rafmacalaba/datause-encoder-data huggingface dataset:
Table Cl1. Percent of Production Hours Lost due to Power Interruption by Region and Employment Size, Indonesia

|**..** **..........**.......................<br>...........<br>PowrInterruption<br>ILoss (X)<br>I..O.......................<br>+.........|
|---|
|Wh**oo**<br>JALL<br>_lIEWA_<br>1<br>6.92<br>C'|
|ountry<br>-<br>I<br>**IN**<br>I<br>191.00<br>---.......<br>4.......|
|S_l LIMEAN<br>1<br>5.93<br><br>I<br>tLr 50)<br>-'''''''''<br>'|
|IN<br>jLmre**NEA**<br>I<br>9|
|....... ...................<br>Il'<br>50 I<br>121.001|
|*~~~~------4--"---.-<br>,<br>**zJakartaiALL**<br>INEAN<br>**6.581**|
|IN<br>I<br>95.001'<br>++|
|.<br>.... ................<br>IS=I IM EAN<br>1<br>5.49!<br>|
|l(XLT50)1 ---------<br>,-------<br>50)<br>I1<br>37.001<br>I<br>I<br>IN<br>+|
|_ ____ _<br>L go<br>1<br>**7.2361**<br>|
|<br>(GE 50)<br>-<br>-<br>-<br>I<br>I<br>IN<br>1<br>58.001|
|_______<br>__....<br>.......<br>.........<br>-tsb<br>L|kfALL**IEAM**<br>1<br>7.65|
|,<br>IN<br>1<br>71.001|
|ISl<br>IMEAN<br>**7** .921|
|(LT 50)1<br>--------------------<br>_IL_<br>IN<br>1<br>24.001<br>|
|______ ____<br>................................<br>_<br>IILgo;<br>**MIEAN**<br>I<br>7p51|
|<br>_(GE_50)1------------------<br>I|
|<br>I<br>IN<br>1<br>47.00:|
|ISear-r-IALL<br>INEAN<br>1<br>6.12I<br> <br> <br>|
|Ing.<br>I---------I----------<br>I|
|<br>I<br>IN<br>1<br>2S001<br>|
|<br>**.**<br> <br>.<br>1.......<br>.....<br>..<br>.|
|I|LT50)1-------------|
|<br>1g<br>_IN_<br>I<br>9.001|
|ILarse**ItEAN**<br>I<br>8.191<br> <br>|
|I<br>tCE 501-------<br>I-<br>|
|<br>)<br> <br>-|
|<br>I<br>I<br>IN<br>I<br>16.001|
|......................................|
10 Table 2 : **Comparison of GFS data with Fiscal design surveys of OECD (1999)** _10 European transition countries_

|**Country**|**Subnational**<br>**share of**<br>**government**<br>**expenditure**<br>**(GFS)**|<br> <br>**Subnational**<br>**share of**<br>**government**<br>**expenditure**<br>**(OECD)**|**Subnational**<br>**share of**<br>**government**<br>**revenue**<br>**(GFS)**|<br> <br>**Subnational**<br>**share of**<br>**government**<br>**revenue**<br>**(OECD)**|
|---|---|---|---|---|
|**Bulgaria**|19,0|19,1|18,1|18,6|
|**Czech**<br>**Republic**|16,3|18,3|20,9|20,8|
|**Estonia**|17,9|19,7|21,8|22,2|
|**Hungary**|21,2|23,7|25,2|26,7|
|**Latvia**|21,6|23,1|24,6|26,0|
|**Lithuania**|22,0|19,6|21,9|22,9|
|**Poland**|28,7|27,6|32,4|28,9|
|**Romania**|9,2|9,4|11,7|11,9|
|**Slovak**<br>**Republic**|5,5|7,0|6,3|4,9|
|**Slovenia**|8,0|11,6|11,6|11,9|
|**Mean**|**17,0 **|**17,9 **|**19,4 **|**19,5 **|

Source : OECD, Fiscal Design Across Levels of Government, 2001, 2002 _Table 3 :_ **_Composition of subnational governments’ revenue (1999)_** _10 European transition countries_

|**Country**|**Tax Reve**|**nue**|**Non-tax**<br>**Revenue**|**Grant**||**Total**|
|---|---|---|---|---|---|---|
||Own-Taxe|sTax-<br>Sharing||General<br>Purpose|Specific||
|**Bulgaria**|0,0|47,2|13,4|36,3|3,2|100|
|**Czech**<br>**Republic**|3,9|43,8|36,3|0,0|16,0|100|
|**Estonia**|6,3|62,1|9,1|13,4|9,1|100|
|**Hungary**|16,3|16,8|17,0|1,7|48,2|100|
|**Latvia**|0,0|56,0|20,4|2,3|21,3|100|
|**Lithuania**|0,0|91,0|4,8|2,3|1,9|100|
|**Poland**|10,4|14,4|24,6|30,5|19,9|100|
|**Romania**|6,1|64,1|14,9|0,0|14,9|100|
|**Slovak**<br>**Republic**|25,2|43,8|21,4|0,0|9,7|100|
|**Slovenia**|10,6|49,3|17,5|15,9|6,6|100|
|**Mean**|**7,9 **|**48,9 **|**17,9 **|**10,2 **|**15,1 **|**100**|

Source : OECD, Fiscal Design Across Levels of Government, 2001, 2002.
||||Inc|ome|||
|---|---|---|---|---|---|---|
||Rural|Urban|Rural|Urban|Rural|Urban|
|Households Reporting Access to Electricity<br>eee<br>(%o ofhouseholds)|37|84|30|90|8|54|
|Access to Improved Water Sources<br>P<br>(%o ofpopulation)|73|98|85|96|54|83|
|Access to Improved Sanitation<br>:<br>(% ofpopulation)|44|86|76|91|28|54|
|Households with Own Telephone<br>H<br>(%oofhouseholds)|6|43|1|17|1|10|

Note: Data used isfrom the fatest observation available between 1997 and 2003.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Dufrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
-29 -

Table 2. Indicators by Category

|Sector|Indi|cators|
|---|---|---|
|External<br>Capital account|(1) international reserves/base money<br>(2) international reserves/GDP<br>(3) stock of international reserves<br>(4) reserves/imports<br>(5) growth in reserves<br>(6) central bank foreign assets/base<br>money<br>(7) growth of central bank net foreign<br>assets<br>(8) net foreign assets/Ml<br>(9) net foreign assets/Mi squared|(10) errors and omissions plus short-term<br>capital<br>(I 1) share capital flows in the form of<br>short-term borrowing<br>(12) short-term capital flows/GDP<br>(13) FDVIdebt<br>(14) capital account balance/GDP<br>_(15)_domestic-foreign real interest rate<br>differential<br>(16) domestic-foreign nominal interest<br>rate differential|
|Debt profile|(1) foreign aid<br>(2) external debt/GDP<br>(3) public debt/GDP<br>(4) share of commercial bank loans<br>(5) share of concessiona**l**oans<br>(6) share of variable-rate debt|(7) share of short-term debt<br>(8) share of public sector debt<br>(9) share of multilateral development<br>bank loans<br>(10) debt service/GDP adjusted for GDP<br>growth|
|Current account|(1) change in real exchange rate<br>(2) level of the real exchange rate<br>(3) drift of the real exchange rate<br>(4) variance of the real exchange rate<br>(5) deviations from PPP in the real<br>bilateral exchange rate<br>(6) deviations from trend in the real<br>exchange rate<br>(7) deviations from historical average of<br>the real exchange rate<br>(8) real exchange rate squared|(9) trade balance/GDP<br>(10) current account/GDP<br>(11) exports/GDP<br>(12) exports/imports<br>(13) change in exports<br>(14) change in imports<br>_(15)_saving/GDP<br>(16) investmentlGDP<br>(17) change in the terms-of-trade<br>(18) change in export prices|
|International|(1) OECD real GDP growth<br>(2) international interest rates|(3) U.S. interest rates<br>(4) foreign price level|
Chancel, Lucas and Thomas Piketty (2021), `Global income inequality, 1820–2020: The persistence and mutation of extreme inequality’, _Journal of the European Economic Association_ , 19(6): 3025–3062 Chetty, Raj, John Friedman, Janet Gornick, Barry Johnson, and Arthur Kernickell (2022), _Measuring Distribution and Mobility of Income and Wealth_ , NBER and The University of Chicago Press, Chicago and London Corak, Miles (2013), `Income Inequality, Equality of Opportunity, and Intergenerational Mobility’, _Journal of Economic Perspectives_ , 27 (3): 79–102 <mark>Das, Jishnu, Quy-Toan Do, Karen Shaines, and Sowmya Srikant (2013), ‘US and them: The geography of academic research’</mark> _<mark>Journal of Development Economics</mark>_ <mark>105 (November): 112-130</mark> <mark>Davis, Kingsley and More (1944), ‘Some principles of stratification’</mark> _<mark>American Sociological Review</mark>_ <mark>10(2): 242-249</mark> <mark>De Ferranti, David, Guillermo Perry, Francisco Ferreira, and Michael Walton (2004),</mark> _<mark>Inequality in Latin America: Breaking with History?</mark>_ <mark>Washington, DC: World Bank</mark> <mark>Elbers, Chris, Peter F. Lanjouw, Johan A. Mistiaen, Berk Özler, and Ken Simler (2004), ‘On the unequal inequality of poor communities’</mark> _<mark>World Bank Economic Review</mark>_ <mark>18(3): 401-421</mark> Ferreira, Francisco (2022) ‘The analysis of inequality in the Bretton Woods institutions’ _Global Perspectives_ 3(1): <mark>39981</mark> Ferreira, Francisco, Christoph Lakner, Maria Ana Lugo, and Berk Özler (2018), `Inequality of opportunity and economic growth: How much can cross‐country regressions really tell us?', _Review of Income and Wealth_ , 64(4): 800-827 Hnatkovska, Viktoria, Amartya Lahiri, and Sourabh. B Paul (2013), `Breaking the Caste Barrier: Intergenerational Mobility in India’, _Journal of Human Resources_ , 48 (2): 435-473 Jantti, Markus and Stephen P. Jenkins (2015), `Income mobility’, Chapter 10 in the _Handbook of Income Distribution_ , Volume 2A, Elsevier Jenkins, Stephen P. (2022), `Top-income adjustments and official statistics on income distribution: The case of the UK’, _Journal of Economic Inequality_ , 20: 151-168 Joshi, Shareen, <mark>Nishtha Kochhar, and Vijayendra Rao</mark> (2022), <mark>`Fractal inequality in rural India: class, caste and jati in Bihar’</mark> _<mark>Oxford Open Economics</mark>_ <mark>, 1: odab004,</mark> <u><mark>https://doi.org/10.1093/ooec/odab004</mark></u> Kuznets, Simon (1955), `Economic growth and income inequality’, _American Economic Review_ , 45: 1-28 Lakner, Christoph and Branko Milanovic (2016), `Global income distribution: from the fall of the Berlin Wall to the Great Recession’, _World Bank Economic Review_ , 30(2): 203-232 10
3 suppliers, constituting the type of spillovers that is economically more important in most developing and emerging host economies than spillovers within a single industry.

Another important feature of the survey is that, by focusing on backward linkages, it provides an opportunity to examine empirical evidence from different research strands that are normally not covered in the same survey (Jordaan 2016). In particular, empirical research in the fields of development studies and economic geography, usually based on case studies or small-scale purposebuilt surveys, often provides detailed information on the scale and nature of linkages between MNC affiliates and local suppliers (see, for example, Ivarsson and Alvstam 2010; Jordaan 2017). Research in the fields of economic development and international economics is characterized by the use of large firm-level data sets to identify the scale and nature of productivity spillovers, in the form of statistical associations between the presence of MNC affiliates and productivity of domestic firms in industries that supply inputs (see, for example, Javorcik 2004; Blalock and Gertler 2008). In combination, these research strands provide a particularly rich set of empirical findings that allow us to study drivers of the extent and nature of backward linkages and their spillover impacts.

The findings of the survey provide new insights and recommendations for the public policy choices that host economy governments are facing. This is important, as they often dedicate considerable resources to attract MNC investments, facilitate the operations of MNC affiliates, and help domestic firms create supply linkages with foreign firms. Host economy governments are searching for new policy measures, in line with the growing acceptance of the notion that restrictive and interventionist policies aimed at influencing the behavior and impact of MNC affiliates are ineffective and often create detrimental economic effects (Battat, Frank, and Shen, 1996; Stone, Messen and Flaig, 2015). Instead, governments need to design and implement a variety of “soft” (Harrison and Rodriguez-Clare 2010) or “light-handed” (Taglioni and Winkler 2016) industrial policies, within the context of creating and facilitating open and liberalized economic environments. Our survey will identify the main areas that government policy making needs to focus on and the types of policies that can promote economic development by facilitating the extent and productivity impact of linkages between MNC affiliates and local suppliers.

The paper is constructed as follows. Section 2 discusses the concept of productivity spillovers and the various channels that transmit these effects. Section 3 presents the key findings from our survey of empirical studies on MNC sourcing linkages and their effects. This section focuses in particular on how characteristics of MNC affiliates and domestic firms and various host economy conditions and mediating factors influence the degree to which MNC affiliates use local suppliers, the nature and degree of technology dissemination, and the materialization of productivity spillovers
marginalized (Boeri and van Ours, 2013; OECD, 2013, 2014b). It has also been shown that where temporary employees have much lower levels of employment protection than their permanent peers, they also have lower job satisfaction (OECD, 2014b). In addition, in countries with very high levels of protection for permanent employees, the probability of transitioning from temporary to permanent employment is lower (Güell and Petrongolo, 2007), productivity is lower, and inequality is higher (Bassanini et al., 2008; Dolado et al., 2012; OECD, 2014b).

Figure 8: Sweden provides a much higher level of employment protection for permanent employees than for temporary ones <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>Permanent employees, 2013 Permanent employees, 2008 Temporary employees, 2013<br>Employment protection legislation index Germany Italy Portugal Belgium France Austria Sweden Greece Denmark Norway Spain Iceland Finland Ireland Japan Canada<br>Netherlands Luxembourg EU15 average OECD average Nordic average G7 average United Kingdom United States<br><!-- End of picture text -->

## Source: OECD (2014b).

Note: The OECD employment protection legislation index ranges from 0 (least stringent) to 6 (most stringent). Countries and country groups are in descending order by level of protection for permanent employees in 2013. The Nordic average excludes Sweden.

# **_Evidence on redundancy regulations from the Doing Business data_**

_Doing Business_ provides detailed data on redundancy rules and the cost of redundancy that are similar to those used in calculating the OECD employment protection legislation index for permanent employees. The _Doing Business_ data include 10 indicators on notification, priority and retraining rules as well as notice periods and severance payments stipulated in labor laws. According to these data, among the 20 EU15, G7 and Nordic countries, 12, including Sweden, do not require employers to notify or get the approval of a third party (such as a government agency) before dismissing one redundant worker (Table 4). Thirteen of the countries, including Sweden, require 18
# **1. Introduction**

Urban transportation in Beirut, the capital city of Lebanon, where more than 40% of the country’s total population lives, is facing several challenges, including inadequate infrastructure, traffic congestion, local air pollution and road accidents. The fifteen-year civil war between 1975 and 1990 caused significant destruction of the transportation infrastructure and contributed to the deterioration of the public transport system (Diab and Obeid, 2012). Expansion of urban transport capacity is not meeting the speed of population growth and urbanization, the centralization of activity around the capital and more recently the huge refugee influx from the Syrian Arab Republic. Traffic congestion is considered one of Beirut’s most serious urban development problems.

The urban transportation problem is such that up to 70% of travel time in the Greater Beirut Area (GBA) is lost in delays due to traffic congestion, and the average reported intermodal road speed is 11 kilometers per hour (calculated as the weighted average speed across all modes). Car speed is within 9.4-13.5 km/hour. Bus speed is 6.5-9.3 km/hour, minibus speed is 7.5-10.8 km/hour and taxi speed is in the range of 8.6-12.3 km/hour (all calculated from Abou Zeid and Hassan (2016)). The congestion problem is increasing due to rapid motorization along with increased household income and growth of middle income households. Almost half of the total vehicles in Lebanon circulate in the GBA and the traffic volume in the GBA reaches 7,000 vehicles per hour in the northern entrance of Beirut (World Bank, 2015).

Traditionally in developing as well as developed countries, supply-side measures are offered to address traffic congestion problems. These include expansion of road networks and improvement of public transportation systems through the introduction of new or the expansion of existing light rail transit, bus rapid transit and metro systems (see, for example Chalak et al. (2016)). In addition to these supply side responses, there is growing interest in using demand side measures, particularly fiscal or pricing reforms to address the broader societal costs (or negative externalities) of transportation systems.<sup>3</sup> A more novel approach is congestion tolls, which economists have long advocated as an effective way of allocating scarce roadway capacity to the highest valued users.

> 3 See Timilsina and Dulal (2008) for an in-depth discussion of fiscal policy instruments to reduce congestion and environmental pollution from urban transportation.

2
the KHDS household sample was stratified. In order to capture a higher percentage of households with a death while retaining a control group of households without a death, stratification was based on agro-climatic features of the region, levels of adult mortality from the 1988 Census (including both high and low mortality areas), and household-level indicators thought to be predictive of elevated adult illness or mortality.

In 2004, another round of data collection was completed (Beegle, De Weerdt, and Dercon, 2006a). The goal of the KHDS 2004 was to re-interview the sample of 6,210 respondents from the 1991-1994 survey; this excludes 169 individuals who died over the course of the baseline rounds. In addition to the household survey, the KHDS 2004 included additional community-level surveys consistent with those carried out in the 1991-1994 rounds. A community questionnaire was administered to collect data on the physical, economic and social infrastructure of the baseline communities, as well as shocks experienced at the community level. Over the course of 10-13 years, it was anticipated that a substantial number of individuals would have migrated from the dwelling occupied in 1991-1994. Considerable effort was made to track surviving respondents to their current location, be it in the same village, a nearby village, within the region, or even outside the region.

Because of the long time frame of the KHDS panel, we are able to study the behavior of children in conjunction with outcomes for these children as young adults. Among children ages 7-15 studied in Beegle, Dehejia, and Gatti (2006), 75 percent were re-interviewed in 2004, 21 percent were not located, and 4 percent were deceased. Among the children we study here (for details on the sample restriction see Section IV.1), 76 percent were re-interviewed in 2004. Of these, 18 percent had moved far from their original village but still resided in Kagera, 11 percent resided outside Kagera but in Tanzania, and 2 percent were residing in Uganda. These children 6
In an event study setting with a discrete and ordered treatment, represents the expected difference gg,ll between governorate ’s actual labor market outcome at year δδ , that is years after it was treated for gg the first time, and the counterfactual outcome gg that would have obtained at that period if it had remained FF + ll ll equal to its period one value from year 1 to gg . In our design, it corresponds to the average effect of gg being treated for years rather than being left with the treatment value of period 1. Dynamic effects FF + ll can be aggregated in different ways that allow to compute estimators for initially untreated groups or a ll+ 1 weighted average of initially treated and untreated groups.

Beyond the dynamic effect, one might be interested in the average treatment effect per unit increase of drought intensity, +. For equal to the last period when there is still a group untreated since the first uu year, + is given by the weighted average of the event study reduced form effect δδ TT , for all the gg,ll δδ }, scaled by the sum of all the treatments . In this case, showing a first stage event δδ ll∈ uu gg gg,FFgg+ll {0, ... , TT −FF DD study is recommended to detect the average treatment intensity after a first treatment switch. To be identified, and + require no anticipation and strong parallel trend assumptions. Placebo estimators, gg,ll δ , allows to compare δδ δδ ’s outcome evolution to that of untreated governorate from period to ppll gg,ll gg −1, which is before governorate gg _g_ is treated for the first time. The placebo estimator measures if FF + ll−2 gg FFtreated and not-yet treated governorate at are on parallel trends when untreated for years, gg the number of years over which the parallel trends should hold for having unbiased gg FF + ll . ll+ 1 gg,ll δδ

# **5. Results**

Our preferred model specification focuses on the 12-month average SPEI shock as discrete ordered treatment, and initially untreated governorates as control group. Table 2 shows the main results for both the dynamic estimators, and the aggregate average treatment effect +. The average effect on the gg,ll agricultural employment is -5.2 p.p. when we aggregate estimators up to 4 years after the drought shock. δδ δδ This means that, on average, governorates that experienced a drought show a share of agricultural employment about 5 p.p. lower than the untreated governorates.

To better visualize the dynamic of the impacts after the shock and test pre-trends, we present the results in an event study setting. In all estimates we cluster the standard errors at the governorate and cohort levels. In the Appendix Figure 1, we present the first stage treatment effect on the average drought 8
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# **I.4 Quantitative results for Section 4.3.3**

Table I.4 shows the calibrated parameter values and the effects of fiscal decentralization on debt levels with alternative calibration of _μ_ and _B_<sup>_o_</sup> .

**Table I.4:** Counterfactual Experiment with alternative calibration of _<u>μ</u>_ and _B_<sup>_o_</sup>

||C|alibrated V|alues||||
|---|---|---|---|---|---|---|
|Parameters|1988|1996|2006||||
| ̄_B_|0.00358|0.137|0.139||||
| ̄_D_|0.316|0.594|0.319||||
|_θ_|0.788|0.652|0.514||||
|_ε_|0.018|0.018*|0.018*||||
|||1988–19|96||1996–20|06|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Moments|1988|1996|Counterfactual<br>1996 (%)|1996|2006|Counterfactual<br>2006 (%)|
|Local debt|0.062|0.130|0.147 (126%)|0.130|0.122|0.104 (332%)|
|Central debt|0.338|0.615|0.389 (18%)|0.615|0.336|0.597 (6%)|
|Total government debt|0.400|0.745|0.536 (**39%**)|0.745|0.458|0.701 (**15%**)|

Note: Using _μ_ = 0.6 and _B_<sup>_o_</sup> = 0.046 and re-calibrating each year. Calibration strategy is identical to the baseline. Total government debt (also known as “general government debt”) is the sum of the local and central government debt. Counterfactual experiments use revenue ( _e_ and _f_ ) and spending responsibility share ( _θ_ ) values from end year, and keep all other parameters at their starting year values. Numbers in **parentheses** show the percent of debt changes that is explained by decentralization. All debt levels are normalized by national GDP. *We assume _ε_ for 1996 and 2006 is the same as 1988.

43