rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Unemployment Rates <!-- Start of picture text --> 1995 2000 2005 2010<br>Year 1995 2000 2005 2010<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU10 Balkan/Turkey CIS<br>Source: KILM, 7th Edition Source: KILM, 7th Edition<br>15 25<br>20<br>10<br>15<br>5 10<br>Unemployment Rate<br>Unemployment Rate<br>5<br>0<br>0<br><!-- End of picture text -->

# **Employment Elasticity of Output**

We will now shift our attention to yet another labor market indicator: the employment intensity of growth, or, alternatively, the elasticity of employment with respect to output. This indicator measures how employment changes with economic output. For example, a value between 0 and 1 implies that output growth of one percent is associated with positive employment growth of less than one percent. Studying the employment elasticity serves two purposes. First, by evaluating how employment growth evolves with output growth, it can shed light on structural changes in the economy over time. Second, employment elasticities are closely linked to labor productivity elasticities (Kapsos 2005). Defining labor productivity as output per employed, for small changes in output, the labor productivity elasticity of output corresponds to one minus the employment elastiticy of output. In case the employment elasticity is positive but less than one, the labor productivity elasticity is also postivite and less than one.

We provide econometric evidence on the relationship between output and employment using a panel of advanced and developing economies from 1995 to 2010. As estimates can be biased due to problems of omitted avriables, endogeneity or measurement errors, we look at a range of estimators. First, we calculate the average elasticities from 1995 to 2010 in the simplest fashion using bivariate OLS (Table 1, Column 1). ECA’s elasticity is 0.18, noticeably less than in Western Europe (0.44) but higher than for peers of ECA and peers of Western Europe. Within ECA, the elasticities are highest among EU10 countries (0.32) and lowest in the CIS (0.12). Second, we include country-fixed effects to capture country-specific factors, such as geography, and economic and institutional environment, along with year indicators to control for general time effects (Column 2). The estimated elasticities are somewhat smaller but the pattern across regions is unchanged. Third, we run robust regressions that correct for outliers (Column 3). The coefficient for ECA declines further, but the broad pattern is the same. Finally, we include lagged employment on the right-hand side to capture the persistence in employment levels, and run system GMM 6
governance settings. In telecom and electricity, elasticity figures rise from 0.60 and 0.63 in low and medium governance settings to 0.83 and 0.75 in high governance settings respectively.

**Table 5: Augmented Solow model with infrastructure variables and the effect of the quality of governance: low, medium or high quality governance**

||Tel<br>e<br>|coms<br>|Elec<br>|tricity<br>|Ro<br>|ads<br>|Sani<br>|ation<br>t<br>|W<br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|st<br>Infra|Bench|st<br>Infra|Bench|st<br>Infra|Bench|st<br>Infra|
|Education|1.24|−0.18|2.49|0.26|0.73|0.15|1.23|0.09|1.02|−0. 02|
|t-stat|3.65|−1.03|4.14|0.85|1.40|0.55|2.73|0.33|2.21|−0. 05|
|Elast(β)|1.24|0.00|2.49|0.00|0.73|0.00|1.23|0.00|1.02|0.00|
|st<br>Total Inv|0.03|−0.54|0.36|−<br>0.15|0.72|0.01|−<br>0.39|0.05|−<br>0. 11|−0. 15|
|t-stat|0.08|−<br>3.06|0.61|−<br>0.78|1.40|0.01|−<br>0.65|0.16|−<br>0. 16|−<br>0.38|
|Elast(α)|0.00|−1.17|0.00|0.00|0.00|0.45|0.00|0.00|0.00|0.00|
|Low|||||||||||
|Infrast||0.60||0.63||0.35||0.90||1.04|
|t-stat||6.48||3.53||2.33||2.80||1.65|
|)<br>Elast (γ||1.30||0.63||0.35||0.90||.00|
|Medium|||||||||||
|Infrast||0.60||0.63||0.35||0.90||1.04|
|t-stat||6.48||3.53||2.33||2.80||1.65|
|)<br>Elast (γ||1.30||0.63||0.35||0.90||0.00|
|High|||||||||||
|Infrast||0.83||0.75||0<br>.03||1.38||0.55|
|t-stat||5.28||2.58||−<br>4.77||6.13||6.54|
|)<br>Elast (γ||1.80||0.75||0.03||1.38||0.55|
|R <sup>2</sup>|0.29|0.90|0.41|0.95|0.24|0.72|0.20|0.84|0.14|0.77|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|e<br>Differenc||Y<br>ES||Y<br>ES||Y<br>ES||Y<br>ES||YES|

Somewhat counter-intuitively, the elasticity of GDP with respect to roads decreases from 0.35 in countries with low and medium governance to 0.03 in countries with high governance quality. This interpretation must however be treated with caution since there is a risk of encountering an omitted variable bias in comparing the results across countries with different levels of governance. For instance, there could be a variable correlated with high quality of governance such as better air transportation, which could potentially decrease the sensitivity of GDP to road infrastructure. Note that in this table and in those that follow, when the coefficients for the dummies are not significant (i.e. when there are no differences with respect to the coefficients associated with different levels of the variable of interest, such as “low” and “medium” quality of governance), we leave identical coefficients and t-values.

20
|Household Owns Irrigation Equipment -<br>SECC 2013|0.062|0.062|-0.2|0.933|
|---|---|---|---|---|
|Percentage of Households having Source of<br>Light: Electricity-Census 2011|42.517|42.777|-0.7|0.808|
|Log of Total Geographical Area (Hectares) in<br>Village-Census 2011|6.006|6.04|-4|0.19|
|Status of Pucca Road in the Village – Census<br>2011|0.788|0.796|-1.9|0.504|
|Log District Head Quarter Distance (kms)<br>from Village–Census 2011|3.888|3.883|0.6|0.82|
|Population Density of the Village – Census<br>2011|2.652|2.536|4|0.214|
|Status of Village Agricultural Marketing<br>Society–Census 2011|0.428|0.428|-0.1|0.986|
|Percentage of Households having Bicycle -<br>Census 2011|39.491|38.618|3.1|0.316|
|Primary Health Centre (Numbers) - Census<br>2011|0.03|0.033|-1.3|0.679|
|Worker force participation rate of females<br>in the Village-Census 2011|0.489|0.494|-3.2|0.285|

**Table 3: Household Selection Model: Generating Propensity Scores**

|**NRLP Intensive vs Non-intensive**||
|---|---|
|Age of Head of Household|-0.01<sup>***</sup><br>(0.00)|
|SC or ST Household (Y/N)|0.87<sup>***</sup><br>(0.13)|
|Dependency Ratio|-0.03<br>(0.05)|
|Status of Primary School in Village in 2012|0.74<br>(0.39)|
|Education Level of Household Head|-0.01<br>(0.01)|
|Household Type Kutcha (Y/N) - SECC 2013|0.07<br>(0.13)|
|Household Owns Telephone/Mobile Phone - SECC 2013|0.00<br>(0.12)|
|Any Member of Household has a Salaried Job - SECC 2013|1.32<sup>***</sup><br>(0.25)|
|Household Owns Any Land - SECC 2013|-0.47<sup>***</sup><br>(0.13)|
|Percentage of Irrigated Land from Total Land in the Household - SECC<br>2013|-0.01<sup>**</sup><br>(0.00)|
|Household Income Main Source: Cultivation - SECC 2013|0.50<sup>***</sup><br>(0.14)|
|Household Income Main Source: Non-Agricultural Own Account<br>Enterprise - SECC 2013|0.86<br>(0.67)|

9
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18
14 than data on total roads because there is less ambiguity about what constitutes a paved road than what constitutes an unpaved road versus a track or trail.<sup>2 9</sup> The size of the national road network is associated with the size of the economy, geographical area, population, income per capita, and population density. Income per capita is a major detertninant of road length at the national level. Both paved and total road length increase at a constant rate with per capita income, as can be seen in figure 2.30 Estimates at the national level (using the techniques employed for vehicle ownership) find no saturation level for road density with respect to per capita income.

**Fgure 2. Per Capita Income and Per Capita Road Length, 50 Countries and 35 Cities**

<!-- Start of picture text --> 100<br>. Nationaltotalroad<br>Australia<br>A Urbanroad<br>-a Nationaltotalroad eq. NZ SA<br>....... Nationalpaved road eq. Brazil<br>9 ~~~Urban road eq. Brzi erF w<br>10 o _*_____* Switzerland<br>.5 Bolivia + + Z r o tBohvia<br>0~~~~~~~~~~~~~~-.<br>Thailand* Korea*Osaka<br>t Malawi 1 .- + SingaporeBeijing .<<br>China Egy Bg T ASeoul<br>Jakarta A * Santiago<br>A<br>B g A Bangkok<br>A A ~~~~A<br>** Surabaya Guangzhou HK<br>O. i *.Suabv<br>0.1 .: .. : lll l l ll -- : :<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text --> Note: Data points for paved roads are not shown for the sake of clarity. _Source:_ Ingram and Liu, "Vehicles, Roads, and Road Use." Paved road length has an elasticity of 1 with respect to income (when income increases by 1 percent, paved road length increases by 1 percent), while overall road length increases only about half as fast as income (table 4). Population is a significant determinant of national total and paved road length, whereas population density affects only total road length. The national level of urbanization, the length of the rail

> 29 Data are available from a number of sources, including _World Road Statistics_ published annually by the International Road Federation. The problems with road definitions are discussed in Canning, "A Database of World Infrastructure Stocks 1950-1995."

> 3 0 Nonlinear specifications produced R2 no higher than those of the simpler linear specification.
outbreak of the war (November 4). Thus, we do not expect uniformly dramatic declines in livelihood activities in the November 2020 round. However, the share of households participating in any activity in Tigray declined sharply from 78 percent in October 2020 to 48 percent in January 2021. Much of this decrease is associated with a concomitant decline in non-farm employment (panel (c)) and wage employment (panel (d)). The change in patterns of non-farm business activities appears slightly nuanced, however. After the war broke out, the share of households who report employment in non-farm business decreased from 29 in October 2020 to 8 percent in February 2021, before bouncing back to 23 percent in May 2021. In the other three regions, both non-farm employment and wage employment remained somewhat stable or increased. Farm activities are much more resilient. The share of households who engaged in farming activities in Tigray changed little immediately after the start of the war, though it decreased considerably in the May 2021 round (15 percentage points less than October 2020 levels).<sup>25</sup>

# **Figure 5: Trends in labor market participation**

<!-- Start of picture text --> (a) (b)<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>(c) (d)<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Tigray Amhara Oromia SNNP<br>Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21 Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21<br>Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21 Aug'19Apr'20May'20Jun'20Aug'20Sep'20Oct'20Nov'20Dec'20Jan'21Feb'21May'21<br>Farm activities<br>Any type of work/employment<br>Wage activities<br>Non-farm business<br><!-- End of picture text --> 25 When compared to the April/May 2020 levels, this drop is between 3 and 13 percentage points.

20
<!-- Start of picture text --> 35<br>Chart 3a: Gender gap in Mathematics score at percentiles - 2003<br>30 vs. 2007<br>25<br>G<br>20<br>e<br>n<br>15 Math-2007<br>d<br>e Math-2003<br>10<br>r<br>5<br>G<br>a 0<br>p 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>Percentile<br><!-- End of picture text --> 14
ensure that households did not believe that participation would render any material benefits. Principal investigators personally conducted recruitment to begin with and households were informed that the purpose of the survey was to ― _gather data and information on health and health seeking behavior among households in Delhi_ ‖.

Given the lack of data on medical facilities that are not registered (the majority) in Delhi, it is difficult to ascertain whether the neighborhoods chosen had more medical facilities or households were more aware of health problems than others in the city. As a check on the external validity of this sample, Das and Sánchez (2002) compared the ISERDD sample to two representative samples from Delhi—the (Delhi) National Family Health (1993) and National Sample Survey (2000). Their comparison suggests that the ISERDD sample is, on average, slightly younger (with a one year difference in the mean age) with a greater density of households in the upper-income groups. On a host of variables including education levels, marital status, characteristics of the household-head, household size and availability of different types of facilities they find few significant differences. The income differences do not appear to be large (they could mirror the different survey years in a growing economy), and assuming a positive relationship between income and health, should actually bias our results towards less morbidity than what is representative for Delhi as a whole.

Recall periods varied for the same household through the two years of the survey. The 7 localities were divided into two ―waves‖ composed of 4 and 3 localities respectively. Between August 2000 and December 2000, every household in Wave 1 (4 localities) was visited weekly by a member of the ISERDD team and asked about health problems during the previous week, whether treatment was pursued, and how much treatment (if any) cost. Care was taken to ensure visits to the households were always on the same day of the week, which became the focal reference-point for the weekly recall questions. This design was followed to avoid the possibility of ―telescoping‖ (the reporting of events prior to the recall period) in the weekly surveys; it also had costs in terms of missing household observations in the weekly data, which are addressed below. Surveyors also attempted to always interview the same respondent in both weekly and monthly surveys; this person, designated as the primary respondent, was usually the head or the spouse of the head of the household. The primary respondent was chosen to partly reflect the belief that they may be better able to report on sicknesses of their children. These repeated visits followed a larger initial interview that covered a much wider range of household level information that was not likely to change much during the study period—household structure, total expenditures (using the standard ―thin round‖ set of questions for household income and expenditures from the National Sample Survey, which is India‘s representative household survey), education, and other household characteristics.

5
# **Box 2. Estimating storm‐surge zones and human resources at risk**

Storm surge zones are locations that would be inundated by a given wave height, assuming the SRTM value represents ground elevation and there are no coastal protection measures. In the calculation of storm surges (wave heights or extreme sea levels), we follow the method outlined by Hanson et al (2011) where future storm surges are calculated as follows: Future storm surge = S100 + SLR + (UPLIFT * 100 year) / 1000 + SUB + S100 * x where: S100 = 1‐in‐100‐year surge height (m) SLR = sea level rise (1 m) UPLIFT = continental uplift/subsidence in mm/year SUB = 0.5 m (applies to deltas only) In the absence of a scientific consensus on where tropical storms will or will not intensify, and by how much; we follow Hanson, et al. (2011) and Nicholls (2010), with a baseline assumption of a 10% increase in storm surges/extreme water levels for the 100 year event. This assumption of 10% increment is conservative, as a review of the regional studies of storm surges reveals predictions of storm surge height in 100‐year events that are generally above 10% (Hardy et al. 2004; McInnes et al. 2005; Karima & Mimura 2008).

x = 0.1 (increase of 10%) applied only in coastal areas currently prone to cyclone/hurricane.

We apply the wave height calculated for the coastline segment closest to a drainage basin outlet to inland areas within that basin. We use mangrove and non‐mangrove wave attenuation functions in estimating wave height for inland cells (see Section 3).

Source: Based on Nicholls et al. (2007), Dasgupta et al. (2011) and Brecht at al. (2012) The joint impacts of SLR and increased storm intensity in a changing climate as described in Step 1 are summarized in Table 5, column 2. Estimates indicate relatively modest increase in the inundation area from 84,222 to 86,257 sq. kilometers, or by 2 percent globally (Table 5, column 2). No region is severely impacted from SLR and increased storm intensity alone 19
Policy Research Working Paper 6699

# **Abstract**

This paper presents empirical evidence of the profound and long-term damages from adverse natural events on poverty. It analyzes 30 years of macro-level damage data from disasters (including earthquakes, floods, and storms), according to income groups, and shows that low-income countries incur disproportionately large damages relative to their assets. Furthermore, the paper reviews the micro-level evidence of disaster impacts on the livelihoods of the poorest households. The evidence suggests that the poor are significantly more vulnerable and exposed to the economic and human capital losses caused by disasters. It discusses detrimental long-term consequences for the income and welfare of the poor and the presence of poverty traps that result from damages to productive assets, health, and education. The roles of migration and ex-ante behavior are also discussed. In the context of climate change, the paper underscores the importance of considering the detrimental impacts of smaller but repeated crises, for instance caused by changes in local precipitation patterns. Lastly, the paper offers a brief discussion of policy options for strengthening resilience and highlights the need for further research for understanding the complex direct and indirect effects of disasters on the poor.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, and Office of the Chief Economist, Sustainable Development Network. 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://econ.worldbank.org. The author may be contacted at jrentschler@worldbank.org, or contact@ junrentschler.com.

_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
25

# **Table 3**

# Public investment in transport and communications

(percent of GDP, 1981-1990)

|Average of 31 LDCs<br>_of which:_|2.2|
|---|---|
|Low income|1.9|
|Middle income|2.4|
|India|1.3|

Source: own calculations using data from Easterly and Rebelo (1993).
interpreted as the incidence of economic growth on five expenditure components: (1) rice, (2) other food, (3) education, (4) health, and (5) other non-food<sup>21</sup> .

Figure 4.2b. Incidence <u>of Growth on Expenditure Components (1999-2002)</u> <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative Percentage of Population<br>Rice Health<br>Other Food Other Non-Food<br>Education<br><!-- End of picture text --> An interesting feature of the aggregate growth curve (figure 4.2a) is that it crosses the benchmark case twice before the head count ratio (55 percent). This means that we cannot use theorem 5 to infer overall pro-poorness. However, the curve lies completely below the benchmark up to the 20<sup>th</sup> percentile and between the 43<sup>rd</sup> and 55<sup>th</sup> percentiles. This represents about 60 percent of the poor for whom expenditure per capita grew less than average. The underlying data show that for this segment of the poor population, on

> 21 The growth pattern curves presented in figure 4.2 (a &b) are more refined than those presented in figure 4.1 for instance. This is because they are computed directly from household survey data, whilst the curves in figure 4.1 come from a parameterization of the Lorenz curve based on aggregate data (a distribution of per capita expenditure per decile).

25
# **Appendix Table 4: Sample means of labor participation**

||1997|2000|2004|2006|
|---|---|---|---|---|
|Participation in agriculture|0.74|0.64|0.57|0.58|
|___Initial age 16-20|0.22|0.35|0.36|0.23|
|___Initial age 21-35|0.79|0.72|0.66|0.7|
|___Initial age 36-50|0.82|0.7|0.67|0.65|
|___Initial age 51-60|0.62|0.54|0.4|0.37|
|___Initial age 61-70|0.53|0.38|0.25|0.19|
|Participation in non-agriculture|0.76|0.67|0.48|0.46|
|___Initial age 16-20|0.19|0.37|0.34|0.21|
|___Initial age 21-35|0.82|0.75|0.56|0.57|
|___Initial age 36-50|0.84|0.73|0.57|0.51|
|___Initial age 51-60|0.65|0.55|0.3|0.25|
|___Initial age 61-70|0.52|0.4|0.16|0.13|
|Participation in family business|0.66|0.56|0.4|0.37|
|___Initial age 16-20|0.1|0.26|0.27|0.1|
|___Initial age 21-35|0.66|0.59|0.44|0.42|
|___Initial age 36-50|0.75|0.64|0.49|0.45|
|___Initial age 51-60|0.61|0.51|0.3|0.24|
|___Initial age 61-70|0.52|0.39|0.16|0.13|
|Participation in wage labor|0.12|0.12|0.09|0.1|
|___Initial age 16-20|0.09|0.12|0.07|0.13|
|___Initial age 21-35|0.18|0.19|0.15|0.18|
|___Initial age 36-50|0.12|0.12|0.09|0.08|
|___Initial age 51-60|0.04|0.05|0|0.01|
|___Initial age 61-70|0.01|0.01|0.01|0|
|Participation in household chores|0.99|0.98|0.81|0.85|
|___Initial age 16-20|0.91|0.93|0.96|0.91|
|___Initial age 21-35|1|0.98|0.81|0.85|
|___Initial age 36-50|1|0.99|0.91|0.93|
|___Initial age 51-60|0.99|0.99|0.86|0.89|
|___Initial age 61-70|1|0.99|0.79|0.85|

Notes: Means are reported for the non-migrant women who have been in the survey for at least three consecutive rounds. The number of observations for 1997, 2000, 2004 and 2006 is 2114, 2689, 2689 and 2354 respectively.

49