They find that overall, more precisely measured studies find much smaller effects, and point out that studies have so far focused on short-term impacts. Notably, in lowand middle-income countries, there is still only very limited experimental evidence about whether intergroup contact actually improves locals’ relations with displaced persons (Zhou and Lyall, 2022). Studies using observational data, such as Ghosn et al. (2019), must be interpreted with caution, given that a positive correlation between contact and lower prejudices might be due to less-prejudiced individuals seeking contact. Future research will also need to explore the importance of the conditions defined by Allport (1954) under which the interactions between in-group and out-group individuals would increase empathy and integration: that is, that (i) both groups must share status, (ii) and the same goals or interests, (iii) both groups need to live in a cooperative environment rather than a competitive one and (iv) they need to benefit from institutional support: that is, operate under a well-defined set of norms, laws, and regulation (Paluck et al., 2019). Studies so far only show that proximity alone (Zhou, 2018) and brief exposure in the framework of transit migration do not seem to amount to the same as contact (Ajzenman et al., 2020; Steinmayr, 2021; Hangartner et al., 2019).

Related to studies inspired by Allport’s contact theory are studies that look at the impacts of perspective-taking and providing personal narratives, as well as studies stressing commonalities (in terms of conflict and displacement and other dimensions). Studies focusing on forcibly displaced populations find positive but not always significant or large impacts (Adida et al., 2018; Alan et al., 2021; Audette et al., 2020; Dinas et al., 2021; Hong et al., 2020; Lazarev and Sharma, 2017; Rodr ́ıguez Chatruc
Women of prime working age who have children at home face higher penalties on the time dedicated to all four activities, compared with the effects for younger and older women. The effects for all age groups, however, are smaller than those associated with marriage (Table 9). The presence of children is associated with a reduction of 11 minutes of market work, 23 minutes of personal care, and 36 minutes of leisure per day. At the same time, women in this age group see an increase of 1.6 hours of unpaid domestic work associated with having children at home. For mature women, the effect of children on the amount of time dedicated to the different activities is nearly half of that observed among women of prime working age.

Compared to women, the presence of children at home does not make a big difference in the way men allocate their time. Among men of prime productive age and mature men, unpaid domestic work increases by 11 minutes per day and leisure diminishes by 10 minutes, but the effect on market work and personal care is not significant. In contrast, the reduction of leisure time among young men is nearly twice as much as that experienced by their older counterparts (19 minutes per day) in the presence of children, but there is no effect on unpaid domestic work.

# **6. Discussion**

In this paper, we contributed to the discussion on gender equality and development by analyzing men’s and women’s time use patterns in 19 countries using recent time use data. Our analysis confirms and adds nuances to the findings of other scholars and those of the 2012 WDR. We focused on the allocation of time to four different activities: market or paid work, unpaid domestic work, self-care, and leisure, social and study activities.

First, we analyzed the average number of hours that men and women allocate to each activity on a daily basis. Our results showed that women specialize in unpaid domestic work and men specialize in market work, but
Most of the data analyzed for this study confirm a female advantage in education, but this does not directly translate into the labor market, where females remain at a distinct disadvantage in most sectors. While females may achieve higher levels of education and obtain better results on assessments, female labor force participation is low compared with males’, wage disparity in many sectors of employment (main industries) is high, and in the civil service women are less likely to be promoted or to hold senior positions. While policies may be gender-sensitive and promote inclusivity, in reality, women and children with disabilities and special needs face disadvantages that will be insurmountable without adequate attention, resourcing and planning. A meaningful examination of how to raise the status of women in society is required for Indonesia to reap the full economic and development benefits from its population. The formation of gender norms starts with formal and non-formal education, both from how girls and boys are taught, and what messages they receive at school and at home, as well as the gender composition of leadership positions and the roles that women and men fill. While Indonesia should be applauded for the progress it has made, there are several areas that require greater attention:

1. **Analyzing data to identify gender imbalances at subnational levels and possible solutions to narrow the gaps:** Provinces and districts should analyze their own enrollment and learning data at the local level from a gender perspective to identify schools and sub-regions that have large gender imbalances. In those schools and sub-regions that have large differences in learning and enrollment, provinces and districts should support communities to identify the source(s) of the problem and possible ways of addressing it. Understanding the local factors driving gender inequality in the area is crucial in helping to identify possible solutions to narrow the gaps in favor of both boys and girls. Continuous monitoring should show improvements over time, otherwise further actions and support will be needed. In addition, a national program to support local research, policy and innovation would help subnational governments
Notably, the data used to evaluate performance with the SDGs can come from different sources. This can pose a cause for concern, since it is common knowledge that even for the same variable, different sources can provide different numbers. As an example, trusted data providers, such as the United Nations, World Bank, and IMF, may publish different country estimates of macroeconomic aggregates such as GDP. As these statistics are founded in the same conceptual framework for all UN member states,<sup>15</sup> why does this happen? There are two reasons. First, many countries with lower statistical capacity publish GDP estimates with a time lag. As international organizations use GDP estimates for operational purposes (e.g., means testing the World Bank’s member countries for IDA eligibility, or informing development policy discussions with member state policy makers), they employ different approaches to fill the data gap between the latest published number and the present. Second, international organizations also forecast economic time series, and the schedules for revising (recalibrating) model estimates can vary among the different organizations.

Similarly, two household surveys implemented by a national statistical office to collect data on the same employment characteristics may not produce the same statistics (see, e.g., Dang, Lanjouw, and Serajuddin (2017) for a discussion for Jordan). Furthermore, this issue is not only pertinent to poorer countries but is frequently observed for richer countries as well.<sup>16</sup> We discuss next a specific example of how analyzing data coming from different sources can complicate the conclusions. In particular, shared prosperity as measured by growth in the income

> 15 This framework is available at <u>https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf. See also the IMF</u> (2018) for an interesting discussion on some potential differences between their macroeconomic databases. 16 A salient case is the U.S.
). Such computations, if done in the same way by all moral agents, should lead to formation of similar normative beliefs about social appropriateness and consequently to coordinated, cooperative behavior [see also Rusch and Vostroknutov, 2023].

KV describe a specific moral psychology of norm formation in any context that is based on the idea of aggregating _dissatisfaction_ . Specifically, it is assumed that the social appropriateness of each given allocation is inversely proportional to the summed dissatisfactions of all agents in that allocation. The dissatisfaction of each agent in a given allocation is high when there are many other counterfactual allocations in the context that give the agent more consumption utility and low otherwise. Thus, dissatisfactions express personal grievances of agents in a given allocation, and the normative value of the allocation is formed by aggregating all these dissatisfactions. In other words, the allocation is considered more socially appropriate, the less aggregated dissatisfaction it evokes.

This construction may seem overly complex and unnecessary. However, tests of existing experimental data have shown that the model of dissatisfaction-based norms can account for many puzzles in social behavior known to behavioral economists.<sup>4</sup> Specifically, it resolves many issues pertaining to the existing models of social preferences and reciprocal kindness [see Vostroknutov, 2020, for discussion] that are not context-dependent enough to accommodate certain stylized facts (e.g., radical context-dependence 4Experiments by Merguei et al. [2022] and Panizza et al. [2021] perform direct and rather stringent tests of KV’s model and find a very good fit with the data.

6
of formality (5%-25%) still have only 14-26% of their firms in the top quartile of predicted probability.

Cluster analysis using the EcC data, shown in Figure 15, also highlights the larger professional (2% of informal firms in the EcC) and non-professional firm clusters (14%) as well as a clusters we call “larger retail” (8%) and “non-retail with 3+ workers” (10%). Like before, firms in these clusters tend to skew older than average and to be more likely to hire wage workers. Although these clusters have the lowest shares of the lowest quartile of predicted probability of formality (8-31%) between less than 1% and 13% of firms have predicted probabilities of formality in the top quartile, although 13-38% are in quartile three.

40
these migrants are available. However, it is the most comprehensive source available on the characteristics of new Filipino migrants, and importantly, does contain information on the destination country and whether this is the first time an individual is migrating or not for contract work. We use data from the 1992-2003 surveys.

In Table 5 we use this data to test whether the age, sex, marital status, place of origin in the Philippines, and education of new migrants going to a particular destination varies with GDP shocks at destination. To do this, we estimate equation (1) with these characteristics as the dependent variables. We find no statistically significant relationships between GDP changes at destination and the characteristics of the migrants going to that destination. The dependent variables are in levels, and GDP is in logs, so to interpret the magnitude of the coefficients, we divide them by 100 to get the impact of 1 percent change in GDP at destination. Thus not only are the coefficients not statistically significant, but we also see they are very small in magnitude. For example, 1 percent higher GDP at destination is associated with a decrease of 0.049 years in the mean age of migrants going to that destination and an increase of 0.024 years in the mean education of migrants going to that destination.

Thus we find no evidence of large selectivity in which individuals migrate over the business cycle, at least in terms of these observable characteristics. We speculate that this composition effect is much less important for the type of migrant labor examined here than it is for examining the pro-cyclicality to domestic business cycles of native wages because of the much greater distortions in global labor markets.

# **5. Analysis of a Change in the Minimum Wage for Domestic Helpers**

The results presented thus far are consistent with the case of binding minimum wages presented in section 2.4 above. To bolster this interpretation of the results, we provide direct evidence (via a natural experiment) that minimum wages bind for an important subset of
of the observed dropout occurs during the transition from primary to secondary schooling (Adelman et al., 2018). Thus, the consequences of the pandemic on educational outcomes are likely to vary across student attributes and, additionally, may also differ across locations since not all districts were exposed to the same levels of infections during the pandemic. In fact, World Bank (2022) shows that department-level dropout rates ranged between 6.6% and 11.1% in Guatemala. Most research on the consequences of the pandemic has focused on estimating the generalized consequences on the entire population without considering that some locations were more exposed to the infection than others. Our objective here is to determine whether greater exposure to COVID-19 had larger effects on educational outcomes compared to lower levels of exposure.

Figure 1. Changes in educational outcomes from 2018-2021 _Source_ : Authors’ calculations from Guatemalan educational administrative records. _Notes_ : The figure presents raw means by year for the entire sample in all educational levels.

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Table 10: Correlates of Politicians’ Integrity

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|Chaiperson|Contenders|Councillors|
|Voter Attachment to National Party - 2011|0.304|2.720*|-1.341|0.290|
||(0.498)|(1.468)|(1.218)|(0.584)|
|Average Herfndahl index - Councilor|-0.017**|-0.047**|-0.002|-0.014*|
||(0.007)|(0.019)|(0.018)|(0.008)|
|Herfndahl index - Chairperson|-0.003|0.005|0.004|-0.005|
||(0.004)|(0.012)|(0.012)|(0.005)|
|Number of radios (HQ)|0.008|-0.106*|0.076*|-0.007|
||(0.020)|(0.056)|(0.041)|(0.025)|
|Poverty rate 2013|-0.638|-1.047|0.112|-0.723|
||(0.473)|(1.158)|(1.433)|(0.548)|
|Log Total Population|-0.016|-0.065|-0.054|-0.024|
||(0.096)|(0.287)|(0.294)|(0.110)|
|Urban population share|-1.614**
stressed by Ley and Steel (2009a), DW jointness statistic may become very sensitive and volatile if one of the variables has high PIP and the other has a very low one. This is indeed the situation we encounter in our analysis. In addition, if the net interest margin and private credit were to be strong substitutes in a sense they would represent very similar factors and thus be mutually replaceable in the estimation process, they should also exhibit the same importance regarding economic growth if considered separately. These findings make us believe that the LS statistics are more appropriate to judge the interdependence between financial indicators.

Table A1. Financial Indicators, Jointness Statistics According to Ley and Steel (2007)

|Net interest<br>margin|.|||||
|---|---|---|---|---|---|
|Bank Z-<br>score|0.335|.||||
|Private credit|0.058|0.025|.|||
|Market<br>capitalization|0.025|0.014|0.011|.||
|Market<br>turnover|0.224|0.158|0.041|0.027|.|

Table A2. Financial Indicators, Jointness Statistic According to Ley and Steel (2007), Alternative

|Net interest|.||
|---|---|---|
|margin|||
|Bank Z-|0.251|.|
|score|||

56
the temperature data from CRU; (iv) the number of days that temperature is above 28<sup>◦</sup> C; (v) dropping regions with temperature being above that level; and (vi) temperature shock, defined as the difference between actual temperature and longterm temperature being greater (less) than 2 (-2) standard deviations (Appendix A, Table A4).

> 8 We also investigate the role of information and communication technologies (ICTs) in poverty reduction by providing access to markets, decreasing transaction costs, and increasing income for a significant proportion of people living in developing countries (World Bank, 2016). Our results show that regions with better access to ICT are less vulnerable to the effects of higher temperature.

17
variables used in the analysis by country and survey year.

**Table 3.3. Household surveys and welfare measures included in the analysis**

|**Country (years)**|**Welfare measure**|
|---|---|
|Ethiopia (2010/11, 2014/15)|Total expenditure per adult equivalent<br>Food expenditure per adult equivalent<br>Nonfood expenditureper adult equivalent|
|Lesotho(2017/18)|Total expenditureper capita|
|Malawi (2010/11, 2016/17,<br>2019/20)|Total expenditure per capita<br>Total food expenditure per capita<br>Total nonfood expenditure per capita<br>Household dietary diversity: based on 12 food categories<br>Household food consumption score: based on food types consumed and<br>frequencyof consumption in a seven-day period(maximum 120)|
|Mauritania(2014,2019/20)|Total expenditureper capita|
|Mozambique (2014)|Total expenditure per capita<br>Total food expenditure per capita<br>Total nonfood expenditureper capita|
|Niger (2018)|Total expenditure per capita<br>Total food expenditure per capita<br>Total nonfood expenditureper capita|
|Nigeria(2011,2013,2016)|Total expenditureper capita|
|Zambia(2015)|Total expenditureper capita|
|Zimbabwe(2017)|Total expenditureper capita|

_Source: World Bank._

> 11 In most countries, the aggregates take spatial variation in prices into account and where this is done, this is retained. In addition, if the consumption aggregate used for national poverty measurement uses per adult equivalent instead of per capita, this is also retained. This makes the aggregate different from the ones reported in the World Bank’s Poverty and Inequality Platform.

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Leaving all these behind to start afresh in a new place seemed scary and uncertain. But move we did, and after going to high school and doing my undergraduate degree in Auckland, I was instead firmly attached to this new home. But then a new opportunity was open to me – the chance to move to America for graduate school. Not only would moving to a new country offer educational opportunities far exceeding those I could get in Auckland, but research I later did with John Gibson found that New Zealanders who were at the top of their academic classes at the end of high school gain an average of US$1,000 a week, or around $50,000 a year, by migrating (Gibson and McKenzie, 2011). Again, deciding whether or not to move would seem an easy decision by economic cost-benefit analysis. Yet as I hopped on that plane, moving once again involved substantial fears and tears, including this time moving

> 1 The 1981 New Zealand census records a population estimate of 6,100 for Huntly:

> [https://www3.stats.govt.nz/New_Zealand_Official_Yearbooks/1981/NZOYB_1981.html#idchapter_1_9605]. 2 Data on incomes from the 2018 Census https://www.stats.govt.nz/tools/2018-census-place-summaries/huntly- <u>east#work-income-and-unpaid-activities. Exchange rate of US$1 = NZ$1.42 used.</u>

> 3 https://www.nzherald.co.nz/travel/huntly-named-worst-town-in-new-zealand-by-publicvote/A4ROMUR4LDUJXHCMGW4S7YARRE/

2
<!-- Start of picture text --> Figure 2<br>Adjusted Weekly Hours Worked, 1996<br>50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Primary Primary L-Secon. L-Secon. U-Secon. U-Secon. The Mix- The The Low-<br>Teacher Pub. Teacher Priv. Teacher Pub. Teacher Priv. Teacher Pub. Teacher Priv. Skilled Group Agricultural Skilled Group<br>Sch Sch Sch. Sch Sch Sch Group<br>Urban Rural<br><!-- End of picture text --> Source: Own estimates based on ENIGH survey

# _Secondary Occupation_

As mentioned above, teachers work fewer hours than the other occupational groups. It is relevant to examine how many hours on average teachers devote to their secondary occupation. Table 4 shows the secondary occupation shares by occupational categories, or the percentage of people that have a secondary occupation. On the whole, for all occupational groups the secondary occupation share is larger in the rural areas than the share of a secondary occupation is for the urban areas, excepting upper secondary teachers in public schools. In particular, note that the secondary occupation share for teachers in basic public schools is significantly larger than the secondary occupation share for the teachers in basic private schools. Yet if one compares the secondary occupation share for the teachers in basic public schools with regards to the upper secondary teachers occupation share, the pattern is the opposite, especially in the urban areas.

In addition, one can compare the teacher secondary occupation shares and the hours-worked in secondary occupation relative to _the mixed-skilled_ group. The table below shows that the secondary occupation shares of teachers in public schools are larger than the shares of secondary occupation for _the mixed-skilled_ group
Policy Research Working Paper 7519

# **Abstract**

Despite significant improvements in per capita expenditures and a marked decline in poverty over the 2000s, a large fraction of Eastern Europe and Central Asia’s population reports their economic situation in the late 2000s to be worse than in 1989. This paper uses data from the Life in Transition Survey to document the gap between objective and subjective economic mobility and investigate what may drive this apparent disconnection. The paper aims at identifying some of the drivers behind subjective perceptions of economic mobility, focusing on the role of perceptions of fairness and trust in shaping people’s perceptions of their upward or downward mobility. The results show that close to half of the households in the region perceive to have experienced downward economic mobility, that is, that their position in the income distribution has deteriorated. The results also show that perceptions of higher inequality, unfairness, and distrust in public institutions are associated with downward subjective economic mobility. The findings from this study confirm that factors beyond objective well-being are associated with the perceptions of mobility observed in Europe and Central Asia and may explain why the region has had such a pessimistic view of economic mobility during the past two decades. Understanding what drives people’s perceptions of their living standards and quality of life is important, because regardless of objective measures, perceptions could influence people’s behavior, including support for reforms and labor market decisions. For Eastern Europe and Central Asia, a region that has undergone substantive transformations and which is still going through a reform process, accounting for these aspects is critical.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ccancho@worldbank.org.

_The Policy Research Working Paper Series disseminates the