rafmacalaba/data-use-mentions-extended huggingface dataset:
illnesses and pay high prices for ―catastrophic‖ incidents that often put them in debt. That an individual‘s health status constantly fluctuated between illness and well-being and that he or she visited different doctors numerous times, incurring potentially large expenditures for seemingly ―minor‖ illnesses, never emerged as a dominant theme.

In an attempt to capture some of the patterns we observed in the field, we designed and implemented a weekly survey module that was eventually interspersed with monthly-recall modules over a 2-year survey period from 2000 to 2002.<sup>4</sup> Given surprisingly large recall effects in the observational data, we returned in 2008 to experimentally evaluate these effects in a sub-sample of households over a 2-month period.

Across the observational and experimental data, both the rich and poor underreport acute health events in monthly-recall surveys, but the poor underreport more and this underreporting seems to arise from the ―forgetting‖ of entire episodes of acute illness. Among the poor, monthly reporting ―erases‖ almost half the morbidity burden of acute illnesses, over a third of doctor visits for both acute and chronic illnesses, and almost half of all self-medication episodes. Monthly reporting also decreases reports that at least one day of work was lost due to an acute illness by close to a third, again with somewhat higher recall impacts among the poor. We explore the possibility that this ―forgetting‖ is driven by a process of normalization of sickness. Consistent with this hypothesis, we are able to show that recall effects are larger for those individuals and households whose initial sickness burden is also higher; arguably, these are households where sickness has become a ―normal‖ event.

Although we are able to present stark differences for reported morbidity and doctor visits, we should note that we are unable to integrate routine and catastrophic bouts of illness into our results and therefore, provide a full accounting of health _expenditures_ and the impact of different recall periods on health spending. Our results do not apply to episodes of illness associated with very large expenditures.
|1588|742|

_Notes:_ Each column represents a regression of the growth rate of real labor income per capita or real GDP per capita on the change in normalized temporary transfers. The regressor is disaggregated by quarter of payment/withdrawal (Column A), or the transfer policy (Column B). All specifications have state and quarter fixed effects, with “benchmark” controls for population growth & US GDP Growth × State FE. Outliers >20% (annualized, absolute value) dropped. Robust std errors are in parentheses (clustered by state).

that payment and withdrawal multipliers are equal (in both labor income and GDP specications; p-values around 0.4 and 0.1), which justies the pooled ~~specication used in the main results in Table 1. For labor income, the 2008 multipliers are around 0.3 for both payment and withdrawal quarters and are remarkably similar to the pooled estimates in Table 1.~~<sup>~~29~~</sup> ~~For the 2001 trans-~~ fers (labor income) and 2008 transfers (GDP), the point estimates are larger when transfers are paid than when they are withdrawn, though these dierences are not statistically signicant. Specically, the 2001Q3 (labor income) 2008Q2 (GDP) payment-quarter multipliers are around 0.9, but multipliers <u>on withdrawal are close to zero. This means states receiving a larger trans-</u> ~~fer may continue to have higher relative income, even after that transfer was withdrawn, though the evidence is not strong enough to be denitive.~~<sup><u>30</u></sup> Column B of Table 2 breaks down pooled temporary transfers into three components described in Section 2.12008 refundable (low-income) rebates, 29 When pooling across 2001 and 2008 payment quarters, and across 2001 and 2008 withdrawal quarters, the
**<u>Table 5.3: Estimated Annual Health Impacts of Indoor Air Pollution (Thousands)</u>**

||**Estimat**<br>**Case**|**ed Annual**<br>**s(000)**|**Estimat**<br>**DALY**|**ed Annual**<br>**s(000)**|
|---|---|---|---|---|
||Urban|Rural|Urban|Rural|
|**_Acute Respiratory Illness(ARI): _**|||||
|Children(under the age of 5years)– increased mortality|19.5|166.4|662|5,660|
|Children(under the age of 5years)– increased morbidity|7,570|47,925|12.5|79|
|Females(30years and older)– increased morbidity|9,401|47,384|65.8|331.7|
|**_Chronic obstructive pulmonary disease (COPD):_**|||||
|Adult females – increased mortality|7.5|53.4|74|363|
|Adult females – increased morbidity|39,000|202.5|127.7|455.6|
|Total Disability Adjusted Life Years (DALYs)-mortality<br>and morbidity|||942.4|6,889.3|

_Source:_ Staff estimates.

Table 5.3 also gives the DALYs lost to indoor air pollution. An estimated 8 million DALYs are lost each year. About 70-80 percent are from mortality and 20-30 percent are from morbidity.

The central estimated costs associated with the impacts identified above are given in Table 5.4. Briefly, the cost of mortality is based on the
# _4c. Limitations of this study_

One potential limitation of this study is that it is based on phone survey data which exclude respondents who did not have access to a phone. This method of data collection was necessary to collect information quickly during the early months of the COVID-19 pandemic while respecting local movement restrictions and minimizing the risk of COVID-19 transmission. Given that having a phone may be non-random, this might have created a risk that the results are representative only of the population with access to a phone. However, in the countries in this sample, access to mobile phones was high, and in countries where the sample was based on a previous survey, sampling weights were used in the analysis to correct for the biases resulting from non-random access to phones. A recent analysis using the same phone survey samples from four African countries demonstrated that the weighting procedures successfully minimized the selection bias in the phone surveys for a wide range of indicators (Ambel, Mcgee, and Tsegay 2021). Nevertheless, the variation in hesitancy estimates from different data sources, such as the phone surveys vs Facebook, also points to the need for methodological research that would explore further the biases introduced by phone surveys, or by online surveys, vis-à-vis face-to-face surveys. These types of experiments would aid the interpretation of findings in environments where data collection modalities are constrained, as was the case with the COVID-19 pandemic, and would also help guide researchers on issues related to sample corrections.

A second limitation is that country surveys were collected at different times, and some surveys were collected before a vaccine was available in the country while others were collected after the vaccine was available. During data collection (and the pandemic more generally), influential events occurred, including adverse news about side effects of certain vaccines, which could have changed respondents’ intentions. Furthermore, there are multiple vaccine manufacturers and types, and the type of vaccine available or anticipated to become available in each country may influence levels of hesitancy.

# _4d. Conclusions and policy implications_

The main
An important stylized fact from the existing research on informality is that the education of owners and managers explains a considerable proportion of the variation in outcomes of businesses (La Porta and Shleifer, 2008, 2014). Our data enables inspection of this relationship. Panel B of Figure 8 divides informal businesses from each country into one of three possible levels of schooling completed by the owner: no formal education (including those with partial but incomplete primary schooling), those that completed primary (high school graduates), and those that completed secondary education or college. Within every country, the management practices index is higher for businesses whose owner’s education is higher.<sup>17</sup> On average, the informal businesses whose owners have secondary education or a college degree scored 32.7 out of 100 on the management practices index, compared with 23.7 for businesses owned by those completing only primary education and 18.4 for businesses whose owners have lower educational attainment. As with the link to productivity, establishing a causal relation between the owner's education and management practices is left for future research.

# **Business Relations**

Data on the relations of informal businesses with their customers and suppliers can help researchers and policy makers better understand the extent to which these businesses are integrated into their local markets. One of the simple but important questions included in the Informal Sector Enterprise Surveys in some countries (namely, Lao PDR, Mozambique, Somalia and Zambia) on these topics is whether the business supplies products or provides services to other businesses. Around 13.6% of businesses are integrated into value chains in this rather narrow understanding, varying from 7% in Mozambique and Lao PDR to 20% in Somalia. By comparison, more than 52% of formally registered micro-enterprises sell to other businesses.<sup>18</sup> While within-country variation in informal businesses supplying to other businesses is low in some countries (namely, Lao PDR and Zambia), cross-city variation within Mozambique
industry attractiveness index. For low and medium technology firms, the IV estimates are in fact larger in magnitude that the OLS estimates, which suggests that any endogeneity present has a small effect. For high technology manufacturing however, the IV estimate is 0.17, which is almost half in magnitude of the coefficient estimated with OLS. Thus, we find that own industry clustering has a significant impact on firm location decisions even after we take into account potential endogeneity problems in identifying the effects of industry clustering / agglomeration economies.

# **5 Conclusion**

In this paper, we examine the relative importance of the local business environment and agglomeration economies on location decisions of manufacturing firms in India. We disaggregate the business environment into (a) regulatory quality, (b) infrastructure quality; and (c) access to inputs such as finance and labor. We estimate a location choice model where a firm optimizes its location decision across Indian cities based on a set of observable business environment and agglomeration variables. We use firm level data from the Indian Investment Climate Survey (ICS) for India and find that the local business environment has a significant bearing on location decisions. Predatory enforcement of business regulations and excessive labor regulations have adverse effects on location decisions, while availability of infrastructure and access to finance and land have positive impacts.

However, agglomeration economies from own industry clustering also positively influence firm location decisions. This means that new firms will choose to locate 30
11

# <u>Figure 2: Structure of the 2018 HEFPI database</u>

<!-- Start of picture text --> Health Equity and<br>Financial Protection<br>Health equity Financial Protection<br>Service coverage Health outcomes Catastrophic expenditures Impoverishing<br>(CATA) expenditures (IMPOV)<br>Prevention Treatment MDG era SDG era CATA 10% IMPOV $1.90-a-day, and<br>other $ PLs<br>MDG era SDG era MDG era SDG era Infant mortality (IMR) Adult BMI CATA25% IMPOV 60% median per<br>capita consumption<br>Cervical cancer screening Skilled birth attendance Under-five mortality<br>Antenatal visits (4+) Inpatient admissions Adult overweight<br>(PAP) (SBA) (U5MR)<br>Treatment of child with<br>Child immunization Breast cancer screening acute respiratory infection Treatment for hypertension Stunting among under-5s Adult obesity<br>(ARI)<br>Sleeping under insecticide- Treatment of child with Treatment for Underweight among<br>treated bednet Hypertension testing diarrhea diabetes under-5s Adult height<br>Prevalence of raised blood<br>Contraception prevalence Cholesterol testing HIV prevalence<br>pressure<br>Raised blood glucose and<br>Family planning demands<br>satisfied Diabetes testing impaired fasting glycaemia<br>(IFG)<br>Condom use during risky<br>intercourse<br><!-- End of picture text --> Figure 3 shows the surveys used in the 2018 HEFPI database, where the size of each block is proportional to the fraction of total datapoints contributed by the survey in question. In contrast to the 2000 database, which was based entirely on DHS surveys, the 2018 database uses a variety of different surveys, albeit still, for the most part, highly standardized surveys from a few multicountry programs. For the MDG-era health service coverage and health outcome indicators, the DHS accounts for the majority of datapoints, but the
Policy Research Working Paper 5997

# **Abstract**

Information, education, communication and interventions based on behavioral-change communication have had success in increasing the awareness of HIV. But these strategies alone have been less successful in changing risky sexual behavior. This paper addresses this issue by exploring the link between action and the intention to change behaviors. In Africa, uncertainty in the lives of those at risk for HIV may affect how intentions are formed. Characterize this uncertainty by understanding the reasons for discrepancies between intentions and actions may help improve the design of HIV-prevention interventions. Based on an incentivesbased HIV prevention trial in Tanzania, the longitudinal dataset in this paper allows the exploration of intended strategies for changing sexual behaviors and their results. The authors find that gender, intervention groups and new positive diagnoses of sexually transmitted infections can significantly predict the link between intent and action. The paper examines potential mediators of these relationships.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ljpackel@gmail.com, wdow@berkeley.edu, ddewalque@worldbank.org, zach.isdahl@ gmail.com and yammtz@yahoo.com.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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/
To solve the model, some following functional form assumptions are made. Specifically: Once the model is solved numerically,<sup>51</sup> the empirical strategy consists of estimating the model using micro data, and then simulating how labor market policies, subsidized health programs and relative prices affect the division of the labor force into unemployment, informal and formal sectors (aggregate effects), the mix of worker types in the two sectors (compositional effects) and the distribution of wages (distributional effects).<sup>52</sup>

# **4 Empirical Strategy**

## **4.1 Descriptives**

To estimate the model, I use data from the Encuesta Continua de Hogares for the second quarter of 2003, representative for 13 metropolitan areas.<sup>53</sup> I choose this period to approximate the prereform’s steady state equilibrium because: 1) An informality module is available;<sup>54</sup> 2) Information on health access disaggregated by contributive or subsidized regime is also available;<sup>55</sup> 3) The first three quarters of 2003 have a roughly constant unemployment rate, and the model is derived under a steady state assumption.

In addition to standard demographic socio-economic variables (age, gender, marital status, educational attainment, etc), the sample is described by the following labor market variables: _Ws_ : Accepted monthly wages for individual _s_ , where _s ∈ Ei_ , so each individual can be employed in sector _i_ (formal or informal)

> _tes_ : Employment duration<sup>56</sup> for individual _s_ , where _s ∈ Ei_ (right -censored)

> 51For details on the computational algorithm to solve the model see Appendix, Section 6.5.

> 52I simulate the steady state distributions of equilibrium productivity and wages in both sectors, instead of deriving them them analytically. See Appendix, Sections 6.2 and 6.3 for the analytical derivations.
shocks may be considered as exogenous to the firms, households, and banks, i.e., not significantly caused by or easily anticipated in advance by any of these economic agents. A number of measures of these shocks are readily available at the national and state levels from several publicly available sources. These are quite useful both for research and for us to discuss the crisis.<sup>5</sup> We focus on these shocks during the first half of 2020, which is when most of the economic damage of the crisis occurred. The public health shocks in the U.S. commenced when the first COVID-19 case was identified on January 21, 2020, and the first government activity restrictions shock in the country started on February 29, 2020, when the Governor of the State of Washington declared a state of emergency.

Berger, Bouwman, Norden, Roman, Udell, and Wang (2021a) provide an illustration of how these shocks are employed in research, and their data help us navigate the first months of the crisis. They construct 14 different COVID-19 shocks from the raw data. Their public health shocks include new and total COVID-19 cases and deaths at the national and state levels, and their government activity restrictions indices include totals of stay-at-home orders, business shutdown orders or limitations, quarantines, etc. at the national and state levels. The rich variation in COVID-19 shocks across the different measures allows us to see how the crisis unfolded at different rates across the nation and helps enable the precision and robustness of research on the COVID-19 crisis. Studies of the GFC and earlier crises often just compare effects during a crisis versus normal times and do not have access to such detailed exogenous shock data.

We first consider here one of the public health shocks, new disease cases per 100,000 (100K) population. National new cases per 100K population intensified considerably over time

> 5 Examples include the Economic Tracker of Chetty, Friedman, Hendren, and Stepner (2020); the Johns Hopkins Coronavirus
In Model 1, we train the decision tree without allowing the algorithm to access the individual's gender. This approach helps correct for potential algorithm biases associated with the detection of gender in the data, as well as the absence of nonbinary options to capture gender identity.

# **_Model 2: Decision Tree on Citizen Participation after Correcting for Bias in the Algorithm: No Gender Variable Nor Gender Proxies (Gender Deproxing)_**

In Model 2, we apply a deproxing strategy, which involves removing not only the gender variable but also any covariates that serve as proxies—those highly correlated with gender and thus likely to carry gender-related bias. In our analysis, employment status was identified as the most strongly correlated proxy for gender. As a result, we remove both the gender variable and employment status from the list of covariates.

# **_Model 3: Decision Tree on Citizen Participation after Correcting for Bias in the Algorithm: Women and Men-only_**

In this model, we analyze how algorithms predict participation separately for female and male samples, excluding any information about the opposite gender.

# **_Model 4: Decision Tree on Citizen Participation after Correcting for Bias in the Algorithm: Gender Switching_**

In Model 4, we simulate a gender-blind society by switching the gender of each individual— assigning males as females and vice versa.

# **_Model 5: Decision Tree on Citizen Participation after Simulating a Less/More Machista Society: Scaling Machismo_**

In Model 5, we use counterfactuals to explore how the success path to citizen participation would differ in alternative scenarios, representing both a less machista and a more machista Latin American society, as reflected in Latinobarómetro data. We construct two main counterfactuals: first, by scaling down individual observations of the composite gender score while preserving the original distribution, simulating a society with reduced levels of machismo.<sup>2</sup> Our primary counterfactual envisions a Latin American society with half the current machismo level, but we also examine scenarios with
persons per square km), and high-density districts are those in the top quartile (over 693 persons per square km).

Table 6 presents the results of interacting the three regressors from our typical approach with indicator variables for the various density bins. The one exception is that all of the nodal districts are above the median density for India, and so we do not have a low-density nodal-district effect. Effects continue to be measured against districts farther than 50 km from the GQ network.

The results in Table 6 suggest that the GQ upgrades have increased new entry the most in high- and medium-density districts that lie 0-10 km from the GQ network. For instance, moderate-density districts, like Surat in Gujarat or Srikakulam in Andhra Pradesh, that lie on the GQ highway registered more than 100% increase in new output and new establishment counts after GQ upgrades. On the other hand, the GQ upgrades are not linked to heightened entry or performance in low-density areas. One interpretation of these results is that the improved connectivity enables manufacturing establishments to efficiently locate in intermediate cities, but that localization economies prevalent for the sector continue to preclude entry in low-density places.<sup>12</sup> Our findings are similar to Baum-Snow et al. (2012) who identify how infrastructure aided the decentralization of industrial production and population in Chinese cities from 1990-2010. Henderson et al. (2001) similarly find that industrial decentralization in Korea is attributable to massive transport and communications infrastructure investments in the early 1980s. These and similar studies form the foundation for development recommendations with respect to infrastructure found in the World Bank’s (2012) Urbanization Review Flagship Report and comparable policy reports.<sup>13</sup>

# **Section 5: Conclusions**

This paper evaluates the impact of a large-scale highway project on economic activity in the Indian manufacturing sector using establishment-level survey data from 1994-2007. The Golden Quadrilateral highway project of India upgraded the quality and width of 5,846 km of highways linking four major hubs in India
**Figure 4.3. Average share of posts with at least one skill demanded in a skill group, by GTI index**

<!-- Start of picture text --> 0 10 20 30 40 50 60<br>Cognitive (analytical)<br>Social<br>Character<br>Writing<br>Customer<br>Project management and business<br>People management<br>Financial<br>Computer (basic)<br>Software and information technology<br>Foreign language<br>Field of study and industry knowledge<br>Zero GTI Low GTI <30% High GTI +30%<br><!-- End of picture text --> _Notes_ : analysis restricted to high-skilled occupations with at least 30 posts. For each occupation, we calculate the percentage of posts that required at least one skill in each of the skills groups. Then, we calculate the average of this percentage by the three categories: Zero GTI, Low GTI, and High GTI.

**Table 4.1. Top 20 skills demanded in high GTI occupations**

|**Skill ranking**<br>**Skill**|
|---|

> 62 See Granata, Posadas, Testaverde (2021) for a discussion on the biases of Indonesia’s online vacancy data.

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