rafmacalaba/datause-encoder-data huggingface dataset:
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OECD (Organisation for Economic Co-operation and Development). 2015. Education at a Glance 2015: OECD Indicators. Paris: Organisation for Economic Co-operation and Development .

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OECD (Organisation for Economic Co-operation and Development). 2017. OECD Economic Surveys: Mexico 2017. Paris: Organisation for Economic Co-operation and Development.

OECD (Organisation for Economic Co-operation and Development). 2018. “Climateresilient Infrastructure.” Policy Perspectives series. OECD Environment Policy Paper 14, Organisation for Economic Co-operation and Development, Paris.

OECD (Organisation for Economic Co-operation and Development). 2021a. Education in Eastern Europe and Central Asia. Paris: OECD Publishing.

OECD (Organisation for Economic Co-operation and Development). 2021b. Education at a Glance 2021: OECD Indicators. Paris: OECD Publishing.

OECD (Organisation for Economic Co-operation and Development). 2022. “2018 PMR database.” Organisation for Economic Co-operation and Development, Paris.

OECD/CAF/ECLAC (Organisation for Economic Co-operation and Development/Corporación Andina de Fomento/Economic Commission for Latin America and the Caribbean). 2016. Latin American Economic Outlook 2017: Youth Skills and Entrepreneurship. Paris: OECD Publishing.

OECD/CAF/ECLAC (Organisation for Economic Co-operation and Development/Corporación Andina de Fomento/Economic Commission for Latin America and the Caribbean). 2018. Latin American Economic Outlook 2018. Rethinking Institutions for Development. Paris: OECD Publishing. Ohnsorge, F., and S. Yu, eds. 2021. The Long Shadow of Informality: Challenges and Policies. Washington, DC: World Bank.

Oleyere, R. U. 2015. “School Enrollment, Attainment, and Returns to Education.” In The Oxford Handbook of Africa and Economics, Volume II: Policies and Practices, edited by C. Monga and J. Y. Lin. Oxford, UK: Oxford University Press.
Table 2: Average effects and standard deviation of effects

||Efe<br>|ct on con<br>|sumption<br>|per unit <br>|of transfer<br>|
|---|---|---|---|---|---|
||W<br>|LS<br>|M<br>|LE<br>|Bayes<br>|
||(1)|(2)|(3)|(4)|(5)|
|**Panel A: UCT**<br>||||||
|_β_: Intercept (Mean efect)<br>|0.298<br>(0.014)<br>[0.000]|0.298<br>(0.029)<br>[0.000]|0.335<br>(0.048)<br>[0.000]|0.335<br>(0.074)<br>[0.000]|0.347<br>(0.054)<br>{0.259, 0.471}|
|_στ_: Std. dev. of efects|||0.083<br>(0.060)|0.083<br>(0.083)|0.117<br>(0.067)<br>{0.011, 0.269}|
|# of RCTs|7|7|7|7|7|
|# of observations|18|18|18|18|18|
|**Panel B: TUP**||||||
|_β_: Intercept (Mean efect)|0.469|0.469|0.517|0.517|0.516|
||(0.071)<br>[0.000]|(0.072)<br>[0.000]|(0.091)<br>[0.000]|(0.118)<br>[0.000]|(0.102)<br>{0.314, 0.719}|
|_στ_: Std. dev. of efects|||0.350<br>(0.080)|0.350<br>(0.134)|0.395<br>(0.096)<br>{0.240, 0.614}|
|# of RCTs|10|10|10|10|10|
|# of observations|20|20|20|20|20|
|Bootstrapped standard errors||X||X||

_Notes:_ Estimates of the model of impacts of interventions on household consumption per unit of transfer in Equation 1 are presented in this table. Estimates for UCT interventions are in Panel A, and estimates for TUP interventions are in Panel B. Columns 1 through 4 report estimates of each parameter, with standard errors in parentheses and p-values in square brackets, while Column 5 reports Bayesian posterior means of each parameter, with posterior standard deviations in parentheses and 95% credible intervals in curly brackets. Column 1 uses robust standard errors clustered at the RCT-level, while Columns 2 and 4 use standard errors block bootstrapped at the RCT-level.

Our estimates of the average effect of unconditional cash transfers are robust to the choice of methods. We estimate an average impact of 0.30 under weighted least squares and 0.34 using maximum likelihood, in comparison to a posterior mean estimate of 0.35 using Bayesian methods. As these approaches appear to yield comparable estimates in our context, we report our preferred estimates using Bayesian posterior means for the remainder of this paper. For inference, Bayesian posterior standard deviations are comparable to block consumption per unit of transfer are robust to the exclusion of any individual RCT in our sample.

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labor market experiences and expectations. While not ideal because of potential fatigue and possible inconsistency in how respondents interpreted questions without the aid of survey enumerators, obtaining baseline data through this method solved the logistical and financial obstacles of physically tracking down a potentially scattered and extremely mobile population for face-to-face interviews, as they had been targeted through universities and radio ads and therefore did not necessarily reside in the cities where pre-assessment testing took place.

We worked with Project Implicit to include Implicit Association Tests (IATs) in the baseline survey. Social psychologists use IATs to measure an individual’s automatic associations between a social group and a stereotypic attribute (Greenwald, McGhee, and Schwartz 1998; Nosek, Banaji, and Greenwald 2002). Economists have used IATs to measure stereotypes related to gender and occupation (Beaman et al. 2009), race and intelligence (Bertrand et al. 2005), and attitudes towards other ethnic groups (Lowes et al. 2015). The IAT requires sorting of exemplars from four concepts – for example male, female, office, home — using just two response options —for example, left for either male or office and right for either female or home. If it is easier for participants to mentally pair pictures of _men_ with words associated _office_ and _women_ with words associated with the _home_ , then subjects should be able to make these pairings faster than the opposite pairings ( _women_ and the _office_ and _men_ and the _home_ ). That is, an individual biased by traditional gender roles should be faster in sorting words when the options are {Left: male or office; Right: female or home} than when they are {Left: male or home; Right: female or office}. Because the differences in sorting times are often less than a second, these associations are considered implicit and automatic, or beyond conscious control. Indeed, they often differ from explicitly expressed attitudes. Females, for example, often exhibit stronger implicit attitudes linking males with career and females with family than males, despite reporting weaker explicit attitudes (Nosek, Banaji, and Greenwald 2002).

The IATs attached to the assessment and baseline survey consisted of tests measuring the ease of associations between gender and a number of attributes relevant for women’s labor market participation in Nigeria. One test measured associations between gender and the concepts of _home_ and _office_ , a test commonly used in the IAT literature to measure gender-related bias (see, for example, Nosek et al. 2002). A second test was designed for the urban Nigerian context and measured associations between gender and the concepts of _office_ and _petty trade_ , as women might 9
and infrastructure than non-hosting districts.

Uganda’s Self-Reliance Strategy (SRS) integrated services for refugees and host communities (by allowing refugees free access to government health and education services) and promoted selfreliance among both communities in the long term (by allocating land to refugees). Since the arrival of large numbers of South Sudanese refugees, levels of international aid targeted at refugees in the country drastically increased from about US32 _millionin_ 2015 _toUS_ 507 million in 2019 (see Figure 1).<sup>8</sup> The SRS was updated in 2004 to address concerns that tensions may arise among locals if the refugees received superior services, and in recognition that refugees were likely to stay for a protracted period. The SRS was replaced by Development Assistance for Refugee Hosting Areas but kept its initial focus. The Refugee and Host Population Empowerment strategic framework updated the SRS model in 2016 (Ronald, 2020). The Comprehensive Refugee Response Framework (CRRF), which replaced the SRS model in 2017, more strongly emphasizes the development approach to hosting refugees.<sup>9</sup> These plans operationalize integration by linking humanitarian and development programming and interventions; the Ministry of Health (MoH) coordinates and implements health services integration through the district health system (Garimoi Orach and De Brouwere, 2006). Aid agencies are expected to support host districts’ health services rather than directly funding refugee settlements. This approach is designed to prevent host communities from carrying a disproportionate burden for hosting refugees, and to ensure that public services made available by the international humanitarian community do not disproportionately favor refugees at the expense of locals.

To descriptively confirm that aid targets refugee settlements and nearby host communities, we map out all current projects conducted by the World Bank related to refugees. These projects cover infrastructural improvements (e.g. bridges, roads, schools, health clinics), environmental improvements (forestry, agriculture, solar panels), and entrepreneurial grants. In SI Section S3, we descriptively show that they are precisely located in and near where refugee settlements are, 8These humanitarian aid levels are from all Refugee Response Plan funders, as reported to us by the UNHCR Uganda country office.

> 9Although the current CRRF falls outside of our study period, it emphasizes the same goal as previous frameworks—that international humanitarian and development actors and government ministries, departments, and agencies coordinate to support both refugees and host communities. For example, "in 2020, Uganda explicitly included refugees in the new National Development Plan III by calling for the integration of refugee programming in all national, sectoral and district development plans and statistics" (p.9, National Plan of Action 2021–2022 to Implement the Strategic Direction for the Global Compact on Refugees and the CRRF in Uganda, Office of the Prime Minister). See https://opm.go.ug/comprehensive-refugee-response-framework-uganda/ for more information.

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external training practices for current employees. Employers also provided information on the main sources of skills supply from education and training institutions. Together, these variables provide detailed insights into skills demand and supply from the perspective of employers and rich data to investigate the relationships between skills, skill deficits, strategies to mitigate them, and their productivity outcomes.

In common with other Enterprise Surveys, TESS elicited the following variables to characterize firms:

- Indicator variables for eight sectors, five regions and three firm sizes

- Firm attributes that research has suggested are related to (often unobserved) productivity attributes, including foreign ownership, female-owned firms, years in operation, and the educational attainment of the firm’s top manager

- Domestic sales and sales to international markets, whether as a direct or indirect exporter

- Engagement in innovative activities such as introduction of new products or services, quality control, R&D expenditure, and technology licensing

- Key variables needed to estimate firm performance using a simple production function model. Besides total sales and employment in the last fiscal year, data was elicited on the value of intermediate inputs and fixed assets, separately for machinery and equipment and buildings.

Skills encompass the broad set of cognitive, non-cognitive (social and behavioral) and technical competencies needed to perform effectively. Skills are acquired from education and training institutions, both before entering the labor market and while employed, in company-sponsored inservice programs. The TESS elicited information on the following skill dimensions:

- Education attainment of the workforce by four levels of schooling: primary, lower secondary, upper secondary and tertiary education, and their distribution in the workforce.

- The share of the workforce with formal pre-employment TVET qualifications as a separate category.

- Skill composition by seven occupational categories: managers, professionals, technicians and associate professionals, clerical jobs, sales, production operators, and other unskilled production or service workers. Each occupational category distinguished between Tanzanian and expatriate employees.

- Qualitative indicators of the adequacy of different workforce competencies (as judged by employers), including a wide range of cognitive, non-cognitive (soft skills) and job-specific technical skills.

- Company-sponsored in-service training, either in-house or external training, the number of workers trained, and the main training provider.

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household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented.

Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN‐OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection.

# Some data challenges common to refugees and IDPs

Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities.

**Sampling** . As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the

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# **6.  Moving Forward**

In this paper we have taken stock of existing cross-country indicators of governance, using a simple framework based on two questions: "what do we measure?" and "whose views do we rely on?". We emphasized the distinction between rules-based and outcome indicators, as well as the distinction between drawing data from experts or respondent surveys of citizens or enterprises. We also discussed the rationale for aggregate indicators, noting that different levels of aggregation are appropriate for different purposes. A sobering perspective emerges from this review, arguing for circumspection: while most indicators have many virtues, all face distinct challenges as well. This points to the need to look at a variety of indicators and sources when monitoring or assessing governance across countries, within a country and over time. We conclude by offering a few principles that may be useful as this work, and the use of governance indicators in public sector policymaking and civil society monitoring, continues.

_Avoid false dichotomies._ Too often discussions of governance indicators overemphasize distinctions between alternative types of governance indicators, with insufficient regard for the strong complementarities between them. For example, artificially sharp distinctions are often drawn between so called "subjective" and "objective" indicators of governance. As we have discussed however, virtually all indicators of governance rely on the judgments or perceptions of respondents in one way or another, and so we suggest that this distinction is largely artificial. And in some cases even the terminology is used in misleading ways. For example, the very recentlyreleased Ibrahim Index of African Governance touts itself as providing "objective" assessments of governance despite the fact that it is based on a number of purely subjective data sources including the Transparency International Corruption Perceptions Index and subjective ratings produced by the Heritage Foundation and the Economist Intelligence Unit.

Distinctions made between aggregated and disaggregated indicators often have an artificial element as well. First, some aggregate indicators transparently disclose each disaggregated source, enabling users to take advantage of the complementarities between both types and blurring the distinction between the two categories. Further, for 30
50 percent parents have zero schooling in our data sets. Thus the results on truncation bias in the intercepts discussed above in subsection (5.2.1) imply that the expected years of schooling at the 25th percentile are likely to be substantially overestimated in the coresˆ ident samples, for both _β_<sup>ˆ</sup> 0 and _ρ_ 0, and this is true in both Bangladesh and India. Also, the extent of upward bias is the largest if one relies on _ρ_ ˆ0. This evidence suggests that even though IGC (ˆ _ρ_ 1) is a robust measure of relative mobility when working with standard household surveys (Emran, Greene, and Shilpi (2018)), the estimates of expected schooling attainment using equation (2) may be the least reliable at the lower tail of the distribution.

The estimates of expected years of schooling for children show that the estimates from the coresident samples are in general larger than those from the full sample at the 25th percentile, and at the 75th percentile, it is true in 9 out of 10 cases.<sup>13</sup> The evidence thus suggests that the upward bias in the intercept dominates the downward bias in the slope estimate. However, the magnitude of bias does not exhibit any consistent pattern across the 25th and 75th percentiles of parental distribution. In Bangladesh, the truncation bias (absolute value of the bias) at the 75th percentile is smaller than that at the 25th percentile in four out of five cases: the average bias is 7 _._ 94 percent for the 75th percentile, while it is 15 _._ 58 percent for the 25th percentile. In India, in 2 out of 5 cases, the bias is higher at the 75th percentile : the average bias estimates are 11.57 percent for the 75th and 13.36 percent for the 25th percentile.

The estimates of expected normalized schooling (i.e., years of schooling normalized by standard deviation) in Table 8B also tell a similar story. The estimates are consistently upward biased in coresident samples, but the magnitude of bias does not follow a consistent pattern across the 25th and 75th percentiles. A comparison of the biases in the expected years of schooling to that in normalized schooling shows that the average bias is substantially higher in the estimates of normalized schooling when compared to the expected years

> 13The only exception at the 75th percentile is the expected years of schooling for sons conditional on father’s schooling. However, the estimates from coresident sample (6.666) and full sample (6.767) are very close to each other.

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Each question has five possible answers: (i) much more than usual, (ii) more than usual, (iii) same as usual, (iv) less than usual, and (v) much less than usual. Respondents who report an item as applying to them “more than usual” or “much more than usual” (“less than usual” or “much less than usual” for positively phrased items) score 1 on this item, whereas others score 0. These binary scores are summed up and summed scores of 3 or higher are classified as cases suffering from psychological distress.

The MHI-5 consists of the following 5 questions:

1. How much of the time during the past month have you been a very nervous person?

2. How much of the time during the past month have you felt calm and peaceful?

3. How much of the time during the past month have you felt downhearted and blue?

4. How much of the time during the past month have you been a happy person?

5. How much of the time during the past month have you felt so down in the dumps that nothing could cheer you up?

Each question has six possible answers: (i) all of the time, (ii) most of the time, (iii) a good bit of the time, (iv) some of the time, (v) little of the time, and (vi) none of the time. Respondents who report an item as applying to them “all of the time” score 5 and respondents who report an item as applying to them “none of the time” score 0 (vice versa for positively phrased items). Summed scores provide an indication of psychological distress.

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