rafmacalaba/data-use-mentions-extended huggingface dataset:
ability of withholding information: before that period, withholding declarations were annual and filed on paper, while after that they became monthly and digital. For those reasons, we do not have access to withholding declarations before 2011. This limits our ability to observe important sources of income at the top, including all capital income and wage income for individuals whose only source of labor or mixed income is wages (and therefore do not file income tax declaration). Below we discuss in more detail how we proceed to impute incomes at the top of the administrative data for those years.

# **3.2.2 Corporate Income Tax & Shareholder Registry**

One key conceptual contribution of the Distributional National Accounts (DINA) program is to use a broader measure of income to compute inequality, considering not only income flowing directly to households but also to corporations and the government, under the assumption that eventually households are the final beneficiaries of all income generated in an economy (Alvaredo _et al._ , 2020, Zwijnenburg, 2019). One large component of national income not flowing to households is undistributed corporate income. The importance of considering income retained at corporations at the household level is illustrated by the significant changes in measurement of inequality and income share in the US around the 1986 tax reform, when incentives to retain income in corporations substantially changed (Nelson, 2016, Smith _et al._ , 2022).

In close collaboration with the tax authority in Honduras, we have created a comshareholder for all active in the We more prehensive registry firms country. provide detail in Appendix B and explain briefly here the available data. We use a series of administrative records to recover shareholders and their participation in each corporation, and then process the data to deal with inconsistencies and produce a final dataset at the firm-by-shareholder level, including the participation of each shareholder. Whenever direct shareholders are corporations, we apply an iterative process to recover individual shareholders and their shares through up to five levels of ownership. This is a static dataset,
constrain their policy choices (Carreri and Martinez, 2022). Matched with personnel data, the detailed procurement data we provide could generate new evidence on how the personal traits of state bureaucrats, such as experience and educational attainment, correlate with measures of value-for-money (Best et al., 2023; Fenizia, 2022). More broadly, we expect _MiDES_ to allow researchers to engage with these and many other questions on subnational public finances.

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- <mark>De Weerdt, J., Gibson, J., & Beegle, K. (2020). What can we learn from experimenting with survey methods?.</mark> _<mark>Annual Review of Resource Economics</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>, 431-447.</mark>

- <mark>Deaton, A., & Zaidi, S. (2002).</mark> _<mark>Guidelines for constructing consumption aggregates for welfare analysis</mark>_ <mark>(Vol. 135). World Bank Publications.</mark>

- <mark>Durazo, J., Herewait, Y., Pattinasarany, G.D.V., & Jolliffe, D. (2017) bridging Surveys: A methodological experiment of food consumption data in the Indonesian Social Economic Survey (Susenas). Contribution presented at the XV EAAE Congress, “Towards Sustainable Agri-food Systems: Balancing Between Markets and Society”, August 29 – September 1, 2017, Parma, Italy</mark>

- <mark>FAO, IFAD, UNICEF, WFP and WHO. (2021). The State of Food Security and Nutrition in the World 2021.</mark> <u><mark>https://data.unicef.org/resources/sofi-2021/</mark></u>

- <mark>Friedman, J., Beegle, K., De Weerdt, J., & Gibson, J. (2017). Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in Tanzania.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>, 94-111.</mark>

- Government of Tanzania. (
<!-- Start of picture text --> Born rural lives rural Born rural lives urban Born urban lives urban<br>.8<br>.7<br>.6<br>.5<br>.4<br>Predicted probability<br>.3<br>.2<br>.1<br><!-- End of picture text --> _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014

# **3. Mobility within Generations**

While the idea of lack of correlation between parental income, education or occupation and the outcomes of their children is the fundamental benchmark of a society with high intergenerational mobility, it is unclear whether the same principle would carry over in the analysis of mobility within a generation. A society where a person’s income or living standards 10 years ago, for example, had no bearing on their income today would be a society with a significantly large amount of inter-temporal variation in these outcomes. The lack of serial correlation would imply a lot of random churning in living standards and would not necessarily be desirable.

So how can mobility within generations be measured and analyzed? One possibility is to consider directional income/living standards movement for the same individual or household over a lifetime. A mobile society would be one in which poorer and socially marginalized households can climb up the economic ladder and attain higher living standards. This would require the income of this group to grow faster than the income growth realized by the average households. So in a way the key statistic to measure intra-generational mobility then is simply the growth rate of income of the household between two points in time. But a pre-condition to being able to measure this is the existence of nationally representative data that allow us to observe the same household at two points in time. Unfortunately for Nepal, the panel data that have been collected with the three rounds of the living standards survey
|**Table A2:**|**Summary**|**Statistics**||||
|---|---|---|---|---|---|
|Variable|Mean|Std.<br>deviation|Min.|Max.|No. of<br>Observations|
|Female Workers (% of total workers)|29.217|28.009|0|100|44539|
|Exports (%, cell average)|9.452|13.96|0|100|44539|
|No. of Workers (logs)|3.061|1.231|0|10.06|44539|
|Herfindahl-Hirschman Index (HHI)|0.102|0.147|0|1|44539|
|Age of Firm (logs)|2.53|0.833|0|5.737|43911|
|Foreign Ownership (proportion)|0.089|0.266|0|1|44217|
|Female Ownership Y:1 N:0|0.34|0.474|0|1|43506|
|Skills Obstacle (0-4)|1.35|1.301|0|4|43892|
|How Much Of An Obstacle: Transport?|1.259|1.282|0|4|44074|
|<br>Firm Purchased Fixed Assets Y:1 N:0|0.481|0.5|0|1|44182|
|Power Outages (days)|1.316|3.541|0|30|42105|
|Courts Obstacle (cell average)|1.016|0.715|0|3.955|43198|
|Crime Losses Y:1 N:0|0.178|0.383|0|1|43173|
|Firm Inspected Y
would be needed to determine whether increases in public spending are feasible and whether they could be financed through a growth in tax revenues, borrowing, or increased external assistance (see Figure 13).

The approach has some limitations due to the poor quality of existing spending data. If we had complete data on spending and outcomes for all countries, this approach would be able to provide much more realistic scenarios on the feasibility of adjusting resource levels and improving outcomes in all countries. As highlighted in Section 2, there is only limited information on private education spending and this can affect the conclusions that can be drawn. For example, trends in outcomes will be affected not only by changes in public spending but also by changes in private education spending. Excluding these changes in private spending has the potential to affect elasticities and the conclusions that can be drawn. Moreover, the quality and coverage of public education spending data also limited the number of countries that could be included in our analysis as well as the precision of the benchmarks that could be estimated. It is expected that as more and better data become available, the benchmarking approach outlined in this section can be further refined and improved. Despite these limitations, the analysis performed on existing data highlights how the proposed approach can help guide spending decisions on education.

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according to the unadjusted REER. Thus, we find a large discrepancy between the two measures of the REER.

Residual testing shows signs of possible misspecification: we fail to reject zero autocorrelation of residuals but find evidence of non-normality<sup>40</sup> and heteroskedasticity for some specifications. As a result, we employ alternative methodologies such as dynamic OLS (DOLS<sup>41</sup> ) and fully modified OLS (FMOLS), which confirm our results. Both DOLS and FMOLS suggest the existence of a cointegrating relation by failing to reject the Hansen test for parameter instability. Both methodologies also fail to reject

> 39 As stressed by MacDonald and Ricci (2003), many studies find the real exchange rate of commodity exports to be strongly correlated with commodity prices though weakly correlated with terms of trade. This can be rationalized by: (i) the inclusion of goods with sluggish price adjustments and incomplete pass-through in aggregate terms of trade indices, (ii) the higher relative accuracy of commodity prices compared to export and import deflators, and (iii) the higher frequency of commodity price data that may influence financial decisions regarding currencies of commodity exporters. In contrast to these studies, we find that real soybean prices decrease the significance of productivity differentials between agriculture and services (tradables vs nontradables).

> 40 We fail to reject skewness in all specifications upon inclusion of two dummy variables taking value 1 in 1982 and 2002, respectively, and value 0 in all other years. Results are robust to this specification. 41 The DOLS estimators use HAC standard error correction and contain leads and lags of order 0 and 1, respectively.

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Using the data sets discussed above, we construct a number of variables. The dependent variable, **_Employment growth_** , is calculated as the annual change in the number of permanent employees divided by the average value over the two periods.<sup>12</sup> In Orbis, we calculate growth rates year over year, while in the ES sample, employment growth rates are annual changes over a two year period. To nullify undue effects from outliers, we truncate the top and bottom 5 percentile observations from the employment growth distribution in Orbis. As the distribution of employment growth is much tighter in the ES sample, we only remove the top and bottom 1 percentile of observations.

In the ES, firms were asked whether they have access to a loan or line of credit from a financial institution. We use the responses to this survey question to construct a dummy variable **_Access to a loan_** which takes the value 1 for firms with access to a loan or line of credit and 0 for firms without access. We measure financial access in Orbis using the amount of loans and long term debt. If any of these amounts are positive, we code the **_Access to a loan_** dummy as 1, and 0 if both are 0 or if either loans or long term debt is 0 and the other is missing. Thus, the financial access measure in Orbis captures access to both short-term and long-term financing. As alternative measures, we calculate the **_Growth of finance_** in Orbis as the annual growth rate<sup>13</sup> of the sum of the loans and long term debt, and **_Debt ratio_** as the ratio of this sum to the total assets. We again truncate the top and bottom 5 percentile of the distribution of these two variables so as to diminish the influence of outliers.

> 12 Employment growth rates calculated using the base year or the average value over the periods as the denominator can yield
Thus, the effect of distance on the freight rate will be mixed with the effect of switching to a different route, which potentially has a different degree of market power.

Ignoring the resulting heterogeneity in distance effects is materially important if the research question goes beyond the average effect of distance. The average effect is sufficient to capture the negative impact of distance on trade. However, focusing on the average effect of distance is of less interest if we are interested in the effects of distance on particular industries and countries with specific and observed characteristics. The rest of the section focuses on determining the extent of the distance heterogeneity across countries and products.

# **4.3.1 Heterogeneous Distance Elasticity of Transport Costs: US Imports**

Using the US Census imports data, we explore whether the distance elasticity of transportation costs varies across export origins and products. For the country-specific heterogeneity, we leverage the fact that the US is geographically large, with ports allocated to different Custom Districts.<sup>34</sup> We first calculate the sea distances between various countries of origin and each of the US Custom Districts.<sup>35</sup> Next, we use these distances when employing the following model featuring the most direct approach to the estimation of the heterogeneity in distance elasticity: 34Therefore, the same country-exporter to the US has sufficient variation in sea distances to the US in large part due to the difference in sea distances to the coasts.

> 35These calculations are described in detail in Terner (2021). These distance measurements reflect the average _traveled_ distance of containerships from country _o_ to Custom District _d_ across 2015-2017.

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**_Perceived discrimination._** In a number of countries, estimated proportion of informal employees who consider themselves belonging to a discriminated group, is well above similar proportion among both formal employees and informal self-employed (Figure 15), once again supporting the hypothesis that exclusion or poverty escape motive plays an important role in the way how employees end up working without a contract. Table 7 provides evidence that within European regions the differences between informal and formal employees are statistically significant (at the country level the relevant sub-samples are too small).

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>LV BG RU SI EE SK PL CZ HU RO UA FI SE NO DK ES GR IL PT CY IT FR UK DE NL BE CH AT IE<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% belonging to a discriminated group<br><!-- End of picture text -->

**Figure 15 Perceived discrimination among formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data

**_Union experience._** Another indicator which helps to understand where the informal workers are coming from is trade-union membership (current or past).  Informal workers with union experience have clearly been working in the formal sector some time ago (and those with current membership probably plan to return). Figure 16 compares  union coverage (current or past) among formal and informal workers. It appears that transition from formal to informal sector is quite common in Nordic countries, Cyprus, Israel, Belgium, Ireland, UK
12|1.41|

Notes: Columns 1-3 report the mean of the named variable by district according to the MNREGA phase of that district. Data from the 2001 Census, the 2011 Census, the 2012 SECC and the VCF data come from the SHRUG dataset (Asher et al., 2019). Fire data is downloaded and assembled from the NASA FIRMS data and is derived from imagery from the MODIS satellite. ICRISAT data comes from the ICRISAT meso dataset (Rao et al., 2012). GDP data is scraped from the Indian Planning Commission website and covers the years 2003-2005 for most districts. Information on combines is scraped from the Indian Ministry of Agriculture website and comes from the 2006 Agricultural Input survey.

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%|71.6%|29.6%|7.1%|

_Note: The educational group includes such variables as secgen specsec univ. The marital status group: wengaged mengaged marrdivwid1w marrdivwid1m. The children group: children: kid1w kid2w kid3w kid1m kid2m kid3m. The early marriage group: earlymarrw earlymarrm. The parentless group: nofather nomother. The education of father: vocsec_f univ_f. The education of mother: vocsec_m univ_m. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 30
[0.23]|
|38|Metals|391|0.16|0.20|0.81|1.16|
||||[0.21]|[0.13]|[0.16]|[0.13]|

PANEL B: Median Output Elasticities

|31|Food|390|0.08|0.25|0.76|1.03|
|---|---|---|---|---|---|---|
|32|Textiles|364|0.19|0.16|0.79|1.11|
|33|Wood|462|0.11|0.20|0.77|1.11|
|38|Metals|391|0.21|0.19|0.83|1.16|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Column (1) refers to number of observations for each production function by sector. Columns (2) - (4) report average (median) estimated output elasticity with respect to each production input for firms in the sector in panel (A) and (B). In panel A, results in brackets report standard deviations (not standard errors). Column (5) reports returns to scale, which is given by the sum of the average (median) elasticities of the three inputs.

indicate that the output elasticities are not statistically different before and after trade openness.<sup>20</sup> This suggests that variations in markups cannot
# **1 Introduction**

The use of randomized control trials (RCTs) in the social sciences has greatly expanded over the past two decades, from economics and political science to public health and education.<sup>1</sup> In parallel, journals, funders, policy organizations, and organizations promoting open science have emphasized that original research data be publicly accessible for others to analyze and use. As a result of this concerted push, hundreds of original RCT datasets from a wide variety of contexts and populations are already published and in principle accessible to researchers, with the potential to benefit numerous areas of research.

However, there remain to date significant barriers to the discovery and use of existing RCT data. Published datasets are scattered across data repositories, journal and university websites, and researcher homepages. In many data repositories, filter options and documentation fields are broad, often consisting of free form text fields, and data quality and provenance are hard to assess. RCT data are often published with a focus on replicating the analysis in a specific paper, and the properties specific to RCTs and features of the data have to be pieced together from inspecting datasets, data appendices, and readme files. Combining datasets across studies is hindered by a lack of harmonization and documentation. Any work in this regard by individual researchers is lost to the next person who wants to conduct a similar study.

In this paper, we propose a metadata schema that can serve as the basis for a catalog of RCT data (or, more broadly, any social science experimental data). The metadata schema defines a set of fields with encoding schemes that can be used to describe datasets associated with social science experiments. The encoding scheme defines the acceptable formats and values to complete the field, such as free text entry, dates, numeric values, or controlled vocabularies (multiple choice options).<sup>2</sup> The proposal organizes fields into thematic modules and includes whether a field should be optional or mandatory.
Young Lives surveys will allow researchers to examine the links between labor earnings, schooling, and skills.<sup>6</sup> The studies from low- and middle-income countries closest to our work are Glewwe, Huang, and Park (2017) and Glewwe, Song, and Zou (2022), who follow a sample of children in rural China. In 2017, they found no correlation between labor earnings and cognitive or SEM skills. By 2022, earnings were correlated with cognitive skills after controlling for years of schooling, but SEM skills were not. This scholarship complements ours by offering insights from a different context. Yet, the studies also differ from ours in their ability to track students over time: Glewwe, Huang, and Park (2022) have some data on 67% of their original sample and face-to-face interviews on just under 50% (compared to 84.5% and 75.1% in our case). In addition, they focus less explicitly on the (potential) heterogeneity in returns by migration status.

It is in this context that our contribution adds value. We first show that our lengthy tool development process produced measures of SEM skills in low-income countries that satisfied desirable measurement properties. We then document the associations between these skills and labor earnings, highlighting the critical role of migration in a sample that started in rural areas but moved to multiple locations over 15 years. As other longitudinal studies either do not (yet) observe labor market outcomes (e.g., Young Lives) or do not include well-constructed measures of SEM skills, this is only the second long-term research project to establish a pattern of correlations between labor earnings and specific cognitive and SEM skills, in a less-developed country. We hope this descriptive evidence helps resurrect the fundamental question raised by Bowles and Gintis (1976), this time for low-income countries: What are the skills that children learn in schools that are then valued in the labor market?

# **I. SAMPLE**

# *
to new technologies, promoting foreign direct investment, and increasing incentives for pursuing balanced domestic macro policies. In general, it can help reduce poverty. Nonetheless, in this paper, only the short-term impacts of trade reforms on poverty are considered, focusing on the potential immediate problems or benefits for the poor, which helps formulate any compensatory measures.

Overall, both liberalization scenarios marginally reduce poverty in Nicaragua, with the non-discriminatory one being more pro-poor than CAFTA. A positive aspect is

> 25 For a full analysis of the poverty situation and its evolution in the last decade see World Bank Report No. 20488 NI, titled “Nicaragua Poverty Assessment, volumes 1 and 2.

> 26 Although informative, the rural and urban poverty measurement obtained using a single national poverty line may be to some extent misleading. Price levels and the corresponding purchasing powers in the rural and urban sectors of the economy can be quite different so the zone specific poverty lines should be used to guarantee more accurate estimates.

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