rafmacalaba/data-use-mentions-extended huggingface dataset:
middle-income countries to build experience and the necessary infrastructure to implement such surveys at scale. Gourlay et al. (2021) provide an overview of how this acceleration materialized, drawing on the experience of the Living Standards Measurement Study (LSMS) survey program as well as other survey initiatives. In this paper, we approach the problem specifically from an agricultural and rural angle with a view to informing data collection activities going forward.

Agriculture, especially smallholder farming, comes with several specific challenges for data collection whether conducted in-person or remotely. Among other factors, agricultural production processes are seasonal and irregular, agricultural outcome variables have different timeframes, non-standard units are very common, respondents are more often illiterate, and access to mobile phones is persistently and comparatively low in rural areas (Carletto et al., 2021). To address some of these challenges, survey programs such as the World Bank’s LSMS – Integrated Surveys on Agriculture (LSMS-ISA), FAO’s AGRISurvey, and the 50x2030 Initiative often plan multiple field visits to improve data quality by reducing the length of recall period, but this can increase the burden for survey respondents and add to the cost of survey operations. Despite improvements brought about by many recent methodological developments, agricultural outcome variables continue to be plagued by measurement error related to respondent recall (Beegle et al., 2012a; Carletto et al., 2013; Deininger et al., 2012; Gourlay et al., 2019; Wollburg et al., 2020). Phone surveys, if adequately integrated with traditional survey operations, offer an opportunity to enrich the toolbox of agricultural statisticians in low- and middle-income countries, by providing an additional mode to collect data in a cost-effective and timely manner, taking some of the burden off the in-person interviews. Far from being a panacea, phone data collection has its own challenges.

Phone surveys cannot be expected to be a substitute for in-person surveys when it comes to administering long and complex survey instruments and can face serious issues with coverage bias when phone ownership
# **Figure C2**

**High school-dropouts wage skill premium and relative supply over time Deviations from each country’s mean**

<!-- Start of picture text --> ARG BOL BRA CHL<br>COL CRI ECU HND<br>MEX NIC PAN PER<br>PRY SLV URY VEN<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Wage Premium Relative Supply<br>.5 .4 .5 .5<br>.2 0<br>0 0<br>0 -.5<br>-.5 -.2 -.5 -1<br>.2 .2 .1 .2<br>0 .1 .1<br>0 0 0<br>-.2 -.1 -.1<br>-.4 -.2 -.1 -.2<br>.2 .2 .4 .2<br>0 .1 .2 .1<br>0 0<br>-.2 -.1 -.2 0<br>High School-Dropouts -.4 -.2 -.4 -.1<br>.4 .5 .2 .4<br>.2 0 .2<br>0 0 -.2 0<br>-.2 -.4 -.2<br>-.4 -.5 -.6 -.4<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

47
are recognized for their capacity to provide scale economies, efficiencies in logistics and in the provision of public services, dense labor markets that foster training and skills acquisition, innovation and creativity, diversification of production, lower environmental footprint through densification, and ultimately greater freedom for the individuals who inhabit them.” (Kharas et al., 2010, p. 1) Whether the urbanization glass is seen as half empty or half full, there is universal agreement among those who deal with urbanization in developing countries that it presents a set of issues that development practitioners and researchers need to grapple with urgently, if an inevitable and key development challenge/opportunity is to be met in the coming decades. At the same time, there is a pervasive sense among urban experts that not enough attention has been paid to the urbanization process by policy makers in developing countries and by the international aid agencies. (Kharas, et al., 2010; Linn, 2010) Recent efforts by the International Housing Coalition to call for recognition of the importance of urbanization in

> 1 This number may well be conservative. The Growth Commission has estimated that countries that wish to grow rapidly need to invest about 5% of GDP in infrastructure. If one were to assume that only one fifth of this requirement would be for urban infrastructure, based on 2008 GDP for lower and middle income countries (World Development Indicators Online puts it at $17.3 trillion) the requirement would be roughly $173 billion.

2
with state control<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**Why did SOEs respond so slowly to the fall in demand of 2008?  The short answer is that management of SOEs is required to meet a variety of quantitative targets rather than only a financial bottom line** .<sup>20</sup> As a result, SOEs did not cut supply sufficiently, accumulated inventories (which increased about 50% between 2008 and 2009, Figure 1.11), and increased their debt to banks (see the evolution of SOEs debts in the third line of Table 1.3).  This was especially the case for those SOEs where exports are a high share of sales. Government policy was aimed to mitigate the negative consequences of enterprise adjustment during the crisis, but such policy may have merely postponed the adjustment of production.

> 19 The reason this is true is that there are decreasing returns to scale in production.  This implies that shedding labor results in an increase in productivity of those employed.

> 20 While it is admittedly simplistic to assume that managers of private firms only pay attention to the bottom line or financial result before they adopt employment decisions it is clear that the room for maneuvering they have is way smaller than that of a firm less constrained by their financiers.

14
We also find that the gap in public investment quality between low-income and higher-income countries, as well as between commodity-exporting and commodity-importing EMDEs, has widened over time.

We use the PIQ index to test whether public investment quality affects the relationship between public investment and sovereign risk. We conjecture that when the quality of public investment is high, an increase of public investment will increase future growth and fiscal revenues and thus reduce sovereign risk. We expect the opposite for countries with low public investment quality. In this case, scaling up of public investment should be associated with worse fiscal fundamentals and higher sovereign risk. The data support these hypotheses and show that the mediating effect of the PIQ index on the link between public investment and sovereign risk is especially important for sub-investment grade countries. We also show that increasing public investment leads to higher debt ratios in low-investment-quality countries and to lower debt ratios in high-investment-quality

> 3 PIMI is a composite index that focuses on the efficiency of public investment management based on 17 indicators evaluating the stages of the public investment management cycle: (i) strategic guidance and project appraisal; (ii) project selection; (iii) project management and implementation; and (iv) project evaluation and audit. Besides this index, there have also been a few attempts to develop a measure of public expenditure efficiency using a combination of proxy variables, a data envelopment analysis, or other methods (IMF 2015; IMF 2021; Afonso et al. 2022; Herrera and Ouedraogo 2018).

3
_Pit_ represents the probability that household _i_ participated in public works in year _t_ . The vector _X_ comprises of covariates controlling for characteristics of the household head, i.e. age, sex, and the level of education; the demographic composition of the household, i.e. the share of children, working-age adults and elderly, and the share of members with a disability; and household wealth and assets measured by the land holding and housing characteristics. We also control for the reported poverty status of the household in some specifications. However, there is a strong discrepancy between the reported poverty status and the poverty status recorded in different administrative databases at the sectoral and national level. Since the discrepancy between the different administrative sources cannot be resolved in the absence of a centralized system, we are putting less emphasis on this, even though it is one of the formal criteria for eligibility. The choice of covariates entering the specification is also informed and validated by the qualitative evidence we obtained from the life-history interviews. We use the qualitative data when discussing the results to illustrate and provide more nuanced insights into the estimation results.

In a second step, we estimate the effect of the income transfer, either through public works or direct support, of household _i_ on the outcome of interest, livestock holding, _yi_ . We use a difference-in-difference estimator to measure differences in beneficiaries and nonbeneficiaries between 2009, 2011 and 2014. Beneficiaries hereby constitute households that have benefited from the VUP at the time of the survey. We look at the overall effect irrespective of the type of support as well as the effect for direct support and public works beneficiaries specifically. The comparison group comprises of households that are formerly eligible but have not benefited from the VUP at any point in time. We estimate the average treatment effect on the treated (TOT) as within estimator based on the following model: Yit = αi + θ<sup>D</sup> ∙1(t = 2011, 2014) + μ<sup>D<
of life satisfaction such as objective welfare measures and other socio-demographic characteristics are robust to the choice of the formulation of the life satisfaction question; and (ii) in the case of the subjective household welfare question (welfare ladder) we look for congruence in the accounts of subjective household welfare and measures of objective household welfare, and we also look for systematic within-household differences in subjective assessments of household welfare.

> 6 A number of other studies have similarly found strong positive associations between measures of subjective wellbeing and income, health, marriage and employment. Current subjective well-being measures also predict future behavior such as marital break-up, or job quits (for reviews of findings, see Clark et al. 2008; Dolan et al. 2009).

> 7 This assumption is further reinforced by other studies that find a correspondence between well-being reported by the respondent and assessments of the respondent's well-being by friends, relatives, or the interviewer (Sandvik et al 1993).

> 8 Ferrer-i-Carbonell and Frijters (2005) examine the more stringent assumption of cardinality, i.e. that the difference between responses 2 and 3 on the satisfaction scale is the same, for instance, as the difference between 6 and 7. Relying on data from the German Socio-Economic Panel (GSOEP) they look at differences between ordinal and cardinal models of life satisfaction using the 11-step response to the following question: “How happy are you at present with your life as a whole? Please answer by using the following scale in which 0 means totally unhappy, and 10 means totally happy.” They find that results are largely unaffected by the choice of cardinal vs. ordinal specification.

3
eliciting information about an illegal act. This raises questions of both research ethics (Duvell et al, 2008) as well as whether one can obtain truthful responses to questions about this sensitive issue. As a result of these issues, while there are a few exceptions (e.g. the Mexican Migration Project), most surveys of migrants (especially in destination countries) do not ask about their legal status.

Of course illegal migration is far from the only sensitive behavior of interest to social scientists, and a variety of methods have been developed to help obtain more reliable data on the incidences of these behaviors. One of the more widely used and simple to apply methods is that of list randomization. Under this method, a sample is randomly divided into two groups. The first group is given a list of _n_ statements (often _n=3_ ) and asked to say how many of the three statements they agree with. The second group is given a list of _n+1_ statements, consisting of the same _n_ statements as the first group, plus an additional statement which is about the behavior of interest. Since respondents only need reveal the number of statements they agree with, and not which ones, this can yield more truthful responses. The difference in the mean number of responses between the two groups then gives an estimate of the incidence of the behavior of interest.

Our purpose in this paper is to explore the use of list randomization as a means of measuring both the incidence of illegal migration, as well as the characteristics of illegal migrants. This is the first paper of its kind to use this method in a migration setting. We do this by adding a list randomization question to surveys being undertaken in four countries: Ethiopia, Mexico, Morocco and the Philippines. We illustrate how the incidence of illegal migration can be obtained via this method, how one can then obtain estimates by socio-demographic characteristic, and in the case of Ethiopia and Morocco, compare direct elicitation to this indirect method. Our results
to traffic congestion is likely to harm less the agglomeration benefits of firms in the tradable sector relative to the non-tradable sector and formulate the following two related hypotheses.

_Hypothesis 2A_ : The effect of urban density on firm productivity in developing economies depends on mobility within metropolitan areas, so that firms in lower mobility cities benefit less from agglomeration economies than firms in higher mobility cities.

_Hypothesis 2B_ : The moderating effect of congestion on the relationship between urban density and labor productivity is stronger for firms in the non-tradable sector than for firms in the tradable sector.

# **3. Data and methodology**

In our empirical investigation, we would like to examine the “pure” agglomeration effects, which capture both the supply-side learning externalities and the demand-side market-size externalities, _net_ of any geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density (reverse causality). We start with a baseline model of the relationship between urban density and labor productivity, in which none of the controls for agglomeration costs are included and in which we do not address reverse causality and sorting. In each subsequent model, we address each of these issues by controlling for geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density.

# _3.1. Data sources_

We use a harmonized and geo-coded data set based on the World Bank Enterprise Surveys for 98 low- and middle-income developing economies for the period from 2009 to 2020. See Appendix A1 for information on the economies included in the analysis. These firm-level surveys are representative of a country’s private sector, but exclude the resource-based sector, the financial sector, and firms with less than five employees. The enterprise surveys are produced using a stratified random sampling strategy and cover in a comparable way a range of businessenvironment topics including sales, number of employees, age of firm, manager experience, ownership characteristics, employees’ education, the degree to which firms see crime as an obstacle, and the location of the
are not yet ready for market are reasons to respond affirmatively to the question when posed to men, but not when posed to women. This is particularly noteworthy with the women who were part of cooperatives growing crops for market. Even though this work occurs outside of the home and is clearly market-oriented, many of these women responded no to the question since they were not being paid until the crops went to market.

A key takeaway from this research is that surveys that aim to measure employment and work need to take into account local definitions of work, especially as relates to differences in gender norms. One especially important tool that has been shown to work in other settings is being diligent with follow-up questions and detailed activity lists. Results from the 2018 Honduran labor force survey suggest that using an activity list and separating the concept of payment from employment improves the measurement of female participation but is not enough to overcome the gendered definitions of work in rural Honduras. Lists need to be crafted to account for local activities – for example, production related to forest products and agricultural production related to vegetable gardens and poultry. In addition to updating the survey instrument to reflect local employment concepts, the training of interviewers should emphasize some of the challenges in measuring women’s economic activities given the overlap of economic and domestic activities.

This mismeasurement matters. Across the world, interventions are being designed and implemented to attempt to mitigate poverty through increasing economic productivity in agricultural and informal activities. A naïve look at the labor force statistics of these areas suggests that it is mostly men who are engaging in economic activities – and hence they who can best benefit from interventions designed to increase productivity. What does this imply for the communities that were interviewed in this study? It feeds a misunderstanding of women’s role as economically dependent rather than as economic agents, potentially limiting the extent to which interventions target their needs. Consider the case of the community in the study in which women collected and sold blackberries to market intermediaries. The interviews and focus group
|9.9|8.7|
|NA|8.5|7.2|7.7|2.7|4.2|3.5|2.7|4.6|3.8|3.3|2.3|2.5|
|Total|100|100|100|100|100|100|100|100|100|100|100|100|
|N obs.|1857|2136|4152|2036|2337|4540|1703|2680|4567|391|1137|1561|

_Notes:_ See Table 1 for definitions of informal employment. Columns ̳Employees‘ and ̳Self-employed‘ here correspond, respectively, to categories 5 and 3 defined in Table 1; column ̳Total‘ includes also family workers. Countries are not weighted by population size. Design weights corrected for variation of sample size across countries in each round are applied (i.e., all respondents are assumed equally likely to be sampled; countries which did not participate in some rounds are, however, under-represented). _Source:_ Calculation with ESS data.

**Table 7 Perceived discrimination on  grounds of race, ethnicity, religion, language, nationality, age or disability among formal and informal workers, by European region. 2004-2009**

||<br>**_South_**|<br>**_East_**|<br>**_West_**|<br>**_North_**|**%**<br>**_Total_**|
|---|---|---|---|---|---|
|[1] Formal Employees|3.5|3.8|4.1|2.3|3.6|
|[2] Informal Employees|7.6|6.4|5.3|2.8|
The data sources pillar is segmented between sources generated by the statistical office (censuses and surveys) and sources accessed from elsewhere (administrative data, geospatial data, private sector data and citizen generated data). The appropriate balance between these types of sources will vary depending on the institutional setting and maturity of the statistical system in each country. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics may reflect a lack of use of (and low score for) geospatial data. This linkage, which is inherent in the data cycle approach, should help highlight areas for investment if country needs are to be met.

# **Data infrastructure**

The data infrastructure pillar is segmented into hard and soft infrastructure segments itemizing essential cross-cutting requirements for an effective statistical system. The segments are:

1. Legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system

2. Standards and methods addressing compliance with recognized frameworks and concepts 3. Skills including level of skills within the statistical system and among users (statistical literacy) 4. Partnerships reflecting the need for the statistical system to be inclusive and coherent finance, both domestically and from donors

# _Construction of the SPI_

We employ Cameron _et al._ ’s (2021) nested weighting structure to construct the SPI overall score. Compared to other weighting schemes, this weighting structure offers properties such as symmetry, monotonicity, and subgroup decomposability. It is based on Atkinson’s (2003) counting method, which was employed to construct a social exclusion index (Chakravarty and D’Ambrosio, 2006) and to measure adjusted multi-dimensional poverty (Alkire and Foster, 2011). Our statistical performance indicators have a three-level structure, and the SPI overall score is formed by sequentially aggregating the indicators at each level.

19
rich nationally representative panel data of about 4,000 (rural sample) households in 290 villages (enumeration areas). There are three rounds of this survey: 2011, 2013, and 2015. In the rural sample, attrition rate is 5% between 2011 and 2013, and 2% between 2013 and 2015.<sup>4</sup> This attrition is not correlated with the households’ 2011 characteristics, such as food and non-food consumption, share of land allocated to coffee, and many other variables, but is slightly negatively correlated with household size and land holding. See table A.1, which reports correlation of attrition between 2011 and 2013 on selected household 2011 characteristics.

This dataset includes information on household production and consumption, disaggregated by crops. In particular, the dataset enables me to calculate the fraction of land and labor a household allocates across crops (or plots), as well as each household member’s on-farm and off-farm labor supply. Importantly, the sample locations cover both coffee producing and non-coffee producing districts (see figure 3). A key variable that enables me to construct a measure of households’ exposure to coffee price shocks is the share of farmland allocated to coffee production – households’ exposure to coffee price shocks is proportional to the fraction of their farmland allocated to coffee production. The dataset also includes information on household demographic characteristics and anthropometric measures for children under five years of age.

There are some shortcomings of the dataset worth mentioning. Its main drawback its relatively small sample size and short panel. The second main drawback is that the child anthropometric measures (height and weight) are poorly measured – height and weight measures reported are biologically implausible for about 3% of the children in the dataset,<sup>5</sup> which I exclude from my analysis.

My international coffee price data comes from the _International Coffee Organization_ , which maintains historical statistics on international coffee prices and trade. I use the monthly coffee price index for the variety of coffee known