rafmacalaba/datause-encoder-data huggingface dataset:
Lagakos, David, Benjamin Moll, Tommaso Porzio, Nancy Qian and Todd Schoellman (2018). “Life Cycle Wage Growth Across Countries”. Journal of Political Economy, forthcoming.

Levitt, Steven, John List, Susanne Neckerman and Sally Nadoff (2016). “The Behavioralist Goes to School: Leveraging Behavioral Economics to Improve Educational Performance.” American Economic Journal: Economic Policy. 8(4): 183‐219.

- Manuelli, Rodolfo and Ananth Seshadri (2014).  “Human Capital and the Wealth of Nations”.  American Economic Review.  104(9):2736‐2762

Mincer, Jacob (1958). “Investment in Human Capital and Personal Income Distribution”. Journal of Political Economy. 66(4):281‐302.

- Montenegro, Claudio and Harry Patrinos (2014). “Comparable Estimates of Returns to Schooling Around the World”. World Bank Policy Research Working Paper No. 7020.

OECD (2016). “PISA 2015 Results (Volume 1). Excellence and Equity in Education”. Paris, OECD. <u>http://dx.doi.org/10.1787/9789264266490‐en.</u>

- Patrinos, Harry Anthony, and Noam Angrist. 2018. “A Global Dataset on Education Quality: A Review and an Update (1965–2018).”  World Bank Policy Research Working Paper No. 8592.

Ravallion, Martin (2011). “Mashup Indices of Development”. World Bank Research Observer. 27:1‐32.

Schoellmann, Todd (2012). “Education Quality and Development Accounting”. Review of Economic Studies. 79:388‐417.

UN (2016). “Guide on Measuring Human Capital” (https://www.unece.org/index.php?id=44704).

- UNESCO, World Bank, UNICEF and Global Partnership for Education (2014).  “Education Sector Analysis ‐

- Methodological Guidelines”. (https://www.globalpartnership.org/content/methodological <u>guidelines‐education‐sector‐analysis‐volume‐1).</u>

United Nations University (2012). “Inclusive Wealth Report”. Cambridge: Cambridge University Press.

Victora, C. G., et al. (2008). “Maternal and Child Undernutrition: Consequences for Adult Health and Human Capital. The Lancet. 371(9609):340‐57.

- Weil, David (2007).  “Accounting for the Effect of Health on Economic Growth”.  Quarterly Journal of Economics.  156‐1306.

World Bank (2018). “The Changing Wealth of Nations”.

57
Table 1 presents the correlation matrix between all variables in CAMU and WAMU and shows that: (1) the correlation coefficients between GDP, and INV, POP and OPEN present the expected positive signs in both regions; (2) TOT are negatively correlated to GDP in both regions; and (3) there is a negative correlation between natural resource rent and GDP in WAMU countries.

Table 2 presents descriptive statistics for all variables in CAMU and WAMU. Based on this table, it can be concluded that GDP growth and inflation rate are the most dispersed variables in both groups of countries.

_Table 1: Correlation Matrix of Variables in Level – CAMU and WAMU_

|**Variables**|**GDP**|**INV**|**CPI**|**POP**|**OPEN**|**TOT**|**NRES**|
|---|---|---|---|---|---|---|---|
|||<br>|**CEMAC/**<br>|**CAMU**<br>||||
|**GDP**|1.000|0.401|0.705|0.484|0.385|-0.244|0.073|
|**INV**|0.401|1.000|0.343|-0.177|0.563|-0.093|0.486|
|**CPI**|0.705|0.343|1.000|0.555|0.144|-0.286|-0.069|
|**POP**|0.484|-0.177|0.555|1.000|-0.210|-0.149|-0.452|
|**OPEN**|0.385|0.563|0.144|-0.210|1.000|-0.109|0.690|
|**TOT**|-0.244|-0.093|-0.286|-0.149|-0.109|1.000|0.140|
|**NRES**|0.073|0.486|-0.069|-0.452|0.690|0.140|1.000|
|||**W**|**AEMU/**|**WAMU**||||
|**GDP**|1.000|0.166|0.560|0.838|0.314|-0.200|-0.411|
|**INV**|0.166|1.000|0.047|0.263|0.210|-0.015|-0.170|
|**CPI**|0.560|0.047|1.000|0.698|0.410|-0.429|-0.250|
|**POP**|0.838|0.263|0.698|1.000|0.288|-0.283|-0.450|
|**OPEN**|0.314|0.210|0.410|0.288|1.000|-0.330|-0.079|
|**TOT**|-0.200|-0.015|-0.429|-0.283|-0.330|1.000|0.116|
|**NRES**|-0.411|-0.170|-0.250|-0.450|-0.079|0.116|1.000|

Page | 16
The time variation of job destruction has been higher than that of job creation in Lithuania, which is a typical phenomenon observed also in developed market economies. For example, while the difference between the highest and the lowest job destruction rates was 7 percentage points during the period under question, the difference between the highest and the lowest job creation rates was less than 4 percentage points. Job destruction has been more sensitive to changes in economic conditions than is job creation. Accordingly, the time variation of job turnover in Lithuania has been largely accounted for by variation in job destruction.

# **The Pattern of Job Turnover**

Let us now focus on the structure of job turnover. First, job destruction has tended to exceed job creation, which has led to a fall in employment.<sup>7</sup> This implies that the extent of excess job reallocation in Lithuania has been limited by job destruction. The number of jobs reallocated was equal to the number of new jobs that were created.

Second, gross job creation has taken place mostly in existing firms. This is a typical pattern in several economies (in the U.S., instead, it is new entrants which tend to create most of new jobs. Still, new entrants played a significant part in job creation in Lithuania, too. For example, out of nearly 11 percent of new jobs that were created in 1999-2000, 7 percent were created owing to expansions of existing firms, and close to 4 percent were due to the entry of new businesses.

Third, job destruction occurs mainly in existing firns, while firm closures play a secondary, although non-eligible, role.<sup>8</sup> For example, out of 15 percent of all jobs that were destroyed in 1999-2000, less than 2 percent were lost due to firm closures, and over 13 percent owing to reductions in employment by existing firms. It should be noted, however, that in the aftermath of the Russian crisis (1998-1999), firm closures accounted for a substantial part of job destruction. So, an increased proportion of jobs lost due to **7** The data on employment growth presented in Table 6 are not consistent with published data on employment **in** Lithuania. Specifically, data on job destruction and job creation imply larger than official loss in employment than. This can be due to the fact that data presented here do not cover all sectors of the economy (e.g., agriculture, public and social services).

> 8 Firm closures are usually substantially underrepresented in most surveys, however this does not seem to be the case in Lithuania, where the statistical agency takes effort to determine in each case whether a non-response is due to **firm** closure or to a refusal to respond.

15
# TABLE II

BASELINE ACCOUNTING RESULTS AND COMPARISON TO QUANTITY LITERATURE

|||Our Estim|ates|||Literature||
|---|---|---|---|---|---|---|---|
||_w_= 0|_w_= .15|_w_= .2_0_|_w_= .25|Hall and C. Jon<br>(1999)|es<br>Hendricks<br>(2002)|Schoellman<br>(2011)|
|h90/h10|1.80|2.45|2.70|3.06|2.00|2.10|4.70|
|h90/h10<br>y90/y10|0.26|0.35|0.39|0.44|0.09|0.22|0.21|
|var[log(h)]<br>var[log(y)]|0.09|0.20|0.25|0.30|0.06|0.07|0.26|

_Notes:_ y: real output per worker (2000-2010); h: human capital based on school and learning estimates (2000-2010). We convert HLO units into standard deviations based on a cross-country standard deviation of 68 to correspond to the returns to schooling parameter value which is given per standard deviation.

_Source:_ Schooling data are from Barro-Lee (2013). GDP data are from PWT 9.0. Learning estimates are from our database. Literature estimates are derived from the referenced papers 19
offsets the consumer surplus increase from reducing taxes, and the total welfare change is negative.

Table 8: Welfare comparison of baseline with status quo and alternative scenarios

|**Variable**|**No**<br>**Policy**<br>**(level)**|**Current**<br>**Policy**<br>**(diff)**|**Sales tax**<br>**reduction**<br>**only (diff)**|**Tariff**<br>**reduction**<br>**only (diff)**|**Carbon**<br>**tax**<br>**(diff)**|
|---|---|---|---|---|---|
||**Panel A: Yea**|**r 2019**||||
|Electric vehicle market share|0.06%|0.51%|0.06%|0.22%|0.56%|
|Hybrid vehicle market share|0.08%|0.42%|0.08%|0.19%|0.46%|
|Average consumer surplus (USD per<br>consumer)|9380.7|30.5|5.2|14.4|-202.9|
|Average profit (USD per vehicle)|2287.5|-3.7|-0.5|-1.5|-40.3|
|Total Consumers Surplus (millions USD)|<br>1150.7|5.9|0.6|1.7|-24.0|
|Total profit (millions USD per market)|343.1|-0.2|-0.1|-0.2|-4.7|
|Fiscal cost (million USD)|0.0|6.4|0.4|2.2|-58.3|
|Total Welfare(million USDper market)|1493.8|-0.7|0.1|-0.7|29.7|
||**Panel B: Yea**|**r 2020**||||
|Electric vehicle market share|0.08%|0.51%|0.07%|0.24%|0.57%|
|Hybrid vehicle market share|0.45%|2.18%|0.41%|1.01%|2.75%|
|Average consumer surplus (USD per<br>consumer)|7004.9|48.6|9.6|23.8|-101.4|
|Average profit (USD per vehicle)|2164.7|-10.3|-2.1|-5.2|-42.4|
|Total Consumers Surplus (millions USD)|<br>552.4|9.1|0.7|2.0|-7.3|
|Total profit (millions USD per market)|208.4|-0.6|-0.2|-0.4|-3.1|
|Fiscal cost (million USD)|0.0|9.3|1.0|4.1|-30.1|
|Total Welfare(million USDper market)|760.8|-0.9|-0.4|-2.4|19.7|

Note: This table shows the results of the counterfactual analysis for market shares for electric and hybrid vehicles and welfare measures for five scenarios. For the "No Policy" scenario, the calculations are presented in levels. For all other scenarios, calculations are the increments from the "No Policy" scenario. "Average consumer surplus" and the "Average profit per vehicle" are the annual averages weighted by the number of vehicle units sold. "Total consumer surplus," "Total producer surplus," and "Fiscal Cost" are calculated annually. 'Total Welfare" is the sum of "Total consumers surplus" and "Total producer surplus" minus the "Fiscal cost." The negative welfare change may at first appear to be surprising. The sales tax and import tariff in the no-policy scenario are distortionary, and one might expect that reducing these taxes should increase private welfare. However, that intuition does not take account of the pre-existing distortion caused by market power. Specifically, because demand curves for hybrid and electric vehicles tend to be flatter than demand curves for gasoline vehicles, equilibrium markups tend to be higher for gasoline vehicles than for hybrid and electric vehicles. In the no-policy scenario, for example, the average markup for gasoline vehicles is about 30 percent higher than for other 24
|_Basic controls + Industry dummies_<br>High school general/ primary|0.567|0.223|0.736|_0.169_|
|---|---|---|---|---|
|High school vocational/ primary|0.438|0.335|0.865|_0.427_|
|Tertiary/primary|0.773|0.624|1.332|_0.559_|

***** ***Changes rather than percentage changes are given.

Table 10b: Returns to skill over time – Employed for wages

||1998|2002|2007-8|Change****<br>1998-2007|
|---|---|---|---|---|
|_Basic controls only_|||||
|Upper secondary and above/<br>Lower Secondary and below|0.315|0.264|0.769|_0.454_|
|At least some tertiary/<br>Secondary and below|0.299|0.376|0.701|_0.411_|
|_Industry dummies included_|||||
|Upper secondary and above/<br>Lower Secondary and below|0.280|0.282|0.743|_0.463_|
|At least some tertiary/<br>Secondary and below|0.286|0.392|0.684|_0.398_|

******** Changes rather than percentage changes are given.

20
# **Table 6: Costs of a Plain Vanilla Domestic Bond Issue**

**(in BRL$ mns; US$ mns in brackets)**

||50|150|300|600|1,200|
|---|---|---|---|---|---|
|**Cost Category:**|(17)|(51)|(101)|(203)|(405)|
|Investment Banking Fees|1,480,000|3,255,000|6,240,000|7,200,000|14,400,000|
|Regulatory fee - CVM|82,870|82,870|82,870|82,870|82,870|
|Commercial registry|127|127|127|127|127|
|ANBID (certification)|5,000|15,000|30,000|30,000|30,000|
|BOVESPA fee|7,800|23,400|61,200|122,400|244,800|
|Depository|24,000|24,000|24,000|24,000|24,000|
|Legal Fees|80,000|80,000|80,000|80,000|80,000|
|Marketing / Disclosure|269,421|269,421|269,421|269,421|269,421|
|Rating Agency|326,000|414,800|414,800|414,800|414,800|
|**Total Initial Costs**|**2,275,218**|**4,164,618**|**7,202,418**|**8,223,618**|**15,546,018**|
|**Costs as % of Issue Size**|**4.55%**|**2.78%**|**2.40%**|**1.37%**|**1.30%**|

Table 6 shows the breakdown for issuance costs for domestic bonds. Surprisingly, one of the highest costs of issuing in the Brazilian debt case stems from the fixed fees associated with publication. In particular, the required publishing fees from start to finish in the newspaper Gazeta amount to over BRL$142,000, and for the Valor Economico, an additional BRL$ 93,000 (as of 2002). These can represent significant fees for small issues.

The total fees for small issues are thus quite high (though slightly smaller than Chile’s case, since there is no large tax). Small issues still exist in the market, however. In 2001early 2003 about 18% of all issues were less than BRL$150 million.

**Table 7:  Costs of an Ordinary IPO**

## **(in BRL$ mns; US$ mns in brackets)**

||50|150|300|600|1,200|
|---|---|---|---|---|---|
|**Cost Category:**|(17)|(51)|(101)|(203)|(405)|
|Investment Banking Fees|2,250,000|6,750,000|12,000,000|18,000,000|36,000,000|
|Regulatory fee - CVM|82,870|82,870|82,870|82,870|82,870|
|Depository|24,000|24,000|24,000|24,000|24,000|
|Quarterly Enforcement fee|13,259|13,259|13,259|13,259|13,259|
|BOVESPA fee|4,160|12,480|24,960|49,920|99,840|
|Internal Registry|240,000|240,000|240,000|240,000|240,000|
|External Auditor|180,000|180,000|180,000|180,000|180,000|
|Legal Fees|45,000|45,000|45,000|45,000|45,000|
|Marketing / Disclosure|175,000|175,000|175,000|175,000|175,000|
|Rating Agency|100,000|300,000|370,500|370,500|370,500|
|**Total Initial Costs**|**3,114,289**|**7,822,609**|**13,155,589**|**19,180,549**|**37,230,469**|
|**Costs as % of Issue Size**|**6.23%**|**5.22%**|**4.39%**|**3.20%**|**3.10%**|

Perhaps due to a relative lack in competition and to a slow equity market, the fees for issuing equity are much higher, as illustrated in Table 7. For the largest deals, the banking fees alone amount to 3% of face value, and go as high as 4.5% for small issues. When issuing equity, firms are required to hire an external auditor, and pay different types of regulatory fees. In addition, they must maintain an internal registry for 16
To extrapolate based on a developing country example, India sees around 5,000 $1m-plus contracts signed in the roads sector a year. Assuming India’s share of developing country road construction is about the same as its share of developing country national income, this suggests a little over 60,000 $1m-plus roads contracts in developing countries each year –or a little under an average of 400 per developing country. Road construction is probably the largest single government-financed construction activity worldwide. As a back of the envelope calculation, it might be expected that the average number of $1mplus government financed construction contracts per developing country might be on the order of 2,000 per year.

# **_Figure Four: World Bank-Financed Contracts by Size: Number and Dollar Value of Contracts_**

<!-- Start of picture text --> 10000<br>8000<br>6000<br>4000<br>2000<br>0<br>$k Size of Contract<br>Number of Contracts Dollar Value of Contracts ($k)<br>All >1 >10 >50 >100 >500 >1,000 >5,000 >10,000 >50,000<br><!-- End of picture text --> With a higher cut-off, fewer contracts would be involved. As we have seen, the state government of Victoria publishes online the full contract for any government tender worth above AUS$10m (approximately USD7.7m). A cut-off of USD5m would affect 4.2 percent of World Bank-financed contracts (326 contracts) accounting for 60 percent of total contract value in FY05. A cut-off of USD10m would affect 2.0 percent of Bankfinanced contracts (158 contracts) accounting for 47 percent of total contract value in 2005.

It is plausible to imagine significant financial savings to governments from contract publication in terms of reduced prices due to reductions in corrupt payments. Investment climate surveys suggest that bribe fees equal an average of about 3.2 percent of contract values–with sector-level estimates ranging considerably higher. Greater threat of scrutiny can lead to significant reductions in corruption. The threat of an audit reduced 12
|Prod function (46)|2.17%|36.96%|60.87%|
|---|---|---|---|
|Cross-country reg (29)|13.79%|37.93%|48.28%|
|Cost function (1)|0.00%|100.00%|0.00%|
|Growth accounting (1)|0.00%|0.00%|100.00%|
|Total (77)|6.49%|37.66%|55.84%|

A number of stylized facts emerge from this initial view of the data. Overall, positive effects of infrastructure are found more often in the sample of developed countries, and when the dependent variable is output level rather than output growth or productivity. As for the independent variable, more conclusive results are obtained by studies using physical indicators rather than measures of public capital. Within these categories, looking at the specific sectors for which more than a few studies are included, positive effects are found mostly for telecom, roads and electricity in that order. Finally, studies based on a production function framework reach more positive conclusion that those relying on cross-country regressions. These results are discussed further in what follows when specific related issues like permanent versus transitory effects, or the quality of different indicators of infrastructure are addressed.

As for the value of the estimates found in the literature, a first generation of studies on US state-level data, such as Aschauer (1989), Munnell (1990) and Ford and Poret (1991), found output elasticities of public capital varying between 0.31 and 0.54. Estimates of the marginal product of a unit of public capital from these elasticities are bound to be approximate, as the results are very sensitive to measurement errors in the ratio Y/G, but the rough implication is a marginal product around 100%, meaning that infrastructure would pay for itself in one year or less (Gramlich, 1994). For this reason, these numbers have often been dismissed as unrealistic. In particular, as Gramlich (1994) pointed out, they raise the question of why capital does not flow to infrastructure investment if rates of return there largely outperform those of other types of investments.

17
**Table 8. Projections for all countries from 2010 to 2030 under populist scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal L**|**ary**<br>**abor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|28%|18%|10%|23%|38%|52%|49%|44%|
|Argentina|46%|24%|8%|36%|37%|32%|18%|39%|60%|
|Australia|87%|57%|26%|5%|21%|25%|8%|22%|48%|
|Austria|87%|55%|24%|5%|16%|20%|8%|30%|56%|
|Burundi|7%|3%|1%|7%|8%|7%|86%|89%|92%|
|Belgium|86%|53%|23%|5%|18%|21%|9%|29%|56%|
|Benin|6%|3%|1%|7%|7%|6%|87%|90%|94%|
|Bangladesh|5%|0%|0%|38%|49%|55%|57%|51%|45%|
|Bulgaria|75%|55%|33%|18%|40%|62%|8%|5%|5%|
|Bolivia|13%|5%|0%|33%|29%|23%|54%|66%|77%|
|Brazil|58%|34%|14%|20%|27%|26%|23%|38%|59%|
|Botswana|17%|3%|0%|73%|91%|88%|10%|6%|12%|
|Central African||||||||||
|Republic|5%|2%|0%|8%|5%|5%|87%|93%|95%|
|Canada|82%|53%|23%|5%|18%|21%|13%|29%|57%|
|Switzerland|86%|51%|22%|5%|13%|17%|9%|36%|61%|
|Chile|56%|37%|17%|21%|34%|35%|23%|29%|47%|
|China|34%|30%|20%|19%|53%|75%|48%|17%|5%|
|Côte d'Ivoire|14%|4%|0%|10%|5%|5%|76%|90%|95%|
|Cameroon|17%|8%|2%|12%|11%|8%|71%|81%|89%|
|Congo, Rep.|12%|5%|1%|17%|15%|11%|71%|80%|88%|
|Colombia|30%|16%|6%|28%|31%|30%|43%|53%|64%|
|Costa Rica|57%|37%|16%|24%|36%|36%|19%|27%|48%|
|Germany|83%|46%|19%|11%|21%|22%|6%|33%|58%|
|Denmark|88%|53%|22%|7%|21%|22%|5%|26%|56%|
|Dominican Republic|30%|16%|5%|34%|39%|37%|37%|45%|58%|
|Ecuador|27%|14%|4%|31%|32%|29%|42%|54%|67%|
|Egypt, Arab Rep.|54%|31%|11%|26%|31%|27%|21%|38%|62%|
|Spain|65%|46%|22%|26%|42%|47%|9%|11%|31%|
|Finland|85%|53%|23%|6%|21%|24%|9%|26%|53%|
|France|82%|49%|20%|11%|24%|23%|6%|27%|57%|
|United Kingdom|85%|52%|20%|5%|15%|16%|10%|33%|64%|
|<br>Ghana|10%|4%|1%|16%|17%|18%|74%|79%|82%|
|Guinea|7%|5%|3%|5%|5%|5%|88%|90%|92%|
|Greece|71%|53%|24%|5%|12%|18%|24%|35%|58%|
|Guatemala|21%|9%|1%|38%|32%|22%|41%|59%|77%|

49
Furthermore, it is also standard to report relative risk ratios, which indicate the effect of changes in explanatory variables x(i) in X on the probability of observing one of the three transitions relative to the probability of exit: In other words, the exponential of the coefficient on each explanatory variable x(i) gives us the marginal effect of variations in x(i) on the probability of observing one of the three firm transitions relative to observing exit. This statistic, the relative risk rate (RRR), varies between zero and positive infinity, implying that any number above one reflects increases in the probability of observing one of the transitions of interest relative to exit. Numbers below one imply lower probabilities. Since the ML coefficients on export-promotion treatment and the RRR are expressed relative to exit, one could compute any relative probability or RRR with respect to any benchmark transition probability. This further implies that the ad hoc imposition of a value to any of the relevant coefficients is immaterial for the interpretation of the results.

We are also interested in how the use of export promotion services affects export intensity. We therefore explain the change in export intensity using ordinary least squares and the same set of explanatory variables as for the Multinomial Logit.

As mentioned above, one problem with these estimates is that export promotion services are not randomly allocated across firms, but rather firms self-select into export promotion services. Blundell and Costa Dias (2002) suggest that a combination of differences-in-differences with propensity matching can help solve the inherent bias in this type of program evaluation exercise. The propensity score matching allows us to estimate the impact of using export promotion services to compare firms that have similar observable characteristics. Indeed if firms that use export promotion services are systematically different from those that do not use export promotion services, and these differences are correlated with export outcomes, then our treatment results may be biased. This is particularly important because, as shown in section 2, firms that used export promotion services tend to export a relatively larger share of their output.

Using 2006 data (i.e., pre-treatment data), we use a Logit model to explain the probability that a firm used export promotion services. The variables are size (log of the number of full time 8
2 where the interactions between trade policies and industrial organization issues give rise to welfare costs not recognized in the more traditional applied general equilibrium trade models where constant returns to scale prevail. The mechanisms incorporated in the model are in the spirit of Harris, though because of QRs, the modelling of trade poltcies and of the linkages between pricing behavior and barriers to entry are different.

The remainder of the paper is organized as follows. Section 2 reviews evidence on linkages between firm behavior, firm size and restrictiveness of the trade regime in semi-industrial developing countries that lend support to our stylized modelling strategy. Section 3 outlines the model. Section 4 reports on simulations from a three sector model that explores the sensitivity of numerical estimates to the parameters describing foreign trade and firm behavior under increasing returns of scale.

# 2. Profitability, and Firm Behavior in Manufacturing Under Different Trade Regimes: Evidence and Modelling Issues

Evidence about the extent to which restrictive foreign trade regimes in developing countries give rise to oligopolistic behavior and suboptimal scale is scant. This is so because few countries have drastically liberalized QRs. One exception is Chile where evidence on firm profitability and concentration duting a regime ridden by tariff and non-tariff barriers (1967) can be compared with firm profitability and concentration during a quasi-free-trade regime (1979). Another example is Korea where many observers agree that conglomerates exercised market power on domestic sales (see World Bank 1987). The evidence from the manufacturing sectors in these two countries is summarized in table 1.