rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Trade Impacts on Additional Outcomes

||OLS||IV||
|---|---|---|---|---|
||(1)|Weighted<br>(2)|Unweighted<br>(3)|Controls<br>(4)|
|Panel A: Statutory Corpor|ate Income Ta|x Rate|||
|CIT rate|-0.002<br>(0.003)|-0.064***<br>(0.017)|-0.051*<br>(0.028)|-0.061***<br>(0.017)|
|Panel B: Self-Employment|as a Share of|the Workforc|e||
|Self-employment|-0.0117<br>(0.0145)|-0.220*<br>(0.126)|-0.185***<br>(0.0460)|-0.174***<br>(0.0560)|
|Panel C: National Income|Components||||
|Corporate profts|0.0339***|0.175**|0.124***|0.206***|
||(0.0128)|(0.0767)|(0.0321)|(0.0726)|
|Employee compensation|0.00848|-0.0749|-0.0964|0.0485|
||(0.0175)|(0.0904)|(0.0669)|(0.0785)|
|Mixed income|-0.0231|-0.0685|-0.0391|-0.202**|
||(0.0182)|(0.105)|(0.0301)|(0.0816)|
|Household operating surplus|0.0002|0.0145|0.0072|0.0171|
||(0.0039)|(0.0146)|(0.0159)|(0.0137)|
|Corporate-sector value-added|0.0396*|0.164|0.0917***|0.274***|
||(0.0210)|(0.109)|(0.0342)|(0.0943)|
|First-stage F-statistic||26.07|19.02|8.415|
|<br>_N_|_4518_|_4518_|_4518_|_4518_|

_Notes:_ This table reports estimates of the impact of trade on additional outcomes. Each cell corresponds to a coefficient on trade from a regression model which varies in the outcome (across rows) and estimation model (across columns). Across columns, the coefficients are based on estimating equation 7 using, respectively: OLS; IV; IV without weights, IV with weights and controls. The controls included in column (4) are: USD exchange rate; gross fixed capital formation (as a percentage of NDP); (log) population; (log) gross domestic product per capita; and _de jure_ capital accounts mobility. Weighted regressions are weighted by annual net domestic product in constant 2019 USD at PPP. Across panels, the outcome is: the statutory corporate income tax rate (Panel A); the self-employed share of the active workforce (Panel B); the share in national income of corporate profits, employee compensation, mixed income, household operating surplus, and the share of the corporate sector in the economy (Panel C). Trade is measured as the sum of export and imports divided by net domestic product. We instrument for trade using the oil-price and the gravity-instruments from Egger, Nigai, and Strecker (2019). All estimates include country and year fixed effects. For more details, see Section 50 7. * p<0.10 ** p<0.05 *** p<0.01. Standard errors in parentheses are clustered at the country level.
# **Exhibit 2.21: Composition former SOEs**

_By sector By year_

||**Year of offering Nu**<br>|**mber**<br>|
|---|---|---|
||1994|2|
|**Sector**<br>**Number**|1995|6|
|Construction and materials<br>6|1996|13|
|Consumer staples<br>17|1997|6|
|<br>Industrial cyclical<br>7|1998|3|
|**Total**<br>**30**|**Total**|**30**|

Source: Coface; World Bank staff analysis Exhibit 2.22 compares simple medians and shows that ROE and ROA are similar for former SOEs and private companies. A standard test shows that the differences are not statistically different. In contrast, with 6 pp., real annual sales growth of former SOEs is significantly lower. In addition, there is some statistical evidence that the operating margin of former SOEs is more than 2 pp lower. Retained earnings and EBIT to interest expenses are slightly higher for former SOEs, but not significantly so. There does not seem to be a difference in the total debt to common equity level. However, looking at other leverage indicators, current to total liabilities and total liabilities to equity are significantly higher for former SOEs (6.2 pp and 29.7 pp, respectively). On the other hand, paid-in capital is significantly lower (6 pp). This result implies that former SOEs are better able to gain access to (trade) financing, potentially because they are perceived to be stable or can rely on some form of government support.

**Exhibit 2.22: Profitability and leverage, 2003-07** _Medians, by type of_ _<u>firm</u>_

|**Type of firm**|**Return**<br>**on**<br>**Equity, %**<br>**Return**<br>**Assets, %**|**on**|**Real**<br>**sales**<br>**%**|**annual**<br>**growth,**|<br> <br>**Operating**<br>**profit mar**<br>**%**|<br>**gin,**|
|---|---|---|---|---|---|---|
|Private firms|14.7<br>7.9||7.4||11.4||
|Former SOEs|16.2<br>6.8||1.4||9.1||
|**Type of firm**|**Retained**<br>**earnings to**<br>**equity, %**<br>**EBIT to interest**<br>**expenses, x**<br>**Total de**<br>**common**<br>**%**|**bt to**<br>**equity,**|<br>**Curre**<br>**liabili**|**nt to total**<br>**ties, %**|**Total liabilities**<br>**to Equity, %**|**Paid-in to total**<br>**capital, %**|
|Private|33.5<br>4.4<br>27.7||84.1||74.5|36.1|
|Former SOE|37.0<br>6.0<br>27.1||90.3||104.2|30.1|

Source: Coface; World Bank staff analysis Since most privatizations occurred in the consumer staples industry, the comparison could be influenced by industry particulars. Moreover, former SOEs could structurally differ in terms of size, biasing the comparison further. Therefore, we again compare profitability and leverage indicators over the period 2003-07, taking into account differences in type of industry and total assets. The findings in Exhibit 2.23 roughly confirm the differences highlighted in Exhibit 2.22: real sales growth is lower and current liabilities are higher.

In a parallel exercise, we analyzed the time since the privatization and its power to explain the above-mentioned differences. We find that there is statistical evidence that the longer ago the 19
|IV.<br>PR|-<br>vi<br>-<br>OJESCIA&<br>........<br>.. .. ...<br>.. ..|||.|..<br>.|. .||.|19|
|---|---|---|---|---|---|---|---|---|---|
|A.|Organization .......<br>. .. .. .. ..|.||.|. ..|I..||.|20|
|B.|Sequence<br>. . . . . . . . . . . . . . . . .|.|.||. . .|. .|.|.|21|
||Nomination<br>. . . . . . . . . . . . . . .|.|.||. . .|. .|.|.|21|
||Contracting .<br>. . . . . . . . . . . . . .|.|.||. . .|. .|.|.|22|
||Mobilization<br>.... . . . . . . . . . . .|.||.|. . .|. .||.|22|
||Preparation of Reference Me.p .<br>. . .<br>. <br>.22|.|.||. . .|. .|.|.||
||Field Work .<br>. . . . . . . . . . . . . .|.|.||. . .|. .|.|.|22|
||Calculation of Unit Costs ... . . . . . .|.|.||. . .|. .||.|23|
||Prepatation of Assessment Roll<br>... . . .|.|.||. . .|. .||.|23|
||Printing of Bills .<br>. . . . . . . . . . .|.|.||. . .|. .|.|.|23|
|C.|Duration. Technology and Scope<br>... . .<br>..|.|.||. . .|. .||.|23|
|D.|ImRact<br>. . . . . . . . . . . . . . . .<br>.|.|.||. . .|. .|.|.|24|
||Projects in 1977 and 1978 . . . . . . . .|.|.||. . .|.<br>.|.|.|24|
||<br>Projects in 1983 and 1984.....<br>.<br>....|.|||. . .|. .||.|26|
|E.|Cot<br>.....<br>.<br>. .<br>. .<br>.<br>................|||||||.|27|
|V.<br>GE|NRALIZABLE LESSONS ......<br>.. . .. . ..|.||.|. . .|. .||.|29|
|A.|IDrroving Administrative Procedures<br>. . . .|.|.||. . .|. .|.|.|29|
||Rapid Urban Growth<br>...<br>..<br>|.<br>|..<br>|.<br>|...<br>|||.<br>|29<br>|
||Scarcity of Skills<br>. . . . . . . . . . .|.|.||. . .|. .|.|.|30|
||i i||||||||30|
||Inaccessble Market Informaton<br>..|||||||||
||Inflation . . . . . . . . . . . . . . . .|.|.||. . .|. .|.|.|31|
||Disputed Liability<br>..||||||||31|
||Slow and Expensive Judicial Processes . .|.|.||. . .|. .|.|.|31|
|B.|Lessons for the Design of Technical Assistance||P|r|ograms|<br>.|.|.|32|
||Method of Delivery<br>. . . . . . . . . . .|.|.||. . .|. .|.|.|32|
||Scope . . . . . . . . . . . . . . . . . .|.|.||. . .|. .|.|.|32|
||Changing Political Incentives?<br>. . . . .|.|.||. . .|. .|.|.|33|
||Selection Criteria||||||||33|
||<br>. . . . . . . . . . .|.|.||. . .|. .|.|.||
|ANNEXES||||||||||
|Annex 1-|CIATA Property Data Fom<br>. . . . . . . . . .|.|.||. . .|. .|.|.|35|
|Annex 2-|<br>CIATA Program Results for Individual Projects||||||||37|
|<br>TABLES|||.||. . .|. .|.|.||
|Table 1-|Program Costs by Components . . . . . . . . . .||.||. . .|. .|.|.|17|
|Table 2-|<br> Staff Allocation<br>. . . . . . . . . . . . . . .||.||. . .|. .|.|.|18|
|Table 3-|CIATA Projects 1973-85<br>. . . . . . . . . . . .||.||. . .|. .|.|.|20|
|Table 4-|<br> Financing of Project Inputs .21|||||||||
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Table 9. Fixed costs compensation: Government spends $1.5 million (�2%).

||Baseline|Industry|EU market<br>US market<br>Documentatio|n<br>Fixed|
|---|---|---|---|---|
||case|entry costs|entry costs<br>entry costs<br>costs|Costs|
|$ compensation per ...rm / entrant|—|318|1,826<br>2,328<br>3,192|2,117|
||||Endogenous quota price case||
||||Change in quota price in US, %||
|Quota price in the US||+1.43%|+3.67%<br>+61.3%<br>+2.85%|+98.6%|
||||Change in Bangladeshi exports,%||
|EU imports from Bangladesh|482.3m|+0.11%|+1.68%<br>+1.30%<br>+1.37%|+6.54%|
|US imports from Bangladesh|233.6m|+0.04%|+0.08%<br>+5.78%<br>+0.06%|+3.19%|
||||Change in mass of ...rms,%||
|Implied mass of entrants into industry|4712|+0.22%|+0.47%<br>+2.62%<br>+0.39%|+12.34%|
||||Change in productivity cuto¤s, %||
|Productivity cuto¤ for EU|0.8508|+0.06%|-1.16%<br>+0.74%<br>-0.95%|+3.25%|
|Productivity cuto¤ for US|1.0355|+0.09%|+0.22%<br>-3.52%<br>+0.17%|+4.37%|
||||Change in demand shock cuto¤s, %||
|Demand shock cuto¤ in EU|0.1856|0.00%|+0.72%<br>0.00%<br>+0.58%|-32.9%|
|Demand shock cuto¤ in US|6.9570|0.00%|0.00%<br>+3.48%<br>0.00%|-32.6%|
||||Share of ...rms invoking ROOs, %||
|Share of ROOs ...rms (model)|70.2%|70.2%|70.2%<br>70.2%<br>95.4%|58.6%|
||||Change in tari¤ revenues, %||
|Tari¤ revenue in EU|447k|+0.12%|+2.14%<br>+1.49%<br>-93.14%|+86.26%|
|Tari¤ revenue in US|46,728k|+0.04%|+0.08%<br>+5.78%<br>+0.06%|+3.19%|
||||Change in EU and US price indices, %||
|Price index in EU|100%|-0.06%|-0.95%<br>-0.74%<br>-0.78%|-3.67%|
|Price index in US|100%|-0.004%|-0.01%<br>-0.53%<br>-0.01%|-0.29%|
||||Change in welfare, $||
|Change in welfare in EU ($)|—|1,887k|28,533k<br>22,077k<br>22,782k|111,733k|
|Change in welfare in US ($)|—|241.3k|456.6k<br>33,409k<br>367.6k|18,403k|
|||Policy e¢ c|iency (dollars of extra net exports per dolla|r of subsidy)|
|Policy e¢ ciency|—|0.4|5.5<br>11.4<br>4.8|24.8|
||||Exogenous quota price case||
|$ compensation per ...rm / entrant||317|1,820<br>2,001<br>3,185|1,912|
||||Change in Bangladeshi exports, %||
|EU imports from Bangladesh|482.3m|+0.28%|+2.07%<br>+8.59%<br>+1.76%|+14.69%|
|US imports from Bangladesh|233.6m|+0.46%|+1.04%<br>+23.6%<br>+0.95%|+27.75%|
||||Change in EU and US price indices, %||
|Price index in EU|100%|-0.16%|-1.17%<br>-4.80%<br>-1.00%|-8.12%|
|Price index in US|100%|-0.04%|-0.10%<br>-2.14%<br>-0.09%|-2.50%|
||||Change in welfare, $||
|Change in welfare in EU ($)|—|4,808k|35,173k<br>146,585k<br>29,402k|252,637k|
|Change in welfare in US ($)|—|2,643k|6,019k<br>136,785k<br>5,497k|159,761k|
|||Policy e¢ c|iency (dollars of extra net exports per dolla|r of subsidy)|
|Policy e¢ ciency|—|1.5|8.3<br>57.1<br>7.1|81.2|

38
7 0 fiscal balance' -- thus potentially turning what should be a one-time change in the price level into a more<sup>persistent increase in the inflation rate.</sup> High open inflation has to be eliminated<sup>for structural reforms to be</sup> effective. Eliminating excess<sup>demand is also important for mi-roeconomic</sup> reasons: studies of firms in<sup>socialist countries suggest that the shift from</sup> sellers' markets to buyers'<sup>markets is essential in modifying firms'</sup> efficiency and customer orientation.

Countries close to macroeconomic equilibrium<sup>have greater latitude to</sup> implement those institutional<sup>reforms most needed to support the operation of</sup> markets before switching over<sup>to market mechanisms." Those needing urgent</sup> stabilization face difficult<sup>decisions of sequencing stabilization</sup> measures (which in the past have typically<sup>involved the tightening of controls) and</sup> structural reforms lee.ding to a market system.

Markets and Decentralization<sup>Turning to the other axis of Figure 1, at</sup> the start of 1990, Czechoslovakia, East Germany, the Sovist Union,<sup>and</sup> Bulgaria had relatively centralized<sup>economies, with a planned materials supply</sup> system playing a major role and<sup>with enterprise management subject to the</sup> constant interventio.. of ministries."2 By contrast, Yugoslavia has<sup>long been</sup>

- 10/ This could happen as a result<sup>not only of the Tanzi effect, in which real</sup> tax revenues decline as inflation<sup>rises, but also because partial price</sup> decontrol may require increasing<sup>government subsidies to maintain the</sup> prices of necessities constant.

- 11/ For examples of authors who<sup>advocate such a pattern, where the institutions</sup> needed to support markets are<sup>put in place prior to each stage of</sup> liberalization, see Svejnar (1990) and the<sup>so-called sequential approach</sup> outlined in Rosefielde (1990).

- 12/ For descriptions of the USSR and Chinese patterns<sup>of management, see Hewitt</sup> (1988) and Lin and Tidrick<sup>(1989).</sup>
Grid-level fixed effects Adding grid-level fixed effects controls for unobserved regional characteristics that might influence both temperature uncertainty and firm growth, as both are measured at the grid level. This approach helps account for spatial heterogeneity, such as differences in climate, infrastructure, or regional economic conditions, which could otherwise bias the results. The results are summarized in Table 5.

The results indicate that incorporating grid fixed effects generally amplifies the impact of temperature uncertainty. For instance, the coefficient for temperature uncertainty in Model 1 decreases from -0.768 to -2.575. Similarly, the coefficients for the interaction terms between Uncertainty and CapIntensity and between Uncertainty and LogIncome change from -1.038 to -1.352 and from -0.903 to -1.569, respectively.

Table 5: Weather uncertainty and fixed asset growth with grid fixed effects

||Dep. varia|ble: Fixed as|setgrowth|
|---|---|---|---|
||(1)|(2)|(3)|
|Uncertainty|−2.575<sup>∗∗∗</sup>|−3.851<sup>∗∗∗</sup>|11.360<sup>∗∗∗</sup>|
||(0.517)|(0.464)|(3.907)|
|Maximum|−0.022|−0.294<sup>∗∗∗</sup>|−0.003|
||(0.114)|(0.079)|(0.114)|
|Capital Intensity||−1.815<sup>∗∗∗</sup><br>(0.412)||
|Income|||−2.840<sup>∗∗</sup>|
||||(1.258)|
|Uncertainty:Capital Intensity||−1.352<sup>∗∗∗</sup><br>(0.187)||
|Uncertainty:Income|||−1.569<sup>∗∗∗</sup>|
||||(0.431)|
|Constant|23.947|22.811|40.973|
||(24.165)|(15.595)|(26.101)|
|Year FE|Yes|Yes|Yes|
|Country FE|Yes|Yes|No|
|Industry FE|No|No|Yes|
|Grid FE|Yes|Yes|Yes|
|Observations|50,249|200,991|49,778|
|R<sup>2</sup>|0.234|0.111|0.236|
|Adjusted R<sup>2</sup>|0.169|0.095|0.172|

_Notes:_ This table regresses fixed asset growth on weather uncertainty variables, measured by the standard deviation temperature. Model (1) includes the weather uncertainty, Model (2) includes the weather uncertainty interacting with the income level, and Model (3) includes the weather uncertainty interacting with the capital intensity. All models incorporate the gridlevel fixed effects. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

18
# **Tables**

**Table 1: Data and sources.**

|Variable|Definition|Year|Source|
|---|---|---|---|
|colonyij|Dummy variable equal to unity if countries i and j were once<br>in a colonial relationship|NA|CEPII|
|common official languageij|Dummy variable equal to unity for country pairs that share a<br>common official language|NA|CEPII|
|complexityk|Task complexity in sector k|1997|PSID survey|
|contiguousij|Dummy variable equal to unity for country pairs that share a<br>common land border|NA|CEPII|
|distanceij|Great circle distance between the main cities in countries i<br>and j|NA|CEPII|
|exportsijk|Dummy variable equal to unity if exports of services from<br>country i to country j in sector k are greater than $1m|2009|Francois and Pindyuk (2011)|
|Herfindahlk|Concentration index of intermediate input use in sector k,<br>calculated as the sum of squared shares of use of each input<br>(multiplied by -1)|1997|Bureau of Economic Analysis|
|high−skill intensityk|Share of high-skill workers in total hours worked in sector k|2009|EU-KLEMS|
|high−skill labori|Percentage of country i’s population with a tertiary education|2009|Barro and Lee (2012)|
|ICT capital intensityk|Share of ICT-related capital used in sector k|2009|EUKLEMS|
|internet users per 100 peoplei|Internet users per 100 people in country i|2009|World Development Indicators|
|legali|Index of the quality of legal institutions and protection of<br>private property rights in country i|2009|Gwartney and Lawson (2004);<br>Fraser Institute|
|non−ICT capital intensityk|Share of non-ICT-related capital used in sector k|2009|EU-KLEMS|
|non−ICT capital stocki|Share of the non-ICT capital stock in the total capital stock in<br>country i|2009|Conference Board; Groningen<br>Center for Economic Growth|
|RTAij|Dummy variable equal to unity if countries i and j belong to<br>the same regional trade agreement that covers services|2009|Authors; WTO.|

33
The evidence of the effect of IPR on FDI is less clear. Maskus and Eby-Konan (1994) find no effect of IPR measures on FDI. Maskus (1998), however, points out that studies that consider FDI in isolation may not be conclusive because investment is only one part of the broader set of decisions that a multinational firm makes in entering a market. He reports regressions that are consistent with a positive effect of IPR on FDI, but only for more developed countries.<sup>8</sup> In principle, multinationals can transfer technology to developing countries even without undertaking FDI. Yang and Maskus (2000) examine the effect of improved IPR regimes on arm’s length licensing by U.S. Multinational Enterprise’s (MNE) and report a strong positive relationship.<sup>9</sup> While these papers are suggestive of some interesting patterns, they all suffer from the usual problems that affect cross-country regressions. The measures of IPR utilized do not generally take into account the effectiveness of enforcement, they are highly correlated with other important country characteristics, and they have limited time-series variation. Since FDI data are typically not disaggregated, even by industry, it has also not been possible to look at the impact of IPR reform on the composition of multinational activity.<sup>10</sup> The data and methodology employed in our paper allow for a more convincing examination of the impact of IPR on technology transfers.

> 8  Lee and Mansfield (1996) also study the impact of IPR on FDI, taking a cross-sectional approach.

> 9 Smith (2001) also examines the impact of IPR on licensing and FDI, obtaining results with the licensing variable that are consistent with those of Yang and Maskus.

> 10  One recent exception is Smarzynska (2000), who examines the composition of FDI in Eastern Europe in the 1990s and relates this composition to differences across countries in IPR regimes.  Because this study focuses on a small number of countries in a single region of the developing world, natural questions arise about the extent to which these findings can be generalized outside their regional context.

6
- Jayaram, K. and others (2005) “The impact of road traffic congestion on supply chain: insights from Auckland, New Zealand, ” _International Journal of Logistics_ 8(2), 159– 180.

- Johnson, P., McKay S. and Smith S. (1990), _The Distributional Consequences of Environmental Taxes_ . Institute for Fiscal Studies Commentary 23. London.

- Karp, L., and D. M. Newbery (1991) “Optimal tariffs on exhaustible resources”, _Journal of International Economics_ , 30, 285–299.

- Karp, L., and D. M. Newbery (1992) “Dynamically consistent oil import tariffs”, _Canadian Journal of Economics_ , 25(1), 1–21.

- Kay, J.A., and M.A. King (1980) _The British Tax System_ , Oxford: Oxford University Press.

- Maskin, E., and D. M. Newbery (1990) “Disadvantageous Oil Tariffs and Dynamic Consistency”, _American Economic Review_ , 80(1), 143-56.

- Mendoza, Enrique G., Razin, Assaf, Tesar, Linda L., (1994) “Effective tax rates in macroeconomics: Cross-country estimates of tax rates on factor incomes and consumption,” _Journal of Monetary Economics_ , 34, 3, pp. 297–323.

- Mendoza, A., Chavarria, J. M., and Mayoral, E. (1998) _Some Measures to Improve Safety on Road Motor Transport in Mexico._ Transportation Research Record 1613, 63–70, Transportation Research Board (TRB), Washington DC, U.S.A.

- Metcalf, G.E., and D.A. Weisbach (2009) “Design of a Carbon Tax.”University of Chicago Law and Economics, Olin Working Paper 447.

- Mirrlees, J. A. (1976) “Optimal tax theory: a synthesis,” _Journal of Public Economics_ , 6(4), 327–358.

- Mohan, D. (2002) _“Social Cost of Road Traffic Crashes in India”, Proceedings First Safe Community Conference on Cost of Injury_ , Viborg, Denmark, pp. 33–38, Indian Institute of Technology. Table available at: http://www.vtpi.org/tca/tca0503.pdf

- Myles, G.D. (1995) _Public Economics_ , Cambridge: Cambridge University Press.

- Ochoa, F. y Asoc. and Radian Corporacion (1997) “Estrategia Integral de Transporte y Calidad del Aire para la Zona Metropolitana del Valle de Mexico” (ZMVM), Estudio 8: Anexo A. C2-4, Externalidades del Transporte en la ZMVM. (Mexico: Comisi ́on Metropolitana de Transporte y Vialidad.)

- Parry, I.W. H. and K.A. Small (2005) “Does Britain or the United States Have the Right Gasoline Tax?” _American Economic Review_ , 95(4), 1276–1289.

- Parry, I.W.H., and others (2006) “The Incidence of Pollution Control Policies” in _The International Yearbook of Environmental and Resource Economics 2006/2007_ , edited by T. Tietenberg, and H. Folmer. Northampton, MA: Edward Elgar.

- Pearce, D. (1991) “The role of carbon taxes in adjusting to global warming,” _Economic Journal_ , 101 (407) 938–48.

- Pigou, A.C. (1920) _The Economics of Welfare_ , New York: Macmillan.

- Ramsey, F.P. (1927) “A contribution to the theory of taxation” _Economic Journal_ , 37,

18