rafmacalaba/datause-encoder-data huggingface dataset:
between legal and illegal activities, leaving aside factors capturing the relative costs such as the efforts to prevent and fight criminal activity. Among the costs indicators, the most common ones refer to expenditure in security, the number of police officers, the prison sentences associated with commission of crime, and detention rates as percentage of committed crimes (Dilis, Miron, and Summers 2010). These are important elements determining the evolution of violence. But, in view of how limited is the systematically collected information of this type, including them would significantly reduce the number of observations in our analysis and introduce an information-availability bias.

The set of controls included in the empirical exercise below are the following: hourly labor income (Gould, Weinberg, and Mustard 2002); income inequality (Bourguignon 1999 and Enamorado et al. 2014); unemployment rate among the population aged 35 to 44 (Freeman 1999); high school dropout rates (Anderson 2014 and Chioda 2015). In addition, we control for the number of youth aged 15 to 24 as a share of the total population, the proportion of workers in the informal sector, and the proportion of total employment accounted for by employees in the manufacturing industries.

# **_3.1 DATA SOURCES_**

In Mexico, like in most developing countries, measuring violence and criminal activities is a challenging task. The ideal measurement would capture all illegal activities occurring in a country during a given period. However, this is impossible in practical terms. By their nature, illegal activities are hidden and, in contexts of weak institutional capacity and a limited application of the rule of law, the great majority of crimes go unreported by victims. For instance, according to the most recent victimization survey, approximately 33 million crimes were committed in Mexico in 2013 of which only 10 percent were reported.

However, there are two crimes that are measured with some degree of accuracy: homicide and car theft. Unlike other offences, for which the opening of a ministerial investigation requires a formal complaint by the victim, homicides are automatically investigated by the judiciary. This generates a generally reliable count of homicides. Moreover, the record of 13
Table 13: Heterogeneous effects of savings default on net deposits, within one week of transfer (+7 days)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Abo<br>Assets|ve median:<br>Patience|
|Savings Default|2790.733***|3574.589**|3751.677***|4305.833***|2724.841**|
||(833.627)|(1452.477)|(1081.432)|(1220.779)|(1016.825)|
|Observations|312|153|234|227|191|
|Mean of dependent variable in control group|2009.78|3143.35|2246.09|1963.03|1871.67|
|R-squared|0.18|0.24|0.16|0.17|0.13|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Bel<br>Assets|ow median:<br>Patience|
|Savings Default|4309.503**|3016.663***|3138.095***|2339.635**|3892.723***|
||(1328.855)|(791.269)|(936.787)|(798.693)|(972.297)|
|Observations|162|321|240|247|283|
|Mean of dependent variable in control group|1588.26|1222.93|1476.79|1744.83|1844.17|
|R-squared|0.17|0.08|0.09|0.10|0.17|
|P-value: equal efect of savings default|0.336|0.734|0.663|0.168|0.400|

The top panel includes respondents from female-headed households, those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values are from tests of equality of Savings Default coefficients in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 39
or per capita income of the lowest quintile of the income distribution in a city needed to make sixty 10 km trips per month.<sup>16</sup>

**<u>assuming 60 trips per person per month</u>**

||**City**|**Afforda**|**bility Index**|
|---|---|---|---|
|||Average|BottomQuintile|
|1|SãoPaulo|11%|107%|
|2|Rio de Janeiro|6%|63%|
|3|Brasilia|6%|59%|
|4|CapeTown|4%|38%|
|5|BuenosAires|4%|26%|
|6|Mumbai|9%|23%|
|7|KualaLumpur|5%|22%|
|8|Mexico City|3%|19%|
|9|Chennai|8%|19%|
|10|Manila|5%|17%|
|11|Krakow|6%|17%|
|12|Amsterdam|6%|16%|
|13|Moscow|4%|15%|
|14|Guangzhou|4%|14%|
|15|Warsaw|4%|11%|
|16|NewYork|3%|10%|
|17|LosAngeles|3%|10%|
|18|Chicago|3%|10%|
|19|Singapore|2%|10%|
|20|Beijing|3%|9%|
|21|Seoul|4%|9%|
|22|Shanghai|2%|6%|
|23|Cairo|3%|6%|
|24|Budapest|3%|6%|
|25|London|2%|5%|
|26|Prague|2%|4%|
|27|Bangkok|1%|4%|

_Source_ : Carruthers, Dick and Saurkar (2005).

16 Formally, Carruthers, et al affordability index is defined as:

> where<sup>_~~x~~_</sup> _i_<sup>, a fixed parameter, replaces the observed number of trips taken by household member</sup><sup>_i_, which in</sup> the case of Carruthers, Dick and Saurkar (2005) is 60 trips per month for each person.

12
|Lagos Metropolitan Development and Governance Project|Hassan Madu Kida|
|---|---|
|Lagos Metropolitan Development and Governance Project|Hassan Madu Kida|
|Lagos Metropolitan Development and Governance Project|Hassan Madu Kida|
|Lagos Metropolitan Development and Governance Project|Hassan Madu Kida|
|Lagos Metropolitan Development and Governance Project|Hassan Madu Kida|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Road Management and SafetyProject|Jacques Bure|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multisectoral HIV/AIDS Control and Orphans Project|Pamphile Kantabaze|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Multi-Sectoral HIV/AIDS Project|Miriam Schneidman|
|Economic RecoveryCredit|Zeljko Bogetic|
|Economic RecoveryCredit|Zeljko Bogetic|
|Economic RecoveryCredit|Zeljko Bogetic|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
|Second National Urban Water Sector Reform Project|Camilo Lombana Cordoba|
defunct Trans-Pacific Partnership (TPP) except the United States. Indonesia has recently signaled its intention to eventually join the CPTPP.<sup>3</sup> The FTAAP is an agreement among 21 Asia-Pacific Economic Cooperation (APEC) economies, which aims to link Pacific Rim economies from China to Chile including also the United States.

**Figure 1 Membership of the TPP-11 (CPTPP),  TPP-12 (CPTPP + USA), and RCEP**

Source: Authors’ elaboration The analysis employs the LINKAGE model, which is a dynamic global CGE model particularly suited to capture the feedback mechanisms of trade shocks across countries. On the basis of trade and relative prices channels, the model evaluates the impact of a reduction of tariffs and NTMs brought about by the prospective agreements. The CGE model is then combined with the Global Income Distribution Dynamics (GIDD) microsimulation tool to study the impact of these potential PTAs on poverty and shared prosperity. In particular, GIDD distributes the macro-economic effects captured by the CGE analysis across households using Indonesia’s National Socio-Economic Household Survey for the year 2014.

The results of the analysis suggest that out of the PTAs under investigation, the EU-CEPA would yield the largest macro-economic gains for Indonesia in terms of income, output and exports. Relative to the business as usual baseline scenario, Indonesian GDP is expected to increase by 2.13% as a result of the EU-IDN CEPA, compared to 0.18% and 0.89% in case of RCEP and FTAAP respectively. The large impact of the EU-IDN CEPA is mainly driven by the highest expected reductions in trade barriers and the high share of international trade between the partners. The signing of the CPTPP in its current 11-member version would have negligible impact on Indonesia due to the very low trade diversion compensated by the trade creation related to the agreement. On the other hand, joining the CPTPP along with other major Southeast Asian economies would generate a positive economic impact for Indonesia with an expected 0.58% increase in GDP.

These macro effects translate into reductions of poverty, albeit the growth incidence curves (along the income distribution) have important differences between the agreements. The EU-IDN CEPA yields the highest expected growth of income relative to the other PTAs at every point of the income distribution. However, the size of the gains grows monotonically with income for the EU-

> 3 See: https://www.cnbc.com/2018/04/18/indonesia-is-on-path-to-joining-the-trans-pacific-partnership-financeminister-says.html.

3
partially, to compensate for the negative impact of these disasters.

Regarding the impact of the different shocks on the current account, the figure suggests that, with the exception of Geological disasters, the current account tends to go to surplus (deficit) as a result of a negative (positive) shock, in other words, in this countries the current account is countercyclical: they borrow in good times and “lend” (increase their position) in bad times. In this regard, these countries do not seem to behave differently from emerging markets, which have been documented to have a highly procyclical access to capital markets (see Agiar and Gopinath, 2004). It is important to keep in mind that the dynamic response of net exports was computed as a residual from a linear approximation using the average shares of the other GDP components, so it also captures inaccuracies in the approximation and in the shares used. However, given the magnitudes observed in the IRFs, it is unlikely that the explicit consideration of these errors could reverse the conclusion about the cyclicality of the current account. Overall, these findings suggest that negative external shocks are not directly linked with significant increases in the level of external debt of low-income countries. On the contrary, these countries seem to accumulate debt while in good times. Of course, the results also imply that, during bad times, these countries are not able to borrow, and most likely have to serve their debt obligations with a reduced level of output and income. It is in this regard that negative external shocks may put these countries under a lot of stress, but, as mentioned above, this situation is not different from that experienced by emerging markets, so, the evidence does not support any structural difference between these groups of countries in this regard.

# **6.2 The determinants of aid**

Much has been recently discussed on academic and policy circles about the adequacy of the level of aid to low-income countries and its effectiveness (see for example, Burnside and Dollar, 2000; Sachs et al., 2004; and Easterly et al., 2004). Despite this recent interest on the effects of aid, little is known about the determinants of aid flows.<sup>14</sup> Here we provide some new evidence on this regard by showing how aid flows respond to the different external shocks that affect low-income countries. This can be seen in Figure 5.

The different impulse-response functions reported in the figure show that both, supply and 14Some recent papers exploring this issue include Neumeyer (2003) and Odedokun and Round (2003).

22
**Figures 11.4: Robustness - HLO vs IRT Primary Reading Scores from 1990-2010**

We conduct a similar exercise using ranks within each respective year. Figures 13.0-13.3 in the Appendix summarize the results. We find consistently high correlations above .95 for all years, except 1990 where the Spearman rank correlation is lower than raw scores at .84. This indicates that the relationship between our HLO database and the LINCS project database is high even when using rank correlations which might be sensitive to small changes in point estimates.

_Comparison to ISAT and RSAT raw scores._ As an additional robustness check, we compare our Harmonized Learning Outcome (HLO) database to raw scores from PISA, TIMSS, SACMEQ and LLECE. If our methodology is able to preserve the integrity of the original scores and rankings, in addition to expanding coverage, this would enhance our confidence in the robustness of our approach. Figures 14.0-14.9 demonstrate the results. Of note, we conduct comparisons from 1995-2015 for all assessments, except LLECE where we compare results form 2005-2015 since we did not include the first LLECE in our HLO database.<sup>17</sup> For PISA and TIMSS we see almost a direct mapping to our HLO database. With SACMEQ and LLECE, we see similar trends, different by a relatively constant factor. This is the expected result: similar trends over time scaled down when placed on an international scale.

> 17 As noted earlier, the first LLECE is not analogous to the SERCE and TERCE due to varying grade-level coverage.

41
<u>Figure 4d: Searching While Employed: Mexico</u> <!-- Start of picture text --> 1987q1 1991q3 1996q1 2000q3 2005q1<br>Searching(F) Searching(S)<br>Searching(I)<br>.08<br>.06<br>.04<br>.02<br>0<br><!-- End of picture text --> Note: Quarterly data from the National Urban Labor Survey (ENEU) 1987:Q1 to 2004:Q4. Searching (j) refers to the proportion of employed workers in sector j who claim to be looking for a new job and have not changed employment status in the previous quarter.

38
|Self-dealing|Index of anti-self-dealing regulation, ranging from 0 to 1 with higher<br>valueindicatingmore controlofself-dealing.|Djankov et al. (2008)|
|---|---|---|
||Index of regulatory oversight of bank, ranging from 3 to 10 with higher|Barth et al. (2004)|
|Capital|valuesindicate greaterstringency||
||Index of power of commercial bank supervisory agency. It measures the<br>power of the supervisory authorities to take specific actions to prevent|Barth et al. (2004)|
|Official|and correct problems, with highervaluesindicating greaterpower.||
||Index of diversification guidelines imposed on banks, ranging from 0 to|Barth et al. (2004)|
|Diversification|2with highervaluesindicatingmore diversification.||
||Index of financial freedom, scaled from 0 to 100 with higher values|Heritage Foundation|
|Financial freedom|indicating greater freedom.||

56
# Research Components of<sup>World Bank Education Projects,</sup> since 1982.

|Area|Country and Loan/Credit<br>Nu.mber|Approved|Research Components|Current Status of Studies,<br>including<br>Completed DocumeritslDatn%|
|---|---|---|---|---|
|LAC<br>LAC|Paraguay<br>LN 2373-PA<br>PeruLN2208-PE|1984<br>1983|Second Vocational<br>Trdining.<br>Studies of: (i)<br>organiza'ional<br>structure<br>of<br>National<br>Vocational<br>Training<br>Center;<br>(ii)<br>analyzealternatives<br>to current<br>budget<br>al locational<br>procedures;<br>(iii)<br>identify<br>long-term funding sources<sup>to</sup><br>finance<br>rural<br>training<br>activities;<br>(iv)<br>17 studies<br>on rural<br>occupations<br>and<br>training<br>requirements;<br>(v) course evaluation<br>(paras.<br>3.28-30 p.18).<br>Higher Agricultural<br>Education.<br>Support for<br>research facilities<br>but no specific<br>studies.|TOR for<br>wantlisis<br>de los flujos<br>del Siste_m<br>Educativo Nacional"<br>(doc. U G34285) and "diagnostico<br>sobretosituacion<br>det eapleo enalsector turismo<sup>y</sup><br>necesidades de capacitacion<br>sectorials<br>(doc. N<br>No information<br>su4pplied.<br>Noinformtion<br>supptied.|
|LAC|PeruLN2465-PE|1985|Primary Education (11). (i)<br>Study to analyze<br>current<br>sctoot<br>administrative<br>and supervision<br>practices<br>(para.<br>4.09 p.i9).<br>(ii)<br>Studyonimplementation<br>of budgeting and<br>accounting system (S300,000)(4.32<br>26).|Noinformation<br>supplied.|

65
practically every indigenous student is in CONAFE, the most accurate conclusions one can reach are that indigenous student performance on Spanish and math exams is improving, that indigenous students are learning more quickly in math than in Spanish, and that indigenous students are catching up to their peers in math.

|**Table 7. Primar**|**y School T**|**est Scores by Ethnic**|**ity, 1999-2**|**002**||
|---|---|---|---|---|---|
||1999|2000<br>2001|2002|Ave|Trend|
|||**Global**||||
|Indigenous|397.5|415.0<br>436.4|453.6|425.6|19.0|
|All non-ind|417.8|461.8<br>464.8|484.2|457.2|20.2|
|Non-CONAFE|425.5|468.8<br>467.3|490.7|463.1|19.4|
|CONAFE|417.2|460.0<br>464.3|482.1|455.9|19.9|
|Diff|20.3|46.8<br>28.4|30.7|31.5|1.3|
|||**Math**||||
|Indigenous|385.4|407.0<br>440.4|463.6|424.1|26.8|
|All non-ind|414.5|460.2<br>467.4|484.8|456.7|21.8|
|Non-CONAFE|421.4|465.0<br>470.2|487.2|460.9|20.3|
|CONAFE|414.1|459.0<br>466.7|484.0|456.0|21.8|
|Diff|29.2|53.2<br>27.0|21.2|32.6|-5.0|
|||<br><br>**Spanish**||||
|Indigenous|407.3|424.7<br>428.0|447.3|426.8|12.3|
|All Non-indig.|419.3|463.1<br>459.6|484.0|456.5|19.1|
|Non-CONAFE|428.5|472.8<br>462.1|493.4|464.2|18.4|
|CONAFE|418.7|460.8<br>459.1|480.9|454.9|18.5|
|Diff|12.0|38.5<br>31.7|36.7|29.7|6.7|

Source: _Estándares Nacionales_ 1999-2002. “Diff” is the difference between indigenous and all non-indigenous students. Universe is students with p-score between 0.9 and 0.99.

29
# 1. **Introduction**

Rapid growth of Internet use in high-income economies has raised the specter of a "digital divide" that will marginalize developing countries, because they can neither afford Internet access nor use it effectively when it is available. Dertouzos (1997) anticipates a widening income gap if the global Internet develops under laissez-faire conditions. The UN (1999) and Sachs (2000) concur, calling for substantial aid flows to narrow the technology gap. While Negroponte (1998) counters that the "leapfrog" character of the Internet will enable the poor to catch up quickly, he also advocates major increases in aid for technology development.

Current Internet access patterns highlight the potential severity of the problem. Figure 1 uses data on Internet connectivity (subscriptions per capita) to plot a digital Lorenz curve for 55 countries whose total population is 4.5 billion.' Severe inequality is evident, with 90% of the world's Internet subscribers in countries whose population is 15% of the global total. If this imbalance persists, it seems likely to damage the future growth prospects of many low-income countries.

Figure 1: International Inequality in Internet Access, 2000 <!-- Start of picture text --> 100.<br>90<br>70°<br>60<br>50<br>;nx 40 r<br>o 30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>% of Population<br><!-- End of picture text --> In this paper, we investigate the sources and evolution of the digital divide using a new dataset. We focus on two dimensions of the problem: Internet use and telecommunications access. In Section 2, we review the anecdotal evidence on Internet use in developing countries. Drawing on this evidence, we specify and estimate a model of Internet diffusion via mainline telephone systems in Section 3. Sections 4-5 extend the modeling exercise to mobile telephone systems, whose rapid technological evolution may accelerate the spread of Internet access. In Section 6, we use the results to assess the potential role of economic and sectoral policy reform in narrowing the digital divide. Section 7 considers the potential for direct promotion of Internet access as a complement to policy reforms, and Section 8 concludes the paper.

1 Internet subscriber estimates have been provided to the World Bank by Pyramid Research, a subsidiary of the Economist Intelligence, Ltd.
# **1 Introduction**

The Coronavirus Disease 2019 (COVID-19) pandemic has disrupted economic activity in India. Until mid-March 2020, the economy was mainly hit by disruptions in cross-border connections. For example, tourism arrivals in India declined due to strict travel restrictions and some value chains were interrupted, especially with China. When COVID-19 started to spread in India through domestic contagion, the Indian authorities enacted a series of measures to combat the pandemic, including a national lockdown from March 25 onwards that strongly disrupted economic activity across the country. When restrictions were stepwise eased in May, economic activity slowly recovered. Shutdowns and other non-pharmaceutical interventions to contain the spread of COVID-19 have high economic costs and consequently tend to be accompanied by policy responses to mitigate their economic impact (Gourinchas 2020). In line, both the Reserve Bank of India and the Government of India announced measures to assist individuals and companies that were negatively affected. Adjusting containment measures and policy responses to mitigate their economic impact require an assessment of the magnitude of the economic situation in near real-time. In addition, since the impact can vary at different locations, an assessment at high spatial granularity is needed.

Indicators traditionally used to monitor the economic situation are available only with substantial lags and often at the national level only, and hence provide little insights into the immediate effect of strong and sudden policy measures like a national lockdown. In response to such problems, economists have suggested different proxies that are available at a higher frequency and with shorter publication lags, as well as at a higher spatial granularity. Two of these are electricity consumption and night light intensity. Electricity is an input to activities throughout the economy, from industrial production to commerce and household activity, so changes in consumption reveal information about these activities in real-time (Cicala 2020a, 2020b). Similarly, nighttime light intensity contains information about economic activity at high spatial granularity. Such proxies have become especially important during the COVID-19 pandemic, as it makes data collection through surveys, which are fundamental for the traditional estimation of gross value added, more difficult. In line, the Central Statistical Office noted that data collection challenges related to India’s national lockdown will likely result in revisions to its growth estimate for the first quarter of 2020.

Both electricity consumption and nighttime light intensity closely track economic activity 1