rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5b: GDP per capita growth with non-financial corporation credit as a share of GDP**

|**Dep. Var:**|OLS|OLS|FE|GMM|2SLS|2SLS|2SLS|
|---|---|---|---|---|---|---|---|
|**growth_gdppc**|(1)|(2)|(3)|(5)|(6)|(7)|(8)|
|nfccredit|0.1402***|0.1537***|0.2465***|0.1511**|1.0213*|1.1371*|0.5856*|
||(0.008)|(0.002)|(0.004)|(0.044)|(0.063)|(0.067)|(0.064)|
|nfccredit<sup>2</sup>|-0.0004**|-0.0003**|-0.0011***|-0.0004**|-0.0052|-0.0059*|-0.003|
||(0.010)|(0.013)|(0.009)|(0.031)|(0.104)|(0.090)|(0.105)|
|fdi|0.1324|0.1301|0.5072**|0.2274|0.1290|0.3211|0.1048|
||(0.398)|(0.341)|(0.026)|(0.114)|(0.466)|(0.240)|(0.588)|
|enrolment|0.0108|0.0301|0.0562|0.0138|-0.0119|-0.0237|-0.0234|
||(0.817)|(0.486)|(0.149)|(0.793)|(0.825)|(0.687)|(0.618)|
|initialgdppc|-7.08***|-6.98***|-18.26***|-6.70***|-10.58***|-10.94***|-8.92***|
||(0.000)|(0.000)|(0.000)|(0.010)|(0.000)|(0.000)|(0.000)|
|initial incomeXnfccredit||||||||
|financialcrisis||-16.08***|-15.36**|-16.02***|-16.58***|||
|||(0.000)|(0.049)|(0.000)|(0.000)|||
|inflation|||||||-0.18|
||||||||(0.198)|
|tradeopen.||||||-0.0313||
|||||||(0.342)||
|constant|23.27***|22.02***|47.07***|23.28***|3.40|1.60|18.11*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.808)|(0.910)|(0.060)|
|# of obs|197|195|195|195|146|146|146|
|R-squared|0.245|0.402||||||
|Prob>F|0.000|0.000|0.000|0.000|0.000|0.001|0.000|
|# of countries|||38|38||||

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

The regressions are re-run using consumption per capita growth as the dependent variable in order to obtain additional insights as to the effect of finance on welfare (Tables 6a and 6b).

19
robustness check, we use the Barro-Lee dataset (available for 48 out of 58 countries in our sample; lagged 2005 values). Using these data, we first compute separately for primary, secondary and tertiary education the total number of years of schooling for women as ratio of the same for total population. Next, we take the average of the three ratios to arrive at the measure of education gap. For women’s education level, we define this as the number of years of schooling among women and averaged over primary, secondary and tertiary education levels.

Supply side effects suggest that ceteris paribus, the proportion of women in the workforce should be higher in countries with proportionately more women in the population. Also, more women in the population may exert greater electoral pressure for non-discrimination and other women friendly laws. The proportion of women population may also proxy for better treatment of women (less female feticide) and hence better job opportunities for women. To raise our confidence against possible spurious correlation, we control for the percentage of women in the total population ( _Women population_ ) taken from WDI, World Bank.

There is some evidence that women in parliament and other law making bodies may benefit the cause of women’s employment by serving as role models for other women and by enacting women friendly laws (Burns et al. 2001, Hallward-Driemeier et al. 2013). It may also reflect cultures that allow women to hold higher status jobs and power. Such cultures are likely to be more favorable towards women’s employment. Furthermore, as referenced in the conceptual framework, one possible reason for gender disparities in employment may be due to preferences. A larger proportion of women in parliament may indicate a preference for women to be engaged in non-household activities including the labor market. Motivated by these factors, we control for the percentage of women in the parliament or lower house of the country ( _Women in Parliament_ ). Data source is Inter-Parliamentary Union (IPU).

11
Finally, we use price data collected by TUIK, in particular the monthly CPI, which includes price information broken down by the 12 main COICOP expenditure groups. The periods of reference available in the data include monthly and annual changes as well as changes relative to December of the preceding year and the 12-month moving average of the change in CPI. In order to capture the inflation episodes that are relevant to each household, all the inflation calculations estimated in this paper are based on 12-month moving averages. This is not possible otherwise for households interviewed in the early months of the year of survey field work using the Decemberto-December price changes rates reported by TUIK.

# **_Methodology_**

The CPI data described in the previous section is used to construct annual inflation figures for each household deriving weighted averages based on budget shares from the consumption baskets in the HBS. Equation 1 formalizes the calculation of household-specific inflation rates as follows: where π represents inflation, _h_ denotes households, _t_ is the survey year, _e_ corresponds to budget shares for each of the 12 COICOP good and services categories, _E_ denotes to total household expenditures and _g_ indexes the 12 good and services groups. Annual price changes for each of the 12 groups of goods and services is published by TUIK. Work by Atuk and Sevinc (2012) presents in detail how subgroup inflation and overall annual inflation are calculated. Once householdspecific inflation rates are constructed, each household is placed into income deciles according to its relative position in the per capita expenditure distribution. Average inflation rates for each decile are calculated as described in Equation 2 using population sample weights in the HBS: where D denotes deciles, n indexes deciles from 1 to 10 and _N_ is the total number of households in each decile. We compute standard errors for decile-specific inflation rates regressing household inflation figures on binary variables that indicate the decile corresponding to each household as follows: 10
|Mercado<br>(Brazil)|(first IPO<br>in 2002)|companies<br>committed to<br>higher<br>corporate<br>governance<br>standards, all<br>sectors|Brazilian Corporations<br>law|Governance) Index,<br>+256%|
|---|---|---|---|---|
|Kosdaq<br>(Korea)|1996|Mostly<br>domestic,<br>SMEs, high-<br>tech and<br>knowledge<br>based<br>ventures|Less stringent than the<br>Korea Stock Exchange|Kosdaq Index,<br>+58%|

In Germany, the _Deutsche Borse AG_ founded the _Neuer Market_ in 1997 in an attempt to enable young and innovative companies to obtain equity capital<sup>54</sup> . In order to gain investors’ trust, the listing requirements in the new market were designed to be higher than those for the “official trading” on the _Deutsche Borse_ . They included the use of at least two designated sponsors, higher equity capital and issue market price requirements, quarterly reports and financial statements that complied with International Accounting Standards or US-GAAP to be published in German and English, and a six-month “lock-up” period following the IPO. These requirements were put in place specifically to address the perceived caution of German investors with regard to investing in stock markets and the absence of an “investment culture.” Despite introducing higher standards, the market objective was to attract listings by firms which had a shorter track record and lower turnover than would be required for listing in the major market segments of the exchange.

The Neuer Market was very successful in its first few years, leading a marked change in German capital markets. Where in the 14 years before the opening of the Neuer Market, the average IPO proceeds amounted to the equivalent of about 2.2 Billion Euro, from 1997 on they mushroomed to reach 25.6 Billion Euro in IPO proceeds in 2000. Significant “underpricing” in the Neuer Market drew more and more investors, not only to the primary market where issue sizes were too small to satisfy the growing demand, but to the secondary market as well. Although institutional investors mostly stayed away from the market, the number of retail investors entering the market grew quickly, from 3.7 million before the Neuer Market began operations to 6.2 million in 2000. During the three years and one month preceding March 2000, the NEMAX-All-Share index which covered the new market increased by about 1636%, outperforming the NASDAQ-100.

However, the index started to fall in 2000, reaching an all time low in October 2002 of about 4% of its March 2000 high. In market capitalization terms, the market fell from €234 Billion in March 2000 to €29 Billion at the end of September 2002. Beginning in 2000, the market was beset by earnings disappointments, followed by

> 54 The description of the Neuer Market’s evolution is largely based on Burghof and Hunger, (2003)

20
Policy Research Working Paper 10308

# **Abstract**

This paper studies the effects of differential exposure to COVID-19 on educational outcomes in Guatemala. The government adopted a warning index (ranging from 0 to 10) to classify municipalities by infection rates in 2020, which was then used by the Ministry of Education in 2021 to establish a “stoplight” system for in-person instruction. Using administrative panel data for all students in Guatemala, the study employs a difference-in-differences strategy that leverages municipal differences over time in the warning index to estimate the effects of the pandemic on dropout, promotion, and school switching. The results show that municipalities with a higher warning index had significantly larger dropout, lower promotion rates, and a greater share of students switching from private to public schools. These effects were more pronounced during the first year of the pandemic. The findings show differential effects by the level of instruction, with greater losses for younger children in initial and primary education. The results are robust to specification choice, multiple hypothesis adjustments, and placebo experiments, suggesting that the pandemic has had heterogeneous consequences.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at a.ham@uniandes.edu.co, evazquez@cedlas.org, and myanezpagans@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
Table 1: Summary of recent studies of catch-up <u>growth</u>

|Study|Context|Outcomes|Metric(s)|Findings|
|---|---|---|---|---|
|M2012|Indonesia|Height|_β_|Incomplete within catch-up|
|O2013|Ethiopia|HAZ|_β_|Incomplete within catch-up|
|S2013|Ethiopia, India, Peru,<br>Vietnam|HAD|_β_|Some within-catch-up,<br>especially before age 5|
|F2014|Ethiopia, India, Peru,<br>Vietnam|HAZ|∆ ̄_y_,_β_|Some within-catch-up, no<br>between catch-up|
|L2014|Brazil, Guatemala,<br>India, Philippines, RSA|HAZ, HAD|∆ ̄_y_|Some between catch-up (HAZ),<br>persistent defcits (HAD)|
|L2015|Ethiopia, India, Peru,<br>Vietnam|HAZ, HAD|∆ ̄_y_|No between catch-up (HAZ &<br>HAD)|
|T2015|Malawi|HAZ, HAD|∆ ̄_y_|Some between catch-up (HAZ),<br>persistent defcits (HAD)|
|H2016|China, South Africa,<br>Nicaragua|HAZ|_β_|Incomplete within catch-up|
|Z2016|Bolivia (Amazon)|Height, HAZ|_β_|Incomplete within catch-up,<br>persistent defcits|
|S2017|Timor-Leste|HAZ, BAZ|∆ ̄_y_|Some between catch-up in<br>height (HAZ), not BMI (BAZ)|

Notes: Studies are classified according to the main parameter on which analysis focuses, which we (re)interpret according to the distinction between within- and between-group convergence; ‘Metrics’ are the parameters and refer to equation (1), where ∆ _y_ ̄ is the unconditional change in the mean sample outcome; HAZ and HAD are height-for-age _z_ -scores and absolute deficits respectively, measured relative to a well-nourished external population; BAZ is the BMI-for-age _z_ -score, also measured relative to a well-nourished external population; RSA denotes South Africa.

Study abbreviations: M2012 = Mani (2012), O2013 = Outes and Porter (2013), S2013 = Schott et al. (2013), F2014 = Fink and Rockers (2014), L2014 = Lundeen et al. (2014b), L2015 = Leroy et al. (2015), T2015 = Teivaanmäki et al. (2015), H2016 = Handa and Peterman (2016), Z2016 = Zhang et al. (2016), S2017 = Spencer et al. (2017).

the first few years of life, where conditions are appropriate. As such, consideration of trends in both height and BMI may provide valuable complementary insights. This is confirmed by Spencer et al. (2017), who find a divergence in trends for HAZ and BAZ scores (BMI-for-age _z_ -scores) in two rural communities in Timor-Leste, calculated with reference to WHO (World Health Organization) charts.<sup>5</sup> Second, the vast majority of studies focus on only one or the other form of catch-up growth. While this is reasonable in specific cases, it means evidence on catch-up growth (broadly defined) is often partial, and little is known about the relationship _between_ the two forms of catch-up. This is important because the forms of catch-up are distinct and bear no necessary (logical) relation to

> 5 The main research goal of Spencer et al. (2017) is to calculate community-specific growth curves. However, their comparison of these curves against WHO standards provides an indication of between-group catch-up.

6
Santos Silva, J. M. C., and Tenreyro, S., (2006), "The Log of Gravity", _Review of Economics and Statistics_ , MIT Press, vol. 88(4), pages 641-658, 09.

Wagner, D., et al, (2002), “Immigration and the Trade of Provinces”, _Scottish Journal of Political Economy_ , Vol. 49, pp. 507-525, 2002.

White, R., (2007b), “An Examination of the Danish Immigrant-Trade Link,” _International Migration_ 45, 5: 62-86 (December).

Winters, L.A., (2003), „The Economic Implications of Liberalizing Mode 4 Trade‟, in A. Mattoo and A. Caarsaniga (Eds) Moving People to Deliver Services, World Bank, Washington D.C.

33
In each household, we monitored PM10 concentrations in the kitchen and living room during the period December, 2003 – February, 2004. For a subsample of households, we also monitored PM2.5 concentrations. We monitored most houses for one day, and a few for two days. Our real-time monitoring instrument is the Thermo Electric Personal DataRAM (pDR-1000) (Thermo Electron, 2004). The pDR-1000 uses a light scattering photometer (nephelometer) to measure airborne particle concentrations.<sup>2</sup> At each of 67 locations, the instrument operated continuously, without intervention, for a 24hour period to record PM10 concentrations at 2-minute intervals.

Our other instrument is the Airmetrics MiniVol Portable Air Sampler (Airmetrics, 2004), a more conventional device that samples ambient air for 24 hours. While the MiniVol is not a reference method sampler, it gives results that closely approximate data from U.S. Federal Reference Method samplers. Our MiniVols were programmed to draw

# **Table 1a:  Sample Composition (Kitchens): Thermo Electric Personal DataRAM**

|**Fuel Type**||**Kitchen Type**||**Construction Mat**|**erial**|
|---|---|---|---|---|---|
|Gas, Electricity,<br>Kerosene|6|Single room dwelling,<br>no separate kitchen<br>Kitchen with a|9|Wall: Thatch|24|
|Firewood, Twigs,<br>Leaves|31|partition (4 walls and<br>a roof)<br>Separate, attached|11|Wall: Tin|22|
|Cow Dung|15|kitchen (4 walls and a<br>roof)|14|Wall: Mud|14|
|Rice Husks, Straw,||Separate, detached||Wall: Brick/ Mud,||
|Jute Sticks,<br>Bagasse, Sawdust|15|kitchen (4 walls and a<br>roof)|31|Roof: Other than<br>Concrete|4|
|||Outside/ open kitchen<br>(0 walls, no roof)|2|Wall: Brick/ Mud,<br>Roof: Concrete|1|
|Total Number of|67|Total Number of|67|Total Number of|65|
|Households||Households||Households||

2 The operative principle is real-time measurement of light scattered by aerosols, integrated over as wide a range of angles as possible.

9
Holmes and Gutiérrez de Piñeres (2006) argue that the drug trade has had negative e¤ects on the economy through: i) higher unemployment rates, (ii) an accelerating increase in the cost of real estate, (iii) a substitution from licit agricultural crops to illicit crops, like coca, (iv) an increase in smuggling that has increased asset laundering in the country, (v) lower investment levels in licit ...rms, (vi) an increase in land concentration in order to produce drugs, and (vii) an increase in long run inequality, among other things. Moreover, drugs have brought much violence to the country, generating forced displacement of people, the creation of illegal guerrillas and the proliferation of paramilitary groups involved in massacres and kidnapping since the 1980s.

Several approaches have been used to understand and estimate asset laundering as well as the shadow/underground activities of the Colombian economy. For example, Schneider and Hametner (2007) use a demand approach to estimate the SE in Colombia between 1976 and 2000. The authors ...nd that the SE grew between 1976-1985 and 1992-2000 and argue that this occurred because of an increase in taxes, higher unemployment and economic crises. They also ...nd a positive relationship between the overall economic growth of the economy and the SE. Arango et al. (2006) uses a latent variable approach to estimate the SE in Colombia using the Kalman Filter and an optimizing algorithm with transition and measure equations. They ...nd that the money supply in the Colombian economy has been a¤ected positively by the SE. Other studies like Amaya and Caballero (2011) try to estimate the aggregate volume of asset laundering in Colombia with basic accounting principles under certain adhoc assumptions.

Our approach combines several of the methodologies used in the literature. We develop a general equilibrium overlapping generations growth model to study the main determinants of asset laundering and the way that this activity a¤ects the capital stock of an economy. On this basis, we build a two linear structural equation macroeconometric model with a transition equation, which speci...es the determinants of asset laundering, and a measure or indicator equation for an observable variable a¤ected by asset laundering, in this case, the capital stock of the economy. This linear system is then taken to the data for the Colombian economy between 1985 and 2013 using two empirical methods: Calibration and Kalman Filter, where asset laundering is understood as a latent variable to be estimated.

The next section builds the conceptual framework on asset laundering to be used in the theoretical model.

# 3 Conceptual Framework on Asset Laundering

In the literature on asset laundering, there appears to be no consensus on the di¤erence between the terms laundered assets and laundered money. In some cases they are considered to be synonymous while in others the former contains the latter. Most de...nitions, though, focus mostly on criminal 10
**~~<u>Tab</u>~~ le 4 The impact of weather shocks on grain prices in places with high road investment and PSNP**

||Impact on<br>ln(price)|p-value|
|---|---|---|
|Percent rainfall loss interacted with|||
|No. of Months after October (t)|0.002***|0.00|
|t<sup>2</sup>|-0.000***|0.00|
|t<sup>3</sup>|0.000***|0.00|
|After 2005 * t|-0.000|0.39|
|After 2005 * t<sup>2</sup>|0.000|0.79|
|After 2005 * t<sup>3</sup>|-0.000|0.83|
|High roads * t|-0.000|0.63|
|High roads * t<sup>2</sup>|0.000**|0.02|
|High roads * t<sup>3</sup>|-0.000***|0.00|
|High roads after 2005 * t|-0.000|0.32|
|High roads after 2005 * t<sup>2</sup>|0.000|0.69|
|High roads after 2005 * t<sup>3</sup>|0.000|0.91|
|PSNP district * t|-0.001*|0.07|
|PSNP district * t<sup>2</sup>|0.000**|0.03|
|PSNP district * t<sup>3</sup>|-0.000**|0.03|
|PSNP district after 2005 * t|-0.001**|0.03|
|PSNP district after 2005 * t<sup>2</sup>|0.000|0.11|
|PSNP district after 2005*t<sup>3</sup>|-0.000|0.31|
|N|126,306||
|Numberof markets|82||

Note: *** indicates significant at 1%, ** significant at 5% and * significant at 10%. The dependent variable is the log of nominal prices (Birr per kg). The regression includes time since harvest and year dummies to control for inflation and seasonality in prices. Grain type (by grade) dummies are included to account for average differences in relative prices across commodities and market fixed effects are included to account for average differences in prices across markets. Standard errors are cluster at market, year and crop level.

# **Figure 6: Impact of rainfall on prices in places with high road investment and the PSNP**

22
# **Table 18.  Impact of WTO Accession on Exports by Sector and Regional Market** (percentage change)

|Good|Central|Siberia|South|North|Urals|Far East|Moscow|St.<br>Peters.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|FME|61.0|30.1|76.6|49.7|34.2|45.1|70.2|423.5||26.3|
|CHM|31.6|39.8|35.4|18.7|32.1|21.0|23.2|21.8|82.8|44.2|
|SCI|21.3|18.6|22.0|42.6|17.2|43.0|19.1|41.9|25.6|39.8|
|NFM|21.2|11.3|47.7|19.3|34.0|14.6|48.3|3.2||-11.2|
|TMS|14.9|5.8|13.8|28.0|-0.3|27.0|-4.3|21.0|20.9|20.4|
|CLI|15.6|7.8|14.7|12.1|11.3|12.1|9.3|4.5|22.9|1.8|
|COL|9.1|1.6|9.4|10.4|3.9|9.7|||||
|OIL|9.4|-1.6|9.0|7.3|-0.4|8.1|2.2|-1.1|13.0||
|TRK|7.0|1.5|5.8|6.5|2.4|7.5|2.6|4.7|8.5|4.4|
|FIN|5.0|0.2|4.9|6.8|1.4|6.9|3.2|5.7|6.6|4.9|
|TPP|0.9|-4.8|-2.0|6.0|0.9|36.1|-6.6|-5.2|28.2|-10.2|
|MWO|-1.4|5.3|7.9|2.2|-3.0|4.2|19.1|4.3|11.0|-7.7|
|AGR|6.5|-3.3|7.2|4.2|1.0|3.4|-0.6|-4.7|16.8|-9.5|
|CRU||1.6|2.4|2.9|1.3|2.1|||2.9|1.3|
|OTI|2.1|-2.9|4.4|0.3|0.5|-1.7|8.4|2.9|4.6|-1.3|
|CNM|3.5|-6.6|6.5|3.1|-1.6|2.2|-5.3|-7.1|17.9|-11.4|
|PST|2.6|-1.7|2.9|1.1|-0.7|0.7|-2.1|-5.0|7.1|-5.8|
|AIR|0.7|-6.6|0.2|2.6|-3.4|1.8|-2.6|-4.3|9.5|-5.3|
|HEA|0.9|-2.9|-0.2|-3.0|0.2|-3.0|-0.2|-3.8|1.3|-5.2|
|CON|0.9|-3.8|1.1|-3.0|-0.2|-3.3|3.7|-6.4|4.7|-9.8|
|ELE|-0.1|-3.1|0.2|-2.6|-1.4|-3.3|-1.5|-5.7|0.9|-4.9|
|TRD|0.9|-3.8|0.0|-2.9|-2.3|-2.0|-2.8|-6.2|1.9|-6.5|
|FOO|-4.6|-9.0|-1.6|-1.8|-6.3|-5.4|-10.8|-6.9|12.9|-14.1|
|HOU|-3.6|-5.7|-2.4|-5.2|-4.1|-5.5|-2.9|-9.6|0.4|-10.6|
|RLW|-1.6|-7.4|-4.1|-7.2|-3.2|-7.6|-5.1|-6.9|-1.4|-9.9|
|MAR|-3.6|-14.7|-6.2|-4.5|-7.1|-3.6|-9.1|-8.5|-3.4|-11.7|
|TRO|-5.8|-13.4|-7.1|-3.3|-16.4|-3.0|-19.2|-6.6|-7.4|-6.8|
|OTH|-6.2|-17.2|-8.8|-15.8|-6.3|-18.5|-7.0|-22.7|-1.9|-27.5|
|GAS<br>PIP||-40.8|-17.0|-33.2|-31.6|-33.9|||2.3|-71.2|

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.

50