rafmacalaba/data-use-mentions-extended huggingface dataset:
We then build on this logic turning it into a strategy to infer entry barriers. Essentially, the strategy involves answering the following question: what is the magnitude of the entry barrier that is required so that, given the estimated elasticity between idiosyncratic distortions (TFPR) and idiosyncratic productivity (TFPQ), the equilibrium average size attains the level in the data? Consider the example of Chile, one of the 21 countries in our collection of firm-level databases<sup>7</sup> . In Chile’s manufacturing sector, firms employed an average of 94 workers in 2013, slightly below the average size of 118 in the US manufacturing sector, our benchmark of efficiency.<sup>8</sup> From the firm-level data, we estimate a productivity-elasticity of distortions of 0.17.<sup>9</sup> According to our quantitative model, the idiosyncratic distortions should have induced a strong decline in average firm size to 36 workers. To reconcile the equilibrium with the data, our model identifies a countervailing entry tax rate of 230 percent, about two times the entry cost of a firm in the undistorted economy. Extending this logic to all the countries in our data, we come up with a model-based distribution of entry barriers.

Equipped with model-based estimates of entry barriers, our final contribution concerns the quantitative assessment of the aggregate effects of entry barriers and idiosyncratic distortions. As stated earlier, the TFP gains from lifting all distortions are substantial, ranging between 20 and 50 percent in the most distorted economies, with both distortions weighing in prominently in accounting for these gains. We complement these findings with two additional exercises. The first one quantifies the role of the interaction between 7Our data combine census-based information of manufacturing sectors in Latin America, South Asia, and sub-Saharan Africa with a selected sample of countries from the Amadeus database, the selection being driven by the extent to which the size distribution portrayed in these data is comparable with the one reported in Eurostat’s Structural Business Statistics database. More
Policy Research Working Paper 10242

# **Abstract**

Interviewer design effects occur when data collected by the same interviewer is more similar than data collected by different interviewers. Design effects inflate survey variance and reduce the precision of estimates. Using household survey data collected via computer assisted personal interviewing (CAPI) in Sudan this paper employs a two-level mixed effects regression model to identify interviewer design effects for key variables. The study finds mean interviewer design effect values of 7 with a maximum of 16, implying a significant loss of precision. Recommendations to mitigate interviewer design effects include simplifying questions, sound survey implementation practices, and utilizing multi-way cluster robust standard errors to account for both area and interviewer clustering during data analysis.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at aetangndip@worldbank.org; christopher.n.root@gmail.com; hfuje@worldbank.org.

_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 conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**|‐306|‐657|3,482|0.1|0.101|0.77|0.269|
|previous year<br>Notes: Baseline survey data (<br>coefficients and standard errors<br>treatment dummies, controllin<br>significance at 1, 5 and 10%.|[28,146]<br>March 2014)<br> (in parent<br>g for strata|(1,952)<br>. Column <br>heses) fro<br> dummie|(1,211)<br> 1: Stand<br>m an OL<br>s (dummi|(1,077)<br>ard devia<br>S regressi<br>es for e|tions <br>on of <br>ach tri|presente<br>the firm <br>plet). *|d in bra<br> owner/fi<br>**, **, *|ckets. C<br>rm chara<br> indicate|olumns 2‐4:<br>cteristic on<br> statistical|

43
describing the impact of a change to HIPAA rules in 2013, the Department of Health and Human Services noted, “We are not able to quantify the benefits of the rule due to lack of data and the impossibility of monetizing the value of individuals’ privacy and dignity....” Modifications to the HIPAA Privacy, Security, Enforcement, and Breach Notification Rules, 78 FED. REG. 5566, 5567 (Jan. 25, 2013) (to be codified at 45 C.F.R. pts 160 & 164). 78 FED. REG. 5567.

> 8 UNCTAD Cyberlaw Tracker, <u>https://unctad.org/en/Pages/DTL/STI_and_ICTs/ICT4DLegislation/eCom-Data-Protection-Laws.aspx.</u>

> 9 Anupam Chander & Uyên P. Lê, _Data Nationalism_ , 64 EMORY L. J. 677 (2015).

3
The data from the Tanzanian Labor Force survey, taken in the same year, used screening questions and confirmed that the actual LFPR of female heads was indeed much higher than was reported in the HBS.

Screening questions for economic activity and unemployment are therefore important to specify clearly to the respondent and the enumerator what constitutes an economic activity or inactivity, and to ensure that all economic activities undertaken by a respondent are captured properly in the survey. Indeed, it is quite surprising that questionnaires are still developed and fielded in low-income countries which do not use this approach. For the analyst, the use of data collected with poorly worded questionnaires will produce errors and biases in the results estimated - both in the dependent and independent variables, with errors likely to be correlated. Equally important, if the perception of the meaning of the word “work” changes over time, for example, as tasks performed by women are increasingly recognized as having an economic value, then the errors and biases would not be stable over time, and trends would be misidentified. The same problem can occur when the wording of an employment module changes over time, possibly reflecting changing perceptions.

An additional challenge in low-income countries is measuring non-wage earnings. In this area, there is less completed survey research. One key recommendation that does emerge from experience is that this type of income needs its own module (Glewwe and Grosh, 2000). This implies separate farm income and non-farm enterprise income modules to be completed at the household level. The measurement of farm income is a particular challenge (Carletto et al, 2011). Research is ongoing under the LSMS-ISA project to test alternative approaches and offer recommendations for survey design and implementation.<sup>10</sup> In sum, although the task of getting consistent measurement of the SNA concept of employment in low-income countries is not easy, techniques exist. Table 1 summarizes the established good practice recommendations from literature covering key issues to be considered in designing a questionnaire to measure employment outcomes in a low-income setting
encounter more complications in policies; Hawkes et al. (2020) propose ten ways to reorient nutrition interventions to address both burdens simultaneously and Luo, Zyba, and Webb (2020) propose an index to measure nutrition in all its forms.

The discussion proceeds as follows. We first outline the important definitions being used throughout this Working Paper and present a basic conceptual framework. We then discuss issues of measurement, and review data sources, focusing on healthy diet metrics collected at national-level across a wide array of countries. This is followed by a brief summary of previous related costing studies, mainly focused on the costs of malnutrition in all its forms. We then propose a methodology for estimating the economic costs of unhealthy diets, along with preliminary cross-country empirical work. The concluding section summarizes key findings in terms of etiological pathways, nutrition and health outcomes, and economic effects, and notes key gaps and areas for future work.

# 2. Definitions and Conceptual Framework

The 2021 UN Food Systems Summit used the following definition: “A healthy diet is health-promoting and disease-preventing. It provides adequacy without excess, of nutrients and health-promoting substances from nutritious foods and avoids the consumption of health-harming substances.” (Neufeld, Henriks and Hugas 2019). Healthy diets respond to the universal principles of nutrient adequacy, dietary diversity, macronutrient balance, and moderation (Verger et al. 2023). In what follows we consider three different diet aspects: energy sufficiency, nutrient adequacy, and food choices which promote health, using Figure 1 published by FAO (2020). These three aspects are also consistent with the “triple burden” of malnutrition discussed above.

**Energy insufficient diets** have large economic consequences in terms of disability-adjusted life-year (DALY) losses from high child mortality and direct costs of treatment of acute malnutrition. Key pathways

> 1 Conversions from the amounts in $ trillion in FOLU (2019), for 2018, to percentage of GDP were undertaken by the present authors, using $86.4 trillion as global GDP
# **4. Empirical Strategy and Identification**

This section discusses the empirical methodology, followed by the identification threats and how I assuage these threats.

## **4.1. Empirical Strategy**

The ESS is a panel household survey, providing room for investigation of the welfare effect of hosting refugees in a household fixed effects specification. In particular, I specify a linear model with fixed effects as follows: with fixed effects as follows: hv h + β β β β _I I v hv hv , (1) jj vv where hvYY is the outcome variable (e.g., one of the welfare outcomes) of a household = αα IIββI II + θθ ′XX + αα + εε h residing in EA during survey round . h stands for household-specific fixed effects. These fixed effects YY absorb any observed and unobserved time-invariant household heterogeneity, which might vv II αα otherwise bias the estimates. β β β I v is an index capturing the refugee intensity in a particular EA during survey round , which varies across EAs and rounds. It is measured as a ββ_I IIββI II weighted sum of the average number of refugees in the two years right before the year the outcome vv II variables are measured over all of the refugee camps or settlement sites in the country, whereby the weights are the inverse geographic distances between a survey EA and each of the refugee II camps or settlement sites , i.e., vv 37 II <u>( )</u> β β β I v , (2) = �� ~~�~~ ss(vv−1) ss vv−2 ββ + ββ ββ_I IIββI II vvss where and stand for the number of refugees in a refugee camp or settlement site ss=1 2DD in the two years (ss(vv−1) ss(vv−2) and , respectively) immediately before the year the outcome ββ ββ II variables are measured during survey round ; 37 stands for the total number
Further details on the sources of data, IEI summary statistics as well as discussion on robustness checks for the index are provided in Appendix 2.

- _4.2 IEI versus other measures of public investment efficiency_

In this section, we briefly discuss some of the other measures of public investment efficiency that are available. These serve as useful checks against the IEI, but do also face some limitations for use in our model – primarily that they indicate relative performance rather than being a cardinal measure.

Afonso, Schuknecht and Tanzi (2005, 2010) and various IMF papers, starting with Albino‐War et al. (2014) take the approach of distance to frontier, where inefficiency is measured relative to best performing peer countries. In the case of the papers by Afonso, Schuknecht and Tanzi (2005, 2010) which respectively cover 23 industrial countries (2005 paper), and 23 emerging and new EU member states (2010 paper), the output measure is a composite of public sector performance based on a series of quantitative and qualitative socio‐economic indicators, while the input measure is public sector spending (i.e. more than just public investment).<sup>16</sup> For our model analysis purposes, we find that the results from these two papers may not be suitable because: one, the outcome variable encompasses broad, indirect macroeconomic outcomes; and two, efficiency is compared within a small group of countries – this may be particularly worrisome in the case of the emerging market/new EU member sample.<sup>17</sup> On the other hand, the IMF public investment efficiency indicator (PIE‐X) covers more than 100 countries. The output variables are directly related to infrastructure ‐ a quantity index (physical infrastructure coverage and provision of social services<sup>18</sup> ) and a survey‐based quality index<sup>19</sup> respectively, as well as a hybrid of the two; while the input variable
2010 floods, individuals who had experienced higher levels of rainfall had lower aspirations than those who had experienced average levels. The negative effect was especially strong among the poor, and among those most vulnerable to weather shocks (Kosec and Mo 2017).

Shocks leading to higher assets can positively influence aspirations, by affecting how much people are able to think ahead. In Mozambique, an agro-input subsidy and a matched savings intervention had a large and significant impact on the planning horizon of households that were not self-sufficient in maize production (Laajaj 2017). Since poor individuals with a longer time horizon tend to accumulate more assets, the betterment of their economic prospects may put them in a virtuous circle in which higher aspirations encourages the individual to take initiatives in the present in order to reach his goals.

# **Achievements**

Data from a four-country study show that average education aspirations evolve nonlinearly during adolescence, differently across countries, suggesting that they may change according the situations faced by adolescents (Favara, Chang, and Sánchez 2018). In India and Vietnam, adolescents downgrade their aspiration to complete university between ages 12 and 15 but eventually raise again their aspirations at age 19. In Peru, which had the highest initial level of aspirations of the four countries, adolescents also reduce their aspirations between ages 12 and 15 but experience an even higher drop between ages 15 and 19. In Ethiopia, adolescents raise their aspirations slightly between ages 12 and 15 but slightly reduce them at age 19. The important changes in average aspiration levels at age 15 may be linked with the completion of secondary education—for example, Indian and Vietnamese adolescents might perceive more difficulties or start to feel higher norms for their future at that age and report aspiring to less education—to readjust their aspirations at age 19, when the possibility of attending university is more tangible.

# **Beliefs about oneself**

A fundamental channel through which circumstances or the social environment influence individuals’ aspirations is through their beliefs about themselves and their abilities. In India
|Table 16: Medium-|term impacts of_Mais Educação_on|Mdi<br>2007 Portuguese te|Low<br>st scores|
|---|---|---|---|
||High Portuguese|eum<br>|Portuguese|
|Dependent variable|<br>test scores in 2007|Portuguese test<br>scores in 2007|<br>test scores in<br>2007|
||(1)|(2)|(3)|
||Panel A: Impacts on fifth-grade va|riables||
|Dummy_Mais Educação_*|year 2011<br>–2.153|–3.266|–2.458|
||(3.357)|(2.047)|(2.070)|
|_R_ <sup>2</sup>|0.816|0.801|0.798|
|Observations|624|732|760|
|Dummy year 2011|YES|YES|YES|
|School fixed effects|YES|YES|YES|
||Panel B: Impacts on ninth-grade va|riables||
|Dummy_Mais Educação_*|year 2011<br>–5.676|–2.510|–1.999|
||(4.287)|(2.614)|(2.451)|
|_R_ <sup>2</sup>|0.743|0.744|0.778|
|Observations|574|666|640|
|Dummy year 2011|YES|YES|YES|
|School fixed effects|YES|YES|YES|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their Portuguese achievement in 2007. This table reports
of consumable income lower than at the level of market income, families with children actually experience an increase in poverty rates after tax-benefit interventions in Moldova. And the most vulnerable in this sense are families with two or three children. For these categories, poverty at the level of consumable income is 16-20 percent higher than at the level of market income. Families with three or more children have also highest post-intervention poverty rates – more than half of households in this category are poor at the level of consumable income if the national poverty line is used (Figure 10).

**Figure 10. National poverty rates inequality at various levels of income in Moldova by types of households, percent.**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>two adults (or two adults (or two adults (or one adult and only working only pensioners mixed adults,<br>more) and 1 more) and 2 more) and 3+ child(ren) age adults no children<br>child children children<br>market market + pensions disposable consumable<br><!-- End of picture text --> Notes: poverty rates at the national poverty line (1562.8 MDL per month per adult equivalent). Source: authors’ calculations using HBS-2017 data.

While poverty reduction across locality types is similar in absolute terms, in relative terms rural households, which are much poorer than urban ones and remain poorer also after the interventions, register the lowest degree of poverty reduction. Poverty at the level of consumable income for rural households is only 17 percent lower than at the level of market income, while for small towns this number is 28 and for big cities, including capital it is 44 percent (Figure 11). The relative reduction in
.

This paper does not attempt a comprehensive analysis of the policy response by the Indian government but only focuses on the extensive vaccination campaign and its role in helping labor markets return to normal. For a review of India’s relief package - the Prandhan Mantri Garib Kalyan Yojana (PMGKY) - refer to Bhattacharya and Sinha Roy (2021) and Chatterjee et al. (2022). In the next sections of this paper, we describe the data and methods used for our main analysis. Section 4 presents our main results on labor market impacts looking at transitions across employment status, earning levels, employment types, sector and location. Section 4 also examines how education and location may have mitigated some of these transitions and contains some additional results on the effect of vaccination on individual employment outcomes. The last section concludes.

# **2 Data and descriptive statistics**

The Consumer Pyramids Household Survey (CPHS) is a continuous panel survey of approximately 170,000 households in each wave. The surveys are conducted by the Centre for Monitoring the Indian Economy (CMIE), a private think tank and data collection 5
both crucially rely on a source of gridded population data for their implementation. Furthermore, while the AI may well be considered preferable to the cluster method to the extent that it more closely approximates an attempt to identify FUAs, it suffers the disadvantage of being more data-intensive. Thus, in addition to gridded population data, the AI also requires travel time data and a settlement point layer as inputs.

There is also a slight contradiction in how the AI approaches the definition of urban areas insofar as it implicitly relies on national definitions of urban in identifying the “sizeable” settlements around which it defines agglomerations. This is because the population data for the settlement points are invariably derived from national sources.

Finally, while, according to the cluster method, grid cells belonging to a given urban area must be spatially contiguous, the AI imposes no such restriction. As a corollary, development within a given agglomeration area in the AI approach can be discontinuous in the sense that urban cells can be interspersed with non-urban cells. Such a pattern of development may be consistent with the existence of topographical constraints (e.g. a river) that divide a city into different parts or with patterns of leapfrog development.<sup>24</sup>

# **3. Data**

As noted above, both the AI and cluster methods rely on gridded population data for their implementation. In addition, the AI also requires a settlement point layer and travel time data.

> 23 Deuskar and Stewart (2016) analyze the sensitivity of results from the cluster method using a variety of alternative threshold values.  Their analysis is, however, restricted to the use of _WorldPop_ and GHS-Pop gridded population data, and does not consider _Landscan 2012_ data.

> 24 It is easy to imagine a methodology which combines different aspects of the AI and cluster methods.  This methodology could, for example, follow the cluster approach of identifying high-density clusters and then define agglomerations around these clusters using criteria of travel time and population density _à la_ the AI.
06)|
|PNB Index_ Public Space**(**α)||-0.04<br>(0.03)|-0.03<br>(0.03)|
|SNE Index_ Public Space #<br>Madheshi**(**β)||-0.12***<br>(0.04)|-0.13*** (0.04)|
|SNE Index_ Public Space**(**α)||0.01<br>(0.03)|0.02<br>(0.04)|
|Madheshi|-0.01<br>(0.13)|-0.04<br>(0.07)|0.05<br>(0.13)|
|Obs.|2,000|2,000|2,000|
|Adjusted R2|0.03|0.02|0.03|
|Controls|YES|YES|YES|
|Regional FE|YES|YES|YES|

_Notes:_ (1) Controls include a binary variable for belonging to the Madheshi ethnic group, respondents’ age, years of education, having children younger than 5 years, log of household’s total income, at least one among mother-in-law or father-in-law living in the household, being a migrant household, living in an urban area, and provinces. (2) Interaction term (β) can be interpreted as the difference between the two subgroups (in this case, Madheshi vs Madheshi respondents) in the association between conservative beliefs and women’s paid work status. If the interaction term is negative and statistically significant, it means that the association is stronger for Madheshi compared to non-Madheshi respondents. If the interaction term is positive and statistically significant, the association is stronger for non-Madheshi compared to Madheshi respondents.

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