rafmacalaba/datause-encoder-data huggingface dataset:
years, the rise has been steeper among ninis aged 15 to 18 years, especially in the case of women. The increasing share of young women—particularly below 18 years of age—starting a new household, suggests that a potential driver of the high rate of female ninis is marriage before age 18, compounded by teenage pregnancy.

**Figure 12: Percentage of ninis that decide to start their own household in LAC by gender and age, 1992-2010**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 52.7 52.9<br>27.7 48.5<br>25.3 46.2 47.1<br>22.3 41.9 41.3<br>19.0 19.9 18.4 38.0 38.6 38.4<br>17.1<br>14.0 15.1<br>12.9<br>1.0 1.2 1.7 2.2 2.5 9.7 9.1 10.6 11.4 11.4<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Source: authors’ own computation with data from CEDLAS and The World Bank (2015)<br>Figure 13: Percentage of ninis that decide to start their own household with children in LAC by gender and age,<br>1992-2010<br>Panel A: 15-18 years Panel B: 19-24 years<br>18.4<br>17.5 43.3 43.6<br>15.8 39.2 40.3 40.9<br>14.2<br>13.1 31.7 32.6 31.9 33.8 33.3<br>11.6 12.0<br>10.6<br>9.8<br>8.9<br>6.0 5.7 7.3 6.8 6.7<br>0.6 0.5 0.9 1.1 1.2<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>30.0<br>50.0<br>40.0<br>20.0<br>30.0<br>10.0<br>20.0<br>Ninis starting own household (% relevant Ninis population) Ninis starting own household (% relevant Ninis population)<br>0.0 10.0<br>20.0 50.0<br>40.0<br>15.0<br>30.0<br>10.0<br>20.0<br>Ninis starting own 5.0 Ninis starting own<br>household with children household with children 10.0<br>(% relevant Ninis population) (% relevant Ninis population)<br>0.0 0.0<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 20
data have been taken from Chao rather than using the data published in the Fixed Asset Yearbook. For the period 1966 to 1974, no annual data are available for capital construction in the government or infrastructure sector. The available data for plan periods have been divided equally across the years concerned.

The data for the business sector have been estimated as the difference between the non-residential FAI and the government and infrastructure FAI.

# **_Estimates of capital formation in the residential sector_**

A particular effort is needed to estimate capital formation in the housing sector due to the importance of land purchases in the cost of housing construction. The purchase of existing assets should be excluded from estimates of gross fixed capital formation, but the Chinese practice is to include land purchases in fixed asset investment. The scale of the bias introduced by these differences between FAI data and the SNA basis of estimates of gross fixed capital formation has been growing over time. In the 1980s and 1990s, there was little bias, as there was no market in real estate. The bias became more important during the decade starting in 2000 as market-based transactions dominated the housing market.

The NBS has published its methodology for overcoming this bias. The steps it takes are set out below: Gross fixed capital formation = Residential fixed asset investment _Less_ land purchases (which includes the costs of land improvement) _Plus_ difference between sales prices and construction cost multiplied by sales quantity _Plus_ investment in land improvement.

Effectively, this means that GFCF is being measured as property sales less the cost of unimproved land purchased for residential development. This definition is not quite compliant with the SNA, as the deduction for land purchases is made when the developer buys the land rather than when the land is sold to the final purchaser. Thus, if the land has increased in value between the date at which the developer made the purchase and when it is sold to a final purchaser, this increase in value will be included in GFCF. Equally, the quantity of land purchased refers to land bought in a given year rather than the quantity of land sold to the final purchaser.

# **_Adjusting sectoral estimates to agree with the published aggregate data_**

The extent of the differences between the published measures of total fixed asset investment and gross fixed capital formation varies according the time period considered. Four different periods can be identified:

|1953 to 1960|Close correspondence between fixed asset investment and gross fixed capital<br>formation.|
|---|---|
|1961 to 1984|Fixed asset investment averages three-quarters of gross fixed capital<br>formation.|
|1985 to 2002|Fixed asset investment and gross fixed capital formation are essentially equal.|
|2003 to 2015|Fixed asset investment becomes persistently and increasing larger than gross<br>fixed capital formation.|

31
_Tkri_ = _α_ + _γkr_ ln _agekri_ + **_βXi_** + _εkri_ (2) where _Tkri_ is the number of disruptive technologies of firm _i_ in region _r_ and sector _k_ . _γkr_ is the coefficient associated with the natural log of firm’s age in years. We allow _Tkri_ to be zero for non-disruptive firms, and _Xi_ contains the same set of controls described above (except for age).

# **3.2.3 Funding and disruptive technologies**

Finally, to investigate whether there are differences in the allocation of funding, we estimate the following regression: where _Fi_ is the outcome variable of funding, which could be either a dummy variable indicating that the firm received funding, or the IHS transformation of the amount of funding in U.S. Dollars received (in total or during the last funding round), as reported in February 2023. _Xi_ is the same set of controls outlined above plus a variable that includes the number of similar companies to also control for competition, and _γR_ are region effects (Africa and Latin America).<sup>10</sup>

# **_An Instrumental Variable Approach_**

A crucial feature of the estimates regarding funding and disruptive technologies is that the direction of causality is not clear. It could be the case that firms incorporating disruptive technologies receive more funding or, on the contrary, that having received more funding allows them to incorporate these technologies into their offering.

To identify the effect of disruptive technologies on access to funding, in this section, we propose an Instrumental Variables (IV) approach. The proposed instrument combines country and firm-specific characteristics to create a vector of exposure to the adoption of disruptive technologies. The main assumption behind the instrument is that firms from sectors that are more intensive in disruptive technologies (defined exogenously by what is observed in the frontier) and that are located in countries with better skills should incorporate more disruptive technologies into their offering. The interaction of these two variables should only affect firm-level financing through the capability of the firm to incorporate disruptive technologies.

The first part of the instrument uses information from countries in the frontier to calculate the share of disruptive firms by industry vertical and impose this on African firms based on their own industry verticals. This strategy based on sectoral intensity follows the idea behind Rajan and Zingales (1998) and has been used recently in the Information Technology (IT) use literature (Bloom et al., 2012; Fernandes et al., 2019; Iacovone et al., 2023). Using only information from the frontier is plausibly exogenous since this excludes country-specific barriers that would prevent the adoption of disruptive technologies in industry verticals that otherwise would be technology-demanding. Crunchbase classifies

> 10In all our estimations, standard errors are clustered at the level of the country.

10
What’s new in the new industrial policies compared to the old? While we have not been able to do a “10 digit” survey of the nature of industrial policies in all the countries of the region, looking at a selected number of different types of countries suggests the following stylized facts:

- Industrial policies are being aimed at much more open economies now led by the private sector which faces international competition in traded goods and services. Moreover, the industrial policies are mostly geared to improving existing international competitiveness, while the promotion of new activities is more limited and generally does not attempt heroic leaps forward that totally ignore comparative advantage or realistic possibilities for building new upgraded knowledge and capacities. Thus the deployment of the new industrial policies generally reflects the prudence of attempting to “walk before running” in terms of industrial promotion.

- One observes the gradual emergence of innovation as a priority in industrial policies— an area where Latin America has seriously lagged. This means that the region is following the lead of successful catch-up developing countries which began to place strategic emphasis on innovation in the 1990s.

- Fiscal management is better and strategies/plans have closer links to budgets; hence, words in national plans/strategies are more likely to translate into deeds.

- There is a much heightened preoccupation about mechanisms for coordinating government action and monitoring outputs of planned industrial policies.

- Public-private policy alliance councils have emerged in many countries to guide governments in the direction, content and implementation of industrial polices. This is important since modern industrial policy recognizes that for market economies in an era of globalization all views are now, more than ever, very partial. Hence, industrial policy cannot be designed “top-down”. Rather it is really a question of finding an effective social process and corresponding institutional arrangement of public-private collaboration that problem solves and elicits information for the effective provision of public goods supporting private sector experimentation, learning and upgrading without capture of the State by special interests.

While these advances are encouraging and major, one also sees serious limitations, some of which are hangovers from the past. These must be addressed if industrial policies are to be done right and progressively raise their ambition.

- Public-private policy alliance councils are a welcome new development in the region. However, they still must be viewed as a work in progress. All the councils exhibit flaws in their structure and the tangible and intangible dimensions of governance. These limit their effectiveness as a social process for the search of information and consensus to support the deployment of strategies and support programs for productive transformation.

36
- 6 -

The deterioration of public infrastructure is imposing a heavy burden on firms. About 68 percent of the survey respondents identified electricity as a major obstacle in 2005. Similarly, 72 percent of firms considered telecommunications as a constraint (World Bank, 2006). These subjective assessments by firms are also available in the BEEPS database. Using these subjective proxies, Carlin _et al_ . (2006) estimates a production function with all the past BEEPS survey data over the world—not only ECA—and finds that manager’s assessment of physical and institutional constraints in the economy explains the extent to which firm productivity would decline for these reasons. As far as the infrastructure is concerned, the low scores of telecommunications and electricity services are likely to decrease firms’ sales significantly.

The current paper is complementary to this but relies on the objective indicators of infrastructure quality, such as the number of days with electricity outages a year and the average duration of water service suspensions per day. Hence, our analysis can explicitly quantify the possible impacts of improving or deteriorating the quality or reliability of infrastructure on business costs. Given the estimates, it will be examined how much cost could be saved as a whole economy by eliminating the existing infrastructure service interruptions. From the public investment point of view, this inference is important to assess the benefits from and costs of public infrastructure investment.

Three types of public infrastructure services are analyzed: electricity, water supply and telecommunications. For each sector, two dimensions are used to measure the level of service quality perceived by individual firms: annual frequency and daily duration of service suspensions. As mentioned above, it cannot be overemphasized that the perceived quality of infrastructure varies from firm to firm, depending on their location. For instance, the average duration of electricity interruptions ranges from 0 to 24 hours, though the majority of firms
than 2 million beneficiaries and, if the rural pensions program is included, the number of beneficiaries exceeds 8 million.<sup>6</sup> Even with high rate of coverage poverty will only be eradicated if benefits are high enough and the family structure is not too burdensome. See Table 1.

# **Table 1.   Pension Policies in Latin American Countries**

|**Country**|**Type of**<br>**pension**<br>**system**|**Year of**<br>**pension**<br>**reform**|**Coverage of**<br>**the**<br>**economically**<br>**active**<br>**population**|**Coverage**<br>**of**<br>**employed**<br>**persons**|**Coverage**<br>**of elderly**|
|---|---|---|---|---|---|
|**Argentina**|mixed|1994|40.9|44.6|70.5|
|**Bolivia**|private|1997|12.5|13.1|89.8|
|**Brazil**|public|-|48.1|51.7|85.3|
|**Chile**|private|1981|62.7|67.3|75.5|
|**Colombia**|pub/priv|1994|31.8|32.3|25.1|
|**CostaRica**|mixed|2001|62.7|65.1|59.2|
|**Dom Rep**|private|2003|20.2|23.7|11.9|
|**Ecuador**|mixed|2004|26.2|26.6|31.0|
|**Guatemala**|public|-|26.8|27.2|15.4|
|**Honduras**|public|-|20.1|20.7|5.3|
|**Mexico**|private|1997|35.9|36.0|23.3|
|**Nicaragua**|private|2004|18.5|19.3|44.9|
|**Panama**|public|-|45.1|54.4|41.7|
|**Peru**|pub/priv|1993|14.0|14.6|27.7|
|**Paraguay**|public|-|12.8|13.5|14.9|
|**Salvador**|private|1998|29.1|31.2|16.2|
|**Uruguay**|mixed|1996|61.1|67.3|85.6|
|**Venezuela**|public|-|35.3|37.8|31.3|

Sources Mesa-Lago (2004) and Rofman et al. (2008) _Evidence on Poverty in Old Age_ At the international level, surprisingly little evidence is available on poverty in old age. World Bank statistical compilations, for example, do not report poverty rates for

> 6  We are grateful to Rafael Rofman for sharing with us his data on coverage.

5
these assessments to allocate concessional resources across countries.<sup>8</sup> This category also includes two discontinued data sources, from the European Bank for Reconstruction and Development, and the French international development agency. Finally, two data sources in this category are based on data reported by the US Department of State, on human trafficking (Trafficking in Persons Report) and on human rights violations (as coded by academic researchers in the Cingranelli-Richards Human Rights Data and the Political Terror Scale).

Finally, four WGI data sources come from commercial business information providers. Three of them are long-running data products providing subscription-based quantitative ratings of a broad range of business environment and political risks, produced by the Economist Intelligence Unit (Country Viewswire), Political Risk Services (International Country Risk Guide) and S&P Global (Country Risk Service). The fourth is Crisis24, a specialized security risk firm, which provides quantitative measures of security risks facing business travelers that feeds into the Political Instability and Absence of Violence/Terrorism measure in the WGI. All four organizations rely on a global network of experts who provide scores following a standardized methodology subject to centralized internal review within the organization.

The source data used to construct the WGI is publicly available at www.govindicators.org.<sup>9</sup> The data are available in their original form as retrieved from the original providers. We also report the data rescaled and reoriented (if necessary) to run from zero to one with higher values corresponding to better governance outcomes, using the minimum and maximum possible scores on each variable to do this rescaling. The rescaled data is comparable within a given data source over time and across countries. However, it is not necessarily comparable across different data sources, because different data sources cover different sets of countries with different underlying distributions of governance. For example, a score of 6 out of 10 might mean something different in a data source that covers only advanced

> 8 In addition, the World Bank and the International Monetary Fund use the Country Policy and Institutional Assessment data as a key indicator in their Low-Income Country Debt Sustainability Framework (LIC-DSF), recognizing that there is a strong empirical relationship between institutional quality and the risk of debt servicing difficulties (see IMF (2017)).  In the same vein, the International Monetary Fund uses components of the WGI as key measures of institutional quality in its Debt Sustainability Framework for Market-Access Countries because their analysis shows that the WGI has statistical predictive power for debt servicing difficulties (see IMF (2021)). 9 For a small number of data sources that are commercially available by subscription only, we cannot report the individual questions from these sources.  Instead, we report averages of questions assigned to each of the six aggregate WGI measures.

8
# **Table 17: Estimated log cell consumption per capita differences**

|**Table 17A1: Evaluated at mean att**<br>|**ributes for refer**<br>|**ence woman a**<br>|**nd different a**<br>|**ge at dissolution groups**<br>|
|---|---|---|---|---|
|Age atmarriage dissolution:|15-30|31-40|41-50|51 +|
|**_Urban:_**Remarried widow|11.921|11.991|12.073|12.235|
|Widow|0.498**|0.516***|0.489**|0.478**|
|**_Rural_**: Remarried widow|11.599|11.668|11.751|11.913|
|Widow|0.148|0.166|0.139|0.128|
|**_Urban_**: Widow|12.639|12.690|12.719|12.664|
|Remarried widow|−0.656***|−0.542**|−0.481*|−0.608**|
|**_Rural_**: Widow|11.966|12.017|12.046|11.991|
|Remarried widow|−0.305|−0.191|−0.131|−0.258|
|**Table 17A2: Evaluated at mean att**|**ributes for refer**|**ence woman a**|**nd different a**|**ge at dissolution groups**|
|Age at marriage dissolution:|15-25|26-30|31-40|41 +|
|**_Urban:_**Remarried divorcee|12.209|12.323|12.441|12.269|
|Divorcee|−0.256|−0.116|0.136|−0.012|
|**_Rural_**: Remarried divorcee|11.920|12.034|12.152|11.980|
|Divorcee|−0.328|−0.188|0.065|0.144|
|**_Urban_**: Divorcee|12.827|12.160|12.330|12.715|
|Remarried divorcee|0.057|0.084|0.144|−0.451|
|**_Rural_**: Divorcee|12.466|11.799|11.969|12.353|
|Remarried divorcee|0.129|0.156|0.216|−0.380|

Note: Women 15 and older.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Significance tests refer to differences relative to the reference marital status estimated consumption. Consumptions are predicted using own parameters and mean attributes of the reference marital status at the given age group. Source: Authors’ estimations using PSF1.

|**Table 17B1: evaluated at mean**<br>|**attributes for r**<br>|**eference woma**<br>|**n and different du**<br>|**rations since dissolution**<br>|
|---|---|---|---|---|
|Time since widowhood:|<7 years|8-16|17-25|26+|
|**_Urban:_**Remarried widow|11.943|11.905|11.984|12.221|
|Widow|0.553***|0.531***|0.518**|0.414*|
|**_Rural_**: Remarried widow|11.568|11.530|11.609|11.847|
|Widow|0.277<sup>+</sup>|0.254|0.241|0.138|
|**_Urban_**: Widow|12.504|12.759|12.709|12.956|
|Remarried widow|−0.439**|−0.459*|−0.549**|−0.662*|
|**_Rural_**: Widow|11.853|12.108|12.058|12.305|
|Remarried widow|−0.163|−0.182|−0.273|−0.386|

|**Table 17B2: evaluated at mean attributes for reference woman and different durations since dissolution**|
|---|

|Time since divorce:|<5 years|6-10|11 +|
|---|---|---|---|
|**_Urban:_**Remarried divorcee|12.212|12.260|12.254|
|Divorcee|0.212|−0.071|−0.333|
|**_Rural_**: Remarried divorcee|11.955|12.004|12.026|
|Divorcee|0.109|−0.174|−0.436*|
|**_Urban_**: Divorcee|12.675|12.644|12.705|
|Remarried divorcee|0.185|−0.451|−0.326|
|**_Rural_**: Divorcee|12.316|12.284|12.346|
|Remarried divorcee|0.289|−0.347|−0.222|

Note: Women 15 and older.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Significance tests refer to differences relative to the reference marital status estimated consumption. Consumptions are predicted using own parameters and mean attributes of the reference marital status subgroup. Source: Authors’ estimations using PSF1.

47
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