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- Falk, A., A. Becker, T. Dohmen, B. Enke, D. Huffman, and U. Sunde (2018). Global evidence on economic preferences. _The Quarterly Journal of Economics 133_ (4), 1645–1692.

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- Giuliano, P. and A. Spilimbergo (2014). Growing up in a recession. _Review of Economic Studies 81_ (2), 787–817.

- Glenn, N. D. (1980). Values, attitudes, and beliefs. In O. Brim and J. Kagan (Eds.), _Constancy and Change in Human Development_ . Harvard Univesity Press.

- Greenstein, F. (1965). _Children and politics_ . Yale University Press.

- Grigorieff, A., C. Roth, and D. Ubfal (2020). Does information change attitudes toward immigrants? _Demography 57_ (3), 1117–1143.

- Haaland, I. and C. Roth (2020). Labor market concerns and support for immigration. _Journal of Public Economics 191_ , 104256.

- Hassan, M., S. Mansour, S. Voigt, and M. Gadallah (2019). When Syria was in Egypt’s land: Egyptians cooperate with Syrians, but less with each other. _Public Choice 191_ , 337–362.

- Ib ́a ̃nez, A. M., A. Moya, M. A. Ortega, S. V. Rozo, and M. J. Urbina (2022). Life out of the Shadows: Impacts of Amnesties in the Lives of Migrants. _IZA – Institute of Economic Labor_ .

- Ib ́a ̃nez, A. M., S. V. Rozo, and M. J. Urbina (2021). Forced migration and the spread of infectious diseases. _Journal of Health Economics 79_ , 102491.

- Lerner, J., R. Gonzalez, D. Small, and B. Fischhoff (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. _Psychological Science 14_ , 144–50.

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- Rodr ́ıguez Chatruc, M. and S. V. Rozo (2021). In someone else’s shoes: Promoting prosocial behavior through perspective taking. Technical report, The World Bank.

16
# **4.3 Data**

For our research we use an official data set provided by public and private sectors, as well as a novel one built by the World Bank. First, by using the official data reported by the firms to their investors and statistical reports of the regulator agencies of each country, we build an unbalanced panel data set of key indicators on outputs, inputs, labor productivity, efficiency, quality, coverage, and prices. Furthermore, we requested information from each of the companies and international organizations like the ITU (International Telecommunication Union), the OLADE (Latin American Organization of Energy), as well as information provided by each regulatory office. We make a particular effort in corroborating the company data with several public sources and with data from the firms provided by different governmental offices. We are also particularly cautious about the consistency and comparability of the data across time and countries (see Andres, Guasch, and Foster, 2006).

Secondly, the novel dataset built by the World Bank describes the characteristics of nearly 1,000 infrastructure projects awarded in Latin American and Caribbean countries from 1989 to 2002, in the sectors of telecommunications, energy, transportation and water. (See Guasch, 2003.) The analysis focuses on several indicators of outcomes, inputs, labor productivity, efficiency, quality, coverage and prices. Some of these variables are used by other authors with other samples, such as, Ros (1999), who employs equivalent indicators for _coverage_ , _labor productivity_ , _quality_ and _prices_ for the telecommunications sector. Ramamurti (1996) uses analogous indicators in _output_ , _coverage,_ and _labor productivity_ for the four Latin American 27
- 21 -

Figure 4: Comparison of effect on prie of 5uswired mwmases m 1985 vs 1992 <!-- Start of picture text --> 2-<br>impacsat 1992 egaLsion<br>on worm<br>price<br>O- - El oc3 B o3 E3-1<br>-2 -<br>-4 185 expawsio<br>-6-<br>1980 1990 2000 2010<br>yen<br><!-- End of picture text -->
# **Figure 6. Systemic Risk Supervision Capacity**

<!-- Start of picture text --> How would you rate your agency’s current systemic supervision capacity<br>relative to international standards?<br>a. Excellent, 3.7%<br>d. Needs<br>strengthening,<br>37.0%<br>b. Good, 40.7%<br>c. Fair, 18.5%<br><!-- End of picture text --> Do you think that recent world events require a fundamental redefinition of the role and functions of the supervisor (particularly by becoming more pro-active)? a. Not really, 14.8% c. Yes, 33.3% <!-- Start of picture text --> a. Not really,<br>14.8%<br>b. Yes, but only to<br>some extent,<br>51.9%<br><!-- End of picture text --> <!-- Start of picture text --> How would you rate your agency’s monitoring of the housing market?<br>a. Excellent, 0.0%<br><!-- End of picture text --> <!-- Start of picture text --> a. Excellent, 0.0%<br>e. Not monitored, b. Good, 22.2%<br>22.2%<br>c. Fair, 11.1%<br>d. Needs<br>strengthening,<br>44.4%<br><!-- End of picture text --> <!-- Start of picture text --> Please rate the key hindrances to strengthening your agency’s capacity to<br>conduct effective systemic supervision<br>100%<br>90% Moderately/Not<br>80% Important<br>70%<br>60%<br>50% Important<br>40%<br>30%<br>20% Extremely/Very<br>10% Important<br>0%<br>Have recent events altered your views regarding the scope for market<br>discipline and the role of price signals in helping enhance the effectiveness<br>of official oversight?<br>15<br>a. Not really<br>12<br>b. Yes, but only to 10<br>some extent 12<br>2<br>c. Yes<br>3<br>Price signals<br>Market discipline 0 3 6 9 12 15 18<br>skills c. Rethinking the organization of supervision supervisor agencies data<br>d. Strengthening the other supervisory foreign supervisors g. Better use of market<br>a. Developing proper staff b. Paying proper salaries powers, legal protection, and independence of the e. Better coordination with the central bank and/or f. Better coordination with<br><!-- End of picture text --> <!-- Start of picture text --> How would you rate your agency’s monitoring of regulatory arbitrage<br>across groups or institutions with different licenses?<br>a. Excellent, 7.4%<br>e. Not monitored,<br>14.8%<br>b. Good, 18.5%<br>d. Needs<br>strengthening, c. Fair, 18.5%<br>40.7%<br><!-- End of picture text --> 23
<!-- Start of picture text --> Number of deals Projection Number of deals Projection<br>5,000 5,000<br>4,500 4,500<br>4,000 4,000<br>3,500 3,500<br>3,000 3,000<br>2,500 2,500<br>2,000 2,000<br>1,500 1,500<br>1,000 1,000<br>500 500<br>0 0<br>1998 2001 2004 2007 2010 2013 2016 2019 2022 2025 1998 2001 2004 2007 2010 2013 2016 2019 2022 2025<br>Source: Authors' calculations Source: Authors' calculations<br>(a) Baseline scenario (b) High-growth scenario<br><!-- End of picture text --> Figure 3: Model projection (hollow and dotted lines) and actual historical data (solid line) for outbound cross-border M&A deals by firms in emerging-market economies, 1998–2025, for baseline (left panel) and high-growth (right panel) scenarios. Shaded areas indicate the projection period. Forward projections based on growth and net IIP assumptions for emerging and advanced economies.

Northern targets, in contrast, occur in greater frequency when these host countries are more closed to trade, which suggests that such deals may be due to an implicit tradeoff that favors siting production directly in the country, rather than exporting to it.

Another important insight that emerges from the analysis is that firms appear to seek to diversify their equity holdings through acquisitions. More specifically, while emerging economy acquirers are likely to be located in economically less volatile economies, they tend to seek out targets in advanced economies that exhibit both lower levels of political risk (to insure the protection of their investments), but higher levels of economic and financial risk (possibly because such economies can offer better returns). The extent to which such diversification occurs is also materially affected by economic policies, such as those governing bilateral investment or access.

In light of this, emerging market firms are likely to press for future economic policies that will strengthen investment climates both at home and abroad. In doing so, emerging market firms can act as catalysts that spur increased integration of developing countries into the global economy, since enhanced integration offers additional support for open trading and investment regimes. But these firms will also serve as a growing source of global competition, especially when they invest in other emerging economies. Emerging market acquisitions are increasingly driven by resource- and efficiency-seeking motives—motives traditionally considered the preserve of firms based in advanced countries—and in making such cross-border investments, these firms will also challenge advanced-country firms’ preeminence in industrial production. Such competition will, in the longer run, drive global factor price equalization in general, and wage convergence in particular. Countries can support such positive competition by enhancing financial access within their countries, which is also positively associated with cross-border M&A.

19
Table 1: Calibration of Common Parameters across Economies

||US data|Model|Parameter|
|---|---|---|---|
|Top 1 % Earnings Share|18.5%|18.5%|_α_+_θ_ = 0_._71|
|Top 10% Employment Share|0.76|0.77|_μ_= 4_._8_e −_05|
|Employment Age 21-25 relative to Age 1|3.95|3.83|_φ_= 10|
|Std Dev. Employment Growth rate|0.25|0.25|∆= 0_._25|
|Empl. Ratio Entrants to Incumbents|18.9%|18.9%|_η_ = 4_._46|

The top 1% earnings share for the US is taken from Khun and RÃos-Rull (2015). We report the average of the top 1% share between 2007 and 2013. The top 10% employment share, the average employment ratio between 21-25 and 1 year old rms, and the average employment ratio between entrants and incumbents were computed from Business Dynamics Statistics database for the year 2007. Numbers are for the manufacturing sector. Standard deviation of employment growth rates for large rms are reported in Atkeson and Burstein (2010).

# 5.2 Reforms

We now turn to discuss the strategy for calibrating the parameter values governing the distortions in the model and their paths of reversal in the reforms. These are given by a sequence of slopes and scale parameters of the revenue tax prole, _υt_ and _ZI,t_ , a sequence of prot taxes, _τt_<sup>_π_,andavalueforthexedcostsofproduction</sup> _fc_ . We rst specify the calibration strategy of these objects in the context of the benchmark reforms, and later describe their connection with the data for the case study.

## 5.2.1 Calibration of Benchmark Reforms

We consider two types of benchmark reforms, one that dismantles idiosyncratic distortions, which we found pertinent for thinking about average acceleration episodes, and one that reverses taxes to the prots of the rms, with which we characterize the barriers to rm entry of communist regimes. The idiosyncratic distortions and the prot tax rate are calibrated to match the observed growth in _TFP_ that occurs between the starting and the ending point of the average acceleration in the data. Specically, we construct the target by subtracting a linear trend to the solid black line in the left panel of gure 3.1. The linear trend is a 0.85% annual growth rate, which corresponds to the average growth of _TFP_ in the United States since 1980, as measured from the Penn World Tables 10.1. The resulting target is a _TFP_ growth of 20%. Then, we identify the slope of the idiosyncratic distortion prole and the prot tax rate that generates, between the distorted and undistorted steady states, a productivity growth of 20% as in the data. The scale parameter _ZI_ in the idiosyn23
18

# References

- Blejer, Mario I. and Mohsin S. Khan (1984), "Government Policy and Private Investment in Developing Countries," _International Monetary Fund Staff Papers,_ 31(2), June 1984, 379-403.

- Blinder, Alan S. and Joseph E. Stiglitz (1983), "Money, Credit Constraints, and Economic Activity," _American Economic Review,_ 73(2), May 1983, 297-302.

- Calvo, Guillermo A. and Fabrizio Coricelli (1992), "Stagflationary Effects of Stabilization Programs in Reforming Socialist Countries: Enterprise-Side vs. Household-Side Factors," _World Bank Economic Review,_ 6(1), January 1992, 71-90.

- Cockcroft, Laurence and Roger C. Riddell (1991), "Foreign Direct Investment in Sub-Saharan Africa" (Washington, D.C., International Economics Department, WPS 619: World Bank, March 1991).

- De Gregorio, Jose, and Pablo E. Guidotti (1993), "Financial Development and Economic Growth" (Washington D.C.: International Monetary Fund, July 1993).

- Dooley, Michael P. (1990), "Comment" in _Taxation in the Global Economy_ edited by Assaf Razin and Joel Slemrod (Chicago: University of Chicago Press for the National Bureau of Economic Research, 1990), 74-78.

- Fry, Maxwell J. (1980), "Money, Interest, Inflation and Growth in Ibrkey," _Journal of Monetary Economics,_ 6(4), October 1980, 535-545.

- Fry, Maxwell J. (1993), _Foreign Direct Investment in Southeast Asia: Differential Impacts_ (Singapore: Institute of Southeast Asian Studies, 1993).

- Ghartey, Edward E. (1993), "Causal Relationship between Exports and Economic Growth: Some Empirical Evidence in Taiwan, Japan and the U.S.," _Applied Economics,_ 25(9), September 1993, 1145-1152.

- Hsiao, Cheng (1979), "Autoregressive Modeling of Canadian Money and Income Data," _Journal of American Statistical Association,_ 74(367), September 1979, 553-560.

- Johnston, John (1984), _Econometric Methods,_ third edition (New York: McGraw-Hill, 1984).

- Keller, Peter M. (1980), "Implications of Credit Policies for Output and the Balance of Payments," _International Monetary Fund Staff Papers,_ 27(3), September 1980, 451-477.
# **III. Data**

Our period of analysis spans 1/1/2014 to 10/15/2016. Overall, we have 700 trading days, and hence 699 observations. We obtain daily the WTI crude oil price from the U.S. Energy Information Administration. The WTI crude is chosen instead of Brent because WTI <mark>is the main benchmark for oil consumed in the United States</mark> . The <mark>WTI refers to oil extracted from wells in the U.S. and sent via pipelines to Cushing, Oklahoma</mark><sup>10</sup> <mark>.</mark> We use the Dow Jones U.S. Market Index (DJUS), which represents about 95% of the U.S. market, to capture U.S. equity. We use the Bloomberg bond indices for bond prices. Daily historical Dow Jones U.S. Market indices, Bloomberg HighYield Bond Indices and Bloomberg U.S. Corporate Bond Indices (investment grade) are obtained from Bloomberg. The 10 sectoral stock indices from Dow Jones are Basic Materials, Consumer Goods, Consumer Services, Financials, Healthcare, Industrials, Energy, Tech, Telecom, and Utilities.<sup>11</sup> These 10 indices together make up the Dow Jones U.S. Market Index. In addition, we also examine two important subsectors: transportation and airlines.<sup>12</sup> The S&P 500 and its sectoral indices serve as a robustness check.

The Bloomberg investment-grade corporate bonds are the aggregate index, Healthcare, Tech, Materials, Financials, Communication, Consumer Discretionary, Utilities, Industrials, Consumer Services and Energy.<sup>13</sup> Similarly, the Bloomberg high-yield corporate bond indices are the aggregate high-yield corporate bond

> 10 For 10/10/2016, we opted for future price (March strike date) to account for Columbus’s Day. 11 Their tickers are, respectively, DJUSBM, DJUSNC, DJUSCY, DJUSFN, DJUSHC, DJUSIN, DJUSEN, DJUSTC, DJUSTL, DJUSUT. These 10 indices together make up the Dow Jones U.S. Market Index (DJUS).

> 12 DJUSTS, and DJUSAR.

> 13 Their tickers are, respectively, BUSC, BUSCHC, BUSCTE, BUSCMA, BUSCFI, BUSCCO, BUSCCD, BUSCUT, BUSCIN, BUSCCS and BUSCEN.

13
- 13 -

**Figure 6: Relation between international achievement score and per capita** GNP, **circa early** 1990s

<!-- Start of picture text --> 60<br>0<br>0 0 0 0<br>0<br>O ~ ~ ~~~~ %o 0<br>50 ~~~~~0 0 0 0O<br>t500 0<br>0~~~~<br>0 ~~~~~~~~0<br>00<br>240<br>0<br>0<br>0<br>200' 400 S0O 1500 3000 10000 20000<br>Pe oapita GNP<br><!-- End of picture text -->

# **2.4 Efficiency of the education system**

By efficiency we refer to two aspects of the education system's operation: student flow patterns, and output per unit of public spending on education.

_Efficiency of student flow._ The available data pertain to the first 8 grades of schooling. In primary education, high dropout rates imply inefficient systems because children who quit before completing the cycle, or who do so with less than four years of schooling, are unlikely to become permanently literate and numerate. Grade repetition also leads to wastage because repeaters use at least twice as much resources as other pupils to attain the same amount of education. To measure the combined influence of these problems we construct an index of student flow efficiency which is benchmarked against a system in which no child repeats or drops out.<sup>15</sup> An index of 100 means that all pupils complete primary school and that no one repeats; a smaller value, say 60, means that with a given amount of resources the system produces only 60 percent as many primary school completers as a system with no dropouts and no repeaters. The relation between the index and the per capita GNP appears in figure 7.

> 5 For details on the constuction of the summary index, see Mingat, Alain and Jee-Peng Tan (1998) "Analyzing problems in the structure of sudent flow," mimeo, Hmnm Development Department, The World Bank.
**41**

World Bank (2005a): Meeting the Challenge of Africa’s Development: A World Bank Group Action Plan, World Bank: Washington D.C.

World Bank (2005b): _Agriculture, Rural Development and Pro-Poor Growth_ , World Bank: Washington D.C..

World Bank (2005c): World Development Indicators and Global Development Finance Database, World Bank: Washington D.C.; April 2005 World Bank (2005d): _PovcalNet_ ; <u>http://iresearch.worldbank.org/PovcalNet/jsp/index.jsp, May 2005.</u> World Bank (2005e) “ _Well-Being and Poverty in Ethiopia: The Role of Agriculture and Agency_ ” Report No. 29468-ET, World Bank: Washington D.C.
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<!-- Start of picture text --> 0.6 i 0.6<br>0.4 ' 0.4 ae<br>+<br>> E&I 3<br>0.2 sodas co 0.2 =<br>0.0 0.0<br>0-2.97 2.97-8.44 8.44-23.03 >23.03 0-2.97 2.97-8.44 8.44-23.03 >23.03<br>1.0 SAS 1.0 SSA<br>08 0.8<br>0.6} pay i 0.6 FF = wi<br>0.4, = as 0.4)...' = '<br><!-- End of picture text --> <!-- Start of picture text --> 0.2 EC ==‘ 0.2 =‘ ==<br>0.0 - 0.0<br>02.97 2.97-8.44 8.44-23.03 >23.03 0-2.97 2.97-8.44 8.44-23.03 >23.03<br>iG YHI<br>0.8<br>3<br>206). == i<br>6 =a<br>5 0.4 = co<br>& —<br>0.2<br>0.0 92.97 297-844 844-23.03 >23.03<br>Income per day (usd)<br><!-- End of picture text -->
**Table 15: Composition analysis – McEwan’s (2014) sample of technology studies**

||**Conn 2014**|**Glewwe et al. 2014**|**Kremer, Brannen, &**<br>**Glennerster 2013**|**Krishnaratne, White, &**<br>**Carpenter 2013**|**Murnane & Ganimian 2014**|
|---|---|---|---|---|---|
|Banerjee et al. (2007) - India|-|Computers &<br>electronic games|Computer-assisted<br>learning|Materials|Computer-assisted learning|
|Barrera-Osorio and Linden<br>(2009) - Colombia|-|Computers &<br>electronic games|Technology|Materials|Computers in schools|
|Carillo, Onafa, and Ponce<br>(2010) - Ecuador|-|-|-|-|Computer-assisted learning|
|Cristia et al. (2012) - Peru|-|-|Technology|-|Computers in schools|
|He, Linden, and Macleod<br>(2008) - India|-|-|-|Additional teaching<br>resources|Scripted lessons|
|Lai et al. (2012) - China|-|-|-|Materials|-|
|Lai et al. (2012) - China|-|-|-|-|-|
|Linden (2008) - India|-|Computers &<br>electronic games|-|-|Computer-assisted learning|
|Mo et al. (2012) -China|-|-|-|-|Computer-assisted learning|
|Mo et al. (2013) - China|-|-|-|-|-|
|Percentage of studies included<br>in review|0%|30%|30%|40%|70%|
|Percentage of RCT studies<br>included in review|0%|30%|30%|40%|70%|

36
Finally, of all structural characteristics considered here, improvement in labor market flexibility has the strongest effect of reducing the impact of terms-of-trade shocks on per capita GDP. The difference in the shock’s cumulative output impact between the first and third quartiles of labor market flexibility is 0.74 percentage points. The ability of firms to adjust their activities on the labor margin seems crucial for the economy to accommodate the shock.

# **Robustness**

We examine the robustness of the basic results to the application of a longer lag structure in the estimated VARs, changes in the sample of countries, the use of alternative measures of financial depth, the inclusion of the exchange-rate regime as an additional country characteristic, and the implementation of an alternative method to estimate the effects of structural characteristics. The results are presented in Tables 5 and 6.

The benchmark results are obtained from panel VARs that include both developing and developed countries and set a common lag structure of 2 lags. The first two robustness checks refer to these features of estimation. First, to dispel concerns on whether pre-estimation diagnostics could have indicated a longer lag structure, we reestimate the shock impacts from VARs with 3 lags for all countries. The results change little if anything: there seems to be some reshuffling in the relative importance of the structural characteristics, but the differences are quantitatively small and unlikely to be statistically significant. A richer lag structure comes at the price of using fewer observations for estimation, particularly in countries where the time series is rather limited, and we prefer to continue working with VARs of 2 lags.

Second, in order to discard the possibility that our results are derived only from the contrast between developing and developed countries, we exclude all OECD countries from our sample, re-estimate the model, and compute again the impact statistics. These results are qualitatively the same and quantitatively quite similar as those obtained using the full sample. This similarity is noteworthy and indicates that our results can be compared to those of studies that focus only on developing countries.<sup>13</sup>

> 13 The results are also unaffected by the exclusion of the 13 countries where were we rejected the hypothesis that output fluctuations Granger caused terms of trade fluctuations.

12
25 Notes: N = 3,884 households. All values are weighted; standard deviations in parentheses. In 2006, US$ 1.00 = 9,000 Ghanaian cedis.

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample).

- Significant at the 0.05 level.

- ** Significant at the 0.01 level.