rafmacalaba/datause-encoder-data huggingface dataset:
12

**Figure 3. Per Capita Income and Per Capita Road Length**

<!-- Start of picture text --> 100 . _____. _ _ - .____. . .<br>* National total road Auskalia<br>A Urban road<br>. National total road eq. USA<br>a --------- National paved road eq.<br>E 10 I Urban road eq. Brazil Pel A<br>lo ~ ~ ~ ~ LmeonSwitzerland<br>F -- ~~~~Bolivia + o p n<br>o~~~~~~~~~~~~~~~~~~~~~~r<br>Maaw * * Thailand * . * Korea * / 49sakarl<br>Beijing reou|<br>- China Egypt - uA<br>Jakalta A .~< ASanfago<br>A<br>Bandung A Bangkok t<br>.' tA Guangzhou HK<br>Surabaya<br>0.1 I I<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text -->

# Ratio of Vehicles to Roads

Our previous paper used the ratio of vehicles to roads as a proxy for road traffic per unit of road length and examined its variation at the national and urban levels in a production function framework. Similar to the production of manufactured goods with factor inputs of labor and machinery, road transport services are produced by a combination of vehicles and roads, and the ratio of vehicles to roads should vary with their relative prices. We hypothesized that the ratio of vehicles to total roads should increase with income at the national level because the prices of vehicles (as traded goods) should not vary with income, whereas the cost of roads (as nontraded goods) should increase with country income level. But the relationship between the vehicle-road ratio and income may not increase with income at the urban level when congestion is present because the value of commuter travel time (a component of vehicle service cost) increases with income--and travel time would increase as congestion worsened. The results from the simple models based on the above reasoning had substantial power to explain the variation in vehicle-road ratios at the national level.

In the current analyses, we test the non-linearity between income and the vehicleroad ratio at the national and urban levels. The results are summarized in Table 4. At the national level, the income elasticity of the vehicle-road ratio based on the constant elasticity specification is roughly 0.4 for total roads, and essentially zero for paved roads.

3 Because the log of vehicle-road ratio can be decomposed into the log of vehicles minus the log of road length, the relationship between vehicle-road ratio and income can be approximately inferred from the income elasticity estimates of motor vehicle fleet minus those of road length.
Bargh, John and Tanya Chartrand (2000) “The mind in the middle: A practical guide to priming and automaticity research”, pp. 253-85 in H.T. Reis and C.M. Judd (eds.) _Handbook of Research Methods in Social Psychology_ . Cambridge University Press, New York.

Barker, Nathan, C. Austin Davis, Paula López-Peña, Harrison Mitchell,A. Mushfiq Mobarak, Karim Naguib, Maira Emy Reimão, Ashish Shenoy, and Corey Vernot (2020) “Migration and the labour market impacts of COVID-19”, _WIDER Working Paper no. 2020/139_ Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen (2014) “Inference on Treatment Effects after Selection among High-Dimensional Controls”, _Review of Economic Studies_ 81(2): 608-50.

Borjas, George and Hugh Cassidy (2020) “The adverse effect of the COVID-19 labor market shock on immigrant employment”, _NBER Working Paper 27243._ Bossavie, Laurent, Daniel Sanchez, Mattia Makovec and Caglar Ozden (2020) “Do immigrants push natives towards safer jobs? Exposure to COVID-19 in the European Union”, _World Bank Policy Research Working Paper no. 9500._ Callen, Michael, Mohammad Isaqzadeh, James Long and Charles Sprenger (2014) “Violence and Risk Preference: Experimental Evidence from Afghanistan”, _American Economic Review_ 104(1): 123-48.

Chakamba, Rumbi (2020) “How Senegal has set the standard on COVID-19”, 8 October, Devex.com <u>https://www.devex.com/news/how-senegal-has-set-the-standard-on-covid-19-98266</u> [accessed February 1, 2021].

Cohn, Alan and Michel Maréchal (2016) “Priming in Economics”, _Current Opinion in Psychology_ 12: 17-21.

Fasani, Francesco and Jacopo Mazza (2020) “Being on the frontline? Immigrant workers in Europe and the COVID-19 pandemic”, _IZA Discussion Paper no. 13963._ IOM (2020) “Effects of COVID-19 on Migrants – Main Findings – Survey in Central America and Mexico”, June, - <u>https://kmhub.iom.int/sites/default/files/publicaciones/surveyeffects_of_covid 19_june_2020_final.pdf</u> [accessed January 22, 2021].

Motyl, Matt, Joshua Hart, Tom Pyszczynski, David Weised, Molly Maxfield and Angelika Siedel (2011) “Subtle priming of shared human experiences eliminates threat-induced negativity toward Arabs, immigrants, and peace-making”, _Journal of Experimental Social Psychology_ 47(6): 117984.

OECD (2020) “Managing international migration under COVID-19”, June 10 update. <u>http://www.oecd.org/coronavirus/policy-responses/managing-international-migration-undercovid-19-6e914d57/ [accessed January 22, 2021].</u> 20
Policy Research Working Paper 1120

# **Abstract**

This study examines the impact of temperature uncertainty on firm fixed capital growth using a unique dataset that merges extensive firm-level financial data with detailed gridlevel weather data. The analysis reveals a strong negative relationship between temperature uncertainty and fixed capital growth. Furthermore, the impact varies significantly across industries with differing levels of investment irreversibility and among countries with varying income levels. Firms in industries characterized by high investment irreversibility and those operating in higher-income countries experience more pronounced declines in fixed asset growth due to temperature uncertainty.

This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at j634yang@uwaterloo.ca and cschoder@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
CLEMENS & TIONGSON recent and growing literature on the micro effects of migration, surveyed by Hanson (2009) and Antman (2012). Though much of the early work on migration stresses the effects of remittances alone,<sup>7</sup> a new literature seeks the effects of migration itself, comprising both channels above.<sup>8</sup> In this paper we contribute to these literatures by offering unusually well-identified estimates of the reduced-form net effects of migration, and by theoretically-grounded tests of the relative importance of different impact mechanisms. Much of the previous research on reduced-form effects faces important challenges of causal identification that are difficult to address with the observational methods most commonly used. In rare cases, researchers have been able to compare observational estimates of migration effects to more reliably identified estimates, and have found large differences (McKenzie et al. 2010; Gibson et al. 2011). Moreover, existing research has only begun to sort out the different mechanisms of migration impact. The latest work suggests that, beyond remittances, also important are the ways that migration shapes household decisionmaking (Ashraf et al. 2011) and perceived returns to investment (Kandel and Kao 2001; McKenzie and Rapoport 2011).

# 3. Mechanisms of impact

We posit three principal channels by which labor migration by one household member can affect household finance and investment decisions. First, new income can alleviate constraints on borrowing and consumption smoothing. Second, changes in the location of family members can affect their power to influence decisions. Third, changes in the composition of the household back home can affect home production technology. Each emerges from a simple dynamic optimization model of household savings and in-

> migrants important changes in transferable skills, cultural attitudes, or technical knowledge.

> 7Rapoport and Docquier (2006) and Yang (2011) survey this literature, which includes Cox Edwards and Ureta (2003); Yang (2008); Alcaraz et al. (2012). A focus of this work has been the effect of remittances and similar transfers on labor force participation by recipients (Rodriguez and Tiongson 2001; G ̈orlich et al. 2007; Ardington et al. 2009; Edmonds and Schady 2012).

> 8Studies of the effects of migration on source households, including but not principally focusing on remittances, include Hanson and Woodruff (2003); Cort ́es (2010); Macours and Vakis (2010); Taylor and L ́opez-Feldman (2010); Amuedo-Dorantes et al. (2010); Mergo (2012).

4
- 38 -

- participation rate 44 percent versus 52 percent for men.

Mali: Rural - areas outside of legally designated "communes". Implicitly settlements over 3,000. Economically active - - not defined. Women's participation rate not given.

Mauritania: Rural - not defined. Taken as all regions except three except with over 85 percent population designated as urban. Essentially all localities under 10,000. Economically active - all persons over 12 years of age. - Women's participation rate 25 percent versus 82 percent for men.

- Mozambique: Rural - all areas outside 12 major cities. Twelve city -

- sizes not cited. Economically active all persons over 12 years of age. Women's participation rate - 81 percent versus 78 percent for men.

- Rwanda: Rural - areas outside perfecture headquarters and two other localities designated as urban. Economically active - all persons over seven years of age. Women's -

- participation rate not given.

- Senegal: Rural - localities less than 1,000. Economically active _ all persons over six years of age. Women's -

- participation rate 23 percent versus 60 percent for men.

- Sierra Leone: Rural - census definition, all towns under 5,000. Since employment not broken down by town, rural defined here as all districts with at least 75 percent of their population in localities of under 2,000. Economically active - all persons over 12 years of age. Women's -

- participation rate equal to that of men. Each accounts for 50 percent of labor force.

|Tanzania:|Rural- regional and district headquarters plus towns<br>over 5,000. Economically<br>active- all persons over five<br>years of age. Women's participation rate-<br>55 percent<br>versus 54 percent for men.|
|---|---|
|Togo:|Rural- all areas outside Lome and six legally<br>designated "communes". Economically active- population<br>over 12 years of age. Women's participation rate- not<br>given.|
|Zimbabwe:|Rural- population living in towns under 2,500. During<br>1969 census urban-<br>any locality over 150 if majority of<br>males employed in non-agricultural<br>occupations.<br>Economically<br>active- population over 15. Women's<br>participation rate- 48 percent versus 80 percent for<br>men.|
|**Table 12**|**OLS:** **Are school/teacher characteristics correlated with scores?**|
|---|---|
||**Dependent variable is percent correct score, Grades 3 and 2, MP**|

||**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**1.68**|**0.71**|**1.74**|**0.76**|**2.41**|**1.81**|**2.48**|**1.85**|
||**(0.15)**|**(0.41)**|**(0.15)**|**(0.40)**|**(0.06)**<sup>**+**</sup>|**(0.07)**<sup>**+**</sup>|**(0.05)***|**(0.07)**<sup>**+**</sup>|
|Index of infrastructure|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.68)|(0.93)|(0.67)|(0.94)|(0.57)|(0.61)|(0.60)|(0.60)|
|% non-sc teachers|-0.02|0.02|-0.04|0.00|0.01|0.03|0.01|0.02|
||(0.56)|(0.66)|(0.36)|(0.99)|(0.71)|(0.33)|(0.81)|(0.46)|
|% male teachers|0.02|0.02|0.00|0.01|0.03|0.04|0.03|0.04|
||(0.75)|(0.59)|(0.97)|(0.87)|(0.50)|(0.25)|(0.57)|(0.33)|
|% teachers with college<br>deg|-0.01|0.02|-0.01|0.02|0.02|0.02|0.02|0.02|
||(0.78)|(0.63)|(0.87)|(0.53)|(0.58)|(0.59)|(0.55)|(0.54)|
|% teachers with graduate<br>deg|0.12|0.10|0.13|0.11|-0.04|-0.06|-0.04|-0.06|
||(0.02)*|(0.01)*|(0.01)*|(0.01)**|(0.33)|(0.08)<sup>+</sup>|(0.38)|(0.10)<sup>+</sup>|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.20)|(0.51)|(0.64)|(0.94)|(0.78)|(0.75)|(0.76)|(0.44)|
|Av. days in service in<br>lastyear|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.14)|(0.32)|(0.19)|(0.41)|(0.87)|(0.98)|(0.97)|(0.89)|
|% teachers with pre<br>service training|0.02|0.00|0.02|-0.01|0.06|0.07|0.06|0.07|
||(0.59)|(0.98)|(0.69)|(0.87)|(0.09)<sup>+</sup>|(0.02)*|(0.11)|(0.03)*|
|% teachers doing multi<br>grade|-0.06|-0.10|-0.06|-0.10|-0.15|-0.12|-0.15|-0.12|
||(0.38)|(0.07)|(0.37)|(0.06)<sup>+</sup>|(0.02)*|(0.02)*|(0.02)*|(0.02)*|
|**% teachers actively**<br>**engaged inteaching**|**0.21**|**0.17**|**0.21**|**0.17**|**0.21**|**0.12**|**0.21**|**0.12**|
||**(0.01)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.02)***|**(0.00)****|**(0.03)***|
|% contract teachers<br>(samvida)|-|-|0.01|0.01|-|-|-0.03|0.00|
||-|-|(0.92)|(0.82)|-|-|(0.60)|(0.98)|
|% shiksha karmi teachers|-|-|-0.08|-0.08|-|-|-0.04|-0.04|
||-|-|(0.07)<sup>+</sup>|(0.03)*|-|-|(0.34)|(0.28)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2239|2239|2239|2239|2239|2239|2239|2239|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

27
which these effects are expected to operate are often not clearly spelt out, either in analytical work or public debate. The reduced marginal effective rates associated with the 2017 Tax Cuts and Jobs Act in the United States, for example, prompted concerns in Canada with the impact on investment there. But how exactly the marginal effective rate in one country, constructed with a closed economy in mind, should impact investment in another is by no means obvious. Similar doubts apply more generally to the helpfulness of the much-noted cross-country ‘league tables’ of marginal effective tax rates<sup>10</sup> in understanding cross-border investment decisions.

The approach taken here is to ground the empirics in a simple but reasonably general model of the investment decisions of a multinational that potentially operate in several countries. This generates a sufficient statistic which summarizes the impact of the international and domestic tax system on inward investment, encapsulating effects through statutory tax rates, in both the host country and alternative locations abroad, and King-Fullerton-type marginal effective tax rates. In doing so, it highlights one form of spillover that has been largely neglected in the international tax literature: the direct effect that investment in one affiliate within a multinational may have on the profitability of others. It might be, for example, that investing in one, and expanding its sales, reduces the price at which others can sell; or expanding one may require using scarce expertise within the group and so reduce the profitability of others. We refer to this, for want of a better term, as ‘implicit’ profit shifting, and distinguish it from ‘paper’ profit shifting of the kind that, as mentioned above, has become the primary focus of attention. In the empirics, we allow for, and attempt to differentiate between, these two forms of profit shifting and the tax effects that operate through them.

The paper is structured as follows. Section 2 sets out the broad framework that articulates potential channels for tax spillovers on real investment and guides the empirical work. Section 3 elaborates on the fundamental differences between FAI and FDI data and Section 4 then develops an estimation strategy. Section 5 sets out baseline results, explores their robustness underlying investment which just yields the investor their required post-tax return (conventionally expressed as a proportion of the former).

> 10As, notably, Mintz (2018) and Bazel and Mintz (2020).

5
<!-- Start of picture text --> Figure 4: Index of per capita income in least cohesive<br>and most cohesive societies (1960=1)<br>3<br>2.8<br>2.6 most cohesive<br>0 least cohesive<br>2.4<br>*~2.2<br>2<br>i 1.8<br>1.6<br>1.4<br>1.2<br>101 o11I ec ~' ell'''''''rTll , ....<br><!-- End of picture text --> (sample of 82 developing and developed countries) By what mechanisms does social cohesion affect growth? Consider first the role of institutions. Using a dataset compiled by Kaufinann, Kraay, and Zoido-Labatan 2000 it is possible to assess whether high-quality institutions have been important for the LDC's. Figure _5_ suggests they have been, i.e. that higher quality institutions (measured here by rule of law -- we will try many different measures in the next section) are positively associated with higher average growth rates over the post-reform period.

18
# ISSUES IN SOCIALIST ECONOMY REFORM

# Stanley Fischer and Alan Gelb'

# I. Introduction

The formidable challenges facing the reforming European Socialist Economies (ESEs) are frequently said to be unique. In fact, most of the individual requirements for socialist economy reform have been faced before, in China, and in Latin American and African countries where the combination of a weak private sector, political monopoly, heavy policy-induced distortions and macroeconomic imbalance is not uncomon. There are also lessons to be drawn from the earlier reform experiences _ih_ Yugoslavia and Hungary. Nonetheless the challenge is unique, in its system-wide scope, in its political and historical context, and in the speed of desired reform.

Few socialist countries remain unreformed planned economies. In the last decade several -- for example China -- have instituted some economic reforms, while trying to preserve the state's monopolies on political power, the savings-investment process, and social ownership. However, countries such as Hungary, Poland, Czechoslovakia, and Bulgaria -- and increasingly, the Soviet Union -- seek fundamental change leading to a pluralist political

- 1/ Fischer is Professor of Economics at MIT, and a Research Associate of the National Bureau of Economic Research; Gelb is Chief of the Socialist Economies Unit of the World Bank. Work on this paper was begun when Fischer was Chief Economist at the World Bank. Opinions expressed are those of the .uthors, and are not official views of the World Bank. The authors are indebted to Bela Balassa, Olivier Blanchard, Rudiger Dornbusch, and Imre Tarafas for helpful comments.
**Figure 18: Jordan’s total export value in manufacturing**

<!-- Start of picture text --> Jordan's Manufacturing Exports<br>by Main Region<br>EU East Asia<br>3000<br>JC JC<br>2000<br>1000<br>0<br>MENA South Asia<br>3000<br>2000 JC JC<br>1000<br>0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>Year<br>Export Value in M US$<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). The vertical red dotted line splits the periods before and after the implementation of the JC.

19 | P a g e
There has been greater acceptance of the usefulness of qualitative information, which has been reinforced by the success of the poverty assessment _Vietnam: Attacking Poverty._

# **_A remaining challenge: institutionalizing participatory methods_**

Few are now disputing the value that the PPAs have brought to the poverty diagnosis work or to the planning activities, which build on the diagnosis. The question that now arises is the longer term role for participatory information in future planning and policy work. The PPAs were highly valuable, but also expensive. Some Government agencies have expressed a desire to integrate participatory methods into their work - MoLISA for example is asking for help in developing a streamlined toolkit of participatory methods which could be useful in targeting poor households, informing their program and monitoring the impact of their activities. This is encouraging. Greater openness and transparency by Government, improved information flows, a more vibrant and vocal civil society will all be necessary to create an environment where participatory methods will work. With regards to poverty reduction, this phase of active national and local level planning presents two clear challenges for Government and its donor and NGO supporters. The first is to ensure that the overall development plans and direction are compatible with those needed to generate equitable, permanent and sustainable improvements in living standards for the poor. The second is to ensure that both the overall plans and Government's strategy for Hunger Eradication and Poverty Reduction a programn of targeted support to the poor which is a very specific component of the overall plan, are as rooted in the realities of poor households lives as possible.

24