rafmacalaba/datause-encoder-data huggingface dataset:
For Kessides and Khan (2009), it was not infrastructure that impeded growth in the 1990s. However, as Eurasian countries returned to more favorable output trends in the 2000s, the deterioration of infrastructure assets (and their quality) added stress on these economies. Using coefficient estimates in Calderon (2007) and its dataset, we estimate the impact of infrastructure on productivity growth for Eurasia. We find that infrastructure development reduced the growth in labor productivity by five basis points per year in 2001-05 relative to 1991-95—an increase in the growth rate of real output per worker of 4.4 basis points due to the accumulation of infrastructure stocks, and a reduction of 9.6 basis points due to the deterioration in the quality of infrastructure services (Annex B).

# **2. ACCUMULATION OF PHYSICAL ASSETS**

Capital formation played an important role in the recent acceleration of GDP growth in Eurasia. A closer look at the 2006-08 period shows that physical capital contributed significantly to GDP growth, explaining between 11 percent (Moldova) and 60 percent (Georgia) of GDP growth performances (Table 2). In earlier years, between 1996 and 2005, physical capital played a less important role in the region, with total factor productivity driving growth performances. Thus, capital accumulation has been a recent phenomenon in the region. In addition, there are distinct differences in the contribution of physical capital to GDP growth between low-income and middle-income countries. In the latter group, the contribution was large (with the exception of Azerbaijan) although slightly below that of countries like China. In the former group, there has been a much wider disparity among countries – from 11 percent in Moldova to 60 percent in Georgia. There is no distinct pattern between resource-rich and other countries in the region. Both groups showed remarkable growth performance over the 2006-08 period. Capital accumulation played an important role in both groups—although with significant variations within each.

**Table 2:** _Sources of GDP growth, 1996-2008_

|||**2006-08**|||**1996-2**|**005**|
|---|---|---|---|---|---|---|
||GDP<br>growth|Contribution<br>(perce|to growth<br>nt):|GDP<br>growth|Contribution<br>(perce|to growth<br>nt):|
|||Total factor<br>productivity|Physical<br>capital||Total factor<br>productivity|Physical<br>capital|
|**Resource-rich**|||||||
|**(Eurasia)**|||||||
|Russia|7.0|38.5%|54.7 %|3.8|128.9 %|-31.5%|
|Kazakhstan|7.3|32.8%|47.7%|6.2|90.1%|-4.8%|
|Uzbekistan*|8.3|71.1%|12.1%|4.5|80%|0%|
|Azerbaijan|20.7|75.3%|19.9%|10.7|60.7%|36.4%|
|**Non Resource-**<br>**rich (Eurasia)**|||||||

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# _4. - QUALITYREGULA TION IN THE AIRPORT INDUSTRY_

# **4.1.- The need for regulation**

The main reason for regulating quality is market failure. Consumers are imperfectly informed about the quality of products at the time of purchase, and are therefore unable to distinguish a bad quality provider from a good quality one. In general, regulation is needed in order to overcome such informational asymmetry. Nevertheless, the quality outcome may differ with the type of market and the temporal dimension. In competitive markets, firms that produce low quality products and sell them at high quality prices will acquire a bad reputation and will be excluded from the market (Klein and Leffler, 1981). In monopolistic situations, the quality of the product is always lower than under a perfect information setting. Imperfect information causes quality deterioration (Shapiro, 1982). Regulators, however, face similar asymmetric information problems regarding product quality.

As we have seen in section 3, privatized airports are usually subject to some sort of pricing regulatory mechanism. Less common is quality regulation, in spite of the likely exploitation of monopoly power in some airport operations. For instance, the British Airports Authority (BAA), although subject to price-capping,28 does not have to comply with a level of quality specified by the regulator. Rather, BAA itself keeps track of their quality records by periodically carrying out quality survey monitoring. It seems that being subject to Civil Aviation Authority (CAA) scrutiny acts as a sufficient incentive to keep high quality standards without any specific regulatory provision. Nevertheless, BAA and the airlines actually agree upon the level of service to be provided. Areas such as check-in, security queues, jetty availability, stand availability and cleaning, project development, and departure and transfer baggage, are usually the main matters under discussion. Performance measures, services standards and compensation payments in case of non-fulfillment, are included in the final service level agreement.

# **4.2.- Monitoring quality: the case of BAA self-regulation**

In order to evaluate quality performance at airports it is necessary to distinguish between the different recipients of airport services and also between the various ways of assessing quality. The main airport customers are the airlines, which in turn depend upon paying passengers. For this reason, any performance measure standards should distinguish between services directly provided to passengers and those intended for airlines. At the same time, there are two main approaches for the assessment of quality. First, there is a subjective approach that bases its analysis on quality surveys that capture the quality perceptions of passengers and airlines. On the other hand, there are also more objective approaches involving the measurement of performance against measurable standards (see Table 4.1).

28 Price-caps might induce quality costs cutting, as operators choose to reduce quality and hence costs, rather than increase efficiency.

37
the standard deviation of the leader’s contribution to growth is around 1.5% (for all leaders) and is statistically nonzero. As leader quality is normally distributed with mean zero in their paper, this is equivalent to Jones and Olken (2005) estimating the distribution of leader quality across all leaders, shown in the solid red line in Figure 1. Their finding is pioneering in showing that leaders matter for growth in general. But it does not tell us much about the contribution of any particular leader, which is the problem faced by constituents and international actors (and is the focus of dayto-day political and academic debates).

<!-- Start of picture text --> Figure 1: Comparing this Paper and Jones and Olken (2005)<br>45<br>40<br>Observe growth data ( ), Seretse Khama (BWA)<br>35 solve signal extraction problem leader contribution<br>ii −ii<br>gg̅ , gg̅ probability density<br>30 (this paper) [Posterior]<br>2)<br>25 ALL leaders growth ii ii −ii ii ii<br>contribribution. μμ |gg̅ , gg̅ ~NN(μμ̂ , RRD<br>20 Probability density from ii ii ii ii −ii<br>Jones and Olken (2005) μμ̂ = ψψ gg̅ −γγ gg̅<br>2 ii μμ ii<br> ) [Prior] RRD = σσ 1 −ψψ<br>15<br>ii μμ<br>μμ ~NN(0, σσ<br>10<br>5<br>95% CI=<br>0 ii ii<br>μμ̂ ± 2RRD<br>-5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8%<br>Leader contribution to per capita GDP growth (value added)<br>Notes: is leader i's effect on growth, and is its estimate. represent the average growth rate residuals under leader i and other<br>leaders in the same country (respectively). See Section 2 for further details.<br>μμii μμ̂ii gg̅ii , gg̅−ii<br>Probability Density<br><!-- End of picture text -->

In contrast, we estimate the growth contribution of every individual national leader since 1950.

Even when we grant the presumption that leaders matter for growth in general, a hugely important question remains: which leaders matter? This is a signal extraction problem, where the true leader contribution is the signal we seek to extract from growth data, but it is obscured by annual countryspecific noise (e.g., measurement error, good or bad luck), country-level growth trends, and global/regional shocks. Our optimal (least-squares) signal extraction method utilizes the overall 3
# **5 Conclusion**

In this paper, we assess publicly-listed star firms in the U.S. We we use financial statement data as conventionally presented, a small percentage of star firms seem to be pulling away from other firms in the economy over time in terms of their return on capital. In particular, star firms in highly skilled industries seem to be pulling away from the others.

However, conventional financial statements do not capitalize R&D expenditures or organizational capital. Once we adjust firms’ returns to capital to address these shortcomings, we see that the differences in firm returns in highly skilled and other industries shrink dramatically and the gap between star firms and other firms does not widen over time. Furthermore, once we adjust markups based on operating expenses for investment in intangible capital, we do find an increase in market power especially in high skilled industries. Star firms tend to be larger, younger, and have higher markups. While they may have more pricing power than other firms, at each level of markup star firms tend to produce more than other firms. In a companion paper Ayyagari et al. [2018], we examine the influences of market power and human capital in an international sample of Overall, our results indicate that there islittle evide nce that the most profitable 10% of firms are pulling away from the rest of the economy. However, there is reason for concern regarding a smaller subset of elite publicly-listed firms. The usual suspects for membership in such an elite group are Apple, Facebook, Google, Amazon, and Microsoft. When we examine these firms individually, the ROIC and markups of most of these elite firms do not seem extraordinary initially and then explode but again only for a couple of firms that have built up a large enough market. However, for these firms, the critical policy concern may not only be the regulation of their use of market power today, but also the need to maintain contestable markets that allow the creation of independent technologies in the future.

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We use maximum likelihood to estimate the relationship between the number of products from country _i_ that economy _j_ subjects to policy investigations and import protection in year _t_ as a function of the lag (year _t_ -1) of the percent change in the bilateral real exchange rate, domestic and trading partner _i_ real GDP growth, and bilateral import growth. The model is identified off inter-temporal variation in domestic real GDP growth and off inter-temporal and cross-sectional variation in bilateral real exchange rates, foreign trading partner real GDP growth, and bilateral import growth.

In interpreting the coefficient estimates from this model, we report incidence rate ratios (IRRs) for the explanatory variables. That is, we report the ratio of counts predicted by the model when the lag of an explanatory variable of interest is one unit above its mean value (and all other variables are at their means) to the counts predicted when all variables are at their means. To better quantify the results of our model, we also frequently present information on the percent change in the predicted counts of imported products becoming subject to new TTBs that our model generates in response to one standard deviation shocks to each of the explanatory variables of interest.

# **3.1 Data and Variable Construction**

There are a number of similarities and differences in our data and modeling approach relative to our companion paper’s (Bown and Crowley, 2013a) estimates on high income economies that require explicit clarification and justification.

Begin with the similarities. Like Bown and Crowley (2013a), we improve upon the prior literature through how we measure TTB import protection. We construct an annual time series of bilateral trade policy actions based on the universally-defined, 6-digit Harmonized System (HS-06) product level. The data for each policy-imposing economy begins either in 1989 or as soon as the country had TTB laws in place and available data on its use of TTBs (see Table 1, column 7). The data derive from extremely detailed trade policy information found in the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012b). Our measure of import protection is comprised of four arguably substitutable temporary trade barrier policies – antidumping, global safeguards, China-specific safeguards, and countervailing duties. Thus the dependent variable in our analysis is the count of HS06 imported products on which the government has agreed to initiate a new temporary trade barrier investigation against trading partner _i_ in year _t_ that results in import protection and against which there is not already an existing TTB in place. This count variable is carefully constructed for each <mark>11</mark>
# **5 Policy implications and future research**

In measuring social welfare, economists have struggled to provide simple statistics that reflect both changes in average income and its distribution. This paper has used a new tool for assessing changes in global income distribution—the GIDD—to illustrate the trends in these two variables in an ex ante framework. According to the data underlying the GIDD, three-quarters of total world income is controlled by only onequarter of the world population.<sup>14</sup> More importantly, we showed that even in a fairly optimistic scenario – where recent trends of strong growth rates for developing countries continues for the next two decades and, as a result, global inequality is in decline – _within_ country income disparities may widen.

This important finding is policy relevant for several reasons. Firstly, the friction between improved convergence of average incomes across countries and widening disparities within them suggests the need to revisit the balance between global and country agendas. Additional multilateral trade liberalization is likely to sustain growth for many developing countries. However, specific groups of people within countries may not be able to adjust to the resulting new wave of globalization and may be left further behind. Worsening inequality can also mute the positive effects of growth on poverty reduction and increase the risk of social alienation of people at the bottom of the distribution. In the end, it may cause counterproductive backlashes against the global liberalization that started the whole process.

One trend that may counter-balance the potential anti-globalization sentiment is the emergence of the global middle class: a group of consumers who demand access to, and have the means to purchase, international goods and service. Our results have shown that the share of these consumers in the global population is likely to more than double in the next 20 years. Even more importantly, the members of the global middle class will

> 14   This statistic is obtained by ordering all countries according to their per capita incomes, from the richest country to the poorest, and then adding up their populations until the resulting group account for 75% of world income. In this way, poor and rich individuals within rich countries are added together to form the final group. When all individuals in the world are ordered according to their incomes and irrespective of their nationality, the threshold of three-quarters of global income is reached by a group that comprises just 20 percent of the world population.

21
are broadly similar when the sample is restricted to country-years with a known diesel price. In column 6, the OLS specification using the diesel price instead of the oil price also has a negative and significant semi-elasticity. In column 7, the effect of the diesel price is larger when the oil price is used to instrument for it.

All results so far have considered transport costs only to the primate city. If mediumsized cities fill a similar role for some small cities, transport costs to them may also drive economic activity. Table 6 reports results controlling for transport costs to these other cities. Column 1 repeats column 1 of Table 3. In column 2, route distance to an alternate destination, the nearest city with a 1992 population of at least 100 thousand, has a magnitude comparable to primate distance, but with a much larger standard error.<sup>46</sup> It does not impact the primate distance coefficient substantially. Column 3 refines column 2’s specification slightly by only considering this alternate distance in the case of cities whose nearest city of at least 100 thousand is not the primate, to reduce the correlation between the two measures. The results are similar. Columns 4 and 5 are analogous to columns 2 and 3, with the intermediate destination now the nearest city in the top quintile (by 1992 population) of sample cities in the country. In essence, the absolute size criterion used in columns 2 and 3 is replaced with a relative one. The effect of the primate distance is reduced a little more, but it is still significant, and the effect of the top quintile city is twice as large or more. This result will be explored further when road surface is considered explicitly in Table 8. Still, no two of the primate city coefficients in this table are significantly different from each other.

# **5.1 Road surface**

The results shown so far have not used the available information on road surface. However, road surface helps to explain under what circumstances transport costs to intermediate cities might matter more than transport costs to the primate. The roads dataset includes (static) information on road surface type, so each route can be characterized by the fraction of its length that is paved. For simplicity, this measure is converted to an indicator denoting whether a city’s route is more paved than the route of the median city in its country. If road surface were randomly assigned, in the context of the model above, in the short run we might expect a less negative _β_ for the more paved routes, because driving on paved roads is cheaper, in fuel, time, and maintenance costs, than driving on unpaved roads. In a study on South African roads, du Plessis, Visser and best considered as a proxy for diesel and gasoline prices together in this context

> 46About a third of the cities in the sample (94 out of 287) have a 1992 population of at least 100 thousand.

20
-5-

potentially alters income distribution in Bulgaria, as they are typically concentrated in the richest groups. Therefore, in this paper, our definition of current income excludes these components.<sup>2</sup> Later in the paper, we assess the effect of this exclusion.

The income unit which corresponds with the income concept employed in the Bulgarian income survey is the household. The household concept adopted in the survey includes one-person households, one family households, and households of more than one family who make common provision for food or other essentials for living. This definition corresponds closely to the definition of the 1980 World Population Census Program (United Nations, 1978). To take into account the differences in households size, our analysis is based on household income per capita. We have not used adult equivalences in this analysis, as the construction of such equivalences is fraught with a number of conceptual and practical difficulties.<sup>3</sup> Household incomes have fallen significantly in real terms during the transition.

GDP has fallen by nearly 30 percent, since 1989 when the political transition began. A 2/ This accords closely to the concept of 'available household income' employed by both the United Nations (1977) and the ILO (1984). This concept includes salaries and wages in cash and in kind (excluding social security and private insurance contributions both by employees and employers), net income from selfemployment including consumption of own production, income from personal property and investment including imputed rent from owner-occupied dwellings, social security and private insurance transfers, minus personal income and property taxes. However, the Bulgarian survey does not include imputed rents. Ultimately, total consumption must be regarded as the best indicator for what we really want to measure. As Nissen (1984), Lipton and Ravallion (1993) noted, it reflects not only current total household income but also past savings, windfalls and expectations of future income. Unfortunately, comprehensive consumption data are not yet available for 1992 and therefore, we rely for this analysis on income only.

3I The literature on the best procedures is controversial. For a survey, see Ravallion (1992).
|Cashin P.,|The paper addresses two|Real annual data of the|Following Watson|No evidence of a break in the|
|---|---|---|---|---|
|McDermott C.J.<br>(2001)|research questions: what is the<br>empirical behavior of<br>commodity prices? Are there<br>any changes in the variability<br>of commodity prices and in<br>the trend growth of prices over<br>time?|nominal industrial<br>commodities price index<br>(dollar based), deflated<br>by the GDP deflator of<br>the United States over<br>the period 1862-1999.|(1994) the paper<br>applies two<br>econometric tests on a<br>“peak-trough”<br>analysis to determine<br>the length of a cycle<br>and its statistical<br>significance.<br>Descriptive statistics<br>complement the<br>analysis.|long-run trend decline in<br>commodity prices. Evidence<br>of a ratcheting up in the<br>variability of price<br>movements. The amplitude<br>of price movements increased<br>in the early 1900s, while the<br>frequency of large price<br>movements increased after<br>the collapse of the Bretton<br>Woods regime of fixed<br>exchange rates.|
|Cuddington J.T.,<br>Liang H. (2003)|The study investigates<br>differences in real primary<br>commodity price volatility<br>across fixed and flexible<br>exchange rate regimes.|The paper combines<br>three datasets: Grilli and<br>Yang (1988), Boughton<br>(1991) and IMF’s_IFS_.<br>The paper examines a<br>_real_commodity price<br>index, defined as the<br>ratio of the chosen<br>nominal commodity<br>index deflated by a<br>manufacturing unit<br>value (MUV) index.|ANOVA tests are<br>performed on long-<br>run data to test equal<br>variance across fixed<br>and flexible exchange<br>rate periods. Dummy<br>variables in a<br>(T)GARCH (1,1)<br>framework are used to<br>test the presence and<br>persistence of a<br>volatility shock.|Strong evidence supporting<br>the conjecture that the<br>flexible exchange periods<br>have been associated with<br>higher real commodity price<br>volatility than the fixed<br>exchange periods.|
|Leon J., Soto R.<br>(1995)|The paper revises the long and<br>short run time series structure<br>of commodity prices in order<br>to answer the questions of the<br>secular decline and the long-<br>run persistence of shocks.|Monthly prices of the<br>twenty-four most-traded<br>commodities in 1900-<br>92.|The paper applies the<br>non-parametric<br>estimator of<br>persistence proposed<br>by Cochrane (1988<br>and 1991) and<br>extended by Lo and<br>McKinley (1989) and<br>Chow and Denning<br>(1993).|15 of the 24 commodity<br>prices have negative trends, 6<br>are trendless and 3 exhibit<br>positive trends implying that<br>Prebisch-Singer hypothesis<br>holds for most commodities.<br>Evidence suggests that there<br>may be substantial room for<br>stabilization and price<br>support mechanisms for most<br>commodities.|

6