rafmacalaba/data-use-mentions-extended huggingface dataset:
. Therefore, we consider all available information, rather than only the poorest economies below an economic gradient – as done by Ravallion, Chen, and Sangraula (2009) – to estimate the regional comparable poverty lines in LAC.

> 20  The existing relationship in the graph may be considered spurious if poverty lines were calculated based the same households surveys used in the graph. This is not the case for the countries covered in this paper; all poverty lines have been computed in different household surveys from the ones used in the figure. 21

> For those cases for which the poverty lines were not reported at 2011 PPP values, we deflated the closest value using national CPIs. Although countries have already published lines for 2012 and 2013, and in some cases also for 2014, we prefer to use the figures corresponding to the same year as the latest 2011 PPP round. 22  For the case of Chile, Ecuador, Haiti, and Nicaragua, the official poverty lines are published at the national level and there is no distinction between urban and rural areas or subnational regions.

> 23  Approximately 80 percent of LAC’s population lives in urban areas.

> 24

> The SEDLAC harmonization uniformly deflates all welfare aggregate to urban prices multiplying rural incomes by 15 percent. This practice might not be consistent with real price differences within each country, but we maintain it in this paper in order to make comparisons with the current methodology used to estimate regional poverty lines.

> 25  A similar result is found by Gasparini, Cicowiez, and Escudero (2013) for poverty lines in 2005 PPPs.

10
# ����� �������������,�

� � ������ ��������������,��� ���������������,� ���������������,� ���,� where i = 1...N and t =1....T. ����� �������������,��� represents the rate of youth unemployment at time t-1 for country i which allows to model the youth unemployment as a dynamic process and thus captures its persistent effect. �����������,� captures the perceived corruption variable. For the purpose of this study we rely on the results of surveys conducted by international organizations among local agents from both private and public sectors to evaluate how they perceive corruption in their countries. More specifically we use the Transparency International (TI) index, which as Treisman (2000:400) notes: “constitutes a “poll of polls” compiled by a team of researchers (...) using information from up to 12 individual surveys and ratings”. �����������,� is the vector of control variables containing macroeconomic, political and social covariates. ��,� is comprised of two components; a country specific component potentially correlated with some of the explanatory variables and an independently and identically distributed vector of disturbances. We use a slightly unbalanced panel of 92 countries (both developed and developing countries) over the period 1985-2008 to account for data availability restrictions.

Table 1 provides the results of various specifications of the relationship between youth unemployment and corruption. Specifications 1 to 3 assume no endogeneity (reverse causality) between youth unemployment and corruption practices
liarities, we focus on the SCP supply from public and private HEIs while taking the supply of SENA’s programs as given.

To open a new program, HEIs need an authorization (or operating license) from the Ministry of Education, which can take a year or two to arrive and must be renewed periodically. In contrast, they do not need an authorization to close a program.

# **2.2 Data Sources**

We leverage multiple Colombian data sources. First is the National Higher Education Information System (SNIES), which covers the universe of higher education programs (bachelor’s and SCP) and contains program-level information on institution, geographic location, field of study, length, and enrollment. We use SNIES data between 2003 and 2019. We define a program as a combination of institution, program code, and department; when an institution offers the same program in two different departments, it counts as two different programs. We focus on in-person SCPs located in metropolitan areas; we exclude online programs because we cannot assign them a geographic location. This yields an SCP sample that accounts for 73% of the national SCP enrollment.

Our second data source is the Labor Observatory for Education (OLE). For the 2007-2013 period, OLE tracks individuals who graduated from higher education beginning in 2001 and work in the formal sector. For each graduate, it includes program identifiers that allow us to find the program in SNIES, and also labor market information such as work location and economic sector of work. Third, we use use annual GDP data by department and economic sector from the National Statistics Agency (DANE) between 2003 and 2019. Fourth, as an instrument for competition we use information on SENA’s department-level budget, which quantifies SENA’s resources by department and year.) All monetary values are expressed in Colombian pesos (COP).

Overall, our data includes 13 departments, 279 HEIs, 3,463 SCPs, and 4,392 bachelor’s programs (the
insucient management training and lack of market competition.<sup>25</sup>

# 4.4 Rent-seeking

Another potentially important source of misallocation in Brazil is rent-seeking. A few recent papers have explored rich data sets to examine the eects of patronage and corruption on public employment selection and rm performance in Brazil. Colonnelli et al. (2020) use a detailed matched employer-employee data on the universe of public employees in Brazil over 19972014 and a regression discontinuity design in close electoral races to examine the role of political connections for public employment selection in Brazil. In particular, they empirically investigate whether discretion in public employment decisions is used as a patronage tool, and the consequences on the selection process in the context of the Brazilian public sector. Patronage represents an obvious friction in the selection of a high-quality public workforce, since political support can act as a substitute for individual competence in the process of government hiring. They establish three main ndings. First, political connections are a key and quantitatively large determinant of employment in public organizations in Brazil, for both bureaucrats and frontline providers. Second, patronage is an important mechanism behind this result. Third, political considerations lead to the selection of less competent individuals. In addition to patronage, there is recent evidence on the eect of corruption on rm performance in Brazil. Colonnelli et al. (2020) estimate the economic eects of a randomized anticorruption crackdown on local economic activity. On average, they nd a 0.9 percent increase in the number of rms and business establishments operating in treated municipalities in the three years after the audit. They also nd that audits increase real economic activity, with an increase in sales by local rms by about 6 percent. There is also evidence of large spillover eects, as a nearby audit has an impact on non-audited municipalities that is similar to their main eects. They also show that their magnitudes can be rationalized by an average corruption tax on rms in the range of
39 ;

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# Conclusion

This database is the product of an effort to bring systematic, cross-country information on political institutions within easy reach of researchers investigating comparative political economy. We have found no other source of information that provides as much objective data on these issues. Others, including some of the authors of this paper, have previously used in other work the data sources relied upon to construct this data these variables (see Clague, et al., 1996, or Durham, 1999). However, we have found no case where all of the information has been compiled, and we have identified many variables that have never been collected. Examples of these are the variables identifying party orientation, party affiliation with the government or opposition, the military affiliation of the executive or minister of defense. The early part of the paper outlined a subset of the questions that the Database of Political Institutions can potentially illuminate. They stretch across political science and economics, ranging from the determinants of democratic consolidation, the political conditions for economic reform, and the political and institutional roots of corruption, to the appropriate and institutionally-sensitive design of economic policy recommendations. To enhance the availability of the DPI for these purposes, it will be placed on several websites, particularly the political economy website of Harvard University and the website of the World Bank’s Development Economics Data Group.

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in sex-disaggregated data caused knowledge of the gender impacts of the pandemic to be incomplete (World Bank 2021). The lack of gender-disaggregated data makes it difficult to fully understand and address the ways in which men and women are affected differently by climate change. By improving the systematic collection and analysis of gender-disaggregated data, policymakers and researchers can gain a more nuanced understanding of the gendered impacts of climate change, which can inform the development of more effective and equitable policies and interventions.

18
positively are located in Latin America. This may reflect the valuable efforts that many countries in this region have undertaken in recent years to improve their remittance data.

<!-- Start of picture text --> Chart 1. Payment of Remittances -<br>Market Share Among Different Service Providers<br>Central Banks that Know Market Market Share Among<br>Share Among Service Providers Different Service Providers<br>100%<br>1. Bolivia<br>2. Brazil<br>80%<br>3. Colombia<br>Yes<br>4. Ecuador<br>No 28% 5. El Salvador 60%<br>72% 6. Guyana<br>7. Honduras 40%<br>8. Mexico<br>9. Nicaragua 20%<br>10. Peru<br>11. Venezuela 0%<br>Money Transfer Firms Banks Other<br>Bolivia Brazil ColombiaEcuadorEl SalvadorGuyanaHondurasMexicoNicaragua PeruVenezuela<br><!-- End of picture text --> As shown in chart 1, in six of the eleven developing countries that had this type of information available, money transfer companies dominate the market for delivery of remittances with more than a 50% market share (Bolivia, Ecuador, Guyana, Honduras, Nicaragua and Peru). In two countries, Brazil and Venezuela, commercial banks account for 100% and 87% of the market for remittance payments, respectively,<sup>5</sup> while in Colombia, exchange bureaus dominate 90% for the market of remittance payments. Because at this time no information is available on the market share among services providers in other developing countries, it is difficult to estimate the amount of remittances inflows that is not reflected in balance of payments statistics.

There are particular reasons that explain why many of the surveyed countries fail to cover in their statistics the remittance transactions conducted by money transfer companies. First, in those countries money transfer companies are neither regulated nor
computed from the location probability estimates of Table 6. A comparison of column 5 of Table 8 with those of Table 5 would seem to suggest that no selectivity bias involved in the estimates reported in Table 5. None of the selectivity terms in Table 5 are statistically different from zero, and individual coefficient estimates are not significantly different in otherwise the same specifications across the two tables. However, the reason for this outcome is the high degree of collinearity between the selectivity term and the region dummies rather than true lack of statistical significance of the former. Indeed the outcome sharply contradicts what we seen Table 9 as strong evidence for the presence of selectivity bias in Table 5. The advantage of the reduced form specification that we estimated in Table 9 is precisely that it avoids the multicollineary problem of Table 8. We also see more plausible coefficient estimates in Table 9 than those in Table 8. This and the high values of the Hausman-Wu endogeneity test statistic reported at the bottom of column (4) to (7) lead to the conclusion that there is selectivity bias in the estimates of Table 5.<sup>26</sup> In estimating equation (12) in the first panel of Table 9, we replace the region dummies by probabilities of selection calculated from the choice probabilities of column 3 of Table 6. In the second panel of Table 9 we report the same correction for selectivity bias, but based on a multinomial selection equation in which location characteristics are assumed not to matter. This latter specification is directly estimated in Table 7, but can also be thought of as what we would obtain by imposing the restriction on equation (10) that all locations are identical in terms of observable characteristics.<sup>27</sup> In spite of the contrast between the two specifications of the selection process, the results are quite similar across the two panels in terms of the direction of selectivity bias though not in the magnitude of bias. The bias is larger when we assume that businesses self select on
children while simultaneously generating income, particularly for households with internet access and in occupations amenable to remote work.

Heggeness and Suri (2021) study the impact of childcare responsibilities on mothers who telework in the United States. They compare how mothers’ labor force participation decreased relative to prime-age women without children and prime-age custodial fathers. They find that flexible work helps to increase women’s labor supply. As Baruch (2001) remarks in his paper, teleworking enlarges the employer’s labor market as more people can be hired (i.e. single parents, second earners with children, people who live in remote places, etc.). Moreover, in another quantitative study, Baruch (2001) found that teleworking was the only option to work for working mothers with young children at home. Chung and vad der Horst (2018) find that a flexible arrangement for women after childbirth helps them sustain their employment status.

In the same line, Berniel et al. (2021) use microdata from the World Bank’s high-frequency phone household survey data (HFPS) to analyze the factors that account for the asymmetric impact of the COVID19 in Latin America. They look at heterogeneities by occupations and construct potential work from home measures for each individual to estimate different labor outcomes. In line with prior studies, they find that the impact of the COVID-19 shock was harder for women, but the ability to work from home mitigated the adverse effect, particularly for women with children. A possible mechanism behind their results is that women in Latin American countries are more likely to be responsible for family and home care; therefore, they are more likely to stay at home to care for the children following school closures. Indeed, they also found evidence that women who were able to work from home were more likely to keep their jobs.

Concerning the effect of the internet on women’s labor force participation, Dettling (2017) finds a positive impact of high-speed internet use on the labor supply decisions of married women by about
0.228|0.266|-0.045|
|(constant 2010 IDR)|(1.140)|(0.628)|(1.466)|(0.533)|(0.468)|(0.403)|
|Growth in GRP per capita|-0.040|-0.164**|-0.149|-0.074|-0.003|0.069*|
||(0.090)|(0.066)|(0.118)|(0.056)|(0.042)|(0.040)|
|Number of observations|272|272|272|272|272|272|
|R-squared|0.530|0.406|0.292|0.754|0.590|0.527|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

23
pollution abatement expenditures in the country increase. For the US, Japan, and Germany, patents on these innovations are typically domestic patents. In contrast, for developing countries, the majority of these patents come from other countries. This is especially true of air pollution control technologies, which tend to be complex. Water pollution control technologies, on the other hand, are more frequently local innovations, as local conditions shape the requirements of these technologies, and are less likely to be patented elsewhere.

Dechezleprêtre _et al_ . (2011) examine climate-friendly innovation using patent data from 1978-2005 for 76 countries and covering a broad range of technologies, including renewable energy technologies, carbon capture and storage, and energy efficiency technologies for buildings, lighting, and cement manufacture. Like Lanjouw and Mody (1996), they find that most climate-friendly innovation occurs in developed countries. In fact, the US, Japan, and Germany together account for two-thirds of the innovations in their sample. Reflecting the role and impact of policy, innovation increases after the Kyoto Protocol in all Annex I countries except the US, which had not ratified Kyoto.<sup>12</sup> Dechezleprêtre _et al_ . (2011) do find some evidence of innovation in emerging economies, as measured by patents. In particular, China, South Korea, Russia and Brazil together accounted for 18.5% of climate-friendly innovations from 2000-2005. However, innovation in emerging economies is often of a different nature. For example, the most prevalent innovations in China, South Korea, Russia, and Brazil include technologies designed primarily for local markets, such as geothermal and cement manufacture. As a result, the share of high-value patents (defined as patent applications filed in multiple countries) from these four countries is just 7.2%. Consistent with Lanjouw and Mody (1996), Dechezleprêtre _et al_ . (2011) find that technologies of wider use

> 12 Annex I countries include all Annex B countries plus Belarus and Turkey.  These are the developed and transitioning
Unfortunately, the monthly weather data from CRU do not allow to identify weather events, such as floods, droughts, and storms, which often depend on extremes within a short time period. Instead self‐ reported data from the commune surveys are used, which include questions about the five main emergencies in the last three years.<sup>15</sup> Based on this data, those communes that have experienced floods, droughts and storms within the 12 months before the survey are identified. Although the data also include information on crop diseases, fires and epidemics, these events are not included as they are supposedly less directly related to weather conditions.

Overall, more than a fifth of all rural households reports some weather event. The variation between zones and years is lowest for the occurrence of storms (Figure 9). In line with earlier findings suggesting that 2010 is the hottest and driest year, the self‐reported data indicates a higher occurrence of droughts in 2010 – mostly in the _Wet‐cold_ zone. Surprisingly, the share of communes reporting floods is highest in 2010 in the _Dry‐hot_ and _Wet‐cold_ zone, which is in contrast to the rather dry conditions in that year and the severe flood events observed in the _Wet‐cold_ zone in 2014. The self‐reported data does not correspond to the flood events experienced by communes in. Auxiliary regressions, however, reveal that self‐reported floods indeed are related to higher annual and seasonal rainfall levels, number of wet months and maximum rainfall (Table A.4).

Figure 9 _Weather events in rural communes by climate zone in 2010, 2012 and 2014_ <!-- Start of picture text --> a. Floods b. Droughts c. Storms<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014<br>.25 .25 .25<br>.2 .2 .2<br
for each woman interviewed data on all births, including whether the child survived and if applicable the date of death.<sup>10</sup> Figure 6 plots the one year and the five year survival probability by birth cohort. It should be noted that infant and under-five mortality will tend to be underestimated for earlier birth cohorts. First, since, the surveys interviews women aged 15 to 49 in 2000, the mothers in this sample who gave birth in the 1970s were young at the time. The risk of infant and child mortality tends to increase in the late fertile years of a woman. Second, given the retrospective nature of the birth histories data, only women who survived until 2000 are interviewed. Those women who did not survived until 2000 might have been more likely to have children who died as well.

Despite this underestimation of infant and child mortality for children from earlier cohorts, figure 6 shows that infant and under-five mortality was very high for children born during the 1970-1979 period. For example, a child born between 1975 and 1979, had a 14.8 percent risk of dying within his first year of life and a 22 percent chance not to make it until his fifth birthday (see the detailed data in Table 3). Compared to other birth cohorts, infant mortality peaks for the 1975-79 birth cohort and under-five mortality is higher for the 1970-74<sup>11</sup> and 1975-79 birth cohorts. It is also very striking that for cohorts born during the 1970s, child mortality (mortality between age one and age five) is a substantial component of under-five mortality, whereas for later birth cohorts, most of the mortality is concentrated during the first year of life. During the mortality crisis of the 1970s, the living conditions were such that survival after the first year was also problematic. The analysis did not show significant differences in the infant and underfive mortality across genders.

Figure 7 compares the one year survival probability for children with rural and urban backgrounds<sup>12
# How Regulation and Enforcement of Competition Affects ICT Productivity: Evidence from Matched Regulatory-Production Surveys in Peru’s ICT Sector<sup>*</sup>

Tanida Arayavechkit<sup>1</sup> , Charl Jooste<sup>1</sup> , and Ana Urrutia Arrieta<sup>1</sup> 1 World Bank

**JEL classification** : C31, D24 **Keywords** : ICT, competition policy, productivity, markups.

> *We are grateful to James Sampi, Ekaterina Vostroknutova, Chad Syverson, Doerte Doemeland, Jorge Araujo, William Maloney, Ana Paula Cusolito, Martin Rama, Elena Ianchovichina and Tania Begazo, for helpful and constructive comments. Jose David Prieto Campo provided excellent research assistance . Corresponding authors can be reached at: tarayavechkit@worldbank.org, aurrutiaarrieta@worldbank.org, cjooste@worldbank.org
with the treatment group dummy variable.

As previously mentioned, our BFL model estimator is a type of “Intend-to-treat” estimator, ITT. In this case, it is easy to transform the _ex post_ TT estimator from SA (results from Table 3 in SA) for comparison with our estimator, as follows:

> 33 Children from the intended treatment and control groups are indistinguishable in the baseline data in terms of their enrollment rate, grade attainment, labor participation, gender, per capita expenditure, assets, parental education and household size,

27