rafmacalaba/datause-encoder-data huggingface dataset:
The working-age population share was stagnant in the 1960s but increased rapidly in the 1970s and 1980s, and then the increasing speed slowed down until 2014. Overall Korea’s working-age population share increased from 55% in 1960 to 73% in 2014. The labor force participation rate also increased during the sample period from 52% in 1960 to 68% in 2014.<sup>8</sup> ,<sup>9</sup> Thus, the changes in these labor market demographic factors positively contributed to growth of the GDP per capita as shown in equation (12).

# **3.3 Decomposition Analysis**

Applying our accounting framework of equations (9) and (12) to the above data, we can decompose Korea’s growth of GDP per capita for the 1960-2014 period by constructing counterfactual GDP per capita measures as follows. Combining equations (7) and (11), we express the GDP per capita such that In order to isolate the contribution of productivity growth to GDP per capita growth, we fix the values capital-output ratio, human capital per worker, working-age population share and labor force participation rate at the 1960 values and vary only the labor-augmenting technology level as in the data. That is, the counterfactual GDP per capita measure due to the productivity changes is

> 8Not surprisingly, this increase of labor force participation rate was due to the rise of the female labor force participation rate from 39% in 1960 to 57% in 2014. The male labor force participation rate increased from 75% in 1960 only to 79% in 2014. Even in the year of 2014, there still exists a substantial gap in labor force participation rate between men and women, although the gap dropped significantly since 1960.

9We observe a noticeable up and down in labor force participation rate between the mid-1970s and early 1980s. Using the national data sources of population census and labor force survey data from the Korean Statistical Information Service (KOSIS), we find that this is an outcome of the combination of the WDI data issue and the reality of the Korean economy. The WDI labor force data for Korea for the mid-1970s is a little overestimated, which generates the more rapid rise of the labor force participation rate than the trend. (It seems to happen because the WDI working-age population and labor force data are based on the estimates from the UN population project and ILO, which can be different from the ex-post national census and surveys.) However, the fall in the labor force participation rate (as well as in the employment rate) for the early 1980s reflects the actual recession of the Korean economy. In 1979, President Park was assassinated and there was a military coup in the following year, which generated economic instability. Furthermore, there was unprecedented cold-weather damage in 1980.

15
39 Table 1: Debt Face Values and DDSR gmllions of dolars **unless** noted otherwise)

||Total External<br>Debt<br>(1)|Commerci<br>(|al Bank Debt'<br>2)|Debt<br>Reduction<br>(3)|Debt<br>' Service<br>Reductimn|RecGpture<br>Cl&ue4<br> <br>(5)|Gro<br>C6)= (|ssDD<br>3)+(4)|SR<br>-MS)|
|---|---|---|---|---|---|---|---|---|---|
|||S<br>X|<br>of Total||<br>|<br><br>||||
||__<br>|<br> <br>_________<br>_____<br>__________<br>|<br> <br>_________||(4)|B<br>_______|_____|o<br>|f Cmercial<br>~~ ~~~Bank<br>Debt|
|MEXICO<br>|$95.416<br> <br>|<br>$47,170<br> <br>|49X|<br>S7.061|S7.948|S858|S14.151||302|
|PHILIPPINES<br> <br>|<br>S28.468<br>|<br>S6.600|23X|<br>S1,340|S0||S1.340||202|
|COSTA<br>RICA<br> <br>|S4.603<br> <br>|S1 608|351|S1.029|5115||S1144||711|
|VENEWUELA<br>US23.<br>|<br>491<br>|S19O1l<br> <br>|59X|<br>S1.921|$2 67|S186|||243412<br>2|
|URGtUiAY<br>|S3.707<br>|<br>S1,610<br>|43X|<br>S628|S169|S11|S786||49X|
|TOTAL|S164.685|<br>S75,999|46X|<br>S119799|<br>11|055|S21,833||29X|
||_Nc-Non?_<br>(7)|met CO<br>(8)=C6)-|SR<br>(7)|Prepayment<br>Equivalent<br>of<br>Piil|Prepayment<br>Equivalen<br>of Interest<br>ll|Prepayment<br>tEquivalentof<br> <br>Collateral<br>|Debt<br>|Reduc<br>(<br>(12)|tionEquivalent<br>DRE)<br>+M8)+(11)|
|||<br>||rncpa<br>|Coatera<br> <br>|7 (11)=(9)+(1|)|||
|||S<br>X of<br>B|Commercial<br>ank Debt|Collateral<br>(9)|<br>(10)||S||X of Commereial<br>Bank Debt|
|MEXICO<br>|S13027<br> <br>|3124|281|S3.465|<br>S1.787|<br>S55252|<br>S18|376|<br>39X|
|PHILIPPINE<br> <br>|S<br>S690|5650|102|S0|SO|<br>S0|<br>|5650|<br>10X|
|COSTA<br>RICA<br> <br>|-<br>|51144<br>|711|50|522|<br>S22|<br>S1|,166|<br>731|
|VENEZtWE<br>U1.16<br>|6<br>|$3.246|17X|SW89|<br>S363|<br>S1.172|S4|.418|231|
|URUGUAY<br>|589<br> <br>|5697<br>|431|557|$14|<br>571||S768|48X|
|TOTAL|S28972<br>S|18,861|25X|S4,331|S2 16|,517|25|.378|331|

Source: World Debt Tables (1990/91 and 1991/92) nd Quarterly Review of financial Flows (various Issues) 'Eligible commercial bank debt.

2Fmoe value of debt reduced by buybacks and discount bonds.

*Present value of debt service reduction an par **bonds** (102 dIscount rate).

"Expected present value of service under recapture clatus.

"Present value of new maone to be disbursed by comercial banks.

'Face value of principal collateral.

Sfince the interest guarantee Is rolling and would only be drain at sam point uncertain in the future, and since interest on guarantee fund goes to the country, the prepayment equivalent of the interest guarantee is less than its face value. The prepayment equivalent of as its face value *ultiplied by the post-operation stripped discount (see Table 3). -. he interest collateral is estimated
<u>FIGURE 2. LOCATION AND RELATIVE SIZE OF THE AMAZON RAINFOREST</u> <u>FIGURE 3. A SMALL PART OF THE AMAZON RAINFOREST AS SEEN FROM THE AIR</u> A recent World Bank study (Vergara, W. & S.M. Scholz (2011): _Assessment of the Risk of Amazon Dieback_ ) describes a development where a substantial fraction of the biomass in the standing Amazon rainforest could disappear over time if the current development continues. There are several, man-made, drivers of such a development. One central such driver is man-made deforestation in the region due to cattle ranching/grazing, other agricultural activities, and timber extraction.

The disappearing rainforest would then be transformed into less dense forest, or savannah. A significant fraction of the trees in the Amazon rainforest would dry out and die. Less oxygen would be produced, the standing biomass 37
and fertility are other major threats to the identication strategy. The conict intensity variable is dened at the level of an individual's village of residence at the time of the survey. Systematic sorting by economic and physical status between the stayers at high conict villages and movers from the high conict to low conict villages is of concern, which will lead to overestimation of the impact of conict. Unfortunately, I do not observe women's village of birth. However, the 2016 NDHS asked each individual how long she has been residing at the place where she was surveyed and if not her entire life, the name of the district from which she migrated. Twenty-six percent of the women in the sample are living in a dierent district. Although the survey did not collect the information, marriage is the most likely reason for women's migration, as most women in Nepal move to their husband's home permanently from their maternal home, which is also likely to be their place of birth.

I dene conict at the level of women's districts of birth (district they moved from) and estimate the same specication as Equation (7) except with district of birth xed eects and region of birth trends and present the results in Table 6. Columns 1 to 3 examine the presences of dierences in migration patterns between the control and treated cohorts. The dierence-in-dierences across the specications are zero, suggesting no selective migration. Columns 4 to 9 re-estimate the main results from Table 4. The dierences-in-dierences estimates, although reduced in magnitude, are statistically signicant and in the same direction as in the main specication for age cohort 0 to 3. Villages within district are highly diverse and dening conict at the district level takes away that variation. Additionally, I limit the sample to women living in the village where they were surveyed their entire life and estimate Equation (7). The results are presented in Table A.3. The eect sizes are comparable to the baseline results in Table 4. These results, overall, suggest there is minimal or no selective migration among the women in the sample.

Limiting the sample to women already born at the start of the conict limits the scope for confoundedness through selective fertility. However, prior to the start of the conict couples that were highly concerned about their children's health may have delayed having children in anticipation of the war. If true, we expect to see signicantly dierent level of births between high and low conict areas periods just before the start of the war. However, the conict lasted for a decade and concerned couples may have had to wait for the full decade to have a child a highly unlikely scenario. To formally examine this issue, I use the information on district of birth and year of 20
which fall after some of the data challenges for 1989 and 1994 evident in Figures 1a and 1b and discussed above. Appendix Table 5 provides calculations for the full sample period.

We undertake these calculations with a cell defined to be state-industry-urban/rural area. Conceptually, these techniques then decompose the national change in electricity usage per unit of output into three terms. The first is the “within” changes in electricity usage per unit output for cells, with cells weighted by initial output shares for the Indian economy. Positive values indicate that cells tended to have increasing electricity usage per unit of output when weighted by initial output.

The second term is the “between” changes across cells in activity. The between component represents changes in shares across cells interacted with the initial deviation of cells from the national electricity usage ratio. Positive values indicate that output tended to be reallocated towards cells that had higher initial electricity usage ratios. Given our construction of cells along state-industry-urban/rural lines, this term captures multiple movements—migrations between urban and rural areas, spatial movements of manufacturing across states, and changes in industry mix. By its design, however, the decomposition does not include migration over districts within states if these migrations retain the same industry and urban/rural composition.

The “covariance” component is the third term. Positive values for this term indicate that fastgrowing cells also experienced rising electricity consumption shares. Fast-growing cells in this context represent urban and rural parts of state-industry pairs that have shown particularly strong growth in output. This term is only separately calculated in the Bailey et al. (1992) approach, while it is subsumed into the first two terms in the Griliches and Regev (1995) approach. As a consequence, the former technique is our preferred decomposition.

The three components by definition sum to the total change in consumption share for India, with some minor deviations due to panel composition changes. Panel A presents the aggregate share change using a balanced panel of state-industry-urban/rural areas employed in the decomposition. Column headers indicate the sector and metric considered. Electricity usage per output unit decreased between 2000 and 2010, for India as a whole and within each sector. These trends are found whether we consider the data with or without imputation and winsorization.

27
Policy Research Working Paper 10351

# **Abstract**

The COVID-19 pandemic has been posing unprecedented challenges for the transport sector. Urban travel has declined all over the world, but not uniformly for all modes; public transportation has taken the hardest blow. As a result, there has been a widespread reduction in public transport ridership: according to recent estimates, passenger numbers in cities around the world have been at the peak of the pandemic down 70 to 90 percent. This paper examines the short-run economic impact of the pandemic, as well as the generic and sector-specific restrictions that were adopted to control the outbreak of the pandemic, based on a rich database tracking on a daily basis public transport usage and traffic congestion. The analysis confirms the significant impact of sector-specific restrictions and broader lockdown measures in terms of reduction in urban mobility and congestion. The analysis finds that the spread of the disease itself had an economic impact distinct from that of the lockdown measures. There are also different results on the magnitude of impact of cross-sectoral vis-à-vis sectoral restrictions on urban mobility and congestion. Whereas the magnitude of the spread of the disease is higher than the overall stringency of the lockdown, the impact of restrictions of public transit has been much greater than the spread of the disease and acts indirectly as a disincentive to move on the road. More effective safety measures, such as those related to the use of facial covering, are associated with higher use of public transport and an increase in the likelihood of low congestion. There is no evidence of intermodal competition between public transportation and road transport. In particular, the expansion of car registration has not led to a decrease in public transport mobility, but it is significantly associated with an increase in traffic congestion, particularly in mega-cities.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at mvagliasindi@worldbank.org.

_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
_Decision de justice_ : Court order resolving a dispute (over land) and defining the right of possession, proprietorship or occupancy and use _Déguerpis_ : Evicted people _District de Bamako_ : Bamako district, which includes 6 _communes_ . _Division parcellaire_ : Partition of land parcel into plots for donation or inheritance purposes only _Document administratif_ : Administrative document _Domaine privé de l’Etat_ : Private domain of the State _Droit réel_ : Real (property) right _Droit d’usage_ : Use right _Droit coutumier_ : Customary right _Droits de mutation_ : Transfer taxes _Enregistrement foncier_ : Land registration _Etats Généraux du Foncier:_ Land Convention _Filièred’approvisionnement en terres_ : Land delivery channel _Frais d’édilité et de viabilisation_ : Local development taxes (infrastructure and services) _Frais d’enregistrement_ : (Land) Registration fees _Génie rural_ : Department of Agricultural Engineering _Géomètre_ : Land surveyor _Gouverneur_ : Governor (representative of State at the region level) _Immatriculation_ : Registration of land under the name of the State; also used to designate registration of land under private ownership _Inspecteur des Domaines_ : Inspector of the State Domains _Lettre de notification_ : Notification letter _Livre foncier_ : Land book / register _Lotissement_ : Land subdivision that complies in principle with planning regulations _Lotissement préfectoral_ : Land subdivision by prefect or subprefect _Maire_ : Mayor. _Mairie_ : Town Council/Mayor's office _Mise en valeur (d’un terrain_ ): Land parcel development _Morcellement_ : Informal subdivision of land “Les _nécessiteux et démunis_ ” : The needy and poor _Occupations spontanées_ : Spontaneous settlements _Ordre des Urbanistes du Mali_ : Urban Planners Professional Order of Mali _Permis d’occuper_ : Administrative permit to occupy a parcel of land “ _Petits papiers_ ”: Petits papiers (“bits of paper”) _Préfet_ : Prefect (representative of the State at _cercle_ level) _Présumé propriétaire_ : Presumed owner _Principe de domanialité_ : Principle of "domanialité" _Promoteur immobilier_ : Property developer/ Property development company _Propriétaire coutumier_ / _propriétaire terrien_ : Customary landholder _Propriétaire occupant_ : Owner-occupier _Purge des droits_ : Extinction / purge of customary rights _Quartier_ : Neighborhood _Quartier informel_ : Informal settlement _Quitus fiscal_ : Tax receipt _Recasement_ : Resettlement _Région_ : Administrative jurisdiction which includes several _cercles Régularisation_ : Regularization/Tenure regularization _Réhabilitation_ : Physical upgrading _Reprise des apports_ : Return of rights to landholders following formal land readjustment and/or subdivision _Restructuration / remembrement foncier_ : Land readjustment _Schéma d’urbanisme_ : Master plan/Urban planning document _Sécurité de la tenure_ : Tenure security _Servitudes_ : Planning restrictions and easements _Sous-préfet_ : Subprefect _Système d’information foncière_ : Land information system _Taxe sur la transaction_ : Transaction tax 41
Table 10: **Sugarcane: descriptive statistics for constant domestic and reference price series**

|||Mean|Standard|Coefficient|Z Statistic|LS|
|---|---|---|---|---|---|---|
||||Deviation|Of Variation||Growth|
|Domestic||19.69|2.42|<br>0.12|0.13|-0.29**|
|Reference|_Importable_|21.28|19.78|<br>0.93|0.79|1.26*|
||_Exportable_|16.99|18.51|<br>1.09|0.97|0.62|

Source and notes: See Annex 16 for details

- ** Indicates 5% level of significance

- Indicates 15% level of significance

The sugarcane NPCs estimated in our study are on average slightly lower than the NPCs estimated in the Goldar and Gulati study for 1981-89 (Table 11). The differences are principally due to:

- Different sources for the world prices of raw and refined sugar and hence the estimated world price of plantation white sugar.

- Except in 1985 and 1986, higher sugarcane reference prices in our study due to higher sugar reference prices. Our estimated NPCs in 1985 and 1986 are higher than Goldar and Gulati's principally because of higher estimated reference mill processing margins.

- Differences in the estimated reference processing margins due to different sources for estimating the fob prices of molasses.

Table 11: **Sugar and sugarcane: alternative NPC estimates, importable hypothesis, 1981-89**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|AVG|
|---|---|---|---|---|---|---|---|---|---|---|
|**SUGAR**|||||||||||
|Goldar & Gulati|0.80|1.25|1.36|1.51|1.85|1.69|1.72|1.39|0.94|1.39|
|Ours|0.74|1.23|1.21|1.42|1.79|1.66|1.59|1.30|0.92|1.32|
|SUGARCANE|||||||||||
|Goldar & Gulati|0.65|1.22|1.43|1.92|3.27|2.93|2.89|1.72|0.87|1.88|
|Ours|0.55|0.98|1.11|1.77|3.81|3.10|2.52|1.52|0.83|1.80|

Sources and notes: See Annexes 1 and 14 and Goldar and Gulati (1991) for details.

# **_Nominal protection: gur and khandsari sugarcane in UP_**

As noted previously, in the 1990s about a third of India's total sugarcane production was used to produce gur and khandsari. More than half of this cane was grown in UP, where one fifth was purchased by khandsari producers and four fifths was used for gur.In one of the research 45
(Herbst 1996, pp. 157-158, Olowu 1993, p. 94). Oil represents 90 percent of Nigeria's exports and most of the government's revenue (Herbst and Olukoshi, 1994, p. 453). The oil reserves are under state control and provide huge windfall gains to those who control them and their political allies. Especially during the years of the petroleum crisis, Nigeria profited handsomely. Thus control of the state became a valuable prize worth fighting for. Those who sought to get rich struggled for a share of the rents, instead of engaging in productive entrepreneurship (Diamond 1993, p. 220; 1995, p. 474, Herbst 1996, Lewis 1996, p. 81)."' As Diamond (1993, p. 218) writes, "plainly, the stakes of politics are too high." An important issue in trying to understand the impact of corruption and rentseeking in Nigeria is the ultimate incidence of payoffs. Do they simply reduce the profits of multinational corporations or do they also impose costs on the country as well? Although definitive data are lacking, the costs appear to have been severe. In 1984, after ten years of oil boom, the per capita income of the average Nigerian was no higher than in 1974. During the 1980s the economy declined at a rate of 0.4 percent annually, and in 1990 Nigeria was the seventeenth poorest country in the world with a per capita income lower than that of India or Kenya (World Bank data cited in Herbst and Olukoshi, 1994, p. 453). The 1996 Human Development Report of the United Nations Development Program ranks Nigeria 137th out of 174 countries in its index of human development (UNDP 1996, p. 137). Foreign direct investment is relatively high compared to other developing countries, but it is heavily concentrated in the oil industry (Jun 1996, Lewis 1996, p. 98). By contrast, Indonesia, another country with large petroleum reserves, is currently attracting foreign direct investment in a wide variety of sectors .

Although we cannot assess the overall cost in lost economic growth, in particular cases, 32