rafmacalaba/datause-encoder-data huggingface dataset:
_Reduction in effective labor supply due to reduced hours worked_ As discussed above labor force ( ) in Pakistan depends on the participation rate ( ) and the working age population ( 15+ ). tt tt WWFF PPRR 1564 PPCCpptt ∗P , tt tt tt WWFF = PPRR PP To account for reduced working days we introduce the notion of effective labor force and substitute this notion for the labor force in the standard MFMod system, where the effective labor force, � is equal to the product of the normal labor force and the ratio of days of work lost to pollution/ divided by total tt WWFF working days (assumed to be 240). ∗�1 −<sup>WWDDWWggCCE</sup> �.<sup>ssttiiC,tt</sup> tt tt The equation above will modify structural employment and hence potential GDP: WWFF� = WWFF tt WWDD _Reduction in effective labor supply due to premature death_ Calculating the macroeconomic impact of a death due to air pollution is more difficult to quantify. World Bank (2020c) provides an age distribution of deaths due to air pollution. With this, we could estimate the number of working years lost due to workers dying prematurely from air pollution. This could then be used to estimate the reduction in GDP.

We could consider the effect of death due to air pollution on GDP per capita. Note that a death reduces not only labor supply, but also the denominator, population size. The death of a retired person would increase GDP per capita. According to World Bank (2020), 57.7% of deaths due to air pollution occur in persons older than 60 years. This means that air pollution deaths can be expected to increase GDP per capita. Notice that this difficulty does not occur for working days lost as sickness caused by air pollution does affect labor supply, and hence GDP, but not the population size. We report deaths due to air pollution separately.

_Fiscal implications of pollution-related disease_ Data show that about 60% of health care expenditure in Pakistan is financed out of pocket while the remainder is effectively financed from the government. In the modeling we assume that these shares are constant so that government’s share of health care expenditures is given by where Θ is the share of health care expenditures financed by households and is set to 0.6. and this PPVVKK,tt (1 −Θ)DDXXAAPP amount is included in the government goods and services expenditure equation.

# 3.8.4 Damages to agricultural productivity

While Pakistan’s mitigation impacts on agricultural damages are negligible, the costs of these given damages are not necessarily small. There is a vast literature on the impact of climate change on agricultural productivity. In an influential study, Schlenker and Roberts (2009) show a strong nonlinearity 46
- Alderman, H. (2000). Anthropometry. Designing Household Survey Questionnaires for Developing Countries. M. Grosh and P. Glewwe. Washington DC, The World Bank. 1: 251-272.

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- Contoyannis, P. and M. Forster (1999a). "Our healthier nation?" Health Economics 8: 289-296.

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- Freeman, J. et al. (1995). "Cross-sectional stature and weight reference curves for the UK, 1990." Archives of Disease in Childhood 73: 17-24.

- Glewwe, P., M. Gragnolati and H. Zaman (2000). Who gained from Vietnam's boom in the 1990s? Washington DC, The World Bank, Policy Research Working Paper #2275.

- Govermment of Vietnam (2000). Analysis of the Multiple Indicator Cluster Survey II (MICSII). Hanoi, Statistical Publishing House.

- Gwatkin, D., S. Rutstein, K. Johnson, R. Pande and A. Wagstaff (2000). Socioeconomic Differences in Health. Nutrition and Population. Washington DC, The World Bank,

- Kakwani, N., A. Wagstaff and E. Van Doorlsaer (1997). "Socioeconomic inequalities in health: Measurement, computation and statistical inference." Journal of Econometrics 77(1): 87-104.

- Kostermans, K. (1994). Assessing the Quality of Anthropometric Data. Washington DC, The World Bank, LSMS Working Paper No. 101.

- Lavy, V., J. Strauss, D. Thomas and P. de Vreyer (1996). "Quality of care, survival and health outcomes in Ghana." Journal of Health Economics 15(333-357).

_18_
Box 2: Who Teaches and Who Manages?

**<u>Issue:</u>** In Indonesia, gender parity is a key issue in the education workforce—while both men and women teach, it is mostly only men who get to manage. Across MoRA and MoEC schools, women represent over half the teaching workforce at the primary, junior secondary, and senior secondary school levels. When it comes to managing and leading schools, however, data show that males dominate these roles. Women only represent 31 percent of principals at MoRA primary schools, and this figure falls as the school level increases—among senior secondary MoRA schools, only 19 percent of school principal positions are occupied by women. The story is similar for MoEC schools as well. While nearly half of primary school principal positions are occupied by women at 43 percent, at the senior secondary school level, this figure drops down to 22 percent.

**<u>Figure 7. MoRA school workforce by gender</u>**

<!-- Start of picture text --> 100%<br>77% 81%<br>69%<br>75% 64%<br>55% 53%<br>45% 47%<br>50% 36%<br>31%<br>23% 19%<br>25%<br>0%<br>Primary (MI) Jr. Sr. Primary (MI) Jr. Sr.<br>Secondary Secondary Secondary Secondary<br>(MTS) (MA) (MTS) (MA)<br>Teachers Principals<br>Male Female<br><!-- End of picture text --> Source: Simpatika 2019

# **<u>Figure 8. MoEC school workforce by gender</u>**

<!-- Start of picture text --> 100%<br>78%<br>74%<br>69%<br>75% 61% 59% 57%<br>50% 39% 41% 43%<br>31%<br>26%<br>22%<br>25%<br>0%<br>Primary (SD) Jr. Sr. Primary (SD) Jr. Sr.<br>Secondary Secondary Secondary Secondary<br>(SMP) (SMA) (SMP) (SMA)<br>Teachers Principals<br>Male Female<br><!-- End of picture text --> Source: Education Statistics MoEC 2018/9 19
laborers for all of the sampled households in a village. The migration rate doubled from 14.22% in 2000 to 28.62% in 2008, reflecting the rapid urbanization in China.

Wage rates used in this study are the average agricultural and nonagricultural wages in a village. The former is calculated from the cost of hired-in on-farm labor and the number of working days (yuan/day) for all of hired on-farm laborers in a village.<sup>14</sup> The latter is the average wage for all off-farm workers in a village (yuan/hour), which is expected to reflect the opportunity cost of farm work in the local economy. Note that workers can find off-farm jobs not only in their village but also in the local economy outside the village. Here, all of the value terms are adjusted at 2000 constant price using provincial CPIs.

Our analysis shows that the average real agricultural wage increased from 26.54 yuan/day (3.20 US$/day) in 2000 to 35.09 yuan/day (4.22 US$/day) in 2008 with an average annual growth rate of 3.5%. The kernel density of agricultural wage rates indicates that the average agricultural wage increased as its distribution moved from left to right during the period but quite mildly (Figure 6a). Nonagricultural wages increased significantly from 2000 to 2008 at different growth rates (Figure 6b). Our data show that hourly nonagricultural wages doubled from 1.92 yuan/hour (0.23 US$/hour) in 2000 to 4.00 yuan/hour (0.48 US$/hour) in 2008. This also suggests that, similar to those of agricultural wages, nonagricultural wages also present regional variations in 2000 and 2008.

Figure 7 plots village-level averages of migration rates and nonagricultural real wage rates in 2000 and 2008 (using provincial CPIs as deflators). Consistent with the Figure 6b, it is clear that nonagricultural wages increased from 2000 to 2008. Migration rates also increased accordingly and are more responsible to nonagricultural wages in 2008 than 2000. Labor outmigration seems to be positively correlated with an increase in nonagricultural wages.

> 14 Since a small number of on-farm hired labors are reported in each village, we expect measurement errors in the variable. We used the county-level average if there is no hired labor case. However, we still think it is important to include agricultural wages due to the fact that agricultural labor demand goes up in a particular season to reflect the demand-supply (im)balance, in contrast with non-agricultural wages.
foreign control is almost 97 percent (93 percent under the control of banking groups from other EU countries, mainly Austria, Belgium, France, and Italy).<sup>4</sup> In some CEE countries, private loans were provided in foreign currency (Chart 10), especially because foreign interest rates were lower. This further increased the risks for the banking sector, because if the domestic currency depreciated, the volume of credit expressed in the domestic currency would rise, debt servicing costs would go up, and foreign exchange risk would turn into credit risk. In some CEE countries, therefore, the aforementioned measures to contain credit growth were targeted primarily at reducing growth in foreign currency loans (Steiner 2011). Furthermore, if a domestic credit boom is financed from foreign sources, as was the case in several CEE countries (except for the Czech Republic, Slovakia, and Poland), the risk of the domestic banking sector having insufficient balance-sheet liquidity (rollover risk) increases. In bad times, domestic banks face a high risk of outflows of short-term foreign funds that cannot be financed by the sale of liquid assets (Hilbers et al. 2005).

**Chart 11. Net external position of banking systems (as % of GDP; end-2010)**

**Chart 12. Development of net external position of the Czech banking sector (as % of total assets)**

<!-- Start of picture text --> 14<br>Czech Republic<br>12<br>Germany<br>10<br>Austria<br>8<br>Euro Area 6<br>Slovak Republic 4<br>Poland 2<br>0<br>Portugal<br>-2<br>Hungary<br>-4<br>Slovenia 2000 2002 2004 2006 2008 2010<br>Net external position<br>-40 -20 0 20 40<br>Net external position vis-a-vis banks<br>Source: IMF IFS, central banks<br>Austria as of end-2009 Source: CNB<br><!-- End of picture text --> Fortunately, the Czech Republic is one of a very few countries of the CEE region that neither experienced a boom in foreign currency lending nor relied on external (foreign) funding during the period of high credit growth of 2003–2008. Households do not take loans in foreign currency, while some parts of nonfinancial corporations do so in order to hedge the inflow of foreign currency, which come mainly from export revenues. Despite high foreign ownership, the Czech banking sector as a whole has a positive net external position vis-à-vis nonresidents of about 7 percent of GDP (Chart 11). The net external position vis-à-vis non-resident banks is

> 4 Foreign banks play a key role in the Czech financial system, and fortunately the ownership is not concentrated into one or two key EU countries. The three biggest state-owned Czech banks were privatized in the early 2000s to three large EU banking groups with origins in different EU countries. This to some extent limits the contagion risk. See Geršl (2007).

7
# Figure 1. Share of between-country inequality in global inequality: Selected years, 1820-2002 (in percent of global Gini)

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1820 1850 1870 1913 1929 1950 1960 1980 2002<br><!-- End of picture text --> Note: Based on Milanovic’s estimates of global inequality for 1820 and 2002, and Bourguignon and Morrisson for all points in-between. Concept 2 inequality is calculated from Maddison (2004) data. Source: See Table 2.

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training to increase the likelihood of employment by 24 percentage points. These expectations far exceed the actual impact of 2 percentage points seen in Table 1. Groh et al. (2016b) likewise show that policy makers in Jordan expected the wage subsidy program to have lasting impacts on youth employment, in contrast to the realized impacts.

Economists are also not immune to this tendency to think active labor market programs will be more effective than they typically are. A first testament to this comes from a number of the studies covered in this review being interventions designed by the researchers themselves, in addition to those evaluating programs that governments were already going to implement. Secondly, Groh et al. (2016b) carried out an expectations elicitation exercise when presenting the results of their Jordan wage subsidy research. They find that development economists on average expected a 10 percentage point increase in employment after the subsidy had ended, compared to the 2.8 percentage point increase seen in Table 3.

However, while revealed preference shows that there are participants who think these programs will be effective and therefore choose to participate in them, revealed preference also suggests that the types of formal jobs and manufacturing jobs that many of these programs think of as “successful” outcomes are not that valued by job-seekers. For example, Blattman and Dercon (2016) randomize job-seekers into industrial jobs in large formal firms in Ethiopia, and find that almost a third of people offered a job quit in the first month, and 77 percent within the first year, and that workers experienced health problems from staying in this work. Similarly, Adhvaryu et al. (2016) find female garment workers in India to have very high quit rates, losing almost 80 percent of the workers in their study over two years. These high rates of turnover are not consistent with formal jobs being so valuable and desired that workers never want to leave once they attain such positions.

The implicit assumption behind search and matching interventions in particular is often that search frictions make it costly and difficult for firms to find workers. Simple queries of firms often find firms _saying_ that they find it hard to find the right workers. But one also sees firms being reluctant to raise wages or spend more money in getting better matches. Groh et al. (2015) conducted a survey in Jordan where they tracked firms as they opened up job vacancies, and found that only 6 percent of positions required more than 4 weeks to find a new employee, and most firms could, in fact, fill jobs quite quickly. De Mel et al. (2016) similarly find firms in Sri Lanka say it would take 16
**Table 1: Estinaited** Effect **of Trade Structure on Growth**

**Cross Section, 1980.1999 Countries: 65**

||**Natural Re**<br>**Net Exports/Labor**|**source Dependence**<br>**force**<br>NRX/GDP|**Export**|**Export C**<br>**Herfmdahl**|**oncentration**<br>**NRXlT**|tl**Et**|**Intra-I**<br>|**ndustry Trade**<br>|
|---|---|---|---|---|---|---|---|---|
|**Basic Cnditinin**||||||a**xpors**|**Grube**|**-Uoyd Index**|
|**oog**<br>+|-0.38<br>(-0.20)|-0.89<br>(-0 31)|-4.98<br>(-202)|**|-3.66<br>3 12|***|3.26<br>|*|
|Capital**Accunulation**|**-0.12**|<br>-3.66|.<br>-580|***|(- )<br>310||((1.79)<br>||
|+|(-0 47)|(-1 44)|.<br>(-372)||-.<br>|***|3.09<br>|**|
|Growth in Terns of Trade|-0.35|<br>-3.01|.<br>-562|***|(-3.65)<br>||(2.12)<br>||
|+|(-0.15)|<br>(-1.29)|.<br>(-328)||-3.09<br>|***|2.85<br>|*|
|Macro**Stability**|-0.09|<br>-3.40|.<br>-650|***|(-3 51)<br>|***|(1.99)<br>||
||(-0.878)|<br>(-1.37)|.<br>(-3 92)||-2.99<br>||2.67<br>|*|
||||||(4 37)||(1.90)||
|NR/GDP||Additional|Controls<br>-6.52|***||||*|
|Leantr**Index**|||(-3.93)<br>656|<br>***|||2.23<br>(1.67)<br>||
|Export Herfirdahl|0.45|0.05|-.<br>(-3.85)||||3.76<br>(1.88)<br>|*|
||(0.81)|<br>(0.03)|||-2.10<br>2|***|1.86<br>||
|_NR_<br>_otal_Exports|||-493|***|(- 69)||(1.63)<br>||
|intra-lIndustry Trade|1.40|-223|.<br>(-3.91)<br>603<br>|<br>***|||-0.20<br>(-0.14)||
||(1.43)|.<br>(-I 02)|-.<br> <br>(-4 68)||-3.07<br><br>|***|||
|HT+Export Herfidahl|1.56<br>*|<br>0.92|||(-3.11)<br>207|**|||
||(1.79)<br>|<br>(060)<br>|||-.<br>(-2 32)<br>||||

The dependent variable is the GDP per capita growth rate. Basic conditioning set includes the log of initial income of the period and a measure of openness (S&W). Capital accumulation includes average ratio of investmentlGDP and log of years of schooling. Growth of terms of trade refers to the growth of the ratio of exports price index to import price index over the period. Macro stability includes the standard deviation of the real exchange rate over the period. T-statistics shown in parenthesis.* significant at 10%, ** significant at 5%, *** significant at 1% 22
<!-- Start of picture text --> 0.15%<br>0.12%<br>0.09%<br>0.06%<br>0.03%<br>0.00%<br>Damages Estimated Losses Estimated Total Impacts<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Figure 4-3 (a). Historical Trends of Climatological Disasters (normalized) <!-- Start of picture text --> 0.40%<br>0.35%<br>0.30%<br>0.25%<br>0.20%<br>0.15%<br>0.10%<br>0.05%<br>0.00%<br>Damages Estimated Losses Estimated Total Impacts<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Figure 4-3 (b). Historical Trends of Geophysical Disasters (normalized) 20
# **The Challenge of Measuring Hunger**

Joachim De Weerdt, Kathleen Beegle, Jed Friedman, and John Gibson JEL Codes: C88, O12, Q18 Keywords: hunger prevalence, measurement error, consumption, survey design Sector board: Poverty Reduction (POV) De Weerdt is with Economic Development Initiatives (EDI Ltd), Beegle and Friedman are with the World Bank, and Gibson is with the University of Waikato. This work was supported by the World Bank Research Committee. We would like to thank seminar participants at the sixth International Conference on Agricultural Statistics (ICAS-VI) for their comments and suggestions. All views are those of the authors and do not reflect the views of their employers including the World Bank or its member countries.
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especially when people work on non-agricultural activities. Thus, our labor variable may be overestimated, resulting in underestimating the true impact.

The coefficient of rain-fed land, _R_ , is always positive and strongly significant. This is consistent with the fact that Ethiopian agriculture is still land intensive. Advance inputs are generally found to be productive. Particularly, the impacts of irrigation and pesticide use are significant. The coefficient of fertilizer, _F_ , is also positive, though not statistically significant. The findings are consistent with the fact that subsistence farming contributes the majority of agricultural production in Ethiopia.

Did the road improvement programs not contribute to improving access to advanced inputs? Equation (3) is estimated using the truncated regression model.<sup>5</sup> Proximity to major roads enables farmers to use more fertilizer: the coefficient of _D_<sup>_RSDP2km_</sup> is positive and significant ( **Table 7** ). On the other hand, feeder roads do not seem to impact on the farmers’ use of fertilizer. Thus, as far as fertilizer is concerned, the main road network, and not feeder roads, is of particular importance to ensure timely and more affordable availability for farmers. This may be attributed to the fact that Ethiopia imports almost all fertilizer and other advanced inputs through the Port of Djibouti, about 800 km away from the capital city, Addis Ababa. Imported inputs are normally distributed through the main road network to regional cooperatives and farmers. This component of transportation cost contributes a substantial proportion of the fertilizer price to farmers (Rashid et al., 2013).

Neither RSDP nor URRAP roads have a significant impact on pesticide use. The coefficient of _D_<sup>_RSDP2km_</sup> is positive but not significant. The coefficient of URRAP is also insignificant. The results are inconclusive, possibly because the use of pesticide is still highly limited in Ethiopia. Recall that pesticides are used by only 10 percent of the surveyed households.

> 5 It may be open to debate whether the other input variable should be included in the equation. From an empirical point of view, there may be omitted variables that affect both simultaneously, for example, proximity to agrobusinesses, possibly making the result biased. On the other hand, from a practical point of view, there must be complementarities between the different inputs used. Both cases were checked. The results were almost the same.
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 20 40 60 80 100 120<br>Share of population with flood exposure (percent)<br>PPP)<br>Average consumption (2011 USD/day<br><!-- End of picture text --> _Figure 22. Scatterplot of share of population exposed by income (2011 PPP USD/day)_

# **5.5. From global aggregates towards local flood risk assessments**

The results in previous subsections that are presented at the subnational, national, regional and global scales are aggregates of flood exposure maps that were generated as part of this study for all countries. These exposure maps with higher spatial resolutions have been retained and can be used to derive more granular insights at the local level.<sup>2</sup> For illustration, Figure 23 shows the population flood exposure estimates for Vietnam in an aggregate form at the national and subnational scales, as well as in its high-resolution raster format for example areas. The raster displays the number of people exposed to flood risk of each 90m x 90m pixel. The map shows a high concentration of flood-exposed populations in Hanoi, as well as urbanized areas along the Red River.

The granularity offered by these pre-processed population exposure maps could be valuable for various applications; e.g. for the development of disaster mitigation plans prior to a flood event, or to estimate the number of affected people during flood events in order to prepare the scale-up of shock-responsive social protection systems. However, several limitations of these maps should be noted: First, poverty estimates are not available with the same resolution and are not incorporated in these maps. Second, while the maps can help to identify the scale of overall vulnerability and the location of pockets of vulnerability, they cannot replace detailed needs assessments for the targeting of post-disaster support or the evaluation of damages after specific events.

> 2 These maps will be made available publicly, and will be available upon request in the meanwhile.

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