rafmacalaba/datause-encoder-data huggingface dataset:
# Table 4: Export Performance and Foreign Partners

|**Case**|Investment<br>to Value<br>Added<br>(1993-94)<br>**(%)**|Investment<br>to Value<br>Added<br> <br>(1995-96)<br> <br>**(%)**|New<br>product<br>lines*|ISO<br>**9001**|**Export** <br>Revenu|Share of<br>es**191***|Export<br>Revenu|Share of<br>es 1996*|Subcontracts<br>1996*w*|<br>Foreign Partners|
|---|---|---|---|---|---|---|---|---|---|---|
|||<br>|||CMEA|ROW|CMEA|ROW|**(%)**||
|1|11|32|35|1994|40|0|0|35|80|Volkswagen, Opel, Daewoo, Volvo, Audi|
|2|32|58|30|1995|30|2|0|25|40|Bayer, Hoechst, Shell|
|3|5|9|40|no|25|0|0|45|52|Austrian and German partners|
|4|21|16|25|1995|15|_5_|0|40|25|Pirelli|
|_5_|17|19|40|1996|70|11|0|90|90|Caterpillar, Hatlapa (Germany), Matorella|
|6|24|40|30|1995|40|0|45|0|0|Chinese partners|
|7|0|15|35|1996|65|3|20|52|30|Pirelli, Uniroyal|
|8|11|30|40|1995|70|30|40|60|40|German and Russian partners|
|9|0|0|15|no|80|0|0|95|95|Belgian and Dutch army suppliers|
|10|0|42|30|1994|65|6|51|23|10|Conti, Pirelli|
|11|3|21|80|no|45|0|_5_|80|80|Austrian and German wholesalers|
|12|16|22|20|no|0|0|0|0|0|services local market only|
|13|14|25|40|1995|82|18|22|78|60|AEG, Samsung, Honda, Aldi|
|14|21|25|30|1995|60|10|20|48|27|Austrian and German partners|
|15|23|31|10|1995|0|87|10|80|0|exports under own trademark|
|16|17|22|50|no|40|0|20|25|20|Philips, Austrian partners|
|17|17|31|25|1994|30|0|10|35|0|exports under own trademark|
|18|23|51|60|1996|60|10|25|55|30|Canon Enginering, Mahindra&Mahindra|
|19|12|23|35|1995|60|_5_|15|80|26|Krupp, Thrall-Chicago|
|20|9|16|20|1995|45|_5_|15|50|23|Motorola, German partners|
|21|0|0|45|no|60|7|10|30|30|Lombardini, Indian and Syrian partners|
|Average|<br>16|24|35||47|9|15|49|36||

- The residual share of revenues comes from the Czech and Slovak markets.

** Product lines that were established after 1991. Does not include *** Revenue from subcontracting arrangements as a share of total revenue.

product lines that had been in operation prior to 1991 but were upgraded 20
Preventing Violence in the Most Violent Contexts: Behavioral and Neurophysiological Evidence<sup>_∗_</sup> Lelys Dinarte<sup>_†_</sup> Pablo Egana-delSol<sup>_‡_</sup>

**Keywords:** After-School Programs, Emotional Regulation, Violence, Behavioral Economics.

# **JEL Classification:** I29, K42, Z13, I25, D87

> _∗_ We are very grateful for comments from Claudia Martinez, Jeanne Lafortune, Rodrigo Soares, Sara Heller, Francisco Gallego, Jishnu Das, Jed Friedman, Adam Wagstaff, Jose Tessada, Nicolas Figueroa, and Tomas Rau. We also appreciate the comments of participants at the World Bank SESI CoP Seminar, 2019 Texas Economics of Crime Workshop, LACEA-LAMES 2018, 10th Maastricht Behavioral and Experimental Economics Symposium, 7th Annual Interdisciplinary Symposium on Decision Neuroscience, 2017 ESA World Meeting, 2017 SECHI Annual Meeting, 5th Antigua Experimental Economics Conference, and seminar at Pontificia Universidad Catolica de Chile, The World Bank-DEC, IADB-RES, Universidad Javeriana, Universidad de Los Andes, and Universidad del Rosario. We are truly grateful for the incredible support of Glasswing International as an implementer partner, especially to Stephanie Martinez and the education sector team. We are also grateful to principals, teachers, students, and instructors of the public schools in El Salvador that participated in this project. This study was funded by CEDLAS-IDRC (Canada) in the context of the project “Fostering capacities in Impact Evaluation in Latin America,” CONICYT Chile “Soporte Operacional para Becas Nacionales,” and the Centre for Social Conflict and Cohesion Studies (CONICYT/FONDAP/15130009). The intervention was funded by USAID El Salvador Office, HANES, and One Link. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. This study was registered in the AEA RCT Registry with unique identifying number AEARCTR-0001602

> _†_ The World Bank. Development Research Group. Email: ldinartediaz@worldbank.org

> _‡_ Asia School of Business, Kuala Lumpur, and Massachusetts Institute of Technology, Cambridge. Email: pegana@mit.edu.

1
# **R~EFERENCS**

Bertaud, Alain, and Bertrand Renaud, 1994. _Cities Without Land Markets,_ Lessons of the Failed Socialist Experiment, World Bank Staff Discussion Paper 227.

Caprio, Gerard, David Folkerts-Landau, and Timothy Lane, 1994. "Building Sound Finance" in _Emerging Market Economies,_ International Monetary Fund and World Bank.

Diamond, Douglas and Michael Lea, 1992a, "Housing Finance in Developed Countries: An International Comparison of Efficiency," Fannie Mae: Journal of Housing Research, 3,1.

Diamond, Douglas and Michael Lea, 1992b, "The Decline of Special Circuits in Developed Country Housing Finance," Fannie Mae: Housing Policy Debate, 3,3.

European Mortgage Federation, 1990, _Study of European Mortgage Markets,_ Brussels.

Jaffee, Dwight and Bertrand Renaud, 1995, "Securitization in European Mortgage Markets," paper presented at the First International Real Estate Conference, Stockholm, July.

Hermalin, Benjamin and Dwight Jaffee, 1996, "The Privatization of Fannie Mae and Freddie Mac: Implications for Mortgage Industry Structure", U.S. Department of Housing and Urban Development.

_International Securitization Report,_ various issues, IFR Publishing, Aldgate House, 33 Aldgate High Street, London EC3 1 DL.

Renaud, Bertrand, 1995, "Housing Finance in Transition Economies, The Early Years in Eastern Europe and the Former Soviet Union," Chicago: _Housing Finance International,_ Vol.X, No.2, December, 35-46.

Stone, Charles A. and Anne Zissu, 1994, "The French Secondary Mortgage Market," Housing Finance International. March, 15-21.

Weiss, Richard, 1991, "A Primer on French Mortgage-Backed Securities," in Charles A. Stone, Anne Zissu and Jess Lederman, _Asset Securitization: Theory and Practice in Europe,_ London: Euromoney.

, 1995, "Titrisation: en attente du demarrage," Paris: Credit Foncier de France, _L' Observateur Immobilier,_ No.30 February.

World Bank,1995, Country Report No. 14929-RU, "Russia Housing Reform and Privatization: Strategy and Transition Issues." 187 pp.

29
# **1 Introduction**

The speed with which new technologies spread across nations is key for explaining crosscountry differences in economic performance (Comin and Hobijn, 2004, 2010; Comin and Mestieri, 2018). Multinational firms and firm-to-firm relationships through global value chains are believed to play an important role in this process of international technology diffusion (World Bank, 2020). However, understanding the role of firm-to-firm relationships in the cross-country spread of new technologies has proved difficult because of the lack of detailed panel data on both the adoption of new technologies across countries and firms, and on different types of international-firm-to-firm linkages.

In this paper, we leverage a rich combination of granular information on online job postings with data on several types of international firm-to-firm linkages to study the role of multinational firms and global value chains in the cross-country diffusion of new technologies. We examine the diffusion of 29 new technologies across 17 countries and 107,981 firms in the period 2014-2022. Following Bloom et al. (2021), we use the raw text of online job postings and earnings calls data to measure hiring patterns and firm discussions relating to frontier technologies, using this as a proxy for technology diffusion. We combine this information with detailed data on international firm-to-firm linkages, which includes information on buyer-supplier relationships, innovation partners and affiliates in multinational firm networks. To study the process of technology diffusion, we employ various measures of dispersion used in the literature, such as the Gini coefficient and the coefficient of variation, which allow us to document the intensity and spread of emerging technology jobs across countries, regions, and firms.

We first establish four stylized facts about international technology transfer. First, emerging technology hiring has become more dispersed across countries and regions over time. This finding is consistent with evidence on the US only by Bloom et al. (2021). Second, emerging technology jobs are highly concentrated in multinational firms and their supply chains. Around one third of all job postings related to these 29 frontier technologies in the 17 countries are posted by Fortune 500 companies and their buyers, suppliers, affiliates or innovation partners. Third, technology jobs spread faster to richer regions and countries, a finding consistent with the existing literature on cross-country technology diffusion. Finally, technology jobs also spread faster to closer regions and countries to the locations where the technologies emerged.

Given the concentration of emerging technology jobs in multinational firms and their supply chains, we next explore the role of pre-existing firm-to-firm linkages in the diffusion of these technologies. For each technology, we use global patents data to identify the regions where the technology emerged, finding that all of these technologies emerged from cities within the USA. We then identify firms that had ex ante linkages to a firm in the 1
By employing an interacted panel vector autoregression model, Huidrom et al. (2019) examine the effects of fiscal shocks not only on private consumption and CDS spreads (as a measure of risk premia) but also on output. The study finds that the relevance of Ricardian and investor sentiment considerations (i.e., private consumption and borrowing cost effects) as the two channels for a government’s fiscal position to matter for fiscal multipliers (i.e., output effects). Similarly, evidence points to less effective monetary policy in the presence of high debt because of poorly anchored inflation expectations in highdebt countries (Kose et al. 2019).

# 4.4. Slowing investment and growth

With higher debt typically comes higher debt service. Spending on higher debt service needs to be financed through some combination of increased borrowing, increased taxes, and reduced government spending. Spending cuts may even include spending on critical government functions such as social safety nets or growth-enhancing public investment (Figure 6.C and 6.D; Debrun and Kinda 2016; Obstfeld 2013; Reinhart and Rogoff 2010). Separately, high and rising government debt may raise long-term interest rates and yield spreads.<sup>7</sup> High debt could also create uncertainty about macroeconomic and policy prospects and weigh on growth (Lo and Rogoff 2015). It includes risks that the government may need to resort to distortionary taxation to rein in debt and deficits (IMF 2018; Kumar and Woo 2010). Higher interest rates and uncertainty would tend to crowd out productivityenhancing private investment and weigh on output growth. By using data on U.S. stock returns, Croce et al. (2018) find that an increase in the cost of capital for innovationintensive firms results in declines in productivity and economic growth. As a result, a rise in government debt is associated with a decline in corporate investment and R&D. Crowding-out of private investment due to high public debt is also shown in Huang, Pagano, and Panizza (2017), with data for China, and Panizza, Huang, Varghese (2018), with firm- and industry-level data across 69 countries. They find that the negative relationship between public debt and private investment reflected tightening credit constraints.

# 5. Debt: How much is too much?

Weighing these benefits and costs of debt, the literature has attempted to identify how much debt is “too much”—a threshold level of debt below which it is sustainable or not harmful to growth. A rich theoretical literature has focused on the interactions between governments, monetary authorities and private agents in response to numerous shocks. The empirical literature has estimated a wide range of threshold values that appear to be tipping points for adverse effects of debt.

> 7 For details about the links between government debt and interest rates, see Ardagna, Caselli, and Lane (2007); Codogno, Favero, and Missale (2003); Laubach (2009); Rubin, Orszag, and Sinai (2004).

12
economic and financial crisis. Preliminary results from DEC research suggests that many developing countries were much better prepared to respond to this crisis than more developed countries, in part because of higher fiscal surpluses, a better build-up of reserves, lower inflation, and avoidance of currency mismatches which could have led to exchange rate speculation and insolvency.

Assessing the impact on poverty and well-being of such events is difficult and, when data are collected, they are often difficult to interpret. Yet such evidence is essential to improve our understanding of how long, and in what manner, shocks reverberate through affected populations. We need to better understand how shocks affect populations, seeking to highlight the way that societies can re-emerge from such setbacks and resume long-term paths of poverty and inequality decline. We also need new paradigms that allow us to incorporate risk into policy recommendations, allowing policies “wiggle room” to face unanticipated shocks.

These conclusions apply with equal force to risks of natural disasters as to risks from disruptions in the global financial system. For example, measuring farmers’ exposure to drought and other weather-related risks has been a longstanding concern. Some Bank research based on pilot programs that tie crop insurance to objective measures of general climate conditions—like total precipitation at a regional level—indicates that such mechanisms could be promising for providing affordable and effective insurance without “moral hazard” problems resulting from difficulties in observing individual farmer behavior. Yet, more work is needed to establish if and how such schemes can be deployed to provide financially sustainable commercial insurance products at a larger scale. In the area of natural disasters from extreme weather events such as cyclones, floods, and earthquakes, research in Bangladesh has highlighted the importance of relatively simple and cost-effective protection measures such as providing emergency shelters large enough to accommodate family members and their livestock, to counteract tendencies to remain in the path of a disaster to protect the bulk of many families’ wealth.<sup>17</sup> Other work on disaster preparedness has emphasized the need for regulatory reform as well as new regulations to lower hazard exposure. For example, land use regulations to limit settlements in flood-prone

> 17   See Susmita Dasgupta, Mainul Huq, Zahirul Huq Khan, Manjur Murshed Zahid Ahmed, Nandan Mukherjee, Malik Fida Khan and Kiran Pandey, “Vulnerability of Bangladesh to Cyclones in a Changing Climate: Potential <u>Damages and Adaptation Cost.” Policy Research Working Paper 5280, World Bank, 2010.</u>

25
# **Table 6: Results with Productive and Non-productive Expenditures (panel OLS and SURE with 3-year averages)**

## **_Dependent variable: Growth rate of GDP per capita_**

||**OLS**|**SURE**|
|---|---|---|
|Initial human capital|0.016<br>(1.524)|0.015<br>(1.685)|
|Tax revenue|-0.039|0.004|
|(% of GDP)|(-0.166)|(0.029)|
|Other revenue|-0.644|-0.583|
|(% of GDP)|(-4.256)***|(-5.456)***|
|Productive expenditure (-1)|0.720|0.669|
|(% of GDP)|(3.176)***|(4.071)***|
|Non-productive expenditure (-1)<br>(% of GDP)|0.082<br>(0.222)|-0.007<br>(-0.037)|
|Budget surplus|0.630|0.529|
|(% of GDP)|(4.971)***|(5.981)***|
|Inflation - consumer price index|-0.232<br>(-3.318)***|-0.188<br>(-3.664)***|
|Growth rate of GDP per capita (-1)|-0.053<br>(-0.205)|-0.098<br>(-0.576)|
|No. of observations|34|34|
|Adjusted R2|0.475|0.579|

Note: The estimation method is the ordinary least squares for panel data in the first column and the cross-section seeminly unrelated regression for panel data in the second column. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

33
- 72 -

Table 31: Literacy Rates, 1981

|Segment|Total<br>Population|Scheduled<br>Castes|Scheduled<br>Tribes|
|---|---|---|---|
|Total<br>|36.23|21.38|16.35|
|Male<br>|46.89|31.12|24.52|
|Female|24.82|10.93|8.04|
|Rural<br>|29.65|18.48|14.92|
|Male<br>|40.79|27.91|22.94|
|Female|17.96|8.45|6.81|
|Urban|57.40|36.60|37.93|
|Male<br>|65.83|47.54|47.60|
|Female|47.82|24.34|27.32|

Source: Census of India, 1981, Primary census abstract.

Table 32: Retention Rates (Class I-V)

|Period|For all<br>communities|For scheduled<br>castes|For scheduled<br>tribes|For other<br>communities|
|---|---|---|---|---|
|1968-72|33.5|27.8|20.3|35.3|
|1969-73|35.0|28.8|21.3|36.9|
|1970-74|36.2|30.3|22.9|38.0|
|1971-75|36.8|31.4|22.3|38.6|
|1972-76|37.2|32.2|21.9|39.1|
|1973-77|36.9|33.5|20.5|38.8|
|1974-78|38.6|35.6|23.3|40.5|

Source: Ministry of Education, New Delhi.
|MDTF_≤_4<br>MDTF_≥_5|0.169***<br>0.082***<br>0.053*<br>-0.014<br>-0.102***<br>-0.066***<br> headers<br>e).|MDTF_≤_4<br>MDTF_≥_5<br>0.026***<br>-0.001<br>0.011<br>-0.006**<br>-0.060***<br>-0.015***<br> headers<br>e).|
|---|---|---|
|SDTF<br>MDTF_≥_5|0.167***<br>0.091***<br>0.221<br>-0.020**<br>-0.132***<br>-0.076***<br>by the column<br>f each variabl|SDTF<br>MDTF_≥_5<br>0.018***<br>-0.003<br>0.009*<br>-0.011***<br>-0.076***<br>-0.031***<br>by the column<br>f each variabl|
|SDTF<br>MDTF_≤_4|-0.002<br>0.009<br>-0.031<br>-0.006<br>-0.030***<br>-0.011<br>s indicated <br>to the right o|SDTF<br>MDTF_≤_4<br>-0.008<br>-0.003<br>-0.002<br>-0.005***<br>-0.017***<br>-0.017***<br>s indicated <br>to the right o|
|MDTF_≤_4<br>MDTF_≥_5|0.148***<br>0.084***<br>0.061**<br>-0.019*<br>-0.093***<br>-0.077***<br>oss equation<br>fect shown|MDTF_≤_4<br>MDTF_≥_5<br>0.025***<br>-0.004<br>0.008<br>-0.005**<br>-0.053***<br>-0.019***<br>oss equation<br>fect shown|
|SDTF<br><br>MDTF_≥_5|0.155***<br>0.086***<br>0.021<br>-0.026***<br>-0.117***<br>-0.080***<br>oefcients acr<br> direction of e|o Table 2<br>SDTF<br><br>MDTF_≥_5<br>0.019***<br>-0.006<br>0.005<br>-0.010***<br>-0.068***<br>-0.032***<br>oefcients acr<br> direction of e|
|SDTF<br>MDTF_≤_4|0.008<br>0.027<br>-0.040<br>-0.007<br>-0.024**<br>-0.003<br>ference in c<br>est (expected|esponding t<br>SDTF<br>MDTF_≤_4<br>-0.006<br>-0.003<br>-0.003<br>-0.005***<br>-0.015***<br>-0.013**<br>ference in c<br>est (expected|
|MDTF_≤_4<br>MDTF_≥_5|0.159***<br>0.083***<br>0.006<br>0.011<br>-0.091***<br>-0.061***<br> show the di<br>one-sided t-t|d tests corr<br>MDTF_≤_4<br>MDTF_≥_5<br>0.032***<br>-0.005<br>-0.0003<br>-0.003**<br>-0.051***<br>-0.018***<br> show the di<br>one-sided t-t|
|SDTF<br>MDTF_≥_5|0.154***<br>0.087***<br>0.011<br>-0.003<br>-0.110***<br>-0.063***<br> cell entries <br>ifcance of a|able 4: Wal<br>SDTF<br>MDTF_≥_5<br>0.023***<br>-0.007<br>-0.002<br>-0.010***<br>-0.067***<br>-0.032***<br> cell entries <br>ifcance of a|
|SDTF<br>MDTF_≤_4|-0.005<br>0.044<br>0.005**<br>-0.013***<br>-0.019**<br>-0.001<br>frst column, <br>with the sign|T<br>SDTF<br>MDTF_≤_4<br>-0.009<br>-0.002<br>-0.002<br>-0.007***<br>-0.016***<br>-0.014**<br>frst column, <br>with the sign|
||+<br>+<br>+<br>-<br>-<br>-<br>the <br>ong|+<br>+<br>+<br>-<br>-<br>-<br>the <br>ong|
||ence heterogeneity<br>-income country assistance<br>loyment rate<br>er of G8 summit pledges<br> activity<br> state assistance<br>or each variable shown in <br>_β_(2) for any coefcient _β_), al|ence heterogeneity<br>-income country assistance<br>loyment rate<br>er of G8 summit pledges<br> activity<br> state assistance<br>or each variable shown in <br>_β_(2) for any coefcient _β_), al|
|(1)<br>(2)|Prefer<br>Middle<br>Unemp<br>Numb<br>Global<br>Fragile<br>Note: F<br>(_β_(1)_−_|(1)<br>(2)<br>Prefer<br>Middle<br>Unemp<br>Numb<br>Global<br>Fragile<br>Note: F<br>(_β_(1)_−_|

19
in the implementation of the República Bolivariana de Venezuela 2006 Enterprise Survey, there were slight deviations in the restrictive criteria (such as the exact length of the survey instrument) typical for World Bank Enterprise Surveys (WBES). This deviation from the WBES afforded the flexibility to run a survey methodology experiment.<sup>2</sup> The paper is focused on methodological, not policy issues. The details of the experiment are as follows. Firms were randomly split into two groups. One group received the standard ES questionnaire with perceptions of the business environment obstacles at the end of every section. The other group received the standard ES questionnaire with one modification - the perceptions of the business environment obstacles lumped together at the very beginning of the questionnaire. We then compare the perceptions of the business environment across the two groups.

Our results indicate that of the 15 business environment obstacle ratings, only 4 show a statistically significant difference across the groups – political instability, corruption, electricity, and business licensing and permits. After using a regression analysis framework to account for several factors, only two business environment elements have a statistically significant difference across the two groups – business licensing and permits, and corruption. The latter finding is consistent with Friesenbichler et al (2017) who uncover answer biases for the corruption perception in firm surveys. We then explore if changes in the order of the perceptions-based questions have any effect on the objective measures in the survey for business licensing and permits, and corruption. We find no significant effects. Thus, objective measures may be unaffected by the placement of the perceptions-based questions. This follows a long literature that has studied the effects of question order in surveys across a wide range of fields (Bradburn and Mason, 1964; Bishop and Smith, 2001; Rohwedder et al., 2006; Holbrook et al., 2007; Lumsdaine and Exterkate, 2013; Bard and Weinstein, 2017).

> 2 The increasing popularity of the use of the WBES has raised questions about the validity of various questions. A program to explore the accuracy of WBES led to the analysis of this experiment. Thus, this experiment was revisited recently, which was not a priority during inception.

3