rafmacalaba/data-use-mentions-extended huggingface dataset:
(unstratified) relationship between spending and the HCI often plateaus above a certain level of spending and is noisy throughout. While the relationship between spending and HCI appears stronger for HICs than for countries at other income levels, these relationships are not statistically significant.

Our conclusions do not change when we examine the relationship between specific inputs into the HCI and their associated sector’s spending. Among the three sectors, health spending seems to display a slightly clearer relationship with HCI than other sectors considered, but this might be partly explained by the higher density of the health data. The clearer relationship between health spending and HCI might also be related to a few factors that could contribute to less noisy data: the health data do not include capital spending (which can be lumpy), and also the WHO-GHED is the result of a long-standing international effort to collect and validate health spending data, possibly resulting in higher quality.

Our findings are in line with other studies that look at the relationship between spending and human capital outcomes, which find that increased spending does not necessarily translate to improved outcomes (AlSamarrai, Cerdan-Infantes, and Lehe 2018; World Bank Group 2018b). There are many possible reasons behind the weak correlation that we observe at the country level. As shown in Al-Samarai (2018), countries display dramatic differences in the ability to transform resources into outcomes, so poor outcomes may be reflective of spending inefficiencies (Al-Samarrai, Cerdan-Infantes, and Lehe 2018). The inefficiencies might either be the result of inability to prioritize resources towards the interventions that are most effective at improving outcomes, or to scale them up in a cost-effective way.

At the heart of our interest in assessing the relationship between social sector spending and outcomes is an interest in providing guidance on how much and what type of investment is needed to achieve high levels 23
and J.P. Morgan; commodity prices from Goldman Sachs and the World Bank’s Development Prospects Group; bilateral investment treaties from UNCTAD; country risk and institutional indicators from the PRS Group’s International Country Risk Guide (ICRG); and technology and innovation indicators from the World Intellectual Property Organization. Depending on the specification, the dataset is an unbalanced panel that includes between 29,995 and 55,497 observations.<sup>10</sup>

# **4.2 Econometric specification and estimation**

The econometric model we use is an augmented gravity model that specifies that the number of cross-border M&A deals originating in country _i_ (“home”) and destined for country _j_ (“host”) at time _t_ , _Mijt_ , is a function of each country’s output in that period, _Yit_ and _Yjt_ , the (timeinvariant) bilateral distance between them, _Dij_ , and additional factors: where **X** and **Z** are vectors of home- and host-country characteristics, respectively, **B** is a vector of other variables capturing the bilateral economic relationship between the home and host countries, and **G** is a vector of additional controls representing global macroeconomic and financial conditions. To maintain parsimony, we nest the two possible host targets within (1), so that the various coefficients— _β_ , _γ_ , _λ_ , _φ_ , and _ψ_ —are allowed to vary by host-country class (advanced, _AD_ , or emerging, _EM_ ), so that _k_ = _{AD, EM }_ .

Variables considered within **X** and **Z** are informed by the different theoretical approaches outlined in Section 3. These include, _inter alia_ , GDP growth (corresponding to the factor proportions hypothesis), trade openness (corresponding to the proximity-concentration hypothesis), patents granted (corresponding to technology transfer arguments), and international reserve holdings (corresponding to political economy explanations). Additional
somewhat biased toward industrialized countries, especially in the earlier years of the panel; this is because many country pairs that consist of less-developed countries do not have bilateral tariff data for either one. Therefore we perform all regressions with and without controlling for tariffs.

We use the sum of absolute value of latitude for a country-pair as an instrument for institutional quality variables. The latitude variables are taken from the CIA Factbook.

In Table 2 we report summary statistics for all our regressors. Sixty-five percent of the country-pairs in our sample do not share the same legal origin. Our institutional quality variables exhibit significant variation. It is noteworthy that the average tariff variable has some extreme outliers, usually due to countries that trade very few products and impose a high tariff vis-à-vis each other<sup>24</sup> . In Table 3 we report correlations that were of interest to us a priori<sup>25</sup> . Notably among these correlations is the small positive correlation between trade flows and different legal origins. The institutional quality variables are highly correlated among themselves. The institutional indicators also exhibit a relatively high correlation with trade flows, telephone lines and with roads. Our two infrastructure indicators are also correlated, which will lead us not to estimate specifications using both in order to avoid multi-colinearity problems. Not surprisingly, tariffs are negatively correlated with trade. They are also negatively correlated with institutional quality.

> 24 It is also indicative that average tariffs between trade-pairs (even trade-weighted) mask huge variation in tariffs among different products for each trade-pair.

> 25 All correlations are statistically significant at the 1% level except for correl(regional,comcol), which is not statistically significant at conventional levels.

18
# **2.2 Sample and data**

## **2.2.1 Sample**

The intervention was delivered in ELA clubs managed and operated by BRAC Uganda. BRAC has established ELA club branch offices throughout the country, each of which oversees several clubs. Eight ELA branch offices in and around the Kampala area were selected for this study, of which six were located in urban areas and two in peri-urban areas. The eight branches can be considered as corresponding to eight distinct geographical areas in and around Kampala. A total of 115 ELA clubs fell under their purview, with each branch managing 8 to 15 clubs. Out of the 115 clubs, 9 were excluded as they were found to no longer exist or function regularly, and the remaining 106 were randomized into one of the three treatment arms.

The sample of adolescents enrolled into the study were selected from the villages and residential areas surrounding the ELA clubs. A radius of 500-meters around each club was drawn and enumeration teams listed all adolescent females aged 13-19 who resided within these catchment areas.<sup>12</sup> During the listing, consenting (and/or assenting, as appropriate) individuals were screened using the eight-item Patient Health Questionnaire depression scale (PHQ-8). Individuals who scored 10 or above, which indicates a risk of moderate or severe depression (Kroenke et al., 2009), were recruited into the study. Overall, 1,919 adolescent females across 106 clusters were successfully recruited into the study.<sup>13</sup>

## **2.2.2 Data**

There were four rounds of data collection during the study. A baseline survey was conducted – between June-August 2019, prior to the IPT-G intervention. The IPT-G intervention i.e., – the 14 weekly group therapy sessions then ran between September and December 2019 at all the clubs assigned to the two treatment arms. A second round of data collection, the rapid resurvey, took place immediately following
country-specific studies on informal firms. Munyegera and Precious (2018) use a census of establishments and representative household surveys in Rwanda and uncover a gender-based productivity gap that persists after the inclusion of controls. Nordman and Vaillant (2014) use informal enterprise surveys in Madagascar and show that even after the inclusion of control variables, female-owned enterprises are 28 percent less productive than male-owned enterprises.

Ours is the first study that we are aware of to decompose the gender gap in labor productivity of informal firms into a wide range of firm characteristics as well as internal and external operating business environment for many economies. Furthermore, using a specialized informal firm survey allows us to account for many factors that are largely unavailable in other data sets, including the ability to produce or sell under contract, use of infrastructure services, owner education and experience. The focus on urban areas is also relevant as this is typically where a large share of formal and informal firms coexist. Cities are the engines of economic growth and a major source of economic activity. The correlation between urbanization and economic development is reflective of agglomeration economies (Glaeser, 2011). Proximity in cities spreads knowledge, making workers more skilled and entrepreneurs more productive (Glaeser and Resseger, 2010). Informal and formal activities are more entwined in cities (Daniels, 2004). Therefore, urban areas may provide different degrees of informality, where for instance informal firms can contract with formal firms – an element that we find is an important contributor to the productivity of informal firms in this study, especially for women-owned businesses. Thus, cities may on one hand provide opportunities for informal firms and on the other hand increase competition between formal and informal businesses.

Our findings matter for policy makers (discussed in section 6). The presence of a gender gap not only means a lower standard of living for women, but also inefficient utilization of their 7
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# **V. Conclusion**

Health care access is an important challenge in many developing countries. In particular in remote rural areas, it is not easy to access good quality health care services. Among others, transport connectivity is often an important constraint to rural dwellers. Particularly in Africa where rural connectivity is still limited, many people do not have access to health care services.

The paper estimated the impact of transport connectivity on access to health care services, using data from Mozambique, especially focused on people’s decision of whether to visit a health facility when they have a fever. In Mozambique, malaria is still a life-threatening serious disease, which accounts for 29 percent of all deaths and 42 percent of deaths in children less than five years old. About three-quarters of the total population does not have access to health facilities by walking.

It is found that transport connectivity is an important determinant of health care access. The marginal effect of travel time to the decision of visiting a health facility is estimated at - 0.029, which is statistically significant. Owning transport means, such as a bike or motorcycle, is also found to be instrumental to promoting people’s access to health care services. Health facilities in rural and remote areas are relatively less attractive, possibly indicating the low quality of health care services provided there. Children are more vulnerable to health risk and thus more likely to be treated, holding everything else constant. The rich take advantage of and benefit more from health care services when they have a fever. The observed income effect suggests a need for other complementary policies, such as a health care subsidy and insurance.
licensed institutions with the authorization to mobilize savings and time deposits from the community at large. However, as table 5 shows, recent changes are more restrictive.

**Table 5** . Minimum Capital and Adequacy Requirements for Licensed MFIs, 2002

|_Requirement_|_Benin:_<br>_credit unions and authorized_<br>_MFIs_|_Ghana:_<br>_licensed banks,_<br>_NBFIs, and credit unions_|_Tanzania:_<br>_licensed banks and MFIs_<sup>_a_</sup>|
|---|---|---|---|
|Minimum capital<br>Capital adequacy (capital<br>as a percentage of risk<br>assets)|No minimum level required<br>No minimum ratio specified|<br>�<br>Rural banks: US$67,000<br>�<br>S&Ls: US$2 million since 2001,<br>up from US$15,000 to –<br>US$22,000 during 1993–99<br>�<br>Rural banks: 6%<br>�<br>S&Ls: 10%<br>�<br>Credit unions: 10%|� Commercial banks: US$5<br>million<br>� Regional banks: US$250,000<br>� Rural banks: US$62,500<br>Microfinance companies:<br>US$1.25 million (stipulated<br>by new law)<br>6%|

a. Data for Tanzania are as June 2003. _Source:_ Gallardo and Randhawa (2003); Ouattara (2003); Steel and Andah (2003).

58. Benin’s licensing framework does not prescribe MCR for the licensed credit unions or for the non–credit union MFIs authorized to operate by the Ministry of Finance. In Ghana,
Leone|3.9|7.6|6.2|10.6|3.7|5.8|8.8|8.2|6.2|6.2|
|Swaziland|1.8|2.8|2.4|5.3|1.1|3.4|3.6|3.5|2.5|2.3|
|Tanzania|1.6|5.8|4.5|10.0|1.5|5.3|10.1|9.7|6.0|3.9|
|Togo|4.3|12.3|8.8|22.2|3.9|7.0|15.4|13.4|11.0|8.2|
|Uganda|4.3|9.2|7.9|15.9|3.3|15.0|16.3|16.2|10.6|6.7|
|Zambia|2.4|11.1|8.0|17.4|1.9|4.9|13.3|12.0|10.4|7.3|
|Median|2.0|4.9|3.9|7.8|1.7|4.4|6.3|6.2|4.8|3.6|

_Source_ : World Bank staff calculations based on utility information and household survey data.

Predictably, electricity becomes markedly less affordable. The 5‐percent threshold is exceeded for the poor in two‐thirds of the countries (against one‐third previously), and for four‐fifths of the bottom quintile (against half previously). These results are consistent with much greater reliance on home‐grown food by the poor than the rich.

Table
socioliogy, consists in putting together a group, for instance users, and through in a series of question to be discussed by the group. This technique is used by Puertos del Estado in Spain complement the more analytical forecasts of the spanish ports.

The more quantitative methods to forecast aggregate demand can be classified into: trends extrapolations-assuming a linear, exponential, quadratic or some other function that seems to fit past observations well--, fancier time series analysis and more sophisticated econometric work. Extrapolation is the weakest but also the most commonly used around the world. The future is assumed to be determined by what happened in the past. It is mostly useful for very short run needs and has a better chance of working on monthly data than on annual data. But this is
# **Table 8. The Effect of Titling firms’ Ownership on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and firm ownership, i.e., whether the firms are SOE or not. The independent variable is the averaged investment (capital expenditure divided by PP&E) between 2013 and 2017 net of investment between 2010 and 2012. Columns (1) and Columns (2) presents parameter estimates associated with SOE subsample and non-SOE subsample, respectively. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _SOE_ is a dummy variable that is 1 if the firm is affiliated to the government and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆(Investment/PP&E)||
|---|---|---|
||SOE Subsample<br>None SOE Subsample|Whole Sample|
||(1)<br>(2)|(3)|
|Titling Firm|0.424***<br>0.075|0.090|
||(4.812)<br>(0.528)|(0.648)|
|SOE||-0.064**|
|||(-2.126)|
|Titling
it in the context of the high pace of turnover. In general, it is difficult to interpret differences across countries in the magnitude of the gap between entering and exiting businesses. For example, it might reflect fundamentals driving market selection with new businesses adopting the latest business practices (or in transition economies, new businesses adopting market business practices relative to incumbents) or it might reflect a very high entry barrier so that only very productive new businesses enter. However, the latter explanation might suggest that firm turnover rates should be reduced which does not appear to be the case for the transition economies. Still, for the transition economies the contribution of net entry is far from proportionate suggesting that there is substantial churning of businesses via entry and exit that is not productivity enhancing in transition economies.

It is also interesting to note that the entry of new firms has variable effects on overall productivity growth in OECD countries. On the whole, data for European countries show that new firms typically make a positive contribution to overall productivity growth, although the effect is generally of small magnitude. By contrast, entries make a negative contribution in the United States for most industries and a stronger than average contribution tends to come from the exit of low productivity firms. Interpreting these findings without more information is difficult. The weak performance of entrants in the U.S. might reflect greater experimentation in the U.S. so that for each entering cohort of entrants there is more selection and potentially more learning by doing. Some evidence in favor of this interpretation is provided in Haltiwanger, Jarmin and Schank (2003), Foster, Haltiwanger and Krizan (2001, 2002) and Bartelsman and Scarpetta (2004). The former paper provides evidence of greater market experimentation in the U.S. relative to Germany. The latter shows that in the U.S. that as the horizon lengthens in the U.S., the contribution of net entry rises disproportionately. Moreover, Foster et. al. show that the increased contribution of net
25</sup> (the absolute component of the AROPE) and low work intensity, which captures exclusion from the labor market, they constitute the anchored AROPE, an absolute measure of multidimensional poverty.<sup>26</sup>

**<mark>To overcome some of the AROPE shortcomings mentioned in section II, we looked at the overlapping of deprivations and expanded the AROPE framework.</mark>** <mark>Given the potential sensitivity of the measure to the dimensions added, we run some sensitivity analysis, including different combinations of individual deprivations.  We also construct a modified version of the AROPE (AROPE +) while ensuring alignment with the framework's essence</mark><sup>27</sup> <mark>. The measure of AROPE + extends the AROPE indicator in two ways. First, it extends the material and social deprivation indicator by incorporating an additional deprivation at the household level to account for housing conditions; we use a variable that indicates if the person lives in an overcrowded dwelling</mark><sup>28</sup> <mark>, that is, if the average number of people per room available to the household is greater than two, following the definition of Weziak-Bialowolska and Dijkstra</mark>

> 23 Source: Gesis, 2021

> 24 Other studies have emphasized the need to measure and monitor an absolute measure of poverty in the EU (Menyhért et al.,2021), but have focused more on constructing absolute measures of monetary poverty, rather than on constructing an absolute measure of multidimensional poverty.

> 25 We follow Eurostat’s criteria for the construction of material and social deprivation for children. Specifically, the same set of 13 items (see Table 2) and thresholds are used for adults and children. However, because individual items are collected for people aged 16 or above, a child is considered deprived in an item if at least half of the adults in the household lack that item.
Table 2: Firm-Level Databases: List of Countries and Data Sources

|**Country**|**Database**|**Description**|
|---|---|---|
|Ethiopia|Central Statistical Agency<br>(CSA): Large and Medium<br>Scale Manufacturing and<br>Electricity Industries Survey|Census of frms employing<br>more than 10 workers, 2011|
|Ghana|Ghanaian Statistical Service<br>(GSS), National Industrial<br>Census|Census of more than 10<br>workers, 2003|
|Kenya|Kenya National Bureau of<br>Statistics (KNBS)- Census of<br>Industrial Sector|Census of all formal frms,<br>2010|
|Bulgaria, Belgium, Finland,<br>France, Hungary, Italy, Latvia,<br>Portugal, Romania, Spain|Amadeus|(see note below for selection<br>criteria)|
|Chile|ENIA (Encuesta Nacional<br>Industrial Annual), 2013|Census of all industrial frms<br>with 10 or more workers|
|El Salvador|EAM (Encuesta Anual<br>Manufacturera)|Census of all industrial frms<br>with 10 or more workers|
|India|ASI (Annual Survey of<br>Industry), 2004–2005|Census of all industrial frms<br>with 10 or more workers in<br>case of power usage; 20 or<br>more workers without power<br>usage|
|Colombia|EAM (Encuesta Anual<br>Manufacturera)|Census of all industrial frms<br>with 10 or more workers|
|Malaysia|Census of Manufacturing<br>Sector|Census of all industrial frms<br>with no size cutof but<br>restricts analysis to 10<br>workers or more|
|Peru|National Economic Census|Census of all frms with no
with family‐care responsibilities or mobility constraints, such as the elderly and the disabled. Finally, a rising share of non‐wage employment may increase the portion of the population uninsured against unemployment and health risks, as well as poverty in old‐age – factors that may add additional strain to social protection systems.

This paper exploits variations in the timing of telecommunications reforms across Europe to study how the expansion of the Internet has been associated with the rise of alternative work arrangements. These reforms, which are intended to increase the availability and affordability of the Internet, have been linked with an increase in Internet adoption internationally (Howard and Mazaheri [2009]; Winkler [2016]). Given that the Internet remains unavailable to a majority of the world’s population (World Bank [2016a]), a better understanding of the unintended consequences of ICT adoption is important for the design of policies to help smooth the disruptive effects of the Internet in countries that have not yet experienced the digital revolution.

Since the timing of the reforms is not necessarily exogenous, instead of estimating their causal effects we focus on the mechanisms by which they would affect the labor market. We thereby construct the following test for the hypothesis that telecommunications reforms have affected labor markets in Europe by fostering Internet adoption. Using sector‐level data, we first classify each sector of economic activity by its technological dependence on ICT. Then, under the assumption that such sectoral ICT dependence or intensity carries over from country to country, we compare several employment outcomes before and after the introduction of telecommunications reforms across sectors with different levels of ICT dependence. By controlling for country and sectoral trends as well as country‐sector pairs fixed effects, we argue that if the Internet is affecting the labor market, we should see greater effects among more ICT‐ dependent sectors than in the rest of the economy.

3