rafmacalaba/datause-encoder-data huggingface dataset:
1961-1966 cohorts to the control group and the treatment group respectively. Our hypothesis is that we should not see any statistically significantly different results between these two groups. Estimation results provided in Table 4, Panel A indeed support this hypothesis where all five measures of educational attainment—for both measures of exposure to the ME program—show statistically insignificant impacts. Another falsification test where we assign the cohorts of 19361939 to the treatment group, keeping the control group the same as in Table 2 (i.e., cohorts 19241935 and 1950-1961), provides qualitatively similar results (Panel A in Table 1.6, Appendix 1).<sup>22</sup> Second, instead of grouping together as the control group for all those who were born during the pre-treatment period 1924-1935 and the post-treatment period 1950-1961, we break this control group into four different and smaller control groups that are composed of those who were born in the periods 1924-1929, 1930-1935, 1950-1955, and 1956-1961. Although the estimation sample sizes were strongly reduced, estimates remain strongly statistically significant for this indicator of education achievement, and other indicators as well (Table 1.3, Appendix 1).<sup>23</sup> Furthermore, we keep fixed the treatment group, and vary the control group in various ways, by either restricting it to pre-war or post-war periods or different combinations of smaller birth cohorts. Figure 3 plots the estimation results and their 95 percent confidence intervals for the years of school variable and shows that estimation results are still qualitatively similar.

Finally, following Duflo (2001) we estimate a more general version of Equation (7)

> 22 Another falsification test (not shown), where we assign the cohorts of 1949-1954 and the cohorts of 1955-1960 to the control group and the treatment group respectively, provides qualitatively similar results. Some of the coefficients are statistically significant however they provide the wrong and negative signs. Those tests suggest that our findings are strong and the difference-in-difference method works well.

> 23 We provide estimates for female cohorts only in this table, since estimates are not statistically significant for all male cohorts.

23
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 9 Productivity We use the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS) nighttime lights (NTL) data to outline urban areas. The NTL data report the recorded intensity of Earth’s surface lights. Nighttime lights products have high correlation to human activities (Hsu, Baugh, Ghosh, Zhizhin, & Elvidge, 2015), and have been previously used for regional and global analysis of urbanization (Cheng et al., 2016; Pandey, Joshi, & Seto, 2013; Sutton, Cova, & Elvidge, 2006; Zhang & Seto, 2011; N. Zhou, Hubacek, & Roberts, 2015; Y. Zhou et al., 2015), population modeling (Anderson, Tuttle, Powell, & Sutton, 2010; Lo, 2001), and economic performance (Cao, Wu, Kuang, Huang, & Wang, 2016; Forbes, 2013).

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (RC) product. For this work we use the latter, given that such data include a correction for the saturation issue likely to be an important issue for most large cities in the region. Further, previous work has suggested that the RC information provides a better proxy for socioeconomic variables than the stable products (Hsu et al., 2015; Ma, Wu, Li, Peng, & Liu, 2014). Radiance-calibrated yearly composites for 1996, 2000 and 2010 obtained from NOAA National Centers for Environmental Information were used to delineate urban extents. These composites have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because the scattering of lights in the atmosphere (Wu, Ma, Li, Peng, & Liu, 2014). A novel deblurring process was applied to address the issue of over glow in the radiance-calibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams, Lozano-Garcia and Oram, 2016).

# _4.2.2 Calibration and interannual correction of DMSP-OLS RC NTL images_

We use the deblurred DSMP-OLS RC yearly composites for the years 1996, 2000 and 2010 to delineate urban extents in Latin America. DMSP-OLS RC NTL from different years
default of financial institutions is currently associated with a 120‐basis points increase in the EMBI spread.

To minimize the adverse effects of the sovereign‐bank nexus, our results imply that policy makers should focus their efforts on: First, _maintaining fiscal and bank buffers_ which reduces both the probability and severity of stress emanating from each sector. The measures proposed in the Basel III capital and liquidity framework aim to strengthen bank buffers and many EMDEs aim to adopt them, notwithstanding various implementation challenges. Second, _strengthening surveillance and supervision_ to identify and mitigate risks in the banking system, since bank stress often emanates from rapid credit growth, weak underwriting standards, and regulatory forbearance. Third, _improving transparency and data quality of bank‐sovereign linkages and contingent liabilities_ not only for surveillance and prudential purposes, but also since during times of stress, investors may assume the worst and indiscriminately act with excessive risk aversion which may amplify stress. Fourth, _addressing the sovereign exposures channel_ by considering measures to deal with the preferential regulatory treatment of sovereign exposures, the home bias of bank portfolios, and financial repression and shallow financial sector development in some EMDEs which creates a captive demand for sovereign debt which may weaken fiscal discipline. Fifth, _addressing the government support channel_ by considering i) ex ante measures to reduce moral hazard and the probability of a crisis and ii) ex post measures to reduce the severity of a crisis and protect tax payers such as proposed in the G20 “too big to fail” reform agenda and the FSB’s _Key Attributes_ for effective resolution frameworks. Again, EMDEs face various implementation challenges. And lastly, _considering policy trade‐offs carefully_ to avoid an unintended tightening of one of the channels which could generate deeper spillovers **.** For example, to weaken the sovereign exposure channel, policy makers may impose restrictions on sovereign debt holdings. However, poorly calibrated risk weights could prove to be pro‐cyclical. As another example, to weaken the government support channel, policy makers may try to adopt explicit measures that rule out any support. This could trigger unintended contagion that leads to a deeper crisis with larger social losses.

The Basel III capital and liquidity accords, the Basel Committee’s review of the regulatory treatment of sovereign exposures (Box 6), the G20 “too big to fail” reforms, the Financial Stability 3
changes that are correlated with project characteristics. With rich enough **X** st, and assuming that systematic differences across the two states remain fixed from 2007 to 2009, ordinary least squares estimation of [1] will deliver causal estimates of the reform’s impact on works procurement outcomes.

# _4.2 Data description_

As mentioned throughout the paper, we utilize works procurement data from two water and sewage utilities operating in Brazil: SABESP, a company that serves most of the São Paulo state market, and COPASA, the largest water utility in the state of Minas Gerais.

SABESP and COPASA are similar water and sewage operators in many respects. Both companies are publicly traded and majority state-owned (and therefore subject to public procurement laws). Each provides water to at least 10 million people and had 2009 revenues in excess of $1 billion.<sup>10</sup> Importantly from a works procurement perspective, both companies have announced sizable capital expenditures for the next several years. For 2009-2010 alone, the combined capital expenditures of both companies will stand at about $3 billion ($2 billion by SABESP, $1 billion by COPASA). They also operate in similar business and socioeconomic environments within their states, as São Paulo and Minas Gerais (a) have consistently ranked among the 10 Brazilian states with the highest per capita income over the past several years, and (b) register a Human Development Index score, as measured by the UN, that is markedly higher than the national average.

The data gathered comprise the full universe of procurement processes for works carried out by each company in 2007 and 2009 under both the _tomada de preços_ and _concorrência_ invitation mechanisms (i.e., the largest projects by size). For each bidding process, we observe data on the type of work being bid on (which makes possible segregating projects by their nature or segment), the duration (in days) of the bidding process (from the opening of the first envelope to the award of contract), the presence (if any) and duration (in days) of legal complaints filed by bidders during the bidding process, the estimated project cost (in R$) provided by each company to the bidders during the bid preparation period, and the actual contract amount (in R$), which is the end result of the procurement process. Note that the period of study purposely excludes 2008, as this was the year that São Paulo state introduced sub-national legislation allowing phase inversion for the procurement of works.

More specifically, data collected from SABESP are organized in two separate datasets: the first describing all bidding processes, the second containing data specifically related to those processes impacted by procurement complaints. Table 1 describes the fields contained in each SABESP dataset. For COPASA, a single dataset was collected, whose detailed contents are described in Table 2.

> 10 Specifically, SABESP provides water to 24 million people and generated 2009 revenues of $4.6 billion; COPASA serves a population of some 13 million, with 2009 revenues of $1.2 billion.

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# **Figure 5:  Entry and Density Rates versus Ease of Doing Business Rankings, by Country, Average 2003-2005**

<!-- Start of picture text --> Entry rate Density rate (percent)<br>20<br>20%<br>15<br>15%<br>10<br>10%<br>5<br>5%<br>0<br>0%<br>0 20 40 60 80 100 120 140 160 180<br>0 20 40 60 80 100 120 140 160 180<br>Ease of Doing Business Ranking Ease of Doing Business Raking<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

More specifically, barriers to starting a business are significantly and negatively correlated with business density and the entry rate. For example, the fewer the procedures required to start a business, the greater the number of registered firms – and the higher the entry rate (Figure 6). There is also a significant relationship between the cost of starting a business (as a percentage of gross national income, or GNI) and business density and the entry rate (not shown). For example, for every 10 percentage point decrease in entry costs, density and the entry rate increase by about 1 percentage point.<sup>19</sup>

# **Figure 6: Business Creation and the Number of Procedures to Start a Business, by Country, Average 2003-2005**

<!-- Start of picture text --> Entry rate Business Density (percent)<br>15<br>20%<br>15%<br>10<br>10%<br>5<br>5%<br>0% 0<br>0 3 6 9 12 15 18 0 3 6 9 12 15 18<br>Procedures to start a business Procedures to start a business<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

19 Countries with entry costs greater than 40 percent of GNI per capita are excluded.

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**Table 4**

**Social Network Effects Among Indigenous Females 20-65 years of age**

|**Dependent variable**||**Rural areas**|**Semi-Urban**<br>**areas**|**Urban areas**|
|---|---|---|---|---|
|**Empleado/obrero**||0.006|**-0.178**|**0.089**|
||t-value|(0.14)|**(-2.27)****|**(2.01)****|
||Adjusted R-squared|0.3921|0.3921|0.2706|
||Number of observations|46,153|13,686|12,871|
|**Jornalero/Peon**||0.041|0.029|0.005|
||t-value|(1.23)|(0.59)|(0.35)|
||Adjusted R-squared|0.2560|0.2560|0.2658|
||Number of observations|46,153|13,686|12,871|
|**Self employed**||0.025|-0.034|0.008|
||t-value|(0.71)|(-0.55)|(0.21)|
||Adjusted R-squared|0.2253|0.2527|0.2228|
||Number of observations|46,153|13,686|12,871|
|**Is occupied in Domestic**|**Services (occupation**||||
|**code=820)**||**0.454**|-0.306|-0.001|
||t-value|**(2.22)****|(-0.93)|(-0.81)|
||Adjusted R-squared|0.2326|0.2162|0.3421|
||Number of observations|46,153|13,686|12,871|
|**Sector of Employment is**|**Agriculture (code=11)**|**0.060**|-0.015|-0.010|
||t-value|**(2.19)*****|(-0.46)|(-1.60)|
||Adjusted R-squared|0.4448|0.4532|0.2750|
||Number of observations|46,153|13,686|12,871|
|**Sector of Employment is**|**Manufacturing (code=31-33)**|**0.130**|**0.212**|**0.130**|
||t-value|**(2.15)****|**(2.44)****|**(2.91)*****|
||Adjusted R-squared|0.3644|0.3317|0.1490|
||Numberofobservations|46,153|13,686|12,871|

Source: Authors’ estimates of the social network effect based on the 2000 Population and Housing Census (10% sample) Notes:

- t-values based on robust standard errors corrected unknown forms of heteroskedasticity

- * p-value <0.10

- ** p-value<0.05

- *** p-value<0.01

The empirical evidence presented so far sheds little light on the determinants of the strength of social network effects. We investigate this issue in more detail by examining how the 16
spending is procyclical and falls with the realization of negative macroeconomic shocks, perhaps due to an inability of governments to borrow during bad times, then OLS estimates of the multiplier would be biased upwards. A further possibility is that, in aid-dependent countries such as many of those studied here, any procyclical tendencies in domestically-financed government spending are offset by countercyclical tendencies in aid-financed government spending, so that total spending could be either procyclical or countercyclical.

I address this endogeneity problem by constructing an instrument based on the lags between commitment and eventual disbursements on loans by official creditors to developing country governments. Some institutional background is helpful in order to better understand this identification strategy. My dataset, described in more detail in Section 3, covers loans from multilateral and bilateral official creditors to developing country sovereign borrowers. Table 1 provides some summary statistics on the lending activities of official creditors included in my dataset. I first report total disbursements, disaggregated by major multilateral and bilateral creditors, for the decades of the 1970s, 1980s, 1990s, and 2000s. Disbursements on these loans are substantial, totaling nearly $1.8 trillion in constant 2005 prices over the past 40 years. Over time, the importance of multilateral creditors relative to bilateral creditors has increased substantially, with the share of the former increasing from about one-third in the 1970s to nearly three-quarters in the 2000s. This reflects a steady shift on the part of most bilateral creditors over the past 40 years to providing aid in the form of grants (which are not reflected in the DRS database), rather than loans (which are).

These loans are a traditional vehicle for aid donors to provide financial assistance to developing country governments. Consistent with this objective, the loans in this dataset are highly concessional on average, typically with long grace and repayment periods, as well as below-market interest rates. The bottom panel of Table 1 shows that, on a loan-weighted average basis, these loans have a maturity between 20 and 25 years, and an initial grace period (during which no payment is required) of approximately 6 years. The interest rates on these loans are also highly concessional, with nominal spreads over 20-year US Treasury Bill rates between approximately -2% and -5%. These simple spreads probably understate the concessional value of the loans to many recipient countries, given that the market rates they would otherwise face on international borrowing from private creditors are likely to be much higher, if not prohibitive.

8
_institutions or the quality of infrastructure.”_ The precise questions about the regulatory environment assessed by each of these sources can also be found in Table 1.

We emphasize that our choice of outcome variables for the GII and DB datasets is not intended to be exclusive in any sense, but rather is purely illustrative. These outcomes are surely not the only ones that policymakers might want to influence by reforms to the policies and institutions captured by GII and DB. Rather, we think these particular outcomes might plausibly be among the many considered by policymakers, and provide a good illustration of the challenges of identifying the partial effects of the many disaggregated indicators making up these datasets. We recognize also that in the case of GII, the objective of the GII is broader than simply measuring corruption, but rather seeks to document the disaggregated ingredients of a wide range of transparency and accountability mechanisms.<sup>11</sup> Nevertheless, one can readily rationalize looking at narrower measures of corruption as a relevant outcome variable for GII by noting that corruption can be viewed as a symptom of the failure of such transparency and accountability measures, and so would be a reasonable proxy outcome for policymakers to consider.

Before turning to the formal analysis of the links between disaggregated indicators and outcomes, we document two important features of the data. The first is that both the overall aggregate GII and DB measures are in fact strongly correlated with each of the outcome variables. We show this in Table 2 , which summarizes the results of regressing each of the outcome variables on its corresponding aggregate GII or DB measure, both unconditionally (in the top panel), and conditionally controlling for log per capita GDP (in the bottom panel). A unit increase in the overall measure of GII will increase a measure of each outcome variables by 0.8 on average statistically significantly. Conditioning on per capita GDP (in logs) only slightly reduce the effects and significance of the overall GII measure on each of the 7 outcome variables. The effects of the overall DB measure on each of the 7 outcome variables are more significant and greater in size, both unconditionally and conditioning on GDP per capita. Of course, we cannot interpret these correlations in Table 2 as purely reflecting a causal effect from the DB and GII indicators to the outcomes of interest – there are many potentially confounding omitted

> 11 We note that Global Integrity does place a disclaimer on its website to the effect that the GII do not measure corruption: _“...it is worth emphasizing that the Integrity Indicators do not measure corruption but rather assess its opposite, that is, anti‐corruption and good governance institutions, mechanisms, and practices. While corruption and bribery are difficult if not impossible phenomena to capture empirically, assessing the performance of key integrity‐promoting mechanisms such as civil society, the media, and law enforcement provides a much more concrete access point through which to analyze and monitor government accountability.”_ .

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# Sachs, Jeffery. 1991. “Response: These arguments are false." Transition 2(1).

Savor, M. 1969. “Price Formation in CMEA Intertrade: Problems and Solutions.” In Foeldi and Kiss. Sobell, Vladimir. 1984. The Red Market; Industrial Cooperation and Specialization in COMECON. Hant, England: Aldershot.

-—--. 1980. The CMEA in Crisis: Toward a New European Order. New York: Praeger.

Scakolczai, G., G. Bagdy, and J. Vindics. 1985. “Dependence of the Hungarian Economic Performance on the World Economy." Facts and Economic Policy Inferences Acta Oeconomica 35 (3-4):/295-314. Szamuely, L. 1986."The Strategic Interrelations of Our Economic Cooperation With the CMEA Countries." Kulturpolitika 5:/86-110. (Translation in FBIS, JPRS-EER-038, March 13, 1987, pp. 7-30.) Tolonen, Yrjaenae. 1988. "Some Macroeconomic Consequences of Trade with Centrally Planned Economies." Journal of Comparative Economics 12:/345-61.

Tardos, M. 1969. "A Model of Intra-Regional Foreign Trade." In Foeldi and Kiss (1969).

Van Brabant, Jozef. 1990. Remaking Eastern Europe: On the Political Economy of Transition. Boston: Kluver.

-——-. 1988. Adjustment, Structural Change and Economic Efficiency. New York: Cambridge University Press.

-----, 1985, “Exchange Rates in Eastern Europe; Types, Derivation, and Application." World Bank Staff Paper. No. 778. World Bank, Washington, D.C.

Verny, Sophie. 1988. “The CMEA and the EC; The Problem of Mutual Recognition," Soviet and Eastern European Foreign Trade XXIV (2): 6-25.

Vincse, Imre. 1984. The International Payments and Monetary System in the Integration of the Socialist States. The Hague: M. Nighoff.

Wolf, Thomas. 1988. Foreign Trade in the Centrally Planned Economy. New York: Harwood Academia Publishers.

----. 1987. "A Note on the Conversion of Ruble Trade Flows into Dollars." Journal of Comparative Economic Systems 11: 558-71.

---~-. 1985. "Exchange Rates, Foreign Trade Accounting, and Purchasing Power Parity for Centrally Planned Economies.” World Bank, PRE Working Paper No. 779, Washington, D.C.

Zeman, Karel. 1988. “Involving the European CMEA Members in the International Division of Labor." Soviet and East European Foreign Trade XXIN (2): 74-91.