rafmacalaba/datause-encoder-data huggingface dataset:
18-35, and a set of community surveys, in a sample of 60 rural<sup>3</sup> communities distributed evenly _among_ 4 departments. The rural population of these departments is highly heterogeneous with regard to ethnicity, language group, economic and social structure, climate and topography.

The individual questionnaire collected information on household composition, background data, birth history, prenatal care and assistance at delivery, infants' and children's health status, contraceptive knowledge and use, marital history, social support, health beliefs, community structure, and economic status.

An extensive community questionnaire obtained information on the presence of various types of health care providers in or near the community, as well as on food prices, infrastfucture, social and religious organizations, and social and political structure of the community. In each community three knowledgeable village informants, one staff/member in the health post or clinic, a private physician, a midwife, and one or two other health providers (including traditional/folk practitioners) were interviewed in Spanish.

Height and weight of children under 5 years of age and the height and weight of the mothers were also collected using standard anthropometric procedures.

# **IV.DESCRIPTIVE ANALYSIS OF ANTHROPOMETRIC DATA: LEVELS AND DIFFERENTIALS**

# **4.1 Prevalence of Stunting, Wasting and Underweight**

Following usual practice in nutritional and epidemiological research, we classify a child as wasted, stunted, or underweight when his or her weight-for-height, height-for-age, or weight-for-age respectively are two or more standard deviations below the WHO/NCHS/CDC reference median.

The prevalence rates of wasting, stunting, and underweight for the entire EGSF sample were 0.7%, 61.6%, and 32.2% respectively. We decided to limit the analysis of the EGSF anthropometric data to height-for-age for two reasons: (1) since weight-for-age is a composite of weight-for-height and heightfor-age, deficits in weight-for-age in Guatemalan children almost entirely reflect deficits in height-forage;<sup>4</sup> and (2) since the prevalence of low weight-for-height is rare in Guatemala, much larger samples would be needed to explore the correlates of this condition.

> 3A community was defined as rural if it contained less than 1800 households. Because the sample entailed selecting an average of 50 women of the specified ages within each community, communities with less than 100 households were replaced with communities from an alternate list.

> 4Keller (1983) used correlation analysis to show that weight-for-height and height-for-age are virtually independent of each other (that is they represent different processes of malnutrition). Regressing weight-for-age on weight-forheight and height-for-age (all expressed in term of standard deviations scores) in several populations with varying prevalence of malnutrition, Keller also found very high coefficients of determination (between 0.95 and 0.98). Combining both findings, he concluded that (1) deficits in weight-for-age are a composite of deficits in weight-

4
Figure 4 (a): Vietnam Skill Premiums – basic controls <!-- Start of picture text --> Vietnam: Skill Premium and Skilled Workforce - Basic controls<br>only<br>0.6<br>0.5<br>Skilled-1: At least Upper<br>0.4<br>Secondary<br>0.3 Skilled-2: Tertiary and<br>above<br>0.2<br>Skill Premium-1<br>0.1<br>0<br>Skill Premium-2<br>-0.1 1992 1998 2004 2006<br>-0.2<br>Year<br>workers<br>Skill premium and<br>proportion of skilled<br><!-- End of picture text --> Figure 4 (b): Vietnam Skill Premiums – industry controls <!-- Start of picture text --> Vietnam: Skill Premium and Skilled Workforce - With Industry<br>controls<br>0.6<br>0.5 Skilled-1:Upper<br>Secondary and above<br>0.4<br>Skilled-2: Tertiary and<br>above<br>0.3<br>Skill Premium-1<br>0.2<br>Skill Premium-2<br>0.1<br>0<br>1992 1998 2004 2006<br>Year<br>workers<br>Skill premium and<br>proportion of skilled<br><!-- End of picture text --> 25
7 <!-- Start of picture text --> Financial development Institutional quality<br>0 .5 1 1.5 0 .5 1 1.5 2<br>Log of financial development (% GDP) Log of institutional quality (1−6)<br>Financial structure Institutional stucture<br>0 .5 1 1.5 2 0 .5 1 1.5 2<br>Log of financial structure Log of institutional structure (0−6)<br>Business environment Tax environment<br>.5 1 1.5 2 2.5 2.5 3 3.5 4<br>Log of business environment (1−12) Log of tax environment (%)<br>Source: Author’s calculations<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Log of fixed investment rate (% GDP) Log of fixed investment rate (% GDP)<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Log of fixed investment rate (% GDP) Log of fixed investment rate (% GDP)<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Log of fixed investment rate (% GDP) Log of fixed investment rate (% GDP)<br><!-- End of picture text --> Figure 1: Scatterplots of fixed investment rate (as a percentage of GDP) to structural variables of interest, unbalanced 5-year average panel, 1980–2009.
<!-- Start of picture text --> Greater than 75 bps Hl<br>75bps 7<br>50-75 bps Ml<br>50 bps<br>25-50bps<br>25 bps<br>125-25 bps<br>12.5bps<br>5-125 bps<br>5bps Hf<br>0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00%<br><!-- End of picture text -->
In the short-run, HBW is what matters for immediate job losses during lockdown. However, as restrictions are lifted, activities intensive in F2F interactions can be slower to recover as consumers may remain apprehensive.

Furthermore, F2F is more related to demand, while HBW is more related to labor supply considerations. F2F indicates the importance of face-to-face interactions _with clients_ and therefore isolates this aspect of demand. In the context of COVID-19, a higher F2F value means a larger potential for loss of demand from social distancing measures. HBW, on the other hand, also includes workers who do not interact with clients – such as those employed in manufacturing activity – and would therefore not experience a direct fall in demand due to social distancing. It primarily captures a labor supply dimension, i.e. the (in)ability of workers to supply labor remotely from home. Neither of these measures captures the general reduction in demand resulting from negative income shocks. This matters for analyzing relative vulnerability to the extent these shocks affect industries asymmetrically. The demand for consumer durables has, for example, typically fallen more during previous crises (Bils and Klenow 1998) and this effect can also be transmitted through international trade and investment links.

Lastly, HBW, is fundamentally binary and therefore cannot capture how well work can be done from home. F2F, in contrast, can capture the quality or productivity dimension related to home-based work. In other words, HBW captures the extensive margin of how social distancing affects jobs, while F2F is more related to the intensive margin.

We compare these complementary, but distinct, measures in the remainder of the paper to assess the implications of the COVID-19 pandemic on jobs across industries, income groups and gender. Section 2 outlines the methodology to construct an index which measures face-to-face interactions with consumers. Section 3 discusses the results. Section 4 provides conclusions.

# **2. Methodology**

We measure the importance of face-to-face (F2F) interactions with consumers based on the extent to which an occupation involves (a) establishing and maintaining personal relationships; (b) assisting and caring for others; (c) performing for or working directly with the public; and (d) selling to or influencing others. These work activities were originally identified by Blinder (2009) to measure the 3
our evidence suggests positive payoffs for both risky and cautious types of innovation, indicating a clearly rational decision by plants when deciding to innovate.

The relationship between innovation and plant survival is important for policy across several dimensions. Plant exit is a major cause of unemployment; therefore, our findings are important for implicitly assessing the innovation-employment link. The implications of our findings are twofold. First, a striking implication for industry dynamics is that risk interferes in the firm selection process in terms of innovation, differently from what is known for productivity, i.e., that in well-functioning markets, firms with higher productivity tend to survive and grow under market competition while others may exit at earlier stages. By contrast, we find that the survival of innovating firms is not necessarily ensured. Second, while innovators tend to be superior performers and often generate positive spillovers to the rest of the economy, our evidence shows that only cautious innovators survive longer. Moreover, despite the incentives, engaging in more cautious types of innovation may not be feasible for all plants in all types of industries. For example, small plants may be unable to add products to their product scope - thus engaging in cautious innovation - since they lack the capacity to maintain a large product range. Moreover, when a radical switch in production is required for innovation, the introduction of new products can occur only on a large scale. Hence, there could be a role for public policy in reducing exposure to risks by promoting investments that potentially result in cautious innovations for certain types of plants and providing guarantees or help to deal with failed innovations. Obviously, such policy interventions would need to be designed so as to set the right incentives ensuring that no moral hazard problems arise.

The paper is organized as follows. We describe the data in Section 2. Section 3 discusses the methodology. Our main results are discussed in Sections 4. Section 5 examines the role of risk and Section 6 concludes.

# **2. Data and Descriptive Evidence on Plant Survival and Product Innovation in Chile**

We use a unique dataset on Chilean manufacturing plants and their products (ENIA) collected by the Chilean Statistical Institute (INE) and spanning the 1996-2003 period. The fact that the ENIA is a census of Chilean plants (with more than 10 employees) is crucial for our analysis of plant survival.<sup>9</sup>

> 9 Details on the ENIA are provided in Fernandes and Paunov (2012). Plant survival in the ENIA was studied by Alvarez and Vergara (2010) and Lopez (2006). The fact that the ENIA covers in principle only plants with more than 10 employees could pose a problem for our analysis in that plants might drop out of the dataset not due to failure but due to their employment falling below the 10 employee threshold. However, that principle is in practice more flexible: in our estimating sample for the period 1996-2003 that includes 19439 plant-year observations, 4.8% or 939

4
44 Table 5: Crime and the Distribution of Welfare in Criminal Catchment Area

||Propert|yCrimes|Violent|Crimes|
|---|---|---|---|---|
||Residential<br>Burglary|VehicleTheft|Aggravated<br>Assault|Rape|
|InequalityInPollce Station|0.55<br>(0.08)**|-0.02<br>(0.10)|0.42<br>(0.09)^*|0.30<br>(0.08)**|
|MeanExpenditureInPoliceStation|0.92<br>(0.05)**|1.67<br>(0.07)**|0.16<br>(0.06)**|0.27<br>(0.05)**|
|Uli CiilCthtA|004|021|009|001|
|nempoymentn rmnaacmenrea|-.<br>(0.06)|-.<br>(0.09)*|-.<br>(0.07)|-.<br>(0.07)|
|RichestJurisdictioninCriminalCatchment|0.20|0.09|0.10|0.04|
|Area|(0.07)**|(0.10)|(0.08)|(0.07)|
|Populatlon<br>Population|~~~~~0.8<br>(°(0.02)**|2<br>0.97<br>(0.03)**|0.78<br>(0.02)*|0.84<br>(0.02)**|
|ProvinceDummies|||||
|EasternCape|-002|101|-001|-002|
||.<br>(0.10)|.<br>(0.15)**|.<br>(0.11)|.<br>(0.10)|
|Northem Cape<br>NorthernCape|~~~~-0.30<br>(0.11)**|0.27<br>(0.17)|0.13<br>(0.12)|-0.05<br>(0.12)|
|FreeState|0.12<br>(0.11)|1.21<br>(0.16)*|-0.30<br>(0.11)9**|0.02<br>(0.11)|
|KlNl|013|144|059|003|
|wazuu-ata|.<br>(0.10)|.<br>(0.13)**|-.<br>(0.11)*|-.<br>(0.10)|
|Pi||11|12|016|
|Northwest rovnce|-0.07<br>(0.11)|.8<br>(0.16)**|-0.<br>(0.12)|.<br>(0.101)|
||0.31|1.54|-0.01|0.35|
|Gauteng|(0.10)**|(0.13)**|(0.11)|(0.110)*|
|Mpumalanga<br>|~~~~~0.31|<br>157<br> <br>|-017<br> <br>|005<br>|
|<br>Mpumaianga|<br>~~~~~~~~<br>~|<br>.<br> <br>~~~~(0.1I1)<br>(0.15)**|.<br>**<br>(0.12)|.<br>(0.I1)|
|NorthernProvince|-0.17<br>(0.12)|0.84<br>(0.17)**|0.15<br>(0.13)|-0.07<br>(0.12)|
|Constant|-8.69|-18.31|-3.34|-6.44|
||<br>(0.33)**|<br>(0.50)**|<br>(0.38)**|(0.35)**|
|PseudoR<sup>2</sup>|.119|.170|.078|..127|
|Observations|1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; t significant at 1%
MacShane, Denis, Martin Plaut and David Ward (1984), ”Power!: Black Workers, Their Unions and the Struggle for Freedom in South Africa.” Nottingham. Spokesman.

Mezzetti, Claudio and Elias Dinopoulos (1991), ”Domestic unionization and import competition.” _Journal of International Economics_ , 31: 79-100.

Michaels, Guy (2011), ”The Long Term Consequences of Resource-Based Specialisation.” _Economic Journal_ 121 (551): 3157.

Moll, P. (1996), ”Compulsory centralization of collective bargaining in South Africa,” _American Economic Review_ , 86(2): 326-29.

Naylor, Robin (1999), ”Union wage strategies and international trade.” _Economic Journal,_ 109: 102-125.

Nunn, Nathan (2008), ”The long-term effects of Africa’s slave trades,” _Quarterly Journal of Economics_ , 123: 139-176.

Nunn, Nathan (2009), ”The importance of history for economic development,” _Annual Review of Economics_ , 1: 65-92.

Nunn, Nathan (2014), ”Historical development,” in: Aghion P, Durlauf S. (eds) _Handbook of Economic Growth_ , volume 2, North-Holland: 347-402.

Pemberton, James (1988), ”A ‘managerial’ model of the trade union.” _Economic Journal_ , 98: 755-771.

Redding, Stephen J., and Daniel M. Sturm (2008). ”The costs of remoteness: Evidence from German division and reunification.” _American Economic Review_ , 98 (5): 1766-1797.

Sachs, Jeffrey and Andrew Warner (1997). ”Natural resource abundance and economic growth.” Unpublished manuscript.

Schultz, Paul (1997). ”Labor unions and the distribution of wages and employment in South Africa,” Economic Growth Center, Yale University, Discussion Paper No. 776.

US Department of Interior (1996), US Geological Survey, Minerals Yearbook: South Africa.

US Department of Interior (2011), US Geological Survey, Minerals Yearbook: South Africa.

Vicente, Pedro (2010). ”Does oil corrupt? Evidence from a natural experiment in West Africa.” _Journal of Development Economics_ 92(1): 2838.

25
**Table 9: Factors influencing decisions to produce cash crops**

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent variable:|Househ<br>>|olds with 5 cash crop pro<br><br>1/3 of HH crop productio|duction<sup>1)</sup><br>n|
|Estimation|Prob|it, with IHS2 samplingw|eights|
|Sample|hous|eholds with crop producti|on > 0|
|ln(agropotential|0.215***|2.818***|0.171***|
|for cash crop production)|(0.022)|(0.967)|(0.023)|
|ln(travel time to the nearest|0.168***|1.415***|2.072***|
|major cityin Malawi)<sup>(2)</sup>|(0.028)|(0.212)|(0.365)|
|ln(travel time to the nearest|||-0.211***|
|Major cityin Malawi), squared|||(0.040)|
|ln(travel time to the nearest cross-|2.156***|4.143***|70.337***|
|border exporting ports)<sup>(3)</sup>|(0.146)|(1.036)|(14.879)|
|ln(travel time to the nearest cross-|||-4.869***|
|Border exporting ports), squared|||(1.061)|
|ln(travel time to the nearest||-0.157***||
|Major cityin Malawi)×ln(agro||(0.026)||
|potential for cash crop production)||||
|ln(travel time to the nearest cross-||-0.262*||
|border exporting ports) )×ln(agro||(0.138)||
|potential for cash crop production)||||
|Constant|Yes|yes|Yes|
|Parabola vertex (peak) of travel time to<br>the nearest major city in Malawi,<br>Hours|||2.2|
|Parabola vertex (peak) of travel time to<br>the nearest cross-border exporting ports,<br>hours|||22.8|
|Observations|8565|8565|8565|

Note: 1. Five cash crops are tobacco, coffee, cotton, tea, and sugar cane.

2. Major cities are Blantyre, Lilongwe, Mzuzu, and Zomba.

3. Cross-border exporting ports are Beira (MZ), Durban (RSA), and Dar es Salaam (TZ).

4. Robust standard errors in parentheses 5. * significant at 10%; ** significant at 5%; *** significant at 1% 33. Prices and quality of inputs, prices of outputs, and access to technology matter in location decisions. Access to social services and amenities are important for attracting skilled workers. These include access to clean water and sanitation, sewerage, and health facilities, as well as the amenities that people get for good air quality and open spaces. But firms often cluster in a few places for reasons beyond exploiting regional endowments. They are likely to cluster in areas that provide good transport infrastructure to help them access markets. _Proximity to markets_ is important _for a_ ctivities that benefit from increasing returns. As a disproportionate share of these activities is attracted to such a location, either the wage rate

- 15 -
**Table 9. Growth and entry and exit regulations**

|VARIABLES|(1)<br>growth|(2)<br>growth|(3)<br>growth|(4)<br>growth|(5)<br>growth|(6)<br>growth|(7)<br>growth|(8)<br>growth|(9)<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)<br>FDI|-7.224***<br>(-6.463)|-7.226***<br>(-6.479)<br>0.00107|-7.233***<br>(-4.986)<br>0.000882|-8.083***<br>(-5.595)<br>0.000380|-8.017***<br>(-6.036)<br>0.000470|-8.020***<br>(-5.960)<br>0.000353|-8.584***<br>(-6.669)<br>-0.000291|-8.579***<br>(-6.594)<br>-0.000353|-9.312***<br>(-6.477)<br>0.00168|
|||(0.742)|(0.971)|(0.367)|(0.455)|(0.350)|(-0.266)|(-0.332)|(1.051)|
|Country risk (ICRG)|||0.193**<br>|0.101<br>|0.0915<br>|0.0927<br>|0.0875<br>|0.0897<br>|0.147*<br>|
||||(2.456)|(1.605)|(1.460)|(1.510)|(1.369)|(1.407)|(1.904)|
|Government consumption||||-0.216***<br>|-0.187**<br>|-0.186**<br>|-0.147*<br>|-0.156**<br>|-0.140*<br>|
|||||(-2.862)|(-2.383)|(-2.386)|(-1.948)|(-2.020)|(-1.744)|
|Rents from natural resources|||||0.0543*|0.0542*|0.00948|0.0230|-0.00680|
||||||(1.717)|(1.708)|(0.299)|(0.731)|(-0.201)|
|Political freedom and civil rights||||||||||
|(FH)||||||0.150<br>(0.520)|0.173<br>(0.633)|0.203<br>(0.729)|0.0465<br>(0.180)|
|Trade (X and M)<br>|||||||0.0540***<br>(3.634)|0.0526***<br>(3.557)|0.0479***<br>(3.226)|
|Corruption (CPI)||||||||0.0342<br>(0.136)|-0.206<br>(-0.656)|
|Credit to private sector|||||||||-0.0143<br>(-1.056)|
|Lagged GDP growth|0.462***<br>(11.80)|0.462***<br>(11.81)|0.401***<br>(6.640)|0.380***<br>(5.784)|0.366***<br>(6.178)|0.367***<br>(6.147)|0.355***<br>(5.803)|0.347***<br>(5.698)|<br>0.312***<br>(4.201)|
|Delta DTF (entry/exit)|0.0188|0.0187|0.0364**|0.0544***|0.0527***|0.0527***|0.0472***|0.0472***|0.0536***|
||(1.337)|(1.323)|(2.389)|(3.267)|(3.013)|(3.035)|(2.907)|(2.850)|(2.802)|
|Constant|<br>65.00***|<br>65.02***|<br>57.51***|<br>73.38***|<br>72.21***|<br>71.61***|<br>72.04***|<br>71.88***|<br>78.39***|
||(6.564)|(6.580)|(4.078)|(5.357)|(5.793)|(5.709)|(6.235)|(6.308)|(6.336)|
|Observations|1,145|1,142|865|795|789|783|778|766|646|
|Number of cod2|176|176|132|126|126|125|125|124|122|
|chi2|142.9|143.1|223.4|201.0|211.6|212.4|138.3|141.7|122.8|

Robust z-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from Arellano-Bond dynamic panel estimator regressions of growth on the change in the distance to the entry/exit regulatory frontier. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

25
3 Time wastage is rarely measured through all the stages in which it is lost, yet such measures are feasible. Unlike many educational measures, time is a well-understood concept that also has equal units and an absolute zero. It also offers the possibility of international comparisons. A variety of methods have been used to assess instructional time, and many studies have focused on specific stages of time loss. Measuring the multiple steps of time loss can be a complex undertaking. Documenting large blocks of time loss (such as strikes) may be easy, but measuring smaller amounts of time requires more intricate methodology.

The opportunity to assess an integrated model of time loss arose with a grant from the BankNetherlands Partnership Program for Global and Regional Initiatives (BNPP).<sup>4</sup> Research undertaken during this grant attempted to answer the following questions:

- a) How much of the classroom time do students spend engaged in learning compared to the theoretically available time?

- b) What is the relationship between the various measures of time use and academic achievement?

- c) What policy options will improve use of time at central government and at local levels?  And what costs and savings might be associated with better time use?

- d) How can the amount of time spent on learning be measured in ways that are simple and easy to administer, reliable, comparable across countries and monitorable across time?

_Rationale for IEG involvement._ IEG had raised the issue of instructional time use repeatedly during Project Performance Assessment Reviews (PPARs) in many countries, but there was no easily usable methodology to permit a holistic measurement of time loss. The BNPP grant made it possible to develop it and study its feasibility. So the study took place in countries where PPARs had been conducted (Tunisia in 1998 and Morocco in 2001), as well as Ghana, which had been the focus of a 2004 impact evaluation. To pilot the use of instructional time data as a baseline for later project evaluation, the Brazilian state of Pernambuco was included in the study. An education project had been prepared and was about to become effective as the study started. In Pernambuco, supervisors were trained after data collection on techniques that would help increase instructional time in class. The effects on learning outcomes would be studied at project completion, around 2010.

Subsequent chapters provide the research background, results, and methodological challenges involved in the measurement of instructional time loss. A follow-on, more detailed study, also financed by the Government of the Netherlands, was under implementation as this document was being prepared.

> 4. A team of staff members (Helen Abadzi-IEG, Robert Prouty-HDNED, and Benoit Millot-SASHD) received a grant of US$227,500, that became effective on July 23 2003 and was completed on June 30 2005, after an extension of one year. The staff volunteered their time for this project and could only travel in connection to grant activities during trips undertaken for other purposes.