rafmacalaba/data-use-mentions-extended huggingface dataset:
association between the book value of machinery and equipment in KED and the establishment technology sophistication measures (MOST and MAX) from FAT, which will be explained in the next section.

**Internal validation.** We conduct an additional validation exercise of the technology measures, by studying whether establishments with larger sales, employment and sales per worker are more likely to use top-tier technologies, which are the more sophisticated technologies in each BF and are marked in bold in Appendix A.1. Specifically, we estimate a linear probability model for each business function, where the dependent variable is binary and equal to 1 if the establishment uses one of the technologies classified as top-tier for the business function and 0 otherwise. The model includes a full set of country- and, for the GBFs, 2-digit sector fixed effects. The independent variables are either (log) sales, (log) employment or (log) sales per worker. We find that the coefficients for these variables are positive and significant in a large majority of business functions.<sup>15</sup>

These ex-post checks further reassure us about the soundness of the survey design, the data collection process, and the accuracy of responses.

# **3 Measures of Technology Sophistication**

We next introduce measures of technology at the business function and establishment levels, constructed using information collected by the FAT survey. Before analyzing these measures, we illustrate the granularity of the grid and how these measures can characterize the sophistication of technology used by establishments, with examples from FAT.

## **3.1 Technology Measures**

We denote by _ANUMf,j_ the number of different technologies from the grid used in business function _f_ in establishment _j_ . When more than one technology is used in a business function, we explore whether the technologies used are contiguous in the sophistication ranking of the grid or, instead, there are sophistication gaps in the vector of technologies used. Formally, we define the sophistication gap of establishment _j_ in business function _f_
have been made by certain individuals (typically program officers responsible for the different thematic areas) largely on the basis of their intuitive (‘gut’) sense of what was achievable. (As one of ADG’s liaison officers put it, “The states actually know what to do and how to do it. But they need space and confidence...they need the leeway to do what they do. You have to clear the way. Our role was to create space.”) It is our impression that after the MICS 2016 survey results came out, highlighting the negative performance of the eight TA states against the six outcome indicators thus far (in our section on impact, below, we question whether this was an appropriate means of measuring the success of the TA), ADG began to operate somewhat more out of a pressure to achieve results than to ensure a locally‐driven process. Having begun with an emphasis on very state‐specific, tailor‐made solutions, it appears that towards the later part of their contract, ADG began to push for more standardized solutions, most likely reflecting their increasing desperation to improve results (and thus obtain a performance payment as well as a potential extension of their contract with the federal government). Apparently, Bank staff also gave explicit guidance that ADG should ‘do more’ to support expansion of two particular activities: Maternal and Neonatal Child Health (MNCH)<sup>17</sup> weeks and Quality Implementation Supervision Support (QISS).<sup>18</sup> ADG thereafter placed various “liaison officers” in the

> 14 Though public sector staff in the TA states did refine interventions based on data analysis, they admitted finding it difficult to drill down into what is driving failure of certain interventions and expressed concern that they were sometimes misinterpreting data.

> 15 One of ADG’s liaison officer notes that in two states there were proposals to do costly launches of the SOML program, which would have used about N
Affairs Canada under the Gender Innovation and Regional Learning (GIRL) initiative, as well as the World Bank’s Umbrella Facility for Gender Equality (UFGE). The UFGE is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment through experimentation and knowledge creation to help governments and the private sector focus policy and programs on scalable solutions with sustainable outcomes. The UFGE is supported with generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, the Bill and Melinda Gates Foundation, and the Wellspring Philanthropic Fund. The study was preregistered in the 3ie RIDIE registry: `https://ridie.3ieimpact.org/index.php?r=search/detailView&id=653` . The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank Group and the IMF, their Board of Executive Directors, or the governments they represent. All errors and omissions are our own.
26 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE with _Zi ≡_ ( _Zi_ 1 _, ..., ZiJ_ ) distributed according to where _dij ≡ Dij_ + _μα_ + ( _σ −_ 1) _μφ,i_ and ̄ _σφ,i ≡_ ( _σ −_ 1) _σφ,i_ .

Using firm-level data from the EDD and China across different origins and destinations, we can estimate the parameters in (32) as well as mean log fixed trade costs (up to a constant) and their dispersion using maximum likelihood methods. The Online Appendix D shows how to derive the density function _gXi_ 1 _,..,XiJ_ ( _x_ 1 _i, ..., xiJ_ ) for the case when we observe sales to _J_ destinations. We simplify the analysis by considering only data for fifteen destinations (USA, Germany, Japan, France and the 11 largest destinations by exports value for each origin), which we label _j_ = 1 _, ...,_ 15 for the year 2007 for each of 39 origins. We compute _gXi_ 1 _,...,XiJ_ ( _x_ 1 _i, ..., xiJ_ ) for each observation in our dataset (which is a realization of _{Xi_ 1 _, ..., XiJ }_ that we observe). Since all random variables are independent across firms, we can compute the log-likelihood function as a sum of log-densities, where _N_<sup>�</sup> _i_ is the number of firms from _i_ that sell to either of the fifteen destinations we consider, and where _ki_ is an index for a particular observation in our dataset (for origin _i_ it takes values in 1 _, ..., Ni_ ) and Θ _i_ is an origin-specific vector of parameters that we want to estimate, where _μ_ ̄ _f,ij_ = ln _σ_ + _μf,ij_ and _ρ_ = _σα,iσαf,σf,ii_<sup>.As the likelihood is potentially not concave in</sup><
C.6, we show that under a VAT system the pass-through of taxes to informal prices is equal to the share of formal input costs in informal retailers’ total input costs. Using the Mexican census data described above we find that this share is on average 10% among all informal retailers.<sup>29</sup>

Table A6 summarizes our progressivity results under the assumption of a 10% pass-

> 29In the 2013 Census, 85% of retailers are informal by not reporting any payment on VAT. Among informal retailers, only 8% report paying VAT on inputs, which applies on average to 40% of their intermediate input purchases. The informal retailers that report positive VAT on inputs account for 25% of all informal sales. Combined, this leads to our estimate of 10%.

18
the ability of remote sensing indicators to track the COVID-19 crisis, across regions of the world and country income groups. Following the beginning of the crisis in the first quarter of 2020, NO2 emissions showed the steepest drops in the Middle East and North Africa (when grouped by regions) and upper-middle-income countries (when grouped by income level). Nighttime lights showed the steepest decline in East Asia and Pacific (when grouped by regions) and lower-middle-income countries (when grouped by income level). Although not every indicator seems to capture the beginning of the COVID-19 crisis in all corners of the world, both NO2 and nighttime lights show recovery starting from the second quarter of 2020.

In addition to these satellite-derived indicators, we compiled a set of indicators that have the potential to reflect the COVID-19 crisis, namely Google mobility data, Google search data, and data on food prices. Of these additional real-time indicators, Google mobility, religious Google search terms, and food prices appear to reflect the onset of the crisis best. Following a sharp change in Google search for religious terms, food prices, and mobility trends at the end of the first quarter of 2020, all these indicators show recovery starting from the second quarter of 2020.

Finally, we also examine whether these real-time indicators predict deviations from projected GDP in a subset of countries, most from the OECD, in which quarterly GDP estimates are available. Selected features from grouped lasso explain around 88 percent of the total variation from predicted GDP in the subsample. Around half of this is explained by real-time indicators, with the remaining half due to quarter and country fixed effects. Two indicators – a price indicator and a mobility indicator – alone explain more than 30 percent of the total r-squared, or just short of 30 percent of the total variation in the dependent variable. The results suggest that real-time indicators can complement existing tools to monitor the economic impacts of a crisis like COVID-19, in which mobility was sharply restricted.

3
Arm 4, while it slightly increases for Arm 2, although the means are not significantly different than those estimated through Arm 1. Indicator 1.4.2 (b), based on perception of land tenure security, shows more variation across arms – Arm 3 estimates 22 percent of individuals perceive their land tenure to be secure, which is significantly different from the Arm 1 estimate of 33 percent of individuals. For 5.a.1 (a), we estimate a higher share of land-secure individuals when comparing the aggregate-level approach (Arms 2 and 4) to the gold standard, and a lower share of landsecure individuals using Arm 3. However, these differences from Arm 1 are not statistically significant. Similarly, there are no statistically significant differences in the estimates of 5.a.1 (b) between Arms 2, 3, 4 and Arm 1.

In operational terms, if financial or technical reasons require survey practitioners to select an aggregated approach using proxy respondents, our findings suggest that in the case of Armenia, the errors stemming from aggregation and proxy respondent bias appear to compensate for each other, specifically in the construction of SDG 5.a.1, which takes an ‘either/or’ approach to indicator construction (that is, if an individual has legally recognized documentation or the right to bequeath or the right to sell, they are considered secure). This finding may be specific to contexts like Armenia, where documentation rates are relatively high. The significant trade-offs in terms of the analytical value of parcel-level versus aggregate-level land data must also be carefully considered if opting for the Arm 4 questionnaire.

agricultural land only, and the consistency observed between the findings of total land and agricultural land only (which is not impacted by the error), we believe the potential bias to be negligible. At the household level, only 3 households of all 301 in Arm 4 reported rights to non-agricultural land only (including a total of 7 women and 5 men). At the individual level
a complex endeavor in a region that lacks long-term longitudinal data, as is the case for Latin America. Nonetheless, we use one of the few nationally representative household surveys in the region, Mexico’s ENOE (the _Encuesta Nacional de Ocupacion y Empleo_ ), which follows the same households for five quarters. We use these data to estimate multinomial models of youths’ decisions to test the predictions of the theoretical model developed by Behrman et al. (2014). Our results contribute to the improved design of policies dealing with the complex topic of at risk youth in Latin America, and will complement previous research on youth unemployment (see Cunningham et al. 2008, for example) and school drop-out (see Bassi, Busso, and Muñoz, 2013).<sup>7</sup> In Section 2, we briefly describe the ENOE data and do some basic analysis of Nini rates in Mexico. Section 3 outlines the theoretical model by Behrman et al. (2014), the framework that guides our empirical endeavor, as well as our econometric strategy. Our results are discussed in Section 4. Conclusions and suggestions for future research are in Section 5.

# **2. Nini Incidence in Mexico**

# **2.1 Data**

We use household data from the Mexican National Survey of Occupation and Employment (ENOE) for our analysis. The survey, conducted quarterly since January 2005, is administered by the National Institute of Statistics and Geography (INEGI), and is publicly accessible on the institute’s website. The total ENOE sample for a single quarter consists of approximately 120,000 households, and is representative at the national and state levels, as well as by the size of locality.

The advantage of ENOE data for our purposes is the rotating panel nature of the survey, which allows us to track education and employment status over time for the same individuals. Each

> 7 Our research builds on previous work by Cunningham and Bustos-Salvagno (2011) who study youth
# **_Indirect Taxes_**

VAT is a major component of indirect taxes, contributing to about 36 percent of total tax collection in 2014. VAT is levied at a standard rate of 23 percent on most goods and services, but a reduced rate of 5 percent applies to certain food, books and magazines or 8 percent is imposed on supplies, such as medicines, hotel and catering services, certain transport and municipal services. There exists also a rate of 7% for products bought from farmers who pay the tax as a lump sum. A zero-rate applies to the intra-EU community supply of goods, exports of goods, some international transportation and related services. Usually, in order to apply zero VAT rate additional conditions need to be fulfilled. Some financial, medical and cultural services are exempt. In particular, small entrepreneurs with yearly revenues below 150 thousand zloty, are exempt from paying VAT and may not deduct the VAT paid on inputs. The VAT gap has been estimated to be between 30% and 50% of VAT collected (PwC, 2013).

Excise taxes contribute 3.4 percent of GDP and they are levied on goods that are deemed to be a harmful for health reasons. The Ministry of Finance in Poland announces every year the excise taxes that apply to products that might be harmful to health or environment. Goods subject to excise duty include energy products, alcoholic beverages and manufactured tobacco products, based on the legislation of the European Union.

# **Social spending**

Overall expenditures in Poland amounted to 42.1 percent of GDP in 2014, down from 45.5 percent of GDP in 2010 (Eurostat). A large part of public spending is dedicated to social protection (16.1 percent of GDP), while education (5.3 percent of GDP) and health (4.6 percent of GDP) are also relatively important. Total social spending in Poland amounts to 26 percent of GDP or 62 percent of total spending. The analysis presented below covers 51
level.

After controlling for a woman’s, partner and household characteristics, attitudes towards and experiences of IPV, ethnicity, religion and state fixed effects, the results of the DD model show that the presence of BH increases the probability of a woman’s experience of physical or sexual IPV by 3.7 percentage points (Table 4).

The individual and household characteristics associated with increased risk of women’s experience of physical or sexual IPV are: having more children (0.3 percentage points for each additional child), marriage as a child (2.6 percentage points), having a sometimes or often drunk partner (14.6 percentage points) and marriage in a polygamous household (1.7 percentage points). Both indicators of attitudes towards and experiences of IPV are also associated with increased IPV risk; having a father who beat their wife/partner (12.4 percentage points) and condoning wife beating (4.4 percentage points). The finding that the highest IPV risk is associated with <mark>having a mother who was beat by their husband/partner and having a husband/partner who is sometimes or always drunk is consistent with other research about generational cycles of IPV (</mark> see Lipsky et al. 2005; Knight et al. 2013; Thornberry et al. 2011 <mark>) and the association of IPV perpetration with drug and alcohol abuse (see L</mark> ipsky et al. 2005; <mark>Thompson and Kingree 2006; T</mark> umwesigye et al. 2012; <mark>Devries et al. 2013;</mark> Hanmer and Klugman 2016).

There is a weak association between receiving earnings in kind as opposed to no earnings and lower IPV risk (-4.7 percentage points) and membership of certain ethnic groups is associated with lower IPV risk. <mark>Our findings are consistent</mark> with other research using Nigeria’s DHS data;
observations across countries and rounds and preserve representativeness. Table 2 presents data on unweighted sample sizes from the round 1 survey of each country.

Table 2. Respondent characteristics, round 1

||Indonesia|Lao PDR|Mongolia|Myanmar|Philippines|PNG|Vietnam|
|---|---|---|---|---|---|---|---|
|Relation (%)||||||||
|Head|93|55|52|52|36|72|63|
|Spouse|3|21|40|23|35|0|25|
|Other|4|24|8|24|30|28|12|
|Average age||||||||
|(years)|46|38|47|41|41|45|51|
|Male (%)|85|60|33|58|35|51|53|
|Sample size|4,216|2,500|1,026|1,500|2,924|3,368|6,213|

Note: Respondent refers to the subject of the employment module. In all countries, respondents were restricted to adults, defined as 15 years old or older. In Indonesia, the respondent was asked to answer questions in the employment module about the household head or breadwinner. In Mongolia Round 3-4, the respondent (if different from the previous respondent) was asked to report on the previous respondent. In PNG, respondents and household heads (if not the same) are pooled.

In the remainder of the paper, we focus on six key indicators to examine the distributional impacts of the pandemic in the context of the aforementioned three questions. Table 3 provides summary statistics by country and round for each of these six outcomes.

Table 3. Summary statistics of outcomes (percentage)

|Country|Survey<br>
- 9 -

_m_ . In our case, travel time is used for _dim_ . Then, the index is normalized to zero to one. Note that the higher _JAI_ means better connectivity, that is, location _i_ is closer to job opportunities.

15. To estimate Equation (1), one empirical issue is that spatial autocorrelation would likely matter to any locational data, such as land prices. This is often ignored in the existing literature. The land value at a particular place is likely to be related to its neighboring values because of unobserved neighboring factors and transport connectivity. Of particular note, transport infrastructure typically forms a network, which creates autocorrelation among neighboring area: Roads must be connected with each other.

16. One approach to mitigate such unobserved spatial autocorrelation is to introduce the location-specific fixed-effects in the model. For instance, Chen et al (2017) use the countyspecific fixed-effects to analyze the relationship between housing market thinness and proximity to the CBD in Maryland. In our case, unfortunately, panel data are not available.

17. Another approach to control for potential autocorrelation is the spatial autoregressive model (Meen, 1996; Orford, 2000; Tu et al., 2007), which the current paper relies on. That is, land value, _Vi_ , at a particular place, _i_ , is correlated to its neighboring values, _Vj_ . That is, Cov�u�, u���0.

where _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011).<sup>1</sup>

> 1 For estimation we applied a STATA command **_spreg_** developed by Drukker, Prucha and Raciborski (2013).
The rigid component of public expenditure reduces the procyclical bias (Table 4, column 9). But the effect is asymmetric over the business cycle: the mitigation of procyclicality through spending rigidity is larger during the upturns of the cycle (column 2 of Table 5). Finally, we found no evidence that fiscal space affects the procyclicality of public spending (Table 4, column 10). However, exploring potential heterogeneity over the period, we find that the fiscal space variable affects procyclicality, with lower fiscal space inducing higher procyclicality, but only when the sample is broken to include the most recent years 2009-2016.<sup>10</sup> Finally, we find evidence of heterogeneous procyclical behavior along the cycle: at first, we find no direct evidence of the asymmetry (Table 5, Column 1), but when the rigidity and fiscal space variables

> 9 Resources-dependent countries are countries for which commodity revenues (GGRC in WEO data set) account for at least 10 percent of total revenues minus grants (GGR and GGRG series in WEO data set) at least 50 percent of the time over the considered period. This definition is derived from Konuki and Villafuerte (2016) and enables us to capture countries with a resource-dependent revenue structure. For more details, see Table E.1. Resources-rich countries are defined by the IMF (2012), here is a direct access to the report (https://www.imf.org/external/np/pp/eng/2012/082412.pdf). According to this definition, we capture countries where production on natural resources reached more than 20% of exports. For more details, see Table A.2.

> 10 The findings are not presented in the paper but available upon request.

12
12 Only about half of the 85% of projects that included an IEC component made provision for systematic feedback from clients about its effectiveness. Where this occurred, this was most commonly (44% of projects) through surveys or studies aimed at establishing changes in awareness and practice. In a minority of projects (about 8%), an effort was made to obtain feedback from service providers on a regular basis about the effectiveness of IEC. A particularly thorough effort to obtain feedback about the appropriateness of IEC messages was made in the Indonesia Nutrition and Community Health Proje.'t (1977, Case 6). This represents the Bank's only systematic attempt co date to apply social marketing techniques in the PHN sectors. Give.. the differentiated nature and distance of client populations and corresponding uncertainty about what IEC strategies and messages are appropriate, the fact that systematic qualitative feedback about IEC is not more widespread is cause for concern.

The same applies to feedback about the appropriateness of service delivery strategies, where only 36% of the projects had made provision for systematic qualitative feedba-k. This was accomplished in 23% of the projects through one-off surveys of client demand for, utilization of, and attitudes to services. Only 19% of the projects planned to do this through the development of regular feedback from clients, principally through the formation of local health committees to liaise with local service providers as for example in India Population 4 (1985) and the Guinea project (1987). Bank thinking about the development of qualitative feedback from clients in the PHN sectors appears to have lagged behind that in the agriculture sector. Feedback from clients is the driving force of the training and visit system of agricultural extension (Case 7), now being implemented in more than 40 countries.

By far the most important form of learning in the Bank's PHN projects has been through the development of management information systems for the collection, analysis and use of routine quantitative performance data. No less than 79% of the projects in the