rafmacalaba/data-use-mentions-extended huggingface dataset:
and dependence on the brick and mortar model is a deterrent to the expansion and uptake of digital financial services.

# **3.2. Methodology**

Having observed the current status of financial inclusion as covered by the NFI Survey in the previous section, we now use several Probit models to understand how certain characteristics are related to financial inclusion. This approach is similar to comparable studies by several authors (Cámara, Peña and Tuesta (2014), Fungacova and Weill (2015), Tuesta et.al (2015), Zulfiqar et.al (2016), Zins and Weill (2016)) who too used probit estimations<sup>20</sup> stemming from the basic formula: ∗ Where is a latent variable, which depends on _x_ being a vector of exogenous variables, **_β_** is a vector of parameters, the subscript ii _i_ represents individuals and _u_ is a normally distributed error yy term with mean 0 and variance 1.

Based on this, a formula popular among previous authors to analyze the determinants of financial inclusion is as follows:

**_fin.Inc.i= α+β*incomei+γ*educationi+ δ*agei+ σ*genderi +_** ii uu

where _finInc_ denotes one of three indicators of formal financial inclusion (i.e. having an account at a formal financial institution (FI), saving at a formal FI, and borrowing from a formal FI) and _i_ is the index for individuals. Given that the endogenous variable is a binary response – i.e. whether the individual uses formal financial service – it can only take the values 0 or 1. It is assumed that the decision to use formal financial services depends on a latent variable, which is determined by

> 20 ⋅ In Probit regression, the cumulative standard normal distribution function Φ( ) is used to model the regression function when the dependent variable is binary. https://www.econometrics-with-r.org/11-2-palr.html
of your home?” =1 if .. paid for any improvements corresponds to “ Have you or an NGO made (or paid for) housing improvements where you live since October 2019?” Index of improvements is an index of structural, cosmetic, mold, insulation and utilities upgrades. FR/HH childcare and chores hours corresponds to the hours spent by the focus respondent/household in the last 7 days on childcare and chores. Any domestic employees is an indicator equal to 1 if the household currently employs any domestic employees, and N refers to the number of employees.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 6 in the pre-analysis plan.

44
s “own boss” as a top reason. Only around 30 percent of entrepreneurs mention the lack of wage employment, and only around 20 percent go into business mainly to avoid paying mandatory social security contributions (IESS). Similar sentiments were expressed in the focus groups, with an additional reason of the business being a source of employment opportunities for family members, and an endowment that can be left to children.

**Figure 1: Entrepreneurs’ reasons for having their own business**

<!-- Start of picture text --> I run my own business because...<br>I prefer to be my own boss and not depend on others<br>I can take care of children/parents while I work<br>I have flexibility in my schedule<br>A business like mine could grow in the future<br>It's too difficult to find wage employment<br>Running my own business is less boring than working as an<br>employee<br>I don't need to pay contributions to IESS<br>Women Men 0% 10% 20% 30% 40% 50% 60% 70% 80%<br><!-- End of picture text --> 2 The recently completed economic census (Censo Economico) was not available at the time of the EMES sample design; moreover, there are several methodological differences such as the fact that Censo Economico only surveyed businesses with a fixed place of establishment and did not cover most street vendors, construction workers, or taxi drivers. However, the correspondence between the EMES results and those of the economic census—for the same cities and firm sizes—is quite high for a number of characteristics, such as having a tax identification number (RUC) and the gender of the firm’s owner.

3
# **<mark>V. Conclusions and Recommendations</mark>**

The national response to HIV/AIDS in Colombia is articulated through a health insurance system and is integrated into the broader area of public health (as per the PNSP), and more specifically, into the SRH area—which is one of the key national priorities in health. The SGSS guarantees the provision of health care to the population – with a growing degree of care coverage and an increasing proportion of public expenditures that have been gradually achieving a smaller proportion of out-of-pocket expenditures in health.<sup>56</sup> This system is integrated by a network of insurance companies and health care provider institutions that compete in a regulated market.

Due to the structural challenges that the health insurance system is currently facing, Colombia is now undergoing a process of rationalization and reform.<sup>57</sup> The existence of different affiliation and insurance regimes makes it an extraordinarily complex and fragmented system. In addition—and contrary to what might be expected—two-thirds of the insured belong to the subsidized regime, and only one-third actively contributes to the contributory insurance scheme.<sup>58</sup> On the other hand, the public health system component carries out collective interventions for health promotion and prevention—channeled through the local entities—which results in a certain degree of local variability in the type and quality of interventions used to respond to the HIV/AIDS epidemic across the country.

## **1. Efficiency of the HIV/AIDS Response in Colombia**

The epidemiological data presented in the Study suggest that the HIV/AIDS response in Colombia is succeeding in containing the epidemic. The prevalence of HIV/AIDS for the entire population of Colombia is low. However, the number of HIV cases reported has been gradually increasing among the groups where the epidemic is concentrated – mainly, MSM, FSWs, IDUs and pregnant women. In addition, Colombia is making a

> 56 ACEMI (Septiembre
parametric methods in Ackerberg et al. (2015) but will also present results based on other methods (Wooldridge, 2008), and with fixed effects to check for robustness. These measures are proxies for revenue productivity, which depends both on physical productivity and markups.<sup>7</sup> The dependent variables in our regressions are 3‐year changes in these productivity measures, employment, sales, average wage and return on assets (ROA). We winsorize these long differences at the 5<sup>th</sup> and 95<sup>th</sup> percentiles of their distribution, calculated separately for each 2‐digit industry‐year combination.

As for the construction of our main explanatory variable, multiple definitions for HGFs exist in the literature. A key difference between these methods is whether they rely (i) exclusively on relative growth rates or (ii) on absolute growth. The former approach tends to identify HGFs as mostly small firms, while the latter provides a less skewed identification. As all theoretical spillover channels – either via vertical linkages or the labor market – are likely to be stronger when they originate from larger firms, the latter measure is more likely to capture these externalities. Hence, that is our preferred approach. Specifically, we choose to identify HGFs based on the Birch index (Birch, 1981), computed for all firms with at least 5 employees in year t : We rank firms according to their Birch�� values for each year and classify a firm as HGF if the Birch index is above the 95<sup>th</sup> percentile in that year. The constant and absolute threshold generates variation across industries, regions and years.

As a robustness check, we will estimate the regressions with the OECD definition of HGFs whereby a firm is high‐growth if it employs at least 5 employees at the beginning of the period and has an annualized growth rate of employment of at least 20 percent for a period of 3 years
# **IV. Summary of Findings and Future Research**

We estimate the average impact of job displacement on labor market outcomes and ask whether these impacts are temporary or permanent. The results identify labor outcomes with the largest and the long-lasting impacts, confirming the existence of scarring effects of job displacement on workers ́ future labor market outcomes.

The empirical analysis focuses on heterogeneous effects on gender and educational groups. We find that the Mexican labor market reabsorbs men and women similarly, as both types of workers present similar recovering paths of wages, hours of work and probability of being formal after a plant closing event. We find marked differences in labor outcomes that follow plant closing events for high and low educated workers. Plant closing has a strong and permanent reduction on wages of high educated workers, while for low educated workers the impact on wages vanishes completely after the sixth year. Displaced workers due to plant closing have a permanent increase in the hours of work per week, with an impact on low educated workers that is double the magnitude of the impact on high educated workers. Additionally, plant closing permanently increases the probability of working in the formal sector for low educated workers. The estimates of the hazard model show that after a plant closing event, unemployment spells –relative to their non-displaced counterpart– are longer for men than for women, and longer for high than for low educated workers.

An important result of this paper is the comparison of OLS and DID estimators. Using the rotating panel of the labor survey, we obtain the DID estimator and remove any time invariant fixed effect that could be biasing the OLS estimates of the cross-section analysis. The DID results show that displaced workers due to plant closing experience a reduction in wages of -4.7 percent, a moderate increase of 0.53 hours of work per week, and no impact on the probability of working in the formal sector. Compared to OLS estimates, DID reduces the magnitude of the impact of plant closing on 28
immigrants’ decisions about when to return to their home countries. Our paper takes a different approach to the migration experience by modeling the central role played by self-employment aspirations upon return. These are important distinctions from the previous work. A joint, dynamic analysis of migration and self-employment decisions is crucial. For example, policies that aim to overcome financial constraints to self-employment might become less effective because endogenous adjustments in migration behavior may offset part of the intended on business creation.

Indeed, we show that this is the case. The data that underlie our findings come from a unique survey, _the Bangladesh Return Migrant Survey_ (BRMS), which was specifically designed to investigate the patterns and determinants of the temporary migrants’ lifetime employment trajectories, earnings, and welfare. We use these data to construct a dynamic framework that models the tightly intertwined nature of pre-migration labor market out51
857|94.9%|15.5%|0%|100%|11,323|96.9%|10.3%|0%|100%|
|Portuguese classes higher field<sup>**</sup>|10,857|84.3%|26.1%|0%|100%|11,323|82.1%|25.4%|0%|100%|

_Note._ Students in the 3rd year of public regular secondary school that participate in ENEM. _Infra PNE_ : _Indicator for school infrastructure_ from Observatory of theNational Education Plan (Observatório PNE). *% classes taught by teacher with higher education. **% classes taught by teacher with higher education in the field. _Source._ ENEM,and School Census microdata.

In the same period, the percentage of classes taught by teachers with higher education increased by about 2 percentage points for both math and Portuguese. This result was accompanied by the increase of 4.6 percentage points in the percentage of math classes taught by teacher with higher education in this field, and the decline of almost 2.2 percentage points in the indicator for Portuguese classes. That is, the growth of teacher-specific training for the discipline, from an aggregate point of view, does not seem to have had a positive relationship with student performance.

Some important changes occurred in the period that may be correlated with students’ results. Regarding the educational offer conditions, the percentage of students attending evening classes decreased from 40.2% to 25.6%, the average duration of classes increased from 4.4 to 4.8 hours, the average number of students per class decreased from 34 to 31, and the school infrastructure indicator increased from 0.193 to 0.305. In relation to students’ profile, the percentage of students who have already worked decreased from 47.3% to 34.7%, the percentage of students
)|1623|453|2076|
|Phone Faults (reported faults/100 mainlines)|73<br>(96)|19<br>(17)|1094|328|1422|
|Telephone Mainlines per Employee|68<br>(57)|175<br>(111)|1787|600|2387|

Source: Own calculations using ITU data. Standard deviations in parenthesis. Countries were classified in developing and developed according to their 2001 GNI per capita.

Regarding affordability, the only information available for a large set of countries is the average price and occasionally, the tariff structure. The information is however widely seen as not reliable since it does not account for many of the “informal” alternatives available to phone users—e.g. calling cards. This may be why only three of the previous cross-country studies have tried to document the impact of reforms on prices. We use the price of a 3-minute local phone call in US cents and the monthly subscription fee (for residents and for business) in US dollars as our proxies for affordability. An issue with these indicators is that they do not take into account prices in the mobile segment, thus, we are using proxies for affordability only in the fixed line market. However, these are the best data available at the moment.

The specific definitions offered by ITU for the selected affordability variables are as follows: The price of a local call refers to the cost of a peak rate 3-minute fixed line call within the same exchange area using the subscriber's own terminal (i.e. not from a public telephone). The monthly residential (business) phone subscription fee refers to the recurring fixed charge for a residential (business) subscriber for using the public switched telephone network. The charge should cover the rental of the line but not the rental of the terminal (e.g. telephone set) where the terminal equipment market is liberalized. In some cases, the rental charge includes
commodity markets, this phenomenon arguably poses significant future challenges. Government interventions including food price subsidies and controls, along with political insecurity in the region, may have contributed to a sluggish market response to downward movements in world food prices. However, this paper does not identify systematically the causes of the asymmetric food price transmission process. They are complex and should be an area for future research.

Following next is section 2 which documents the sources for the MENA food time-series price data and presents statistics that explore MENA‟s potential exposure to food price shocks, including dependency ratios by food commodity. Section 3 reviews the empirical literature on food price pass-through effects, with the aim of establishing benchmarks and informing the empirical methodology. Section 4 details the econometric approach, while section 5 presents pass-through coefficients for 18 MENA countries. It shows also food price transmission dynamics and decompositions of pass-through estimates into approximate sources. Section 6 concludes with a summary.

# **2. What Do the Data Tell Us?**

Historic and current price data for MENA are scarce and for most countries not readily available. For the 18 individual countries, monthly food consumer price data are compiled from various sources. The primary data sources are the national statistical offices, either directly, or collected over time by World Bank country economists. The food consumer price data are complemented with historical information from the International Labor Organization (ILO) and several updates provided by national statistical offices themselves.

Efforts were made to ensure data accuracy. Specifically, we compared trend and annual growth consistency of different time-series from 1998–2011. The data were further corroborated with general market information from various press releases, field documentation from USDA, and country updates by World Bank economists. In some cases, such as Djibouti, Jordan, Lebanon, and Tunisia, the level data in different series show small divergences from original series due to rebasing, yet the effects on annual growth rates are negligible. A small number of missing monthly observations were interpolated for the Gulf Cooperation Council (
# **Figure C5.4: Evolution of the premium on industry-level Suharto connectedness – competition index – broad connections**

<!-- Start of picture text --> Z competition index<br>'93 '94 '95 '96 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09<br>Suharto era Post Suharto era<br>6<br>4<br>2<br>PC broad<br>β<br>0<br>-2<br><!-- End of picture text --> _Note:_ The figure depicts annual variation in the estimated impact of the market share of broadly defined politically connected firms on the competition index Z. The dots depict the coefficient estimates ′93 ′94 ′09, estimated using the regression: = ∑ Y Y which are αα , αα , ... ... . αα presented in Table 8, with 1996 as the omitted year. jjtt ii=1993,..,2009 ααiiPC (MMSS)jj ∗DD(Y = tt) + ∑measures the aggregate Suharto-era market share of firms with ii=1993,..,2009 γγiiSSjjii ∗DD(Y = tt) + ππjj + ττii + εεjjii ZZ broad connections to Suharto (the average of their aggregate market share in 1996 and 1997). jj represent Suharto-era industry PC (MMSS) characteristics. is a vector of industry fixed effects, and is a vector of year fixed effects. The vertical bars indicate the jjii SS 95% confidence interval associated with the estimates. jj ii ππ ττ 70
but has had relatively little to say about whether, how and to whom they have diffused across countries, industries, and firms. An important exception is a recent paper by Bastos, Greenspon, Stapleton, and Taglioni (2024), who draw on textual analysis of online job postings to document the diffusion of low carbon technologies across 35 countries, and examine econometrically the role of the 2022 global energy crisis in accelerating their diffusion across countries and firms. The current paper provides complementary stylized facts on both the emergence and diffusion of green technologies by drawing on the textual analysis of patents and ECTs in a wide range of countries; examining the link between green technology adoption and subsequent changes in emissions at the firm-level; and documenting the role of buyer-supplier links in the diffusion of green technologies. Finally, our paper is also related to the broader literature on technology diffusion, including Comin and Hobijn (2004), Comin and Hobijn (2010), Comin and Mestieri (2018), Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021) and Bastos, Stapleton, Taglioni, and Wei (2024). Our approach for examining the role of firm-to-firm linkages in the diffusion of low carbon technologies is related to that adopted in Bastos, Stapleton, Taglioni, and Wei (2024), who use data on online job postings and information on firm-to-firm linkages to examine the spread of new technologies, but do not focus on green technologies.

The remainder of the paper is organized as follows. Section 2 describes the various sources of data, and our approach to identify green technologies and associated keywords. Section 3 presents stylized facts on innovation and dissemination of green technologies based on patent data from the WIPO green database, which allows us to assess the dissemination of the most novel and influential green technologies. Section 4 presents evidence about the diffusion of green technologies among firms, sectors, and countries using textual analysis of earnings calls transcripts. Section 5 reports evidence of patterns of diffusion of green technologies through firm-to-firm relationships. Section 6
|
|Ethnicity|0.005|0.005|0.004|0.006|0.006|0.004|
||(0.94)|(0.91)|(0.23)|(1.88)|(1.88)|(0.13)|
|Constant|0.095|0.095|0.093|0.277|0.277|0.285|
||(28.86)**|(33.29)**|(7.84)**|(123.40)**|(123.36)**|(9.84)**|
|Obs|23,871|23,871|20,503|109,278|109,278|96,141|
|R-squared|0.04|0.04|0.04|0.01|0.01|0.01|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Tables 5 and 6 display predicted correlations from the GLS regressions by locality of residence and SES.<sup>5</sup> Middle SES is assigned to paired status for individuals who are neither High SES nor Low SES. Both tables display the highest correlations for paired individuals with Low SES in a common locality. Conversely, paired individuals with High SES in different localities have the lowest correlations. Typical correlations are higher for general environmental changes than for livelihood-related changes, but livelihood-related changes have greater relative effects. From highest to lowest case, the correlation falls by 69% for livelihood-related changes and 49% for changes in general environmental conditions.

> 5 Ethnicity has no meaningful effect on these results; the table entries are calculated for paired individuals who do not have common ethnicity.

10
tax income or even after other deductions such as medical insurance or pension fund contributions (which could be important in the top tail even in low-income countries and more important in middle income countries).

It is hard to find direct evidence of under-reporting because this requires matched survey and administrative data. We noted above that Flachaire et al. (2022) obtained such data for Uruguay. The authors showed that under-reporting in the household survey they used is, on average, highest at the top of the tax income distribution, as might be expected, but that there is over-reporting at the bottom of the distribution. In the top five percentiles, the tax data values are, on average, around 2 to 2.5 times as large as the values reported in the surveys. This is novel evidence that under-reporting is indeed an important issue when measuring the top tail of the income distribution, although the data requirements are so exacting there are, to our knowledge, no other similar studies for LMICs. Bollinger 7
not a household has an infrastructure service such as electricity or piped water supply; if a household does have a particular service, it is said to be "covered."

> 5 Coverage data can aid in the description of an existing infrastructure situation, but they cannot be used to determine why such a situation exists, even if one were able to go back to the original data sets. This is because most surveys on which the coverage summaries are based do not ask respondents what services they could have chosen (but did not) and the attributes of such service options (e.g., price, quality, reliability). What we see in the coverage statistics is the _outcome_ of both supply and demand factors that bear on a household's infrastructure choices, but policy analysts cannot generally disentangle such factors from the coverage statistics.

I