rafmacalaba/datause-encoder-data huggingface dataset:
Promoting Democracy in Fragile States where _Yit_ is the outcome for person _i_ in town _t_ (with the polling place data, we simply aggregate up to town _t_ ), Civ. Ed. _t_ is the indicator for whether the town received civic education, Sec. Com. _t_ is the indicator for whether the town received a security committee, Combined _t_ is the indicator for whether the town received the combined treatment, _Xt_ accounts for both blocking and the low- and high-density security conditions by including centered block and high density indicators and their interaction with the treatment variables, and _✏it_ is an error term assumed to be clustered at the clan level.<sup>18</sup> All models are fit using weighted least squares, where the weighting accounts for each town’s probability of being included in its treatment condition given varying numbers of towns per clan. Thus, our weighted least squares estimate of _β_ 0 is consistent for the expected value in the study areas under control, while the estimates for _β_ 1, _β_ 2, and _β_ 3 are consistent for the average treatment effects (ATEs) of civic education, security committees, and combined programs, respectively. Section H in the supporting information presents results that control for pre-treatment education levels, literacy rates, and ethnic group composition, as these were found to be out of balance in our analysis of covariate data.

As Table 2 shows, we have multiple measures for each of the outcomes. Following Anderson (2008), whenever possible we boost the power of our tests while also ensuring that we are not led astray by multiple comparisons problems by constructing indices in the

> 18Centered block fixed effects with interactions estimates the stratified sample average treatment effect, appropriately accounting for the blocking without introducing any functional form assumptions (Gerber and Green, 2012; Lin, 2013, pp. 71-77). For the purposes of this paper, we do not interpret the effects of the differing density conditions. As it turns out, there were no significant effects, and so we simply average over the density conditions to simplify the presentation.

23
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A second, more fundamental problem, involves not so much the choice of any particular creditworthiness indicator, but how to assess whether the value of the indicator chosen is too high or not (high values indicate low creditworthiness). An indicator is too high (creditworthiness too low) if at that value the burden of servicing the debt exceeds the likely penalty on noncompliance to repayment terms. The problem with this definition is that nobody really knows how high that penalty is. This section follows Cohen in a very simple but forceful approach to this issue. The cost of default is not known, but if a country has not defaulted at the current value of its debtresource ratio, that value is, by implication, not yet too high. Otherwise the country would have defaulted already. A cautious borrowing policy than is a policy that will prevent a rising debt-resource ratio.

One important caveat: it does not follow from this analysis that a borrowing policy designed to rapidly lower debt-resource ratios is necessarily a good idea. While it is true that lower debt-resource ratios indicate higher creditworthiness, the transitional costs of reaching that lower ratio clearly raise the cost of servicing the existing debt. Since creditworthiness involves comparing the cost of default with the cost of servicing the current debt, such a strategy, which has been imposed on many high-debt countries, would lower rather than increase current creditworthiness. (ii) Sustainable Current Account Deficits How much foreign borrowing is compatible with maintaining the debtresource ratio at its current value, and hence maintaining the level of creditworthiness? Since the debt-resource ratio is a weighted average of the debt-output and the debt-export ratio, it will depend on the growth rate of the borrowing country and of its trading partners. The growth rate of its Chhibb.rDisk/chhibber-Turkey Paper/ac :0<sup>7</sup> -28-88:lc
Beginning in mid-2003, the _Bono Solidario_ was gradually replaced with a new program, the _Bono de Desarrollo Humano_ (BDH). The BDH differs from the _Bono Solidario_ in that it is meanstested. Starting in 2001, the government of Ecuador invested significant effort into developing a family means-test. Fully 85 percent of families in rural areas and poorer urban areas of Ecuador were surveyed and assigned a poverty index (called the Selben index). This index is used to assess eligibility for the BDH—only families in the first two quintiles of the Selben index are eligible for transfers of $15 per month.<sup>2</sup> BDH transfers are relatively small in magnitude—in our sample, they account for approximately 8.9 percent of expenditures of the median household. Transfers are made through the banking system. The program rules state that transfers must be made to women if there are any adult women in the household, and women must physically go to a nearby bank to receive cash.<sup>3</sup> (There is a much smaller version of the program that covers the elderly, regardless whether they are men or women; these households are not included in our sample.) Earlier work on the BDH program has focused on its impact on overall consumption, schooling, and child health and development. A joint document by the World Bank and the _Secretaría Técnica del Frente Social_ (2006) in Ecuador shows that households that received BDH transfers did not have significantly higher overall per capita consumption. This is surprising, but can be explained, in an accounting sense, by large reductions in child labor among households that received transfers. This same analysis finds no evidence of changes in adult labor market participation. Schady and Araujo (2006) show that the BDH program had a large, positive effect on school enrollment. Finally, Paxson and Schady (2007) show that BDH transfers resulted in improvements in measures of nutritional status, motor development, cognitive development, and psycho-emotional development among children of pre-school age in rural areas, especially among the poorest households. There is no earlier work on the effect of BDH transfers on the composition of household expenditures.

# B. <u>The experiment</u>

In a similar fashion to the roll-out of _PROGRESA_ , an evaluation design was explicitly incorporated into the BDH. The sample for this evaluation was drawn from the Selben rosters of four of the 22 provinces in the country: Carchi, Imbabura, Cotopaxi, and Tungurahua. All four provinces are in the sierra (or highlands) region of the country. The sampling framework followed a two-stage

> 2 In January 2007, the administration of newly-elected President Rafael Correa announced that transfers to all eligible households would be doubled from $15 per month to $30 per month.  However, this reform falls well after the time period considered in our study.

> 3 There are only 4 households in our sample that receive BDH transfers in spite of the fact that there are no adult women in the household.

5
Across fiscal categories, fiscal policy produces the largest reduction in inequality for the _workers, schoolage children_ group, from market to final income (Figure 9) while each group keeps its market income ranking visà-vis within-group inequality. That is, the _workers, no dependents_ group shows the most within-group inequality, and the _no workers and children in school_ shows the least within-group inequality at either pre-fiscal or post-fiscal income. Across care categories, the _married couple with dependents_ group also shows the largest fall in its within-group inequality when moving from market to final income (Figure 10). For the fiscal categories, however, within-group inequality rankings do not change from pre-fiscal to post-fiscal income.

Figure 9. Contribution to Within-Group Inequality by Fiscal Categories <!-- Start of picture text --> 0.08<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0.00<br>Market Disposable Consumable Final<br>income income income income<br>No workers, no dependents No workers, children in school Pensioners<br>One/two worker, no dependents One/two worker, children in school<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

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- Lopez Murphy, Ricardo. "Asbectos fiscales de la Cuenta de Regulecion M2ngtara"ia Enayvos Economigos, September 1984.

- Machinea, Jose Luis and Jose Maria Fanelli. "Stopping Hyperinflation: The Case of the Austral Plan in Argentina, 1985-87," in Bruno, Michael, &I Al, eds., Inflation Stabilization: The ZXgerience of lsrael. Argentina. Brazil. Bolivia and Mexico, (MIT Press, Cambridge, Mass., 1988), pp. 111-152.

- Piekarz, Julio A. "C=2ansacion de raserva. dg efectiv min: La Cuenta_ de RegHlacion Monetaria. e1 resultadocui fiscal del Banco C2ntraj v la transfgrmacion del sistema financiero argentino," Ensavos Economic2os, September 1984.

- Piekarz, Julio A. "El deticit cuasi fiscal del Banco Central," paper presented in the Seminar on Central Bank Quasi-Fiscal Operations (Brasilia, August 1987), Center for Latin American Monetary Studies.

- Rodriguez, Carlos A. and Aquiles Almansi. "Reforma monetaria y financiera en hiperinflacion." Center for Macroeconomic Studies of Argentina (CEMA), Serie Documentos de 2ZrahatJ No. 67 (August 1989).

- Sargent, Thomas and Neil Vallace. "Some Unpleasant Monetarist Arithmetic," Federal Reserve Bank of Minneapolis Ouar° terIX Review, Fall 1981, pp. 1-18.

- World Bank. "Arqentina: Tax Policy for Stabilization and Economic Recovery," Report No. 8076-AR, November 8, 1989.
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Sai, F.T. (1983). _Ethical issues in contraceptive technology._ In: _Draper Fund Report_ 12: 24-26.

Sai, F.T. _(1984). Adolescent sexua.ity and reproductive health._ In: Mati, J.K.G. _et al.,_ (eds). _Reproductive health in Africa._ Baltimore: JHPIEGO, pp. 16-31. Sai, F.T. (1984). Contraceptives and their politics. _Br J Fam Plann, 10,_ 38. Senanayake, P. (1984). _Tho politics of contraception._ In: Runnebaum, B. _et al._ (eds). _Future aspects in contraception._ Part 2 _Female contraception._ Lancaster: MTP Press, pp. 351-360.

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World Health Organization (1982). Facts about injectable contraceptives: Memorandum from a WHO meeting. _Bull WHO,_ 60(2): 199-210.

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World Bank (1984). _World development report 1984._ New York: Oxford University Press.

World Bank (1986). _Population growth and policies in Sub-Saharan Africa._ Washington, DC.: World Bank.
Acemoglu, Daron, and Pascual Restrepo. 2020. “Robots and Jobs: Evidence from US Labor Markets.” _Journal of Political Economy_ 128 (6): 2188–244.

Acemoglu, Daron. “Harms of AI.” In _The Oxford Handbook of AI Governance_ , ed. Justin B. Bullock et al., 660–706. Oxford, U.K.: Oxford University Press.

Acemoglu, Daron, David Laibson, and John A. List. 2014. “Equalizing Superstars: The Internet and the Democratization of Education.” _American Economic Review_ , 104 (5): 523–27.

Agarwal, Sumit, Shashwat Alok, Pulak Ghosh, and Sudip Gupta. 2019. “Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech”. December, Indian School of Business, Hyderabad; available at SSRN: https://ssrn.com/abstract=3507827.

Agrawal, Ajay, Joshua S. Gans, and Avi Goldfarb. 2019. “Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction.” _Journal of Economic Perspectives_ 33 (2): 31– 50.

Agrawal, Ajay, Nicola Lacetera, and Elizabeth Lyons. 2016. “Does Standardized Information in Online Markets Disproportionately Benefit Job Applicants from Less Developed Countries?” _Journal of International Economics_ 103 (November): 1–12.

Agrawal, Ajay, and Avi Goldfarb. 2008. “Restructuring Research: Communication Costs and the Democratization of University Innovation.” _American Economic Review_ 98 (4): 1578–90.

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within sector growth has been broad-based-including in agriculture as well as manufacturing, trade, transport and finance services, while the between-sector productivity gains mainly reflected a move out of agriculture into services. In particular, the share of workers employed in agriculture fell from about 70 percent in 1975 to about 30 percent in 2018. In LICs, between-sector productivity gains in LICs reflected a broad-based shift out of agriculture into services such as trade, transport and finance. During the 2010s, the contribution of between-sector slowed down due to small movement to higher productivity sectors such as manufacturing and trade.

Figure 4 shows that contributions of the between-sector effect have been non-negligible in the East Asia and Pacific (EAP), European and Central Asia (ECA), South Asia (SAR) and Sub-Saharan Africa (SSA) regions whereas those in Latin America and Caribbean (LAC) and Middle East and North Africa (MENA) have been small.

# **3.2 Baseline regression**

# **3.2.1 Aggregate convergence**

Table 1 and Figure 5 show the results with three different balanced panel datasets: 60 countries in 1975-2018, 60 countries in 1975-1995, and 91 countries in 1995-2018. At the aggregate level, regression (2) shows there has been weak unconditional convergence prior to 1995. However, since the late 1990s aggregate convergence emerges (Table 1 and Figure 5, World Bank (2020)). Over this period, countries with lower initial levels of productivity have begun to catch up to high-productivity economies. Nonetheless, at the estimated rate of convergence it would take about 140 years for countries at the bottom 10 percent of the productivity distribution to reach the level of the top 10 percent.<sup>8</sup>

# **3.2.2 Decomposing within and between sector convergence**

Even though many sectors are not converging to the frontier, the reallocation of labor to other sectors with higher productivity levels could be an important engine of aggregate convergence. Estimating the decomposition of aggregate convergence from regression (3) suggests that since 1995 both within and between sector effects

> 8137 years ≒(ln(0.9)/ln(0.1)-1)/0.00695, using Table 1.

7
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE pressure on prices, however. Given the size of Kenya’s market, it may be desirable to consider introducing competition in the fixed-line services, as well. Nigeria has done so with considerable success and today is the only country in Africa where fixed-lines services are not in decline. The Nigerian experience also illustrates the willingness of private providers to invest significantly in inter-urban telecommunications backbones.

Submarine cables could substantially reduce costs as long as access is competitive (table 8). Based on experience elsewhere in Africa, the imminent completion of three submarine cable projects—EASSy, SEACOM, and TEAMS—has the potential to cut Kenya’s Internet and international telecom charges at least by half. But these benefits will materialize for the economy only if there is more than one operator providing a physical point of access to the submarine infrastructure, and hence competition between alternative landing stations. Countries in which international gateways remain under monopolistic control do not experience full price reductions from increases in international connectivity, essentially because the benefits of the technology are retained as monopoly profit (table 8).

**Table 7. Benchmarking ICT indicators**

||Unit|Low-income<br>countries|**Kenya**|Mid<br>c|dle-income<br>ountries|
|---|---|---|---|---|---|
|GSM coverage|% population|48.2||**86.2**|97.2|
|International bandwidth|Mbps/capita|5.8||**6.3**|30.2|
|Internet|subscribers/100|people<br>0.1||**0.3**|2.0|
|Landline|subscribers/100|people<br>0.8||**0.9**|9.4|
|Mobile phone|subscribers/100|people<br>15.1||**30.2**|86.7|
||**Keny**|**a**<br>Countries wi<br>access to sub<br>cables|thout<br>marine<br>|Other de<br>regi|veloping<br>ons|
|Price of monthly mobile basket||**15.9**|11.12||9.9|
|Price of monthly fixed-line basket||**21.0**|13.58||—|
|Price of 20-hour Internet package||**81.5**|67.95||11.0|
|Price of a 3-minute call to the United States||**2.00**|2.59||2.0|
|Price of inter-Africa calls, mean||**1.04**|0.72||n.a.|

Source: Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable.

**Table 8. High international call charges driven both by technology and market power**

|US$|Percent of<br>cases|Call within<br>region|Call to U.S.|Internet<br>dial-up|Internet<br>ADSL|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
|monopoly on international gateway|16|0.70|0.72|37|120|
|competitive international gateway|16|0.48|0.23|37|98|

Source: Minges and others 2009.

21
year.<sup>4</sup> Table 2 gives the (compound annual) growth rates for the measures of average income or consumption and the poverty measures; the table also gives the growth rates of the total population, so the growth rates in the numbers of poor can be readily calculated. (Notice from Table 2 that the growth rates of the survey mean for India have been appreciably lower than consumption per capita as measured in the NAS.) The data suggest that, around the time its reforms began, China had one of the highest proportions of the population living in poverty in the world. In 1981, a staggering 84% of the population lived below a poverty line of $1.25 per day in 2005 prices and converted to RMB at purchasing power parity in 2005. The best data available suggest that only four countries (Cambodia, Burkina Faso, Mali and Uganda) had a higher headcount index than China in 1981. By 2005, the proportion of China’s population living in poverty had fallen to 16%—well below the average for the developing world of 26%. The proportionate rate of poverty reduction over 1981-2005 was an impressive 6.6% per annum (and slightly higher using the mixed method), with the number of poor falling by 5.5% per annum.

Using the same poverty line for Brazil, the proportion of the population in poverty is appreciably lower than in China, and fell from about 17% to 8% over 1981-2005. The proportionate rate of poverty reduction of 3.2% per annum is certainly not China’s rate but it is still impressive.<sup>5</sup> The rate of poverty reduction rose from 2.3% to 4.2% between the periods 1981-1993 and 1993-2005. Given population growth rates (which declined between the two periods), the number of poor went from being virtually constant in the pre-reform period to a decline of 2.7% per annum. The difference between the two periods is even more marked using the mixed method, which indicates no progress against poverty in the 1981-1993 period, but a rate of reduction in the headcount index of 5.1% per annum post 1993. Using the $2 a day line, we see a somewhat slower pace of poverty reduction and a narrowing of the difference between the “reform” and “pre-reform” periods using the surveys alone, though the mixed method also suggests that virtually all the poverty reduction was in the latter period.<sup>6</sup>

> 4 The proportionate rate of poverty reduction is calculated by a compound growth rate between end points. Alternatively one can use a regression of the log of the variable in question on time using all observations. The two will only give (to a close approximation) the same answer if the end points are on the regression line. I also used the regression method for the poverty measures and I shall note anything more than non-negligible differences.

> 5 The rate is slightly higher (3.6%) using a regression of the log poverty rate on time.

> 6 Ferreira et al. (2009) provide estimates for Brazil’s national poverty line, which is about $3 a day.

4
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# **Table 1**

# Chile: Macroeconoaic Indicators

|(|GDP<br>growth rate)<br>Z|capacity<br>utilization<br>(index )|Investment<br> <br>rate<br>(Z of GDP)|<br>Current<br>Account<br>(Z of GDP)|rate of<br>inflation<br> <br>( Z)|Public<br> <br>Savings<br>(Z of GDP)|
|---|---|---|---|---|---|---|
|1981|5.7|100.0|19.5|-<br>14.5|9.5||
|1982|-<br>14.7|77.6|15.0|-<br>9.5|20.7|- 0.9|
|1983|-<br>0.8|74.9|12.9|-<br>5.7|23.1|0.0|
|1984|6.3|81.8|13.2|-<br>10.7|23.0|0.5|
|1985|2.4|83.4|14.8|- 8.3|26.4|3.7|
|1986|5.7|87.6|15.0|-<br>6.5|17.4|4.6|
|1987|5.5|91.4|16.5|-<br>4.8|21.1|5.3|
|168O|7.4|,5.7|17.0|-<br>o07|12.7|5.6|

Source: Chilean National Accounts and Balance o. Payments Statistics Central Bank of Chile (1988). The index of capacity utilization draws from Marfan and Artiagoitia (1989).
We present our final piece of evidence on the effects of LEAP on social capital and prosocial action in Table 10. The first column of Table 10 present linear estimates of the effect of LEAP on the number of village meetings respondents reported attending. According to these estimates villagers in program communities attended more village meetings than did members of control communities, although most of this increase appears to have been among the non-poor, who were not targeted by the program. Non-poor members of LEAP communities report attending over two more meetings than their counterparts in non-LEAP communities. The increment is smaller among the poor. Poor members of program communities attended about one-and-a-half more meetings than did poor members of control communities. Both results, those of the poor and the non-poor, are statistically significant at the five percent level or better.

The remaining columns of Table 10 present linear probability estimates of respondents’ answers to yes-or-no questions about whether they participated in the voluntary activities listed. The estimated differences between the program and the control communities were very close to zero (and in some cases negative) and statistically insignificant.

In summary, other than the increase in the number of village meetings attended, there was no difference in the voluntary community action of LEAP-community and controlcommunity members as reported by respondents in a retrospective survey. The standardized average effect across these four measures shown in column 5 of Table 10 captures this lack of difference between treated and control communities. The average effect is small and insignificant suggesting that there was no significant difference between program and control communities in the voluntary actions of their members.

# **5. Conclusion**

LEAP delivered more than feared and less than hoped. The program clearly had a positive effect on the poor’s savings behavior, dramatically increased associations among the poor via SHGs and may have produced substantial improvements in the poor’s meat production and income derived from it (although the evidence on this latter impact was somewhat contradictory). These positive effects have materialized after a one-year intensive pilot 27