rafmacalaba/datause-encoder-data huggingface dataset:
in the raw leader growth average is only associated with a 0.17 percentage point increase in the LS leader effect, suggesting high average growth rates are only weakly informative about an individual leader’s performance (Online Appendix Figure 1).<sup>32</sup>

**Statistically significant leaders.** We define the best (and worst) leaders for growth as any leader for whom the estimated LS leader effect is positive (or negative) and significant at the 95% level. Figure 2 plots the combination of the LS leader estimate and the size of its standard deviation for autocracies and democracies (each leader with tenure ≥ 3yrs is a dot).<sup>33</sup> Leaders who are significantly good for growth are in the bottom right corner and their negative counterparts are in the bottom left corner. Both have leader effects (in absolute value) more than twice as large as their error. The significant leaders are listed in Table 3. Leaders with insignificant growth contributions are in the center region.

Our first main result is that the vast majority (93.5%, 602/646) of leaders with tenures ≥ 3yrs and complete growth data have a contribution insignificantly different from zero.<sup>34</sup> That is, these leaders have errors too large relative to their LS leader effect. This does not cast doubt on the existence of leader effects in general, since Table 2 shows sizable variation in underlying leader quality (as in Jones and Olken 2005). Rather, the low number of significant leaders instead μμ exposes the difficulty of detecting which leaders represent a draw of high or low quality amid the σσ

> 32 Online Appendix Figure 2 shows that most of this difference is due to the shrinkage factor . 33 We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding  We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding ψψ

33 We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding errors for leaders with shorter tenures. We also exclude leaders where we have missing growth data for some of their tenures - leaders who are in office outside 1951-2014 - as we do not observe their full growth record.

34 Nothing guarantees that only a small fraction of leaders are significant. Monte Carlo evidence shows that if growth were less noisy (an iid SD of 1% rather than 5%), 40% of leaders would be statistically significant at the 95% level.

23
|Trade Connectivity per<br>capitat=0<br>FDI Connectivity per capita<br>t=0|||0.61***|0.59***|||||
|---|---|---|---|---|---|---|---|---|
|Migration Connectivity per<br>capitat=0|||||0.34*||||
|ICT Connectivity per capita<br>t=0||||||0.12|||
|Portfolio Flows per capitat=0<br>Airline Connectivity per<br>capitat=0|||||||0.17|0.19*|
|Adj‐R<sup>2</sup>|0.54|0.53|0.59|0.58|0.56|0.54|0.55|0.56|

|Table 2. Network effects on overall incomegrowth<br>The dependent variable in each model is the annualized income growth (%) between 2000‐2016. All right‐hand‐side variables<br>we take the first available observation for the growth period. There are 86 countries for which we are able to estimate each ve<br>connectivity variables /PageRank are normalized using the standard normal distribution. Therefore the size of the coefficient r<br>impact of one standard deviation change.|are tra<br>rsion<br>epres|nsfo<br>of th<br>ents t|rmed i<br>e mod<br>he gro|n logs<br>el. The<br>wth|and<br>|
|---|---|---|---|---|---|
|1|2|3|<br>4|5|6|
|‐<br>1.|‐<br>1.|‐<br>1.|‐<br>1.|‐<br>1.|‐<br>1|
|3|2|1|0|1|.<br>|
|GDP per capita‐t0<br>1|9|5|6|1|1<br>|
|<br>*|*|*|*|*|1<br>|
|*|*|*|*|*|*<br>|
|*|*|*|<br>*|*|*|
||1.|1.|2.|1.|2.|
|1.<br>|8|6|0|9|0|
|Years of schooling‐t0<br>7<br>*|7<br>|0<br>|6<br>|9<br>|4<br>|
||*|*|*|*|*|
|*<br>*|*|*|*|*|*|
||*|*|<br>*|*|*|
|G i0<br>‐<br>5.|‐<br>5.|‐<br>4.|‐<br>5.|‐<br>5.|‐<br>5.|
|overnment sze‐t<br>8|2|9|4|2|7|
|1|4|1|<br>4|1|9|
|2.|2.|2.||1.|1.|
|Inflation‐t0<br>9|7|5|2.<br>|8|7|
|2|5|4|<br>1|2|3|
||0.|1.|1.|1.|1.<br>|
|<br>1.|||||6|
|Governance‐t0<br>|9|0|4|3||
|<br>2|9|4|<br><br>3|2|3<br>*|
|0.|0.|0.|0.|0.|0.|
|Investment Rate ‐ t0<br>1|2|2|2|2|2|
|9|1|0|0|0|0|

19
A two-standard deviation increase in public enforcement (roughly from Sweden to the U.S.) is associated with a 42% increase in listed firms per capita and adds 1.6 to the IPO-to-GDP ratio. In contrast, anti-director rights, but not public enforcement, typically matters for the other measures of stock market development (with the exception of earnings manipulation for which neither variable matters).

Finally, Panel C presents the results of a horse race between our proxies for private and public enforcement. The key result is that private enforcement is significant in all but one of the regressions (earnings manipulation). In contrast, public enforcement is never significant when combined with private enforcement (i.e., private enforcement knocks out public enforcement from the regressions for domestic firms and IPOs).

To ensure the robustness of these results, a few additional issues are addressed to correct for possible weakness of the results stemming from measurement problems, from the possibility that omitted variables may explain the strength of the results on private enforcement and for the possible endogeneity of private enforcement.

Public enforcement may only be effective in countries with efficient government bureaucracies. To address this concern the authors reran the regressions for the sub-sample of countries with per capita GDP above the median. They find no consistent evidence that public enforcement is correlated with larger securities markets in these countries (See Table 3). A related concern is that public enforcement may be ineffective if the Supervisor lacks adequate resources. To address this concern, they collected data on the number of employees that work for the Supervisor and found them to be insignificant when included in the regressions. To capture any interactions between public enforcement and the resources of the Supervisor, they separate 25
**Table 7. Sector Exports as a Percent of Total Exports of the Regional Market**

|||||||||St.||North-|
|---|---|---|---|---|---|---|---|---|---|---|
|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|Petersb.|Tumen|west|
|MAR|3|2|2|1|2|2|2|2|1|2|
|AGR|2|1|0|0|2|1|0|0|0|1|
|AIR|3|3|2|2|3|2|8|3|0|2|
|CHM|14|1|2|7|8|12|6|5|0|22|
|CNM|1|0|0|0|1|0|1|1|0|0|
|COL|0|4|3|5|1|0|||||
|CON|2|0|0|1|2|1|3|1|0|1|
|CRU||8|26|3|27|20|||50|2|
|ELE|1|0|0|0|0|0|1|0|0|0|
|FIN|0|0|0|0|0|0|1|0|0|0|
|FME|26|1|4|5|5|11|3|4||41|
|FOO|5|10|2|3|10|1|11|8|0|5|
|GAS||2|3|1|5|3|||45|0|
|HEA|0|0|0|0|0|0|0|0|0|0|
|HOU|0|0|0|0|0|0|0|0|0|0|
|MWO|12|15|2|9|10|13|40|28|0|7|
|NFM|2|16|18|42|5|13|7|10||1|
|OIL|19|15|4|10|13|15|5|19|1||
|OTH|1|0|0|0|0|1|1|1|0|1|
|OTI|4|0|0|0|1|1|2|1|0|1|
|PST|0|0|0|0|0|0|0|0|0|0|
|RLW|0|0|0|0|0|0|1|0|0|0|
|SCI|0|0|0|0|0|0|0|0|0|0|
|TMS|0|0|0|0|0|0|1|0|0|0|
|TPP|2|18|28|7|1|2|1|12|0|12|
|TRD|1|1|1|1|1|1|2|1|0|1|
|TRK|0|0|0|0|0|0|0|0|0|0|
|TRO|0|0|0|0|0|0|1|0|0|0|
|CLI|1|1|1|1|1|1|4|1|0|1|
|Total|100|100|100|100|100|100|100|100|100|100|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

43
Ederveen et al. (2006) look at the impact of EU funds on 13 EU countries (EU-15 minus Luxembourg and Germany) and find that European support as such does not improve countries’ growth performance. Rather, this support is more effective when it goes to countries with the right institutions – openness to competition, quality governance and low corruption.

In this sub-section, we showed that the literature provides evidence of cross-national convergence in the EU. However, as we shall show in the next sub-section, this masks lack of regional convergence within countries, as most of a country’s growth could be accounted for by its richer or “core” regions.

# <u>4.2 What do cross-regional studies say?</u>

As Exhibits 3a through 3e show, the evidence on regional convergence is more mixed. Five key strands emerge from this evidence that shed light on the hypotheses we are testing. First, there is stronger evidence of regional convergence in the 1950s, 1960s and 1970s, and slower or absence of convergence after that. This is important as most EU regional policies took effect in the 1980s and the 1990s. Barro and Sala-I-Martin (1991) find evidence of conditional β -convergence rates similar to the US of about 2% per year from 1950-1985 in 73 regions across Germany, UK, Italy, France, Netherlands, Belgium, and Denmark. This is perhaps to be expected, as these were all more developed countries in the EU, and perhaps did not have the problems associated with underdevelopment as in other less advanced countries such as Ireland and Greece. The authors also claim that convergence to steady state independent of government polices, while steady state output per worker may differ due to policy differences.

Boldrin and Canova (2001) find some evidence for national convergence (supports findings in previous sub-section) but not for regional convergence during 1980-1996. According to Boldrin them “Most of the observed inequality in regional income levels can be accounted for by a combination of three factors: differences in total factor productivity, differences in employment level, and differences in the share of agriculture in regional income... except for a few 'miracles' (Ireland, the Italian north-east, the East German Lander, Lisbon's metropolitan area and, Inner London) most of Europe seems to have achieved a form of long-run convergence in growth rates.

20
# **References:**

**Acemoglu, Daron, Simon Johnson, and James A. Robinson.** 2001. “The Colonial origins of Comparative Development: An Empirical Investigation,” _American Economic Review,_ 91(5): 1369-85.

**Aghion, Philippe, and Olivier Blanchard.** 1994. “On the Speed of Transition in Central Europe.“ _NBER Macroeconomics Annual,_ 283-320.

**Andreyeva, Tatyana.** 2003. “Company performance in Ukraine. What governs its success.” Economics Education and Research Consortium Working Paper 03/01.

**Angelucci, Manuela, Saul Estrin, Jozef Konings, and Zbigniew Zólkiewski.** 2002. “The Effect of Ownership and Competitive Pressure on Firm Performance in Transition Countries: Micro Evidence from Bulgaria, Romania and Poland.” The William Davidson Institute Working Paper 434.

**Baldwin, Richard E., Joseph F. Francois, and Richard Portes** . 1997. “The Costs and Benefits of Eastern Enlargement: The Impact on the EU and Central Europe.” _Economic Policy,_ 12(24): 125-76.

**Barnett, Steven** . 2000. “Evidence on the Fiscal and Macroeconomic Impact of Privatization.” IMF working paper 130.

**Bena Jan, and Jan Hanousek** . 2008. “Rent Extraction by Large Shareholders: Evidence Using Dividend Policy in the Czech Republic.” _Czech Journal of Economics and Finance_ , 58(3-4), 106-130.

**Bennett, John, Saul Estrin and James Maw** . 2005. “Why Did Transition Economies Choose Mass Privatization?” _Journal of the European Economic Association_ , 3(2-3): 567-75.

**Bennett, John, Saul Estrin, and Giovanni Urga.** 2007. “Privatization Methods and Economic Growth in Transition Economies”, Forthcoming in _Economics of Transition_ .

**Bennett, John and James Maw** . 2003. “Privatization, Partial State Ownership, and Competition.” _Journal of Comparative Economics_ , 31(1): 58-74.

**Berkowitz, Daniel, and David N. De Jong.** 2001. “Policy Reform and Growth in PostSoviet Russia.” _European Economic Review_ , 47(2): 337-52.

**Bevan, Alan, Saul Estrin, and Mark Schaffer.** 1999. “Determinants of Enterprise Performance during Transition.” Centre for Economic Reform and Transformation Working Paper 99/03.

31
- 32 -

# Table 11: Cost Per Predicted Points on Posttest Mathematics Test and Effect Size, by School Type, with Background Characteristics Held Constant and Selection Bias Controlled for

||Predicted<br>Posttest<br>Score a/|Effect<br>Sizeb/|Cost per<br>Student t/|Cost per<br>Point on<br>Math Test|Cost per<br> <br>Standard<br>Deviation<br>Unit|
|---|---|---|---|---|---|
|Public 15%<br>5|9.79<br>9.79|-<br>-|323<br>289|33.0<br>29.5|-|
|Private 0-type|12.86|.89|209|16.3|234.8|
|Private F-type|17.26|2.16|472|27.4|218.5|

- */ From Table 5a.

- b/ Effect size is defined as the difference between the predicted private school mathematics score and the predicted public school mathematics score divided by the standard deviation of the public school scores; the measure is the standard deviation unit.

- d Tuition and other fees reflect the long-run average cost pe_ student in the private schools. For the public schools, the following formula is used: C - X/(l - N) where C - cost per student, X - salary expenditure -

- per student and N non-salary cost as a proportion of total cost.

F-tyDe Versus 0-tyne Private Schools. In comparison with the private 0-type schools, the private F-type schools have a 70% higher cost to achievement ratio. A major reason for this higher ratio is much larger expenditures for salaries; private F-type schools spend 83.5% more per student for salaries than do the private 0-type schools. The additional cost is associated with mathematics achievement scores that are 34.2% higher at the
18 Table 4: Basis Risk for Latin American Crudes and Brent. 1986-1990

|Crude|Country|Basis Risk (%)|
|---|---|---|
|Istmo|Mexico|12|
|Oriente|Ecuador|22|
|Cano Limon|Colombia|15|
|Lagotreco|Venezuela|31|
|Lagotreco 1/|Venezuela|22|
|Tia Juana Light_2/_|Venezuela|35|
|Tia Juana Light|Venezuela|27|
|Bachaquero|Venezuela|40|
|Brent|United Kingdom|11|

I/ Regression results for the sub-period August 1988 to December 1990. Z/ Regression results for the sub-period October 1988 to December 1990.

Source: Claessens and Varangis (1991).

The quality of Venezuelan crudes is different from that of WTI and Brent crudes; this difference is due to the differences in API gravity and sulfur content (see Table 5). However, the qualities of the Mexican, Colombian and Ecuadorian crudes are quite similar to the Venezuelan crudes. Thus, if quality were the only difference, we would expect basis risk Table 5: Quality Characteristics of Selected Crude Oils

|____|____<br>API|%Sulfur|Vanadium|
|---|---|---|---|
|Brent|38.2|0.3|0|
|WTI|39.0|0.4|0|
|Venezuela (average)|28.4|_1.5_|+|
|Mexico (average)|27.4|2.4|+|
|<br>Colombia (Cano Limon)|29.4|_0.5_|NA|
|Ecuador (Orito)|29.9|0.75|NA|

# Source: PDVSA, ECOPETROL.

Note: Tia Juanna Light has 310 API, Bachaquero 170 API and Lagotreco 300 API, and Lagotreco 300 API.
**<u>Table 3: Effects of land rights on agricultural investment (IHPS)</u>**

||<br>**Organic ma**|<br>**nure applied**|**Cash cro**|**p planted**|
|---|---|---|---|---|
|Has right to decide on crop|0.029|0.030|0.035|0.036|
|cultivation (_β_11)|[0.631]|[0.558]|[0.325]|[0.284]|
|Has right to invest (_β_12)|-0.091|-0.083|-0.037|-0.034|
||[0.299]|[0.333]|[0.647]|[0.686]|
|Has right to bequeath land (_β_13)|0.118**|0.121**|0.059|0.059*|
||[0.019]|[0.021]|[0.135]|[0.095]|
|Have right to sell land (_β_14)|0.078***|0.068**|-0.018|-0.023|
||[0.006]|[0.029]|[0.791]|[0.745]|
|Parcel characteristics|No|Yes|No|Yes|
|No. of observations|2,011|2,011|2,011|2,011|
|R-squared|0.775|0.777|0.576|0.579|
|Mean of dependent variable|0.233|0.233|0.070|0.070|
|Sd. of dependent variable|(0.416)|(0.416)|(0.238)|(0.238)|
|Mean of right to bequeath land|0.724|0.724|0.724|0.724|
|Sd. of right to bequeath land|(0.447)|(0.447)|(0.447)|(0.447)|
|Mean of right to sell land|0.656|0.656|0.656|0.656|
|Sd. of right to sell land|(0.475)|(0.475)|(0.475)|(0.475)|

_Notes_ : IHPS data are used and the sample includes owned parcels only. Household fixed effects and parcel size are controlled for throughout. Parcel characteristics include soil type (sandy, between sandy and clay, or clay), land slope (flat, slight slope, moderate slope, or hilly), and whether land is in swamp/wetland. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

18
For example, a World Bank Institutional and Governance Review of Honduras (World Bank 2009: 23-26) highlights inefficiencies, corruption and capture in the judiciary, which forms part of a broader narrative of capture and clientelism across all branches of the state. However, an understanding of the histories of violent conflict in Honduras can shed a different light on the provenance of such behavior, and situate in a set of broader intersubjective meanings shared and contested amongst (parts of) society. Honduras, under the influence of private and state actors and a key route for control of the banana trade, suffered from civil and transnational violent conflict through the first half of the 20<sup>th</sup> century, and was repeatedly occupied by American troops during this period (Langley 2002: 170-186). At the same time, there was aggressive action by banana companies to undermine collective labor movements, moving thousands of workers of different ethnicities from one country to another to saturate labor markets and reduce wages (Bourgois 1989: 217; Euraque 2002: 240-2). In the Honduran context, the notion that patron-client relations are intersubjectively understood as providing security and safety, rather than simply being an issue of corruption, allows collective action problems to be reconceptualized in light of historical contingencies. Such an understanding would undermine a competition-based approach to reform – people are unlikely to flock to the “best-“performing institutions but will rather stay within the safe confines of neopatrimonial networks - and opens the door for policy and programming based on security concerns rather than or alongside governance and anti-corruption interventions.

# **III. Conclusions and Implications for Analysis, Policy and Programs**

In this paper we have argued that current understandings and analyses of collective action are limited, emphasizing reified institutions and the incentives and information that structure action around them. This is particularly true of fragile and conflict-affected contexts, which cannot be fully understood without reference to their social and historical production. We have used examples of collective action around common pool environmental resources, and collective use of (and subordination to) rule of law institutions in situations of fragility, conflict and violence to demonstrate the analytic and explicatory bounds of such an approach.

Specifically, we have sought to show that the concept of “intersubjective meanings” – shared understandings that give rise to shared meaning systems – is essential for understanding the social 27
# **Box 2: What about financial openness?**

Economic models have traditionally identified financial markets as the most important shock absorber because they help diversifying macroeconomic risk and shifting resources where they are most needed. Evidence remains however inconclusive. While, among others, O’Donnell (2001), Loayza and Raddatz (2007), and Calderon _et al_ . (2007) find evidence that financial openness helps countries reduce their growth volatility, Easterly, Islam, and Stiglitz (2000) do not find a significant impact of financial openness on output growth volatility.

Our estimations lean towards a beneficial impact, as they find that more openness to financial markets reduces output growth volatility. Our data show that East Asian countries have improved their financial openness although they remain below OECD levels (Figure 12), and that such an improvement has contributed to lower output growth volatility by an average of 7.6 percent.

**Figure 12: Financial openness in the world**

<!-- Start of picture text --> 81.2<br>59.0<br>44.0<br>34.8<br>31.1<br>20.7 22.1<br>13.1<br>8.9 9.8<br>5.4 3.8<br>0.6 1.2 0.2<br>NON-OECD NON-EAP OECD EAP NON-OECD<br>1961-70 1971-80 1981-90 1991-00 2001-05<br>Sources: author’s own calculations using Lane and Millesi-<br>Ferretti (2007). Financial openness is measured as the<br>unweighted group’s average of the sum of Assets and Liabilities<br>as a percentage of GDP.<br>Financial Openness<br><!-- End of picture text --> 16
**Figure 6: Sudan's Democratic Performance vis-a-vis the Median Sub-Saharan Country (19722005), Freedom House Political Rights**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>Sudan<br>Sub-saharan Africa<br>3<br>2<br>1<br>0<br>Note: The index ranges from 1 (free) to 7 (not free).<br>Source: Freedom House 2007.<br>1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Freedom House Political Rights Index<br><!-- End of picture text --> Given the current state of democracy and the legacy of political oppression, it might not be realistic to expect fundamental change in the level of democracy in the next three years. Still, we contend that free and fair elections in 2009 (as mandated by the CPA) that would lead to more democratic governments in both Khartoum and Juba would more accurately reflect the interests and will of all of the people of Sudan, including the interests of Juba.

We contend that because both actors (especially Khartoum) face a credibility constraint, even limited democratization in the upcoming election could serve as a useful commitment device and resolve some of the credibility issues that are currently contributing to excessive militarization by both actors and the possibility of unwanted 31