rafmacalaba/datause-encoder-data huggingface dataset:
and 0.584 log points larger on average than places with routes less paved, in terms of population and lights, respectively, after controlling for distance to the primate. In column 5, after controlling for distance to the primate, more paving is correlated with a larger fraction of adults working in the manufacturing sector, in a sample of districts in 4 countries (Ghana, Guinea, Senegal, and Tanzania) for which census data are available from IPUMS.<sup>48</sup> These results are all consistent with the idea that cities connected by more paved roads could be more hurt by higher oil prices because they are more economically connected to the primate, whereas cities that are connected by mostly unpaved roads are smaller and closer to autarky.

The regressions in Table 8 exploit the paving information by including two terms of the main effect _βptxi_ in Equation (11), one for cities with routes to the primate more paved than the median and one for cities with less paved routes. Column 1 demonstrates that the transport cost effect is similar in the two categories of cities. While paving status was likely determined before the study period in most cases, it is potentially endogenous to local economic activity, limiting the scope for causal interpretation. It is likely that transport costs affect the two sets of cities in slightly different ways. Routes to some cities were paved for any number of reasons (early manufacturing promise, political or military importance, corruption), and then this paving helped these cities to grow more, at least in part because of transport-sensitive firms that were then penalized by increases in oil prices. On the other hand, unpaved roads require slower and more fuel intensive travel, so given the same demand for transport services, cities along them are penalized more per mile.

However, without a mostly paved road to the primate, firms in a city may seek alternate trading connections, relying on intermediate cities instead. Column 2 adds the distance to the nearest city in the top population quintile if that city is not the primate, separately based on the paving status (high or low) of the route to the primate. As in Table 6, higher transport cost to a top quintile city decreases output. However, this effect is limited to cities with relatively unpaved routes to the primate. This suggests that these cities, relatively unconnected to the primate, are essentially consumer cities as in the formulation of Jedwab (2011). In essence, their trade is funneled through a regional hub, not the primate. Conversely, among cities that are relatively wellconnected to the primate, it is the primate distance that matters, not the top quintile

> 48Produced by Ghana Statistical Services (2000), National Statistics Directorate (Guinea; 1983), National Agency of Statistics and Demography (Senegal; 1988) and National Bureau of Statistics (Tanzania; 2002), respectively, and distributed as: Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 6.1 [Machine-readable database]. Minneapolis: University of Minnesota, 2011. https://international.ipums.org/. Accessed 28 April 2011.

22
- Arnold, Jens M., Beata S. Javorcik and Aaditya Matoo (2011), ―Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,‖ _Journal of International Economics,_ Vol. 85 (1), 136-146.

- Arnold, Jens, Aaditya Mattoo and Gaia Narciso (2008), ― Services Inputs and Firm Productivity in Sub-Saharan Africa: Evidence from Firm Level Data,‖ _Journal of African Economies_ ,‖ Vol. 17(4), 578-599.

- Baldwin, Richard.E., Rikard Forslid and Jan Haaland (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), _Dynamic Issues in Applied Commercial Policy Analysis_ , Cambridge: Cambridge University Press

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- Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr  (2009), ―Modeling Services Liberalization: The Case of Kenya,‖ _Economic Modeling_ , Vol. 26 (3),May,  668-679.

- Balistreri, Edward J. and David G. Tarr (2011), ―Services Liberalization in Preferential Trade Arrangements: The case of Kenya,‖ World Bank Policy and Research Working Paper No. 5552, January.

- Baranov, S.P. (2000), ―Compiling the disaggregated version of ―input-output‖ tables of the Russian Federation for 1995 year for consequent use as a basic data for building the general equilibrium model for the Russian Federation,‖ mimeo.

- Bourguignon, Francios, Jaime  de Melo and C. Morrison (1991), « Poverty and Income Distribution During Adjustment: Issues and Evidence from the OECD Project,‖ _World Development_ Vol. 19, 1485-1508.

- Bourguignon, F. and L. Pereira da Silva (2003), ―Conclusion: Where do we go from here?‖ in Bourguignon, F. and L. Pereira da Silva (Eds.), _The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools_ , World Bank and Oxford University Press, New York, pp. 1-26.

- Broda, Christian  and David Weinstein (2004), ―Variety, Growth and World Welfare,‖ _American Economic Review_ , Vol.94, 139-144.

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83
analyze and evaluate deposit insurance in developing countries. The reason is that developing countries, 'r fact, typically do intervene to protect depositors in failing bank situations. Consequently, if an analysis and evaluation of deposit insurance is to be relevant and useful, it is necessary to compare these explicit deposit protection systems with the implicit deposit protection schemes that are now widely employed in countries without deposit insurance. As might be expected, the conclusions of this study regarding deposit insurance differ significantly from those that have emerged from the existing deposit insurance literature.

The second objective of this study is to identify the major features of deposit insurance systems, and then review the pros and cons of alternative ways of structuring each major feature. Some of the major features that will be analyzed include the types of financial institutions that should be eligible for deposit insurance; whether the deposit insurance system should be public or private; whether the system should be compulsory or voluntary; the amount of insurance coverage that should be offered depo--.tors; and how the deposit insurance system should be financed.

As part of this study, we have prepared a survey of deposit insurance systems throughout the world, with particular emphasis on those in the developing countries. Appendix A contains a discussion of the major fcatures and performance of a dozen deposit insurance systems in the developing world, as well as the system in Spain, which has recently served as a model for one or more developing countries. Appendix B contains a table of the major features of deposit insurance systems in both the developing and developed world. This table is designed to allow the reader to make a quick comparison of the major features of these systems. The survey updates and considerably expands an earlier survey
(b) For recipients of matching grants _Note:_ The adjusted confidence intervals are obtained from regressions that use (a) recipients of cash grants and the control group only and (b) the matching grant recipients and the control group only. _Source:_ Estimates based on Bagré Growth Pole Data Set.

# _6.6 Spillovers_

Given that the treatment status varies within each of the nine municipalities and given that many of the treated firms operate within the same sector, it is worth exploring possible spillover effects. These are interesting for two reasons. First, it is important to check whether firms in the control group are affected by beneficiary firms which would reduce their suitability as a control group. Second, it is worth exploring whether the gains of beneficiaries depend on the competition they face (McKenzie 2017). In our case, especially, the slightly negative treatment effects on profits and turnover that we find for recipients of cash grants despite increased investment could, for instance, be due to the competition they face with other treated firms. Competition might make it difficult to increase sales especially in a context where the market size is relatively small and offers little possibilities to expand, for example, through exportation. Table 7 shows the effects of the number of treated firms (with cash) alternatively within 500 m, 2 km, and 5 km for recipients of cash grants, recipients of matching grants, and control firms.<sup>10</sup> These results suggest that overall, there are only very small, if any, spillover effects. The only remarkable pattern that comes out of these estimations is that the negative treatment effect on profits decreases slightly with the number of cash beneficiaries within a radius of 5 km. Yet, again, overall, we see little spillovers so far, neither negative nor positive. This issue will be further explored as more rounds of data become available. The survey includes various variables that measure in one way or another the competition firms face.

> 10 On average there are 7.1, 20.7 and 32.5 cash beneficiaries within a radius of 500 m, 2 km and 5 km respectively.

26
**Table 1 – Green occupation categories**

|**Green job category**|**Definition**|**Examples**|
|---|---|---|
|**Increased Demand**<br>**Occupations**|Occupations that already exist. They may<br>face increased demand with an expanding<br>green economy. While their work context<br>may change, the tasks and skills they use will<br>not.|Carpenters and welders that will<br>be required in the construction of<br>new energy-efficient buildings or<br>agricultural workers needed for<br>more organic farming.|
|**Enhanced Skills**<br>**Occupations**|Occupations that already exist. A growing<br>green economy may or may not increase the<br>demand for these occupations, but it would<br>affect the type of tasks or skills required.|Construction<br>inspectors,<br>agricultural<br>technicians,<br>and<br>architects. Workers in these jobs<br>may need to learn how to use new<br>materials or adhere to new<br>energy-efficient building codes.|
|**New and Emerging**<br>**Occupations**|The “purest” forms of green jobs. These<br>occupations emerge because of the green<br>economy creating the need for new and<br>unique work and worker requirements. As<br>such, these occupations emerged more<br>recently.|Wind energy managers, climate<br>change<br>analysts,<br>or<br>water<br>resource specialists.|

Source: Own elaboration based on O*NET database and Dierdorff et al. (2009, 2011).

This paper uses the International Standard Classification of Occupations (ISCO) or its adaptations to classify occupations, instead of O*NET. Previous studies have mapped the O*NET-SOC 2010 classification of green occupations into the latest ISCO-08 taxonomy to allow for more straightforward cross-national comparisons of green occupations (Hogarth, 2011; Sofroniou and Anderson, 2021). However, their mappings are produced at the ISCO three-digit level at best. More importantly, their methodologies are based on a dichotomic definition of green jobs, in which a whole ISCO category is considered either green or non-green. This paper maps the O*NET-SOC classification of green occupations into the most detailed four-digit ISCO-08 taxonomy using a probabilistic instead of a dichotomic approach. Specifically, the paper uses data on total employment at the 2-digit level ISCO for 120 countries from circa 2001 to circa 2020 (676 country-year observations). Since each ISCO code may have several O*NET-SOC occupations associated with it (not all necessarily green), it would be misleading to consider a whole ISCO occupation as green or non-green based on whether the number of O*NET-SOC associated occupations are primarily green or non-green. Therefore, instead of constructing a binary classification, the paper estimates the share of employment within an ISCO-08 category that corresponds to O*NET-SOC occupations considered green and then considers this share as the probability that an occupation with that ISCO code is green. Since the O*NET-SOC classification is based on the US structure of employment, employment shares are 6
Table 10: Effects of LEAP on self-reported community action

||(1)<br>Nr. of village<br>meetings|(2)<br>Repair<br>school|(3)<br>Repair road|(4)<br>Clean<br>public<br>space|(5)<br>Average<br>effect|
|---|---|---|---|---|---|
|_Full_<br>_sample_||||||
|LEAP|1.78**|-0.00|0.01|0.04|0.14|
||(0.69)|(0.10)|(0.10)|(0.06)|(0.11)|
|constant|6.37**|0.36**|0.60**|0.24**||
||(0.43)|(0.07)|(0.07)|(0.04)||
|N|548|548|548|548||
|_ID Poor 1_<br>_and 2_||||||
|LEAP|1.46*|0.04|0.02|0.06|0.18|
||(0.78)|(0.10)|(0.10)|(0.06)|(0.12)|
|constant|6.22**|0.34**|0.58**|0.22**||
||(0.45)|(0.06)|(0.07)|(0.04)||
|N|367|367|367|367||
|_Non-poor_||||||
|LEAP|2.40**|-0.09|-0.03|0.01|0.07|
||(0.86)|(0.11)|(0.11)|(0.08)|(0.12)|
|constant|6.69**|0.39**|0.63**|0.28**||
||(0.55)|(0.08)|(0.07)|(0.06)||
|N|181|181|181|181||

Note: Columns 1-4 report OLS estimates with village-clustered standard errors. Column 5 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-4

* = significant at the five percent level, **=significant at the one-percent level

40
_Matthew Crawford got a PhD in Political Philosophy from the University of Chicago. Then he abandoned academia after a year, abandoned a Washington DC think-tank job after five months, and opened a one-man motorcycle repair shop. He thinks more now than when he worked at think-tank. He’s part of a vibrant, intuitive, well-educated community. He’s proud of his work, which matters deeply to his customers. His decisions aren’t arbitrarily changed by a superior. His job won’t suddenly be shipped to India. Of course, most people assume fixing motorcycles was the only job he could get._ Business Insider, May 24, 2009.

Among the key questions trade economists need to be able to answer is: When a trade shock strikes such as liberalization, trade agreement, or expansion of a foreign export power, who benefits and who is hurt, and by how much? There are as many ways of approaching these questions as there are ways of dividing people into economically meaningful subgroups. The oldest literature divided people by what can be called ‘class’ lines, making a distinc– tion between workers and the owners of physical or human capital the Stolper-Samuelson approach. More recent approaches have divided up workers based on their industry of employment (Revenga (1992), Pavcnik, Attanasio and Goldberg (2004), Artu ̧c, Chaudhuri and McLaren (2010)); region of residence (Topalova (2007), Kovak (2010), Hakobyan and McLaren (2010)); and age (Artu ̧c (2012a)), in each case attempting to quantify how trade shocks affect people in the different groups differently.

More recently, several studies have focussed on a division of workers by _occupations_ , often making use of a data set explored by Autor, Levy, and Murnane (2003) that breaks down the ‘task’ composition of a wide range of occupations in US labor data. Authors who exploit these distinctions to look at the differential effects of trade shocks on workers with different types of occupations include Peri and Sparber (2009), Ebenstein, Harrison, McMillan, and Phillips (2009), and Liu and Trefler (2011). Some of the results in Ebenstein et. al. (2009), in particular, suggest that occupational distinctions may be more important than industry in identifying who loses from globalization, that it is workers in vulnerable occupations (namely, those that are the most offshorable) in affected industries who lose.

2
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38
- 37 -

Virmani, Arvind, "Tax Reform in Developing Countries: Issues, Policies, and Information Gaps," mimeo, World Bank (May 1985).

- Wheeler, David, "Sources of Stagnation in Sub-Saharan Africa," Worl Development 12 (1984): pp. 1-23.

- World Bank, World Tables (3rd ed.), Baltimore: Johns Hopkins University Press (1984).
**<mark>Table 5 – Republic of Korea Composition of Foreign Currency Debt and Reserves</mark>** <mark>as of end December 2016, billions USD</mark>

|||USD|EUR|JPY|GBP|CNY|Other|Total|
|---|---|---|---|---|---|---|---|---|
||USD bil. eq.|232|39|6|11|63|15|366.0|
|Reserves*|Percent of total|63.3|10.8|1.8|2.9|17.1|4.1|100.0|
||Percent of GDP|16.4|2.8|0.4|0.8|4.5|1.1|25.9|
||USD bil. eq.|2.6|0.81|-|-|0.25|-|3.7|
|External Debt|Percent of total|71.2|22.0|-|-|6.8|-|100.0|
||Percent of GDP|0.2|0.1|-|-|0.02|-|0.3|
|Concentration|Ratio FX debt/reserves|0.01|0.02|n/a|n/a|n/a|n/a|0.01|

Source: Ministry of Strategy and Finance and Kim and Lee, April 2017.

*Estimates of the reserve composition as of 2014 data, Kim and Lee, April 2017.

Korea maintains a large amount of foreign reserves, and an estimate (Kim and Lee, 2017) of the composition indicates that the largest share is denominated in USD followed by CNY and EUR (Table 5). Korea has a de jure and de facto floating exchange rate regime.

**Figure 5 – Republic of Korea: FX Currency Composition of:**

<!-- Start of picture text --> Reserves Debt<br>Other<br>4%<br>CNY<br>CNY 7%<br>GBP 17% EUR<br>3% 22%<br>JPY<br>2% EUR<br>USD<br>11% USD<br>63%<br>71%<br><!-- End of picture text --> Considering that the FX debt/reserves ratio is only 0.01, mismatches between debt and reserves composition are not a concern. Similar to South Africa, the flexible exchange rate regime works as a mechanism to adjust the exchange rate against external shocks, and the level of reserves is high enough to defend the exchange rate.

# **IV. II. Countries with Zero FX Positions**

# **A. Albania**

Albania has almost equal shares of domestic debt and external debt (see Table 6). In recent years, Albania has issued bonds in EUR, including a EUR450 million international bond in 2015.

16
Table 4: The Impact on Time Use for Employed Women and Employed Men

||(1)|(2)|(3)|
|---|---|---|---|
||Ln(Travel Time)|Ln(Time for HH)|Ln(Time for Work)|
|_Panel A. Employed Women_||||
|Treat_×_ Post_×_ Married|-0.215***<br>(0.026)|0.196**<br>(0.059)|-0.248***<br>(0.061)|
|Control Mean (Level)|0.70|2.69|7.21|
|_R_<sup>2</sup>|0.52|0.54|0.43|
|No. of Individuals|2,916|2,916|2,916|
|N|9,906|9,906|9,906|
|_Panel B. Employed Men_||||
|Treat_×_ Post_×_ Married|-0.010<br>(0.037)|-0.142**<br>(0.054)|0.111***<br>(0.029)|
|Control Mean (Level)|0.79|1.51|7.57|
|_R_<sup>2</sup>|0.52|0.71|0.30|
|No. of Individuals|1,867|1,867|1,867|
|N|7,318|7,318|7,318|
|Individual FE|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

_<u>Notes:</u>_ In panel A, we restrict the sample to employed women; and in panel B, we restrict the sample to employed men from _households_ with employed women (the sample in panel A of Table A4). In this table, we categorize individuals by their current employment status. A (wo)man is unmarried if (s)he is divorced, unmarried, or widowed. In panel A, the omitted group is employed unmarried women; and in panel B, the omitted group is employed unmarried men. “Travel Time” = Time spent on travel. “Time for HH” = Time spent on household activities. “Time for Work” = Time spent on work done for the employer. “Married” = A dummy takes the value of one if a (wo)man is married and zero if the (wo)man is divorced, unmarried, or widowed. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is hours per day. The control mean is the average for employed unmarried women in control states during pre-periods in panel A and is the average for employed unmarried men in control states during pre-periods in panel B. Standard errors are clustered at the state level. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

29
investments of $3.4 billion and Nepal’s $2.9 billion. Maldives, Bhutan and Afghanistan’s PPP programs have been the smallest with investments of $469 million, $240 million and $39 million, respectively.

**Table 1 Sectoral distribution of public-private partnership projects in infrastructure in South Asia, by country, 1990–2018**

|**Country**||**ICT**|**Energy**|**Transport**|**Water and**<br>**Sewerage**|**Total**|
|---|---|---|---|---|---|---|
|Afhanistan|_Projects_|0|2|0|0|2|
|g|_Mn. US$_|0|39|0|0|39|
|Banladesh|_Projects_|5 (2)|31|8 (1)|1|45 (3)|
|g|_Mn. US$_|76|5,372|1,097|333|6,877|
|Bhutan|_Projects_|1|1|0|0|2|
||_Mn. US$_|0|240|0|0|240|
|Idi|_Projects_|7 (1)|414 (3)|543 (27)|20|984 (31)|
|na|_Mn. US$_|1,624|162,375|117,595|1,337|282,932|
|Maldives|_Projects_|0|0|1 (1)|0|1 (1)|
||_Mn. US$_|0|0|469|0|469|
|Neal|_Projects_|2|34|1|1|38|
|p|_Mn. US$_|12|2,491|378|0|2,880|
|Pakistan|_Projects_|0|73|8|0|81|
||_Mn. US$_|0|28,159|3,005|0|31,164|
|Si Lk|_Projects_|1|75|3|0|79|
|r ana|_Mn. US$_|81|2,244|1,062|0|3,387|
|Ttl|_Projects_|16 (3)|630 (3)|564 (29)|22|1,232 (35)|
|oa|_Mn. US$_|1,764|200,919|123,605|1,670|327,988|

_Source_ : PPI database.

_Note_ : The number of canceled projects is in parentheses. Investment totals are in 2019 million dollars. ICT = Information and communications technology.

PPPs in South Asia have been predominantly used in the energy and transport sectors. Of the 1,232 PPP projects in infrastructure with financial closure in the region since 1990, 97 percent are in energy or transport sectors (table 1). The remaining 38 projects are in the water and sewerage sector and the information and communications technology (ICT) sector. The composition by country shows that aside from India, Bhutan, and Maldives, the country programs are dominated 8