rafmacalaba/data-use-mentions-extended huggingface dataset:
data from between 1970 and 1980.9 Second, total public spending on health has had much less impact on average health status that one might have expected, and certainly less than hoped. Although the lack of data on public spending has, until recently, limited the direct examination of the issue, Musgrove (1996) summarizes studies of the impact of public spending on health on health status: "[m]ultivariate estimates of the determinants of child mortality give much the same answer [as his results on life expectancy or DALY burden of disease]: income is always significant, but the health share in GDP, the public share in health spending, and the share of public spending on health in GDP never are." Even using instrumental variables to account for data and endogeneity problems Filmer and Pritchett (1997) find that public expenditure on health as a share of GDP is a small, and statistically insignificant, determinant of child mortality. At the point estimates _doubling_ public spending from 3 to 6 percent of GDP would improve mortality by only between 9 to 13 percent (appendix Table A-I reproduces those regressions).<sup>1</sup> Other empirical studies that add measures of health resources, such as physicians, nurses, or hospital beds per capita to the basic socio-economic variables in this type of regression rarely find large and significant impacts for these variables (Kim and Moody, 1992).

9 Some have been confused about Anand and Ravallion (1993), claiming that they showed that income was unimportant, in spite of their explicit claim otherwise. What they showed is that they could not reject the restriction that average income affected health status only insofar as it affected the level of poverty, highlighting the fact that it is increased income among the poor that is most effective in improving health. However, since the correlation between average incomes and poverty is very high this still implies average income, through its poverty reducing effects, explains most health status variation.

10 Bidani and Ravallion (1997) show
# **Table 1: Estimates of Crypto-assets Adoption**

|Panel A: Statista Global<br>Used or Owned Crypto-a<br>%<br>**Country**|Country Survey<br>ssets (2020)<br>**Share(%)**|: Share of Respondents wh<br>**Country**|o Indicated That They<br>**Share(%)**|
|---|---|---|---|
|Nigeria|31.9|Lithuania|8.7|
|Vietnam|21.1|Egypt,Arab Rep.|8.3|
|Philippines|19.8|Norway|8.1|
|South Africa|17.8|Portugal|8.1|
|Thailand|17.6|Australia|7.8|
|Peru|16.1|Korea,Rep.|7.6|
|Turkey|16.1|Serbia|7.5|
|Colombia|15.3|Russian Federation|7.3|
|Argentina|14.4|Austria|7.2|
|Indonesia|13|Poland|7.2|
|Brazil|12.5|China|6.9|
|Malaysia|12.3|Hungary|6.4|
|Chile|11.7|Romania|6.4|
|Saudi Arabia|11.4|Belgium|6.3|
|Switzerland|11.1|United States|6.2|
|Greece|11.1|France|5.6|
|Kenya|10.5|Pakistan|5.6|
|Dominican Republic|10.3|Canada|5.2|
|Netherlands|10|Germany|5.2|
|United Arab Emirates|10|Finland|5.1|
|Mexico|9.7|New Zealand|
self-reporting by all household members, the household’s primary respondent will then report on behalf of other members as well, “proxying” their responses.<sup>3</sup> Recently, the global penetration of cell phone access has introduced phone surveys as a method of data collection that could allow for more frequent interview intervals along with shorter recall periods at a lower cost. While weekly interviews have been shown to be less vulnerable to recall loss than longer windows (Arthi et al. 2018), weekly interviews across an entire year or season would be prohibitively expensive for most research budgets and highly fatiguing for respondents. In this paper we use a survey experiment to test an intermediary option, comparing quarterly (90day) with an annual recall question using a series of phone surveys.

Labor outcomes measured using the long recall responses result in much lower recorded levels of any work involvement (22%), number of unique activities reported (24%), and number of months worked (20%) compared to those based on the quarterly measures, although we do not find significant losses in total hours worked. These gaps increase with the amount of time that has passed. Proportional losses from long recall (relative to short recall averages) are, depending on the outcome variable, between two and ten times larger for proxied individuals than for households’ primary respondents who self-report. Splitting the sample by self and proxy reported individuals, we explore heterogeneity by gender and age of the respondent as well as of the proxied household member. We do not see clear patterns of heterogeneity for self-reports or by the primary respondent’s characteristics when reporting for others. We do, however, see that younger household members have greater relative losses from long recall when their responses are proxied than older proxied household members. The profile of primary respondents (generally household heads) further biases annual recall data against women and younger household members, as they will be exposed to larger losses from long recall as a consequence of having their labor
mobility’, which tells us the extent to which households move around in the overall income distribution. This is the measure that most closely corresponds to the idea that mobility can lower lifetime inequality and provide equality of opportunity. Estimation of equation (18) in contrast can be thought of as giving an estimate of ‘conditional mobility’, telling us whether households move around relative to their own average income. This relates somewhat to the concept of mobility as a measure of flexibility and efficiency of the labor market. We will provide estimates of mobility under both specifications and discuss further the interpretation of these two measures in Section 6.

# 4 Data

To investigate earnings mobility in Mexico we use the Encuesta Nacional de Empleo Urbano(ENEU), Mexico’s national urban employment survey, conducted by the Instituto Nacional de Estadística, Geografía e Informática (INEGI). The sampling unit is a dwelling or housing structure, and demographic information is collected on the household or households occupying each dwelling. An employment questionnaire is then administered for each individual aged 12 and above in the household, providing detailed information on occupation, labor hours, labor earnings, and employment conditions. The survey is designed as a rotating panel, with households interviewed for five consecutive quarters before exiting the survey. In each new round the household questionnaire records absent members, adds any new members who have joined the household, and records any changes in schooling that have taken place. If none of the original group of household members is found to be living in the dwelling unit in the follow-up survey, the household is recorded as a new household (INEGI, 1998). As in many labor force surveys in developing countries, the interviewers do not track households which move, so any household which moves attrits from the panel.

We use data from the first quarter of 1987 through to the second quarter 15
**|-0.007***|0.010***|0.009***|0.006***|0.005***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|FDR q-values|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|R-squared|0.650|0.657|0.725|0.732|0.960|0.959|
|Observations|47,840|47,840|47,840|47,840|47,840|47,840|
|Mean Dep. Variable|0.035|0.033|0.089|0.085|0.460|0.414|
|_Controls (All Panels)_|||||||
|Grid FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Panel A presents the results of our main specification, as outlined in equation 1. Panel B shows the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. It shows the results of the main specification in equation, controlling for an indicator variable of differential linear pre-trends between the treatment and control groups. SID denotes standardized values of the inverse distance from each grid to the refugee camps, and I(Post2017) is an indicator variable with a value of 1 post-2017, marking the onset of the substantial Rohingya inflows to Bangladesh. Dependent variables are constructed using the MODIS Land Cover Type dataset, which captures Earth’s land surface imagery every 1 to 2 days, providing measurements of area in square
a maximum of 10 workers we also find significantly positive effects on the retention of white incumbents. This suggests that in small peer groups, where relationships among coworkers may be deeper, losing a coworker of a different race may make also white workers more likely to stay at the firm.

# **4.3 Heterogeneity**

We will now exploit our diverse sample of workers and peer groups to investigate potential heterogeneities in how incumbent workers’ retention is affected by losing same-race coworkers.

**Occupation characteristics.** We start by examining whether effects differ across occupation characteristics. First, we split our sample into white-collar and blue-collar occupations.<sup>18</sup> The nature of coworker interactions may differ substantially between these occupations. Moreover, if the demand for workplace amenities – specifically, the preference for same-race coworkers – increases with income (Hamermesh, 1999; Pierce, 2001), we expect the effects to be more pronounced in white-collar occupations. Second, we differentiate workers by the importance of teamwork in their occupation, using O*NET data on occupational task descriptions.<sup>19</sup> Two contradictory mechanisms could be present here: On the one hand, one could expect that in jobs requiring more teamwork, coworkers interact more strongly and thus may be more affected by the racial composition of their colleagues. On the other hand, the contact hypothesis would predict that racial biases are mitigated when workers interact more (Allport, 1954), such that workers in occupations with more teamwork might be less affected by a change in the racial composition of their peers. Results are presented in Panel A of Table 3. We find that the negative effects of a non-white vs. white death on the retention of non-white incumbents are only driven by workers in white-collar occupations, while we do not find significant effects on bluecollar workers. Moreover, the effects appear to be weaker in jobs that involve more teamwork, suggesting that in these jobs, coworker interactions may have reduced preferences for same-race coworkers
in return areas that informs programming, as well as communication with refugees and IDPs about conditions so as to inform their decisions; this information should complement all statistics about returns – both assisted and spontaneous.

> 97 JIPS, under the leadership of the Special Rapporteur on the Human Rights of IDPs, is developing a methodology and tools to apply the IASC Framework’s eight criteria for durable solutions (Beyani, Baal and Caterina 2016).
industry structures might be subject to similar general trends. The equivalence is achieved by including exposure-weighted averages as controls in model (3).<sup>32</sup> Nevertheless, in our setting, similar industry structures which are affected in similar ways, are not necessarily worrisome as mentioned in Section 4.<sup>33</sup> Column (1) in Table A.6 shows the results at the municipality level and using robust standard errors at the municipality level. Column (2) redoes the analysis at the industry level and uses robust standard errors at the exposure level. While our first stage F-Statistic stays significant, the significant level of our coefficient decreases; however, is still significant at the 10 % level. We acknowledge the fact that our coefficients differ slightly, however, we suspect that the difference arises due to the use of fixed effects in the panel data setting. Overall, we conclude that our instrument is still relevant at the industry level; however, the second-stage results have a lower significance level.

# **5.3 Heterogeneities**

We next conduct heterogeneity analyses to examine how differences in municipal and individual characteristics shape families’ exit dynamics in response to “rising tides”.

**Positive versus negative shifters** First, we are interested in the question of whether the positive or negative shifters drive our results. Panel A of Table 6 shows the results of splitting the data in those families that live in a municipality that experienced on average positive employment growth and those that experienced on average negative employment growth. Results are driven by positive shifters, causing an increase in the probability of leaving BF. While those results display an interesting heterogeneity, they also increase the credibility of our main question – if employment growth (instead of decrease) causes an increase in the likelihood of leaving the program.

**Informality** Second, we aim to disentangle the effect for areas with historically high informality rates and areas with historically low informality rates. We classify municipalities
and new versus existing firm) to form a treatment group of 729 firms, and a control group of 1,112 firms.

The awarded grants were paid in four tranches. The first payment averaged about 10% of the total, while the second tranche was of approximately 45%. The third and fourth payments were conditional on employment and sale thresholds set individually.

Data was collected in three follow-up surveys. The first follow-up took place between November 2012 and May 2013; the second follow-up was administered between October 2013 and February 2014; the last follow-up took place between September 2014 and February 2015.

# <u>Variable details:</u>

- Self-employment: indicator equal to one if respondent currently operates a firm (survey section 2, question 1, and attrition section 0, question 3).

- Employed: if respondent is self-employed, or if she worked during the last month as wage or salary worker, casual worker, paid on commission or other basis, agricultural worker, or other form of paid work (section 11, question 1).

- Migration: indicator equal to one if respondent claims to ”live in a different Nigerian state now than where I lived at the start of 2012” (section 1, question P2). Also consider answers to tracking module, question 5 (whenever untracked individual is reported to live ”in a different location in Nigeria” or ”outside of Nigeria”).

65
Policy Research Working Paper 5647

# **Abstract**

What drives governments with similar revenues to publicly provide very different amounts of goods for which private substitutes are available? Key examples are education and health care. This paper compares spending by Brazilian municipalities on pre-primary education—a good that is also provided privately—with spending on public infrastructure like parks and roads, which lacks private substitutes. Panel data from 1995–2008 reveal how the distribution of income affects public investment. Revenue is endogenous to investment outcomes, and the analysis addresses this problem by exploiting a 1998, nationwide education finance reform and several revisions to the policy. The author constructs a variable that captures exogenous variation in revenue generated by nonlinearities of the law to instrument for observed revenue. Municipalities with higher median income and more inequality are less likely to allocate revenue to education or to expand pre-primary enrollment. They are more likely to allocate revenue to public infrastructure. There is suggestive evidence that this occurs for two reasons, hypothesized in two separate literatures. In rich and unequal municipalities, fewer total people support public education spending (the collective choice channel), and also, any given poor person wanting public education has less influence over policymakers there (the political power channel).

This paper is a product of the Human Development Department, Latin America and the Caribbean Region. 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 author may be contacted at kosec_katrina@gsb.stanford.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and
preferred to asking questions about earned income. Wealthier households tend to underreport earned income (Carletto et al., 2022). In this study, however, we examine specifically the sources of income, and so a consumption approach would provide no information on the sources from which a household earns its livelihood. Second, diversification as a coping strategy is not about how many different sources of income one has but about spreading the risk by diversifying to income sources with different risk profiles. To that end, one would want to have information on crop choice and the sectoral composition of wages. Unfortunately, the data, particularly the HFPS data, does not capture this information. Third, the way in which we have measured diversification cannot distinguish between voluntary and involuntary changes in diversification. For example, a person may be separated from a job, which would appear to be a decrease in diversification, though an involuntary one, not a deliberate coping strategy. Ideally, we could differentiate between involuntary and voluntary diversification actions, but we can only observe the level of diversification and the subsequently related associated food insecurity. Finally, as job searches, crop production, and starting a new business typically extend over several months, one would want to have data over a long enough time frame to adequately allow for inter-household adjustments to livelihood sources. While our data span a time period of more than two years, we acknowledge that this time relates to food insecurity.

8
percent of GDP)** 40

**Figure 15. Selected tax rates (1990-2022)**

<!-- Start of picture text --> 34.1 40<br>35<br>1.4<br>30 29.1 Unconventional 35<br>Taxes<br>9.5 30<br>25 9.1 Social Security<br>25<br>20.6<br>20 2.5 20<br>9.8 VAT & Excise<br>5.2<br>15 4.3 15<br>10 8.5 8.0 Income & 10<br>Property<br>13.4 5<br>5<br>6.3 5.8 0<br>0<br>OECD ARG LAC VAT CIT PIT Export Duties Financial Transaction<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Source:_ Ministry of Economy and OECD stats. _Note: LAC excludes Argentina. Unconventional taxes in Argentina are represented by the turnover tax (ingresos brutos), financial transaction tax, export duties, tax on FX purchase, among others. Data refers to 2021 or closest._ _Source:_ Ministry of Economy. Note: Grey areas denote economic recessions.

In recent decades, Argentina has experienced limited changes in its top statutory income and value-added tax rates, while "emergency" taxes have become more prevalent and unstable. While VAT, CIT, and PIT top statutory rates have remained relatively stable since the 1990s ( **Figure 15** ), it is important to note that the current tax rate database may not fully capture all policy changes. For instance, modifications in income brackets, income tax floors, and lower marginal tax rates are not reflected
classes (Hout and DiPrete, 2006). Those who argue for the first approach propose, based on empirical evidence, that the occupational structure follows a continuous hierarchical scale with differences in degree, which are not qualitative (Hout and DiPrete, 2006). The continuous occupational scale most frequently used in international studies on social mobility is the Socio-economic Index of Occupational Status (ISEI) developed by Ganzeboom, De Graaf and Treiman (1992), which measures the socioeconomic status associated to each occupation.

Meanwhile, those who adhere to the second approach, the approach of classes, argue that social stratification may be represented by means of categorical grouping, which does not necessarily have a vertical structure but rather, mainly, qualitative differences. They consider that this perspective makes possible the analysis of individual mobility within the structural context and, thus, mobility may be seen as a process of mediation between the micro and macro-social levels.

Various social classes schemes, seeking to represent the stratification in society and which result from different theoretical perspectives, have been proposed. The CASMIN scheme (Erikson and Goldthorpe, 1992),<sup>14</sup> which has been applied in most contemporary studies on social mobility at an international level, seeks to capture the differentiation in the labor markets and production units related to the distribution of life opportunities (Hauser and Warren, 2001; Breen,

> 14 The acronym refers to its creation within the project _Comparative Analysis of Social Mobility in Industrial Nations._

10
19 Figure 4b: Productivity Growth and Vertical Specialization in China, Czech Republic, Indonesia and the United States by Industrial Sector, 1986-2014 <!-- Start of picture text --> CHN CZE<br>31<br>29 34 34 35<br>33 3429<br>26 30<br>3626 33 28 25 31 362626 3333 28 2525 2930 3429 31 35 3232<br>31<br>25<br>36 28 36 28<br>0 20 40 60 0 20 40 60<br>IDN USA<br>3626 33 2828 2525 29 343429 35 313135 32 36362626 3333 2828 2525 293034342930 313135 32<br>3626 33 30 32 35<br>32<br>30<br>0 20 40 60 0 20 40 60<br>1986-2000 2001-2014<br>Vertical specialization(world imports of parts and components<br>as a share of world total exports, average 1986-2014, percent)<br>300<br>200<br>150 200<br>100<br>100<br>50<br>0<br>0<br>-50 -100<br>300 200<br>200<br>100<br>100<br>0<br>0<br>-100<br>Productivity Growth (average by period, percent) -100<br>-200 -200<br><!-- End of picture text -->

# _Source:_ UNIDO (INDSTAT2), UN Comtrade, and authors’ calculations.

_Notes:_ ISIC3 industry codes are used; sample includes 85 countries; productivity is normalized using average industry productivity; weights are represented by value added.

ISIC3 codes: 25 Manufacture of rubber and plastics products; 26 Manufacture of other non‐metallic mineral products; 28 Manufacture of fabricated metal products, except machinery and equipment; 29 Manufacture of machinery and equipment n.e.c.; 30