rafmacalaba/datause-encoder-data huggingface dataset:
The relationship between democracy and inequality is likely to be non-monotonic (Figure 2) –it shows the form of an inverted U. Acemoglu and Robinson (2002) suggest that, “economic development tends to increase inequality, but this inequality contains the seeds of its own destruction, because it induces a change in the political regime towards a more redistributive system.” In this setup, where I measure contemporary inequality, this is a plausible mechanism if it acts through an intergenerational channel –a channel that can be influenced by long-periods of democracy, for example education (Saint-Paul and Verdier, 1993). Indeed, the historical evidence suggests that democratization led to a surge in redistribution: The increased supply of educated workers that came to be as a result, and the direct impacts of these redistributive efforts, are key factors in the non-monotonic pattern of inequality (Acemoglu and Robinson, 2002).

Figure 2: Stock of democracy vs. labor income Gini <!-- Start of picture text --> (a) (b)<br>R2=0.18 R2=0.11<br>−300 −250 −200 −150 −100 −50 0 50 100 150 200 −300 −250 −200 −150 −100 −50 0 50 100 150 200<br>Democracy stock between 30 years before birth to 20 years after Democracy stock between birth year to 20 years after birth<br>70 70<br>60 60<br>50 50<br>40 40<br>Standard deviation of years of education Standard deviation of years of education<br>30 30<br><!-- End of picture text --> Note: The figures above assume _r_ = 0 _._ 01, however the U-shape also appears for values higher than 0.01 (figures available upon request to the author). Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

**D** is endogenous. Indeed, some researchers attribute the persistently high level of inequality in Latin America to the long lasting effects of colonial institutions. The encomienda (which gave Spanish conquistadors the right to Amerindian labor), the mita (a system of forced labor used in the mines), the repartimiento (the forced sale of goods to Indians, typically at highly inflated prices), the resguardo (which organized the Amerindian labor to be provided in the _Haciendas_ and mines) and slavery, established important sources of economic and political inequality that endured after the region gained independence from their colonial masters and despite significant social, economic and political changes during the 20th century (Perry et al., 2003). This would likely lead to omitted variable bias, _E_ ( _Dc,p,_ _<u>ε</u> c,g,p,e_ ) = 0.

11
MINIMUM WAGES IN CAMBODIA 16 <!-- Start of picture text --> All workers<br>Garment sector Other sectors<br>-8 -6 -4 -2 0 2 4 6 -8 -6 -4 -2 0 2 4 6<br>Quarters from minimum wage hike<br>Point estimates 95% CI<br>Female workers<br>Garment sector Other sectors<br>-8 -6 -4 -2 0 2 4 6 -8 -6 -4 -2 0 2 4 6<br>Quarters from minimum wage hike<br>Point estimates 95% CI<br>.2 .05<br>.15<br>0<br>.1<br>-.05<br>.05<br>-.1<br>0<br>Impact on log(hourly wages)<br>-.05 -.15<br>.2 .15<br>.1<br>.15<br>.05<br>.1 0<br>.05 -.05<br>-.1<br>0<br>-.15<br>Impact on log(hourly wages)<br>-.05 -.2<br><!-- End of picture text --> Figure 5. Impact of minimum wage hikes on hourly wage rate _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _αw_ + _βw_ (left panel), and _αw_ (right panel) from Equation (4), with _q_ = _−_ 1 being the reference group. The brown lines show the average of the coefficients 1-8 quarters before the change, and 2-8 quarters after the change. Sample restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock.
Table C.1: Descriptive Statistics

||(1)<br>Overall|(2)<br>Brazil|(3)<br>Vietnam|(4)<br>Senegal|
|---|---|---|---|---|
|Employment|39|49|46|22|
|S.D. of Employment|(215)|(229)|(265)|(125)|
|P10 of Employment|5|7|5|5|
|P50 of Employment|12|18|12|8|
|P90 of Employment|57|74|62|35|
|Firma Age|15|19|9|16|
|Export|0.124|0.041|0.169|0.162|
|Foreign Owned|0.059|0.008|0.120|0.048|
|Sectors:|||||
|Agriculture|0.044|0.016|0.006|0.110|
|Food Processing|0.044|0.042|0.016|0.073|
|Wearing Apparel/Leather|0.084|0.051|0.028|0.172|
|Moto Vehicle|0.003|0.006|0.002|0.000|
|Pharmaceutical|0.001|0.000|0.002|0.000|
|Wholesale & Retail|0.387|0.495|0.419|0.248|
|Finance|0.003|0.000|0.002|0.007|
|Land Transport|0.006|0.002|0.002|0.016|
|Health Service|0.003|0.003|0.003|0.004|
|Other Manufacturing|0.175|0.138|0.207|0.179|
|Other Service|0.250|0.248|0.314|0.190|

Note: Overall is the average of Brazil, Vietnam, and Senegal. Estimates are weighted by the sampling weights. Standard deviation of employment is reported in parenthesis.

Table C.2: Average Technology Sophistication by Country

|||MOST|||GAP||
|---|---|---|---|---|---|---|
||ABF|GBF|SSBF|ABF|GBF|SSBF|
|Overall|1.85|1.90|1.66|0.73|0.77|0.63|
|Brazil (BR)|2.32|2.49|1.92|0.83|0.85|0.83|
|Vietnam (VT)|1.91|1.92|1.80|0.80|0.84|0.74|
|Senegal (SN)|1.31|1.29|1.27|0.55|0.61|0.32|
|Gap: BR - SN|1.01|1.20|0.65|0.28|0.24|0.51|
|Relative Gap|25%|30%|16%|7%|6%|13%|

Note: Overall is the average of Brazil, Vietnam, and Senegal. Relative gap is the difference between Brazil and Senegal relative to the maximum technology gap (( _Brazil − Senegal_ ) _/MaximumGap_ (4)). For the columns on SSBF, the sample is restricted to the firms having sector-specific technologies. Technology measures are weighted by the sampling weights.

72
8 Table 4. Level of Protection in Environmentally Sensitive Goods In Main Industrial Country Markets (1988)

||Teriffs(|weighted average)|Non-tar|iff barriers(% of importc covered)|
|---|---|---|---|---|
|Country|World|Developing|World|Developing|
|EEC|1.5|0.7|9.0|9.0|
|**Japan**|**2.8**|2.3|43.5|48.1|
|United States|2.7|2.7|16.2|32.7|

Source: World Bank SMART **data base.** Revealed comparative advantage (RCA) indexes. Another way to analyze changes in comparative advantages or trade patterns in specific products is through calculating indexes of revealed comparative advantage<sup>7</sup> . Traditionally it has been used to analyze a specific country's revealed comparative advantage in different industries. In this study it is used to measure different countries' RCA's within a specific industry. The RCA measures changes in the share of a country's exports of a product in world exports of the product compared to changes in the country's total share in world exports, i.e, relative to the size of the country. The index allows taking account of the impact of changes in a country's or countries' overall importance in world trade on changes in trade shares in a specific industry. If the ind;x is above one, a country is deemed to have a comparative advantage in a product, i.e. its share in the market of a product was larger than its overall share in world trade. Calculated RCA indexes are presented in Table _5._ The indexes confirm many of the results of the analysis of trade shares. Industrial countries as a whole have maintained their comparative advantage in environmentally sensitive goods (index around 1.0), while that for developing countries has remained below one at 0.9. The aggregate data hide large differences between countries. Austria, Finland and Sweden have 7 The formulae used for calculating the RCA index is RCAji - (xji/Xjt)/(rit/Xtw) where j i industry, i country, w world and t total. The index goes up, for exmnple, when the country inceases itu **share** in **the** world market of the product; it can go down if the country's other export go up or if the country **share** in world trde declines. The RCA ignorec the impact of some protectionist barriers in distorting trade patterns between altenative source of supply, or tade not taking place because of protectionist barriers. This study used total trade in the denomninator versus manufactures, because mAny of the environrnentally sensitive goods are outuide the traditional definition for mnanufactures (SrrC 5-9 less 68, 67). The share of mAnufactures in the sensitive goods **was** 40% in 1970, which increased to 54% in 1990. The use of manufactures **as** total has been justified in other studies due to the distorted nture of world trade in igriculture.
24 Table 3: Trade reduction indexes, OECD countries, all covered products, 1960 to <u>2007 (percent)</u>

||1960-69|1970-79|1980-89|1990-99|2000-07|
|---|---|---|---|---|---|
|**Western European Countries**||||||
|**European Union**|**73**|**53**|**71**|**40**|**24**|
|**EFTA**|**40**|**27**|**24**|**27**|**57**|
|Austria|67|22|2|15|30|
|Denmark|-35|29|72|44|24|
|Finland|28|-8|-43|-51|31|
|France|66|46|70|41|23|
|Germany|105|65|77|45|26|
|Iceland|—|—|59|23|40|
|Ireland|-8|51|123|72|44|
|Italy|47|33|50|28|18|
|Netherlands|104|91|97|52|32|
|Norway|—|—|60|175|120|
|Portugal|**13**|**22**|**26**|**23**|**18**|
|Spain|20|-1|23|27|17|
|Sweden|44|47|-11|-6|33|
|Switzerland|—|—|124|31|23|
|UK|59|50|86|54|33|
|**Europe's transition**||||||
|**economies**|—|—|—|**9**|**11**|
|Bulgaria|—|—|—|10|8|
|Czech Republic|—|—|—|-7|7|
|Estonia|—|—|—|11|4|
|Hungary|—|—|—|-9|-17|
|Latvia|—|—|—|26|17|
|Lithuania|—|—|—|22|-5|
|Poland|—|—|—|10|-12|
|Romania|—|—|—|16|37|
|Russia|—|—|—|-2|22|
|Slovakia|—|—|—|3|3|
|Slovenia|—|—|—|-13|-20|
|Turkey|—|—|—|19|13|
|Ukraine|—|—|—|14|12|
|**North America**|**4**|**3**|**8**|**5**|**4**|
|Canada|7|10|22|17|13|
|US|3|2|7|4|3|
|**Japan & Korea**|**58**|**81**|**126**|**140**|**118**|
|<br>Japan|69|87|123|133|112|
|<br>Korea|10|56|138|177|144|
|**Oceania**|**-5**|**-4**|**-5**|**-3**|**0**|
|Australia|-9|-4|-5|-5|-1|
|NewZealand|2|-3|-6|2|1|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008)
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31
PRE Working Eaper Series

||v|kls~~~~~~~~~ubr|DMQ|Contact<br>forpaper|
|---|---|---|---|---|
|WPS689|Do Tax Policies Stimulate Investmen<br>in Physical and Research and<br>Development Capital?|t Anwar Shah<br>John Baffes|May 1991|A. Bhalla<br>37699|
|WPS690|The Terms-of -Trade Effects from the<br>Elimination of State Trading in Soviet<br>Hungarian Trade|Gabor Oblath<br>- David Tarr|May 1991|J. Smith<br>37350|
|WPS691|Can Debt-Reduction Policies Restore<br>Investment and Economic Growth<br>in Highly Indebted Countries? A<br>Macroeconomic Framework Applied<br>to Argentina|Jacques Morisset|May 1991|S. King-Watson<br>31047|
|WPS692|Health Financing in the Poor<br>Countries: Cost Recovery or Cost<br>Reduction9|J. Brunet-Jailly|May 1991|0. Nadora<br>31091|
|WPS693|Report on Adjustment Lending Il:<br>Lessons for Eastern Europe|Vittorio Corbo|May 1991|_A._Oropesa<br>39075|
|WPS694|Labor Markets in an Era of<br>Adjustment: An Overview|Susan Horton<br>Ravi Kanbur<br>Dipak Mazumdar|May 1991|M. Schreier<br>36432|
|WPS695|Long Term Prospects in Eastern<br>Europe: The Role of External Financ<br>in an Era of Change|Ishac Diwan<br>e Fernando Saldanha|June 1991|S. King-Watson<br>33730|
|WPS696|Macroeconomics of Public Sector<br>Deficits: The Case of Chile|Jorge Marshall<br>Klaus Schmidt-Hebbel|June 1991|S. Jonnakuty<br>39074|
|WPS697|Volatility Reversal from Interest<br>Rates to the Real Exchange Rate:<br>Financial Liberalization in Chile,<br>1975-82|Paul D. McNelis<br>Klaus Schmidt-Hebbel|June 1991|S. Jonnakuty<br>39074|
|WPS698|Tax Policy Options to Promote<br>Private Capital Formation in Pakistan|Andrew Feltenstein<br>Anwar Shah|June 1991|A. Bhalla<br>37699|
|WPS699|Regulation and Deregulation in<br>Industrial Countries: Some Lessons<br>for LDCs|Ralph Bradburd<br>David R. Ross|June 1991|E. Madrona<br>37496|
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# **What Happens When the State Is Bossing around Markets?**

An Analysis of the Performance Differentials between Businesses of the State (BOS) and Private-Owned Enterprises (POEs).

**Dennis Sanchez-Navarro**<sup>1</sup> World Bank, Washington, D.C., USA

**JEL:** L32, L38, L21, L25, D22.

**Keywords** : SOEs, public enterprises, state ownership, firm performance, competition, private investment.

> 1 Email: dsancheznavarro@worldbank.org. The author wants to thank Juan Felipe Rodrigo, Carla Scarlato, Regina Onglao, Francis Ratsimbazafy for their research assistance as well as comments and suggestions received from Martha Martinez Licetti, Denisse Pierola, Xavier Cirera, Mary Hallward-Driemeier, Mariem Malouche, Birgit Hansl, Fausto Patino-Pena in the World Bank and professor Ufuk Akcigit (University of Chicago). The author also thanks for the comments received as part of the academic workshops conducted as part of the elaboration of the WB Business of the State (BOS) flagship. The author also wants to thank to the peer reviewers Dorothe Singer and Yuheng Ding for their valuable comments and suggestions, which are reflected in this final version of the paper. This paper is linked to the programmatic ASA State Footprint in markets: database and indicators for SOE reforms (P179791).
guaranteed private sector debt is defined precisely as its name suggests and is the complement to PPG. Otherwise, most datasets using a sectoral breakdown conform to the standard definition of the main institutional sectors and subsectors given above. We consider PPG vs. PNG debt in Appendix C.2.

## **A.1.3 Sign of Flows**

There remains some confusion about the sign of capital inflows and outflows in the data. This is primarily due to a change in sign conventions that occurred when the BOP data switched from the BPM5 to the BPM6 version. In BPM5, a negative sign indicated that capital was leaving the country on net, regardless of whether it was an asset or liability flow. In the current version of the BOP data (BPM6), a positive asset flow represents capital leaving the country on net by domestic residents, while a positive liability flow represents capital entering the country on net by foreigners. We use the updated convention, where a positive sign indicates an increase in either assets or liabilities, and adjust our interpretation accordingly.

# **A.2 Balance of Payments Data**

The IMF’s Balance of Payments (BOP) data is the most comprehensive dataset available on international capital flows and the basis for our dataset. It comprises two main accounts – the Current Account and the Financial Account.<sup>44</sup> The current account records transactions

## from the real side, capturing imports and exports, factor income, and transfer payments.

> 44A third account, the Capital Account, is generally much smaller than these two. Since the BOP uses double entry bookkeeping, the sum of the accounts should be zero, so a Balancing Account called ”Net errors and omissions” is defined to satisfy the identity: current account + financial account + capital account + net errors and omissions = 0. Errors and omissions are usually interpreted as unrecorded private capital flows (see Forbes and Warnock (2012)).

49
finding of the former study on the role of rainfall in predicting civil wars (through its effect on income) is attenuated, sometimes to the point of statistical insignificance, by the inclusion of spatial effects in the explanatory equation. The presumed importance of such regional effects, supported by the Buhaug-Gleditsch and Jensen-Gleditsch studies, is exactly the conclusion of our narratives.

These spatial interdependencies also have implications for authors such as Goldstein and Pinker (2011), who attempt to conclude that there is a decline in the prevalence of wars. Because these wars form complexes, the countries that are involved may tend to return to a peaceful status at nearly the same time. If it were assumed that the end of violence in a number of countries represented independent events, it might make sense to infer a trend toward peace and to look for its causes. However, if the end of violence in a number of countries is not independent, then there is not a sufficient number of observations to make confident inferences. For this reason among others, the type of inferences that Angell (1910) made about the prospects for peace are always risky.

Fundamentally, the interdependencies among countries raise the question of whether the unit of observation is the individual country or the regional war complex. Econometric studies seem to struggle to incorporate these interdependencies. By contrast, an analysis based on the narratives can easily focus on the regional complex as the unit of observation.

# **4. Conclusions**

As we initially stated, we believe that a list of country-years classified into civil war or membership in a regional war complex or the absence of these situations requires a detailed compilation of country histories based on the case-study literature. This task is feasible for the 35
was not a causal by-product of the Colombian conflict, but an extremely common strategy of war used by illegal armed groups to weaken the enemy’s popular support, clear regions for illegal crop growing and drug trafficking, and expropriate lands and natural resources (Ib ́a ̃nez and V ́elez, 2008). According to Ib ́a ̃nez, Moya, and Vel ́asquez (2006), for instance, between 1993 and 2002 displaced individuals lost 1.2 million hectares of land.

# **II.1 Characterizing internally displaced migrants**

The annual cumulative displaced population from 1995 to 2010, according to the _Registro Unico_<sup>_ ́_</sup> _de V ́ıctimas_ (RUV) from the Colombian Human Rights Observatory is presented in Figure I.<sup>7</sup> The figure shows that the number of displaced individuals began to increase dramatically between 1996 and 2002 in Colombia, when the internal conflict was most intense, as shown by the evolution of conflict-related variables such as armed actions and clashes between armed groups (see panel b of Figure II). Since then, forced displacement has been decreasing slowly in conjunction with a softening of conflict intensity and violence.<sup>8</sup> Data from RUV also suggests that the cumulative population of forced migrants is balanced in terms of gender (51 percent women) and that most are of working age. Colombia’s forced migrants are also young. In particular, 39 percent of forcefully displaced individuals were 15 years old or younger at the time of displacement, this percentage is disproportionately larger than this age group within the population of Colombia as a whole (28 percent). Indeed, 15.5 percent of forced migrants were younger than 5 years of age at the time of migration. Households also tend to be bigger as several members of the extended family tend to live together to save on housing costs (Unidad para la Atenci ́on y Reparaci ́on de V ́ıctimas, 2013). Previous studies, using surveys given to migrants who were forcefully displaced, also report that this population has low education

> 2005. Many of the combatants that had been part of the AUC, however, later fused into new criminal groups that are known today as _Bandas Criminales_ (BACRIM), illegal armed groups that obtained financial resources, mainly through extortion and drug trafficking, to carry on their activities.

> 7 The data is publicly available at: _http_ : _//rni.unidadvictimas.gov.co/RUV_

> 8The problem is still ongoing, however. At the time of writing, in mid-2017, the year’s forceful displacement rate has already surpassed 800.

8
**Table 3: Reviews and Their Composition**

|**Review**|**Learning studies reviewed**<br>**(Total studies reviewed)**|**Inclusion criteria (in brief)**|
|---|---|---|
|Conn (2014)|56<br>(56)|Any formal education level<br>Learning outcomes<br>RCT & quasi-experimental<br>Sub-Saharan Africa<br>1980-2013|
|Glewwe et al. (2014)|67<br>(79)|Primary & secondary school<br>Learning or access outcomes<br>RCT and quasi-experimental<br>Low & middle income countries<br>1990-2010|
|Kremer, Brannen, & Glennerster<br>(2013)|30<br>(34)|Primary school<br>Learning or access outcomes<br>RCT only<br>Low & middle income countries|
|Krishnaratne, White, &<br>Carpenter (2013)|43<br>(76)|Primary & secondary school<br>Access outcomes<br>RCT & quasi-experimental<br>Low & middle income countries<br>1990-2009|
|McEwan (2014)|66<br>(66)|Primary school<br>Learning outcomes<br>RCT only|
|||Low & middle income countries|
|Murnane & Ganimian (2014)|92<br>(130)|Primary & secondary school<br>Learning or access outcomes<br>RCT & natural experiments (no matching or<br>fixed effects)|
|||Low & middle income countries|
|Total learning studies reviewed|227||
|Total studies reviewed|301||

Notes: RCT stands for randomized controlled trial. Learning outcomes are scores in language or reading (in local language or English), mathematics, science, cognitive outcomes, or a composite assessment including any of these. Notably, learning outcomes do not include assessments of computer skills. Access outcomes include enrollment, attendance, and years of schooling. Note that we describe inclusion and not exclusion criteria; for example, where the inclusion criterion is access (learning) outcomes only, this means that only studies that have at least one access (learning) outcome are included in the review, although studies may include other outcomes in addition.

25