rafmacalaba/datause-encoder-data huggingface dataset:
|re-Primary Quality|Infrastructure quality<br>index (mean = 0)<br>(7)<br>(8)<br>-0.176<br>-0.178<br>(0.032)**<br>(0.031)**<br>0.077<br>(0.010)**<br>0.276<br>(0.029)**<br>0.186<br>0.218<br>(0.067)**<br>(0.066)**<br>-0.602<br>0.203<br>(0.111)**<br>(0.025)**<br>-0.089<br>-0.015<br>(0.040)*<br>(0.031)<br>0.040<br>0.306<br>(0.196)<br>(0.171)+<br>0.247<br>-0.042<br>(0.241)<br>(0.200)<br>56765<br>56765<br>4713<br>4713<br>simulated revenue and its<br> All specifcations include<br>venue, per capita transfer<br>d pre-primary enrollment<br>erage per capita income is<br>00,000s.|
|---|---|
|on Public P|quality<br>ean = 0)<br>(6)<br>-0.240<br>(0.052)**<br>1.074<br>(0.051)**<br>-0.002<br>(0.114)<br>0.062<br>(0.041)<br>-0.277<br>(0.109)*<br>-0.131<br>(0.278)<br>1.057<br>(0.484)*<br>51701<br>4685<br>ariates using <br>ipality level. <br> per capita re<br>enrollment, an<br>e mean of av<br>measured in 1|
|of Revenue|Teacher<br>index (m<br>(5)<br>-0.209<br>(0.058)**<br>0.329<br>(0.019)**<br>-0.152<br>(0.131)<br>-3.453<br>(0.231)**<br>-0.578<br>(0.173)**<br>-1.173<br>(0.403)**<br>2.146<br>(0.734)**<br>51701<br>4685<br>ith these cov<br> at the munic<br> 1997 levels of<br>pre-primary<br>nue is 9.61, th<br>Population is|
|Table 9: IV Results, Showing How Income and Inequality Moderate the Efect|Dependent Variable:<br>Pre-primary students per<br>Fraction PP teachers with post-<br>PP teacher (mean = 19.2)<br>secondary educ. (mean = 0.22)<br>(1)<br>(2)<br>(3)<br>(4)<br>Revenue<br>1.498<br>1.693<br>-0.034<br>-0.036<br>(0.394)**<br>(0.368)**<br>(0.011)**<br>(0.011)**<br>Revenue x average income<br>-2.051<br>0.024<br>(0.125)**<br>(0.004)**<br>Revenue x median income<br>-6.672<br>0.079<br>(0.367)**<br>(0.010)**<br>Revenue x gini<br>0.422<br>-0.441<br>0.059<br>0.068<br>(0.890)<br>(0.803)<br>(0.023)*<br>(0.023)**<br>Average income<br>22.295<br>0.158<br>-0.245<br>0.007<br>(1.498)**<br>(0.281)<br>(0.043)**<br>(0.009)<br>Population<br>3.993<br>2.158<br>-0.043<br>-0.021<br>(1.383)**<br>(0.993)*<br>(0.016)**<br>(0.013)+<br>0-6 population<br>15.532<br>9.042<br>0.088<br>0.164<br>(4.055)**<br>(3.133)**<br>(0.078)<br>(0.082)*<br>7-15 population<br>-16.360<br>-9.671<br>0.149<br>0.070<br>(6.554)*<br>(4.924)*<br>(0.092)<br>(0.090)<br>Observations<br>51942<br>51942<br>52546<br>52546<br>Municipalities<br>4691<br>4691<br>4708<br>4708<br>_Notes:_ An observation is a municipality - year. I instrument for revenue and its interaction w<br>interaction with the same covariates. Robust standard errors are in parentheses, and clustered <br>municipality and year fxed efects, as well as a linear time trend interacted with the pre-reform, <br>revenue, the fraction of public primary school students in state or federal (non-municipal) schools,<br>squared. ** indicates p_<_.01; * indicates p_<_.05; + indicates p_<_.10. The mean of per capita reve<br>0.64, the mean of 2000 median per capita income is 0.22, and the mean Gini coefcient is 0.39. <br>_Sources:_ Author’s calculations based on data from IBGE, INEP, and Tesouro Nacional.|

47
there is an unaubiguous increase in indoor pauperism (Maclinnon, 1986, p.333). As we would expect, recessions are bad for the poor but we should not confuse fluctuations with long term growtb and industrialization, we will have more to say about macro instability below.

# Rp ional Variation in PovertZ: Ingdustrialization and Urbanization

Can we get a better impression of the aggregate effect of long term economic developsent on the poor by comparing pauper rates in different regions? Since developmen **t** ook the form of industrialization, it might be useful to compare agricultural with iudustrial counties.

Table 10 reports pauperism rates for industrial and agricultural counties for 1802-3, in the early stages of the British industrial revolution. when London is excluded, the last two rows of the table suggest that there was no obvious difference in the generosity of relief policy (as measured by the proportion of paupers forced into the workhouse) between agricultural and industrial counties. Bowever, despite the bigh agricultural prices at that time caused by the Napoleonic war, there vas a far higher proportion on relief in the agricultural countries. This suggests either that poverty was more widespread in agricultural areas or that it was more widespread in those areas (the South) most distant from new industrial job opportunities. The latter seems more likely given the favorable prices facing agriculture during the war.

Unfortunately, this parliamentary return omits most nortbern agricultural counties so it is hard to distinguish whether it is agriculture uer Le or location that is driving the high pauper rates. But the data we have 43
Policy Research Working Paper 8042

# **Abstract**

This paper is a study of the effect of Brazil’s staggered Internet rollout between 2000 and 2014 on municipality employment and wages. The study uses a new, annual data set on Internet availability from the Brazil school census, with the assumption that the share of schools that have Internet access in each municipality reflects the general accessibility of Internet connections. These data are combined with Brazil’s rich, matched employer-employee survey, - which contains annual occupation and wage earnings infor mation for all formally-employed workers in Brazil across all sectors, including primary, secondary, and tertiary industry groups. Contemporaneous and lagged effects are considered. The analysis finds that increased Internet access has no statistically significant net effect on aggregate employment, and has a negative effect on average wages, with a reduction in measures of wage dispersion. Brazil’s Internet rollout results in employment shifts from sectors with more limited expansion opportunities (wholesale and retail trade, public administration, and largely publicly-owned utilities, which jointly comprise almost half of the formal workforce in 2010) to sectors with more output expansion opportunities. The employment effects are positive and most pronounced in the manufacturing, transport and storage, finance and insurance, and hospitality industry groups. In the manufacturing sector, Internet access induces positive employment and wage effects in medium- and high-skill occupations.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mdutz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
||_εi,d_ (model 1), where _Dratioi,d_ is excess<br>xcess demand by all investors in the same<br>or each group of Active Share. Models 2,<br> (calculated from the S&P500 index).|Number<br>Number of<br><br>Lag U.S. returns<br>of Funds<br>Observations<br>-.02<br>190<br>20,863<br>(0.9)<br>0.01<br>185<br>18,108<br>(0.43)<br>-0.01<br>198<br>15,105<br>(0.65)<br>0.03<br>185<br>11,768<br>(1.58)<br>-0.01<br>247<br>107,748<br>(1.23)<br>-0.01<br>813<br>135,573<br>(1.51)<br>-0.01<br>591<br>62,024<br>(1.22)<br>-.01<br>357<br>29,780<br>(1.15)|
|---|---|---|
|**style**|_iDratio−i_<br>_d_ +<br>_o−i_<br>_d_<br>is the e<br>egressions f<br>ock returns|(4)<br>Lag Returns<br>0.04**<br>(2.05)<br>0.03<br>(1.52)<br>0.04**<br>(2.2)<br>-0.02<br>-0.82<br>-0.02**<br>(2.2)<br>-0.04***<br>(5.25)<br>-0.04***<br>(3.81)<br>-0.02**<br>(2.05)|
|**investor **|_,d_ =_αi_+_β_<br> and_Drati_<br>me series r<br> lag U.S. st|_Dratio−i_<br>0.13***<br>(6.61)<br>0.05***<br>(2.78)<br>0.04***<br>(2.82)<br>-0.01<br>-0.81<br>0.02***<br>(3.23)<br>-0.03***<br>(5.34)<br>0.01<br>(1.6)<br>0.00<br>(0.38)|
|**Table 13**<br>**Correlated demands by**|he linear regression _Dratioi_<br>_tioi,d_ =<br>�_S_<br>_s_=1_buysi,s,d−sellsi,s,d_<br>�_S_<br>_s_=1_buysi,s,d_+_sellsi,s,d_ <br> averaged across investor ti<br>olombian stock market and|(3)<br>_Dratio−i_<br>Lag U.S. returns<br>0.13***<br>0.00<br>(7.22)<br>(0.03)<br>0.04**<br>0.03<br>(2.19)<br>(1.17)<br>0.04***<br>0.01<br>(2.77)<br>-0.7<br>-0.01<br>0.03<br>-0.82<br>(1.52)<br>0.02***<br>-0.02**<br>(2.84)<br>(2.47)<br>-0.03***<br>-0.02***<br>(5.05)<br>(3.51)<br>0.01*<br>-0.03***<br>(1.93)<br>(3.48)<br>0.00<br>-0.02<br>(0.5)<br>(1.44)|
||efcients of t<br>fned as_Dra_<br>efcients are<br>ns for the C|(2)<br>Lag Returns<br>0.03**<br>(2.17)<br>0.02<br>(1.24)<br>0.04**<br>(2.27)<br>0.00<br>(0.19)<br>-0.03***<br>(2.86)<br>-0.04***<br>(6.37)<br>-0.04***<br>(5.24)<br>-0.03***<br>(2.68)|
||mated co<br>ch day de<br>ment. Co<br>rket retur|_Dratio−i_<br>0.13***<br>(7.02)<br>0.03***<br>(2.62)<br>0.04***<br>(2.7)<br>-0.01<br>-0.56<br>0.02***<br>(2.9)<br>-0.03***<br>(5.04)<br>0.01*<br>(1.68)<br>0.00<br>(0.25)|
||sents esti<br>nd_i_on ea<br>e manage<br>de lag ma|(1)<br>_Dratio−i_<br>ors<br>0.14***<br>(7.87)<br>0.03*<br>(1.9)<br>0.04***<br>(2.89)<br>-0.01<br>(0.71)<br>stors<br>0.02**<br>(2.46)<br>-0.03***<br>(4.99)<br>0.02**<br>(2.55)<br>0.00<br>(0.31)|
||The table pre<br>demand of fu<br>group of activ<br>3, and 4 inclu|Foreign Invest<br>Group 1<br>Group 2<br>Group 3<br>Group 4<br>Domestic Inve<br>Group 1<br>Group 2<br>Group 3<br>Group 4|

38
# **Annex 4: Bibliography**

# **_Articles_**

Asunka, Joseph. “What People Want from Government: Basic Services Performance Ratings, 34 Countries,” AfroBarometer Network (2013).

Awortwi, N. and Nuvunga, A. (2019) Sound of One Hand Clapping: Information Disclosure for Social and Political Action for Accountability in Extractive Governance in Mozambique, IDS Working Paper 523, Brighton: IDS Ballivian, Amparo, and William Hoffman. “Public-Private Partnerships for Data: Issues Paper for Data Revolution Consultation,” World Bank & World Economic Forum (January 15, 2015).

Barr, Abigail, and Lawrence Bategeka (eds.). “Management and Motivation in Ugandan Primary Schools: An Impact Evaluation Report,” Working Paper 2012–14 (December 2012).

Barr, Abigail, Frederick Mugisha, Pieter Serneels, and Andrew Zeitlin. “Information and Collective Action in Community-Based Monitoring of Schools: Field and Lab Experimental Evidence from Uganda” Working Paper (2012).

Bjorkman, M., and J. Svensson. “Power to the People: Evidence from a Randomized Field Experiment on Community-Based Monitoring in Uganda,” _The Quarterly Journal of Economics_ (2009): 735–69.

Bjorkman, M., D. de Walque, and J. Svensson. “Information Is Power: Experimental Evidence on the LongRun Impact of Community-Based Monitoring,” Policy Research Working Paper 7015, World Bank (2014).

Calland, R. “Background Paper on Freedom of Information: Review of Impact and Effectiveness of Transparency and Accountability Initiatives,” Institute of Development Studies, Brighton, UK (2010).

Carothers, T., and S. Brechenmacher. “Accountability, Transparency, Participation, and Inclusion: A New Development Consensus?” Carnegie Endowment for International Peace (2014).

Carruthers, Celeste K. and Marianne H. Wanamaker. 2015. "Municipal Housekeeping: The Impact of Women's Suffrage on Public Education," _Journal of Human Resources_ 50 (4) (2015): 837-72.

Collier, David. "Understanding Process Tracing," _PS: Political Science and Politics_ 44: 4 (2011): 823–30.

Dahl, Robert. “Can international organizations be democratic? A skeptic's view.” in I. Shapiro & C. HackerCordón (Eds.), _Democracy's Edges_ . Cambridge: Cambridge University Press (1999).

Davies, T. “Open Data in Developing Countries: Emerging Insights from Phase 1,” World Wide Web Foundation (2014).

Devarajan, Shantayanan, Stuti Khemani, and Michael Walton. 2011. Civil Society, Public Action and Accountability in Africa. HKS Faculty Research Working Paper Series RWP11-036, John F. Kennedy School of Government, Harvard University.

Devarajan, Shantayanan, and Khemani, Stuti. 2016. If politics is the problem, how can external actors be part of the solution ? (English). Policy Research working paper; no. WPS 7761. Washington, D.C. : World Bank Group.

41
to dominate. While we leave a of these uncongested mobility appears deeper investigation results for future research, we emphasize that they are inconsistent with typical claims that rapid urban population growth in developing countries is necessarily associated with worse worsen with but this effect is more mobility. Congestion may population growth negative than offset by faster roads.

in column 8 we no consider income but instead introduce two measures Finally, longer for the share of population with access to a car (or equivalent) and (separately) a motorcycle. The insignificant positive coefficient for cars in explaining mobility in table 10 results from two offsetting effects where more cars are strongly and positively associated with both un11. congested mobility and congestion in table Motorcycles are associated with faster travel via less congestion, consistent with them taking up less room than cars, but inconsistent with them a to causal is our here being response congestion. Again, identification beyond scope but we would like to highlight that standard indicators of urban economic development such as higher incomes, faster population growth, and more cars are generally associated with better mobility outcomes despite higher congestion.

Although our findings above are generally stable across a wide variety of specifications, they may be subject to bias due to omitted city-level variables. In results reported in Appendix D, we control for city fixed effects, using within-city variation in population, area, and roads, at the level of concentric rings (0 to 2 kilometers from the center, 2 to 5, 5 to 10, 10 to 15, and 15 and beyond) to gain further insights about variation in mobility. Within cities, rings with more population and less urban area are slower, just as in the across-city results above.

# **7. Transit and walking**

While roughly half the households in the average city in our data have access to a private vehicle – sometimes a car but more often a motorcycle – we recognize that city dwellers in India also often walk and use transit. To investigate these two alternative modes of travel, we also collected travel time data for walking and transit for all our trip instances.

For walking trips, speeds typically do not vary much across our trips and remain constant within trip. Mean walking speed is 4.8 kilometers/hour with a standard deviation of 0.1 kilometers/hour. We first estimate a city effect for walking trips in the same spirit as our baseline mobility index above. The standard deviation for the city effects is unsurprisingly 34
<!-- Start of picture text --> Pinto Curicó Caldera<br>Families: 1215 and R2: 0.60 Families: 7549 and R2: 0.38 Families: 2612 and R2: 0.41<br>395 420 445 470 495 419 444 469 494 519 454 479 504 529 554<br>CAS CAS CAS<br>Antofagasta Lonquimay Lo Prado<br>Families: 34238 and R2: 0.00 Families: 2213 and R2: 0.01 Families: 15195 and R2: 0.11<br>435 460 485 510 535 390 415 440 465 432 457 482 507 532<br>CAS CAS CAS<br>1 1 1<br>.75 .75 .75<br>.5 .5 .5<br>Entry in 2002.25 Entry in 2002.25 Entry in 2002.25<br>0 0 0<br>1 1 1<br>.75 .75 .75<br>.5 .5 .5<br>Entry in 2002.25 Entry in 2002.25 Entry in 2002.25<br>0 0 0<br><!-- End of picture text --> Figure 1: Participation in CS and _effective cutoff_ and _official cutoff_ in illustrative municipalities (2002).

Note: The dots on the graphs are the proportion of families in municipality entering in CS in each year (see y-axis) by intervals of CAS score of 4 points in distribution of CAS in 2002 in each municipality. The solid vertical line represents the point identified as _effective cutoff_ and the dashed line is the _official cutoff_ . Where only one line is shown, the two coincide. The _R_<sup>2</sup> in the top of each graph is the _R_<sup>2</sup> for the cutoff score of CAS that maximizes the _R_<sup>2</sup> of equation (2) - the _effective cutoff_ . These figures are zoomed around the _effective cutoff_ , so that only families at most 40-points apart from it are depicted (the CAS score varies between 380 and 770 points).

of the program computed as described in expression (3) (for very small ε): Families just above and just below the cutoff differ in their eligibility to CS, but they are likely to be similar in all other (observable and unobservable) dimensions.<sup>22</sup> We also restrict the sample to those families whose CAS is near the cutoff for the program, since

> 22See table A.4 in Appendix A for the main correlates of participation in CS. This table uses only the sample of families who were eligible according to the official cutoff and information for the first time observation in the Ficha CAS. Families who are selected to CS are more likely to be connected to the welfare system. Within municipality, families who are selected to CS are less likely to have adequate walls or ceilings in their homes, less likely to be legal occupants of their home, and more likely to have a connection to the sewage network. Participant families have lower CAS scores than non participants, but heads and spouses in participant families are more likely to be working than those in non-participating families. Selected families are more likely to have younger heads, married heads, female heads, and children. They are less likely to belong to the dominant ethnicity in the neighborhood, and to live in urban areas (typically, more accessible).

13
# **1. Introduction**

The story of how the Republic of Korea leveraged its growth strategy to begin closing the income gap with OECD countries and in less than three decades transformed itself into an innovation-grade leader is well known.<sup>2</sup> The catching-up game is also being pursued by many other emerging market economies such as Brazil, India, and China (the BICs), South Africa, Chile, and Russia. Interested readers are invariably drawn to two headlines with important implications for public policy. Are trade liberalization and global integration a hindrance or a help in the process of catching up with high-income economies? And why is it that after implementing trade reforms, only a handful of countries is indeed catching up?

Developing countries have good reasons to study the South Korean growth experience. It is true that even though income levels doubled (in 2000 constant US dollars) in South Korea, Brazil, and other middle-income countries between 1965 and 1980, a distinct divide persisted between this group and the OECD countries (figure 1). Around 1981, however, in a break from its steady growth trajectory, the South Korean economy experienced a growth-acceleration and began catching up with the high-income non-OECD countries. By 2007, its income per capita was $19,690 in nominal terms. In comparison, Brazil’s income per capita was $5,910.

China’s tale of catch up is equally impressive. Around the mid-1980s, when its per capita income was still below $200 (2000 constant US dollars), China began closing the gap with India (figure 2). By 2007, its per capita income had risen to $2,360 while India’s income per capita was only $950 in nominal terms. In sum, starting at levels of income per capita typical of a low-income country in the 1950s, Korea was already a middle-income country by the early 1980s, joined OECD in 1996, and has been catching up with the OECD average income per capita – in spite of the temporary setback associated with the Asian financial crisis (1997-98). At the same time, the income gap between Korea and the BICs has widened.

> 2  The denominations South Korea and Republic of Korea are used interchangeably in this text.  For details about the Korean growth strategy, see, for example, Commission on Growth and Development (2008), Suh and Chen (2007) and World Bank (1998).

2
children. Ferreira and Schady (2009) propose a conceptual framework in which an increase in infant/child mortality is driven by increased food insecurity and reduced access to (or demand for) essential health services (including preventive services such as vaccination), and review the available literature. Baird et al. (2011) document that indeed, downward deviations in GDP from trend increase child mortality in a sample of 1.7 million births in 59 countries. The food security/nutrition channel does appear to be important - not only is child mortality counter-cyclical, but so is child wasting and other measured nutritional outcomes (Headey and Ruel, 2020).

There is an emerging body of empirical evidence that food insecurity and access to healthcare services have worsened during this pandemic period (e.g. Egger et al., 2021; Shapira et al., 2021b). Osendarp et al. (2021), for example, project between 47,000 to 283,000 additional child deaths in 2020-2022 due to increases in child wasting and declines in nutrition intervention coverage in 118 low- and middle-income countries.

The survival of other vulnerable groups could also be impacted by income shocks. Unfortunately, there is very little existing evidence on economic contractions and mortality in non-child populations. Cutler et al. (2002) show higher elderly mortality during economic crises in Mexico, Khvan et al. (2020) show a similar relation in Russia, and Hone et al. (2019) find a generalized increase in adult mortality in Brazil.<sup>7</sup> We are not aware of a data source that would allow us to estimate the impact of economic downturns on elderly mortality in a systematic way across countries in the same way we estimated it for children. We rely on standardized Demographic and Health Surveys for child health measures as vital statistics in low- and lower-middle-income countries are unreliable.

Allowing for a mortality increase among non-children due to lockdowns would only increase the ratio between the non-COVID-19 deaths caused by the lockdowns and the number of COVID-19 deaths averted, conditional on a given lockdown severity. However, we would expect the overall impact via the elderly mortality rates to be much smaller. One reason is that, as we highlight below, adults above 60 account for a small proportion of the population in developing countries.

> 7These three studies are from middle-income countries with relatively reliable vital statistics data, a rarity in lower income countries. These results are of course in contrast to the literature from wealthy countries that finds recessions to lower mortality rates at adult ages.

16