rafmacalaba/datause-encoder-data huggingface dataset:
companies. As a result, ROIC measures the return that an investment generates for the providers of capital and reflects management’s ability to turn capital into profits.<sup>12</sup> In calculating ROIC, we also subtract cash stocks in excess of those estimated required for transactions purposes. Following Koller et al. [2017], we treat cash above 2% of sales as excess cash and subtract it from the firm’s invested capital. In section 4.1 we undertake robustness tests allowing for varying percentages. Our estimates are not affected by firms’ decisions on whether to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities, as is the case of many large U.S. multinationals.

We define _ROIC_<sup>_unadj_</sup> _Star_ as a dummy variable that takes the value 1 if the firm’s ROIC is above the 90<sup>_th_</sup> percentile of ROIC across all firms in the US economy in a particular year and 0 otherwise. To replicate the figure in previous studies such as Furman and Orszag [2015] and Koller et al. [2017], we restrict our sample to large firms (defined as firms with assets more than $200 Million in 2009 dollars, adjusted for inflation) and drop firms with negative invested capital. As noted before, Figure 1 shows that there is a large rise in capital returns over the past three decades where the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. We also see that the divergence of the top decile of firms from the rest of the economy really takes off in the 1990s.<sup>13</sup> These results are qualitatively consistent with Furman and Orszag [2015], Koller et al. [2017] and the Council of Economic Advisors [2016], all of which were produced using a proprietary dataset of US firms from McKinsey & Co.

# **1.1 Role of Human Capital**

Firms differ in the complexity of tasks that they perform and the product market may reward certain capabilities more than others. To address this issue, we construct industry-level indices of the composition of tasks firms perform and assess how they affect the likelihood of a firm from that industry becoming a star firm. In creating these indices, we draw on a large labor market

> 12In particular, if we do not subtract _GDWL_ from _INTAN_ we would run the risk of capitalizing future monopoly rents reflected in high acquisition premiums, thereby incorrectly attenuating the relation between ROIC and pricing power when one firms buys another.

> 13In an early version of the paper, we find much higher increases in ROIC (over 190%) for the 90th percentile without the sample restrictions to large firms and firms with positive invested capital.

9
- Lewis, Karen, “Trying to Explain Home Bias in Equities and Consumption,”  Journal of <u>Economic Literature, XXXVII (1999), 571-608.</u>

- Lucas, Robert E., “Why doesn’t capital flow from rich to poor countries?,” American <u>Economic Review, LXXX (1990), 92-96.</u>

Merton, Robert (1990). Continuous-Time Finance. Cambridge: Basil Blackwell.

- Obstfeld, Maurice and Kenneth Rogoff, Foundations of International <u>Macroeconomics, Cambridge, MIT Press, 1996.</u>

- Rider, Mark, “External debt and Liabilities of Industrial Countries,” Research Discussion Paper 9405, Reserve Bank of Australia, 1994.

- Rogoff, Kenneth, Carmen Reinhart and Miguel Savastano (2003).  “Debt Intolerance”. <u>Brookings Papers on Economic Activity.</u>

- Sinn, Stefan, “Net External Asset Positions of 145 Countries: Estimation and Interpretation,” Institut für Weltwirtschaft, University of Kiel, 1990.

- Tesar, Linda, and Ingrid Werner, “Home Bias and the Globalization of Securities,” National Bureau of Economic Research,  Working Paper No. 4218, 1992.

34
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# Table 6. Real wages, productivity and output in the Great Depression

# average annual percentage changes

||Real<br>1929-32|wagesa<br>1932-37|Produc<br>1929-32|tivitya<br>1932-37|out<br>1929-32|putb<br>1932-37|
|---|---|---|---|---|---|---|
|Germany|8.9|-4.9|-2.9|3.6|-5.6|8.5|
|JapanC|5.5|-5.0|1.8|3.2|2.0|7.8|
|Sweden|1.2|0.4|-2.6|4.9|-1.4|4.4|
|United Kingdom|<br>3.5|-1.2|-1.4|1.5|-1.7|4.3|
|<br>United States|<br>-3.6|4.5|-7.6|4.0|-10.4|6.5|

a. In the corporate sector.

b. Total GDP.

c. Manufacturing sector only.

Sources: Ohkawa and Shinohara, 1979; Donnell, 1981; Maddison, 1982.
A comparison of the regions over the last five years points to some interesting differences. The massive privatizations in Eastern Europe resulted in a total number of transaction of 373 for Europe and Central Asia, but only $10.6 billion in revenue were generated.<sup>17</sup> In general, the average asset value in Eastern Europe is significantly smaller relative to other middle income countries, rendering these privatization programs far less attractive from a budgetary perspective. But, of course, revenue generation only plays a secondary role in the effort to restructure the economic system as a whole. Privatizations in these countries primarily served as a mechanism to support the transfornation process by rapidly expanding private ownership, even if at the expense of government revenues. Latin America, on the other hand, privatized a total of 266 entities for $40.9 billion during the period, providing the governments concerned with substantial amounts of additional income.

Figure 3 PRIVATIZATIONS IN THE DEVELOPING WORLD, 1988-1992 Aggregated by Geographic Region <!-- Start of picture text --> Prooeeds hi USS Bilion Number of Transactions<br>50 - 400<br>10- Transactions|<br>I....... ..... ... . ............. -_ - 1300<br>4 0 -, . . .. ... . ......... ... ' I ... ....<br>........... ~~~~~~~~~~~~~~200<br>20 -<br>100<br>10 - . .. . .<br>0 ~~~~~~~~~~~~~~~~~~~~~0<br>Europe and Latin America East Asia and South Asia Sub-Saharan North Africa £<br>Central Asia and Caribbean Pacific Africa Middle East<br><!-- End of picture text -->

In terms of econonic sectors, privatizations in infrastructure, which includes electric and water utilities, transportation, and telecommunications, are clearly dominating, accounting for 34 percent of total revenues for the developing world as a whole. Industrial production, comprising chemical production, heavy industry, and manufacturing is the second most important sector. having generated a fifth of total revenues. Privetizations of financial institutions was also very strong during the period, accounting for almost 26 percent, resulting primarily from the re-privatization of commercial banks that were nationalized in many countries during the early stages of the debt crisis. With respect to the primary sector, privatizations were relatively strong in petroleum-related activities. The number of mining transactions was relatively minor, and while a not insignificant 17 Excluding Portugal and Turkey, the only non-Eastern European countries in the group, 294 entities were privatized for a total of only $5.7 billion.

14
on Rybowiak, Garst, Frese, & Batinic, 1999). Entrepreneurial identity captures the degree to which the respondent self-identifies as an entrepreneur (based on Hagger & Chatzisarantis 2006). Career fit confidence is measured in two ways: an individual’s assessment of their commitment to the sector relative to others; and capacity to form good relationships with customers and suppliers in their sector of operation (based on Cech et al. 2011).

The survey weights for the _Future of Business_ survey were constructed to match the Facebook Page administrator population on variables that are highly predictive of survey response. Given the way in which the sample was selected, as well as the selective attrition across questions, this sample should not be regarded as representative of a clear underlying population.<sup>14</sup>

> 14 The sampling was designed to be representative of the Facebook business page administrator population and not of any country’s business population.

7
# **Table A22: Second stage: Effects of refugee presence on household welfare disaggregated by initial education level, with analysis at cluster level and the distance -** **<u>weighted refugee index at 100km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||||||Panel|A (Didn’t comp|lete primary ed|ucation)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.14933*|0.14708*|0.16349*|0.14838*|||||||||
||(0.07896)|(0.07846)|(0.08814)|(0.07872)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.15538*<br>(0.08211)|0.15351*<br>(0.08168)|0.19706**<br>(0.09611)|0.18312**<br>(0.08819)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.14797*<br>(0.07970)|0.14563*<br>(0.07914)|0.13935*<br>(0.08333)|0.12718*<br>(0.07400)|
|Observations|3,224|3,224|2,616|2,614|3,224|3,224|2,616|2,614|3,224|3,224|2,616|2,614|
|Kleibergen-Paap rk Wald F|13.77|13.79|11.28|10.73|16.22|16.23|13.02|12.27|11.45|11.47|9.630|9.248|
|Root MSE|0.545|0.545|0.511|0.373|0.546|0.545|0.513|0.376|0.545|0.545|0.509|0.372|
||||||Pane|l B (Complete|d primary educ|ation)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.04234<br>(0.08489)|0.03804<br>(0.08348)|0.01991<br>(0.10615)|0.06518<br>(0.09722)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.04782|0.04089|0.01971|0.06886|||||
||||||(0.09161)|(0.08982)|(0.11696)|(0.10638)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.05133<br>(0.08818)|0.04891<br>(0.08696)|0.03551<br>(0.10662)|0.08399<br>(0.09970)|
|Observations|2,015|2,014|1,489|1,488|2,015|2,014|1,489|1,488|2,015|2,014|1,489|1,488|
|Kleibergen-Paap rk Wald F|13.68|13.66|12.56|11.88|15.60|15.57|14.27|13.44|11.48|11.46|10.70|10.17|
|Root MSE|0.546|0.541|0.476|0.340|0.546|0.541|0.476|0.340|0.546|0.541|0.476|0.341|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|

35
Figure 7 Korea MOY, MlY, QMY and DCPY overtime <!-- Start of picture text --> 0.8-<br>0.5<br>0~.4<br>0.4 M MY ........-<br>...............<br>0.2 ..... ..............<br>0.1-<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991<br><!-- End of picture text -->

# year

j-MCY -M1 ....... Ql MY ---DCPY
## **THE INDIAN SUGAR AND SUGARCANE ECONOMY**

# _Producers and Consumers_

In India most sugar cane is a 10 to 12 month crop planted during January to March. In northern Maharashtra and parts of Andhra Pradesh and Karnataka there is also an 18 to 20 month crop. In most areas the 12 month crop is followed by just one ratoon crop, that is, a new crop grown from the stubble of the harvested crop. Most sugarcane is irrigated ( about 85 percent of the total area since the mid 1980s) and uses large quantities of fertilizer. It is a sturdy crop, unaffected by heavy rains and hailstorms. It is cultivated in both tropical and subtropical regions, but is best suited to the tropical areas where yields are considerably higher. In the tropical regions the states with the largest production are Maharashtra, TN, Karnataka, AP and Gujarat and in the sub-tropical regions UP, Haryana, Punjab, Bihar and Orissa.

Figure 6: **Sugarcane production by state for the crop year 1993-94** <!-- Start of picture text --> Haryana<br>TN 2.8% Karnataka<br>0.8% 6 -<br>Mahiarashtra ... Gujarat<br>12.3%<br>2.1%<br>Rajastha<br>46.2%<br>0.4~~~~~~~<br><!-- End of picture text --> Source: Agricultural Statistics at a Glance, 1995.

Yields are highest in TN (105 MT/HA) followed by Karnataka (85 MT/HA), Maharashtra (81 MT/HA), Gujarat (80 MT/HA) and AP (77 MT/HA). The average yield at all India level was 67 MT/HA, due to lower yields in the sub-tropical regions. Between 1951 and 1994 the area under sugarcane increased from 1.7 mln HA to 3.4 mln HA. In 1994, UP accounted for 1.8 mln HA, 16
Table 3: Debt Dynamics Decomposition under the Baseline Scenario (Percentage Points)

||2015|2020|2025<br>(p|2030<br>ercenta|2035<br>ge point|2040<br>s)|2045|2050|
|---|---|---|---|---|---|---|---|---|
|Change in Debt to GDP Ratio|1.18|0.80|0.73|0.63|0.56|0.69|0.97|1.12|
|Growth Eect|-0.49|-0.48|-0.40|-0.43|-0.43|-0.39|-0.36|-0.33|
|Interest Payments|0.76|0.42|0.33|0.37|0.40|0.43|0.47|0.53|
|Primary Balance|0.85|0.86|0.80|0.68|0.59|0.65|0.86|0.91|

across ages, and the impact of technological improvements in health services production on expenditures.

The next aging-related expenditure category in Panel (c) is the budget transfers to the pension fund. These transfers are comprised of two components. First, an amount that is proportional to the total current pension contributions from employee payrolls is transferred from the general budget to the pension fund, as enacted by law. Second, if the pension fund runs a current (accounting) decit within a year, then the gap is closed by a second tier transfers from the general government budget within the same year. As shown in the graph, the sum of these two types of transfers exhibits a small decrease from about 6.1 percent of GDP in 2012 to about 4.9 percent in medium term, and then bounces back to reach about 5.6 percent by the end of projection horizon.

As demographic transition leads to a shift in both the composition and size of scal revenues and outlays, the next question is whether these changes pose a threat to scal sustainability. To this eect, we present simulation results that trace the evolution of primary balances, as well as the share of debt in the economy, which takes the debt dynamics into consideration. Panel (d) in Figure 7 shows that the scal budget continues to incur primary decits throughout the projection horizon. Although there is a modest improvement in the medium term, it changes direction quickly, and the decit remains close to 1 percent of GDP. As a result, the debt-to-GDP ratio increases from its current level of about 18 percent to about 51 percent by 2050. Even though this shows a substantial deterioration, this indicator, in itself, does not breach the common debt distress threshold identied in the literature, and it is also within the limits dened by Maastricht criteria. However, the protracted upward trend indicates that a breach of these thresholds is likely in a longer time span.

Table 3 shows the debt dynamics decomposition for the projection period. Protracted primary decits, which follow the U-shape pattern in the transfers from general budget to the pension system, are the main driver of the change in debt-to-GDP ratio. The growth eect is large enough to counteract the burden imposed by the interest payments, however, in the long run it falls short of doing this.

19
the following criteria: they needed to have more than 10 conflict events during 2012–14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database (Raleigh et al. 2010).

Conflict-affected areas were oversampled to create a sample of individuals who had experienced conflict events to shed light on the types of events that had occurred. A random sample of the zones might have presented an insufficient sample of conflict-affected households; this, therefore, would have restricted the analysis of the types of conflict events. Due to the oversampling, however, the sample drawn was not representative at the level of the geopolitical zone, as is the case in the GHS. Indeed, out of the 717 households that are spread across 99 LGAs, all conflict events were concentrated in 52 LGAs—that is, almost half (47) of the LGAs did not have any household conflict exposure. This suggests strong geographical clustering of conflict events.<sup>9</sup> Even though the conflict was widespread in these areas, the LSMS panel data set contains a low fraction of households that have migrated during the data collection. All households in the telephone survey had lived in their LGA of residence during the entire time of the LSMS panel.

To account for the biases arising from the sampling, we present our results using probability weights. This renders the estimates representative at the level of the geopolitical zone (North East, North Central, and South South), as is the case with the GHS panel. The weights used were constructed using the wave 3 GHS panel weights as a benchmark, adjusting for the probability of being in the sample. The weights correct for the biases arising from the oversampling of the high-intensity conflict LGAs, for nonresponse, and for the fact that a minority of the households surveyed in wave 3, visit 2, of the GHS did not have a phone or did not provide a phone number. The mobile phone penetration rates are close to 90 percent in all three regions, however.<sup>10</sup> Additionally, in appendix E we show that the results are robust to unweighted regressions. Appendix A, section 2, provides additional information on the administration of the telephone survey.

> 9 There is an average of 7.24 households per LGA.

> 10 In the North East, the mobile phone penetration rate is 84 percent. In the North Central and South South regions, it is 90 percent and 83 percent, respectively.

9
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<!-- Start of picture text --> Inequality<br>a \ \<br>' !<br>08 ! !<br>| ' !<br>\ I<br>23 \ I<br>I<br>i 0 64 | 1' |<br>a '| !!<br>J<br>02 } i }<br>| t if<br>Colder temperature ; H Hotter temperature<br>)<br><0 (0,5) [5,10) (10,15) (15,20) (20,25) (25,30) 30%<br>‘Temperature bin<br>4 Gini + Theil<br><!-- End of picture text -->
- 87-

government funds (50%), Bergen was able to pursue a road construction program within half the time horizon previously envisaged for its road construction program. Citing problems of congestion, air and noise pollution and traffic safety, eight tenths of toUl revenues are earmarked for road construction and improvement and two tenths for bus lanes, etc. (to improve pubLic transport).

# B. Analysis of Results

94. Even though the principal purpose of the toll ring was not traffic restraint, traffic was projected to fall by 3%. Given that the overall traffic growth of 10% was due to ecozemic growth, it was stiU found that the isolated (i.e., _ceterisparibus)_ impact of the toll ring was that traffic declined by 6%-7% in the first year. In surveys conducted by the Institute of Transport Economics, Norwegian Centre for Transport Research (arsen, 1987, 1988), the effects of the toll ring were found to be as follows: a) change in route choice was negligible since the topography allows almost no detours; b) there was an internal adjustmr- t for households which are seasonal pass holders and have more than one car, resulting in more car trips undertaken since the additional toll is zero; c) it is unclear whether a change in mode choice from car to public transport had taken place; d) through trfic declined by 6-7%; e) car occupancy seems to have increased slightly; and f) changes in trip timing are observed at the end of the charging period but the effect is not sgniicant.

95. Even though only a quarter of all vehicle owners in Bergen are hol&drs of seasonal passes, slightly over half (55%) of the CBD-destined trips are undertaken by these holders every day.68 In particular, seven tenths of trips during the morning rush hours are undertaken by these holders. It was found that seasonal pass holders undertake about 1.7 trips per day on average, implying an effective charge of about _NOI2.90 [US46_ cents], (a 40% discount or more) as compared to the standard toll rate of _NOKS [US8O_ cents]. From a road authority's

**Leus** th **amo tenth** of Bergui's **populion** live inside do **toil ing (Sderut6m,** 1988).
**Figure 7: Foregone gains from trade rise with mobility costs**

<!-- Start of picture text -->
1.8<br>Peru<br>1.6<br>1.4<br>1.2<br>Denmark<br>1<br>Turkey<br>Chile<br>0.8<br>France<br>0.6 Lithuania<br>0.4<br>Ethiopia<br>Indonesia<br>0.2 Ireland<br>BelgiumSenegal<br>0<br>0 2 4 6 8 10<br>Labor mobility cost<br>Foregone gains from trade<br><!-- End of picture text -->

_Source_ : _Adapted from Artuç, Lederman, and Porto (2013)_ Note: Figure 7 plots estimated foregone gains from trade for each country measured as the ratio of the labor adjustment cost to potential gains from trade. The horizontal axis measures labor mobility costs as a ratio to annual average wage.

Countries with a large share of primary employment tend to have high labor mobility costs. To the extent that poorer households tend to be employed in the primary sector, including agriculture, they may face higher costs of transitioning into other sectors of an economy. Using cross-country estimates to test for various labor market-related correlates, Artuç, Lederman and Porto (2013) find a positive correlation between labor mobility costs and the employment share in the agriculture sector, suggesting that for countries in which employment opportunities are more diversified away from primary sectors, workers encounter lower mobility costs. They also find evidence that labor mobility costs are positively correlated with other frictions and constraints such as time to export, while countries with higher levels of tertiary education tend to have lower mobility costs.

Mobility costs vary by sector of employment. There is significant heterogeneity in the mobility costs for workers entering different industries, and these costs may be positively related to the amount of “specific” rather than “general” skills needed in specific industries. In countries where panel data are available on workers’ employment transitions over time, it is possible to estimate mobility costs disaggregated at the sector level, as well as for different worker characteristics (e.g., skilled vs. unskilled, formal vs. informal, by gender) or disaggregated by firm size. In Mexico, for example, Kaplan, Lederman, and Robertson (2013) find that for formal sector workers, industries with the lowest entry costs for formal workers were construction, services, and retail and wholesale trade; transportation/communications and utilities had the highest costs of entry. By identifying relative entry costs, policy makers would be better equipped to anticipate the potential labor market impact of a policy reform in one sector and which alternative sectors displaced workers were likely to enter. Assistance can then be better targeted to reduce the cost. For costs related to geographical 16