rafmacalaba/datause-encoder-data huggingface dataset:
Table 1 confirm that using the FO definition, it is still the case that the typical level of education of workers in occupations at risk of automation is secondary education.

Table 1: Share of employed workers in occupation at high risk of automation, by educational attainment.

||(1)|(2)|
|---|---|---|
|**Educational attainments**|**GM**|**FO**|
|Primary|.39|.42|
|Secondary|.59|.56|
|Tertiary|.02|.02|

The table reports the share of employment in production occupations at high risk of automation, by the educational attainments of Indonesian workers in 2007. Primary education includes up to completed primary school. Secondary education includes junior and senior high-school. Tertiary education includes education levels from diplomas to PhD. FO indicates that the list of occupations at risk of automation is based on the methodology in Frey and Osborne (2017). GM indicates that the list of occupations at risk of automation is based on the methodology in Graetz and Michaels (2018). Sources: Sakernas (LFS); Frey and Osborne (2017); Graetz and Michaels (2018).

The finding that occupations at risk of automation are dominated by secondary educated workers is consistent with two pieces of evidence. First, the literature on employment polarization suggests that automation technologies tend to replace occupations with an intermediate level of skills.<sup>6</sup> While we do not observe the skill level of plants’ workforce directly, secondary education is both the median and the mean level of education of workers in manufacturing plants in 2006.

Second, an analysis of the characteristics of production occupations compiled by the World Bank for Indonesia, Thailand and Malaysia, suggests that secondary education is the typical educational attainment for all of the occupations at greater risk of automation across countries with similar economic characteristics.<sup>7</sup> The information provided in the World Bank occupation profiles is similar to the Occupational Information Network database (O*NET) for the United States, but it is based on analyses of labor force survey data for comparable Asian economies. An example of the Occupation Profile for “Welders and Flame Cutters” is provided in Figure A3. According to the World Bank, this is an occupation at high risk of automation. The key piece of information provided by the Occupation Profiles is the typical educational attainments of the occupation: Junior High

> 6E.g. Goos et al. (2014); David and Dorn (2013); Goos et al. (2009); Goos and Manning (2007)).

> 7We thank Mauro Testaverde for sharing these unpublished profiles with us. More details on the Occupation Profiles are presented in Section B.

7
that the instruments are extremely weak. Human capital does show up marginally significant, and governance remains an insignificant predictor of income, but we heavily discount this result due to poor test statistic performance.<sup>31</sup>

# **3.6 Extension to Panel Setting**

Due to data limitations, the estimates that have been presented thus far have been cross-sectional in nature. It is possible to expand the sample to a panel, but it is important to keep in mind two considerations. First, while the educational attainment data are available for five-year intervals from 1960–2000, the panel is unbalanced, and consequently the 116-country sample has an average of only 4 observations per country. We report the fixed effects regression, analogous to ( _B1_ ), in column ( _P1_ ) of Table 5.<sup>32</sup> Second, given that the governance and educational attainment data overlap for only one year (2000), we need to use an alternative measure of human capital if we wish to expand the panel in a way that allows us to preserve the use of government effectiveness as an instrument. We do so by substituting our human capital measure with data on enrollment rates. The panel with enrollment rates alone is much larger—176 countries, with an average of 7 years—and for reasons of comparability we report the fixed effects regression using this human capital measure in column ( _P2_ ).

The coefficients for human capital in both of these specifications are relatively small: 0 _._ 409 and 0 _._ 323, respectively, although both are statistically and economically significant. Physical capital also appears significant in both of these specifications, although the magnitudes of their coefficients are also correspondingly smaller. As before, however, we discount these estimates because of endogeneity concerns.

Our benchmark panel, which uses enrollment data but is otherwise analogous to ( _B2_ ), is reported in column ( _P3_ ). It comprises 95 countries, with an average of about 5 time periods per country. As noted in the introduction, the danger that enrollment is a poor proxy measure for human capital is less of a concern as long as our instruments are valid. The Anderson and Hansen tests confirm that this is indeed the case, although it is important to point out that we are forced to use contemporaneous (instead of lagged) government effectiveness as an instrument; it is perhaps for this reason that the coefficient on governance in the first stage is indistinguishable from zero.

The results largely corroborate the findings of the cross section estimates, with the coefficients on human capital being statistically significant. While the magnitude of the contribution is somewhat

> 31We also explored including the settler mortality instrument, with even more disastrous results: The instrument fails _both_ the exclusion and relevance conditions, and none of the variables in the second stage are statistically significant.

> 32The Hausman test detects systematic differences between coefficients and hence a preference for fixed over random

20
# TABLE 7 **EGYPT 1975-80 THE DIRECT IMPACT OF CHILD DEA'TH ON NEXT PREGNANCY OF MOTHER (before age 60 months)**

% per cent dead (child death prior to pregnancy of mother) RR relative risk of pregnancy of mother (child death / no child death) AR attributable risk of pregnancy of mother

-------------FERTILITY CONTROL--------------ALL (N-7375 children) NO (N-4714 children) YES (N-2661 children) EST (95 % CI) EST. (95 % CI) EST. (95 % CI)

CHILD DEATH at any age % 9.98 ( 9.30 - 10.66) 10.73 ( 9.85 - 11.62) 8.64 ( 7.57 - 9.71) RR 3.22 ( 2.73 - 3.81) 3.22 ( 2.62 - 3.95) 3.16 ( 2.34 - 4.26) AR 18.1C (14.32 - 21.83) 19.22 (14.28 - 23.88) 15.73 ( 9.49 - 21.54) CHILD DEATH before 12 months % 8.88 ( 8.23 - 9.53) 9.50 ( 8.67 - 10.34) 7.78 ( 6.76 - 8.80) RR 3.25 ( 2.74 - 3.85) 3.19 ( 2.59 - 3.92) 3.23 ( 2.39 - 4.36) AR 16.64 (13.03 - 20.11) 17.21 (12.62 - 21.56) 14.76 ( 8.82 - 20.31) CHILD DEATH at 12 months or later % 1.81 ( 1.42 - 2.20) 2.30 ( 1.72 - 2.89) 1.17 ( 0.70 - 1.65) 1KR * * * AR * * * based on log-logistic model controlling for: weaning, parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to married women iast two children only no multiple births no marital disruption prior to child death Muslim only note: some children still alive at survey

*-not statistically distinguishable from 0.0 for per cent (%) and attributable risk (AR) and from 1.0 for relative risk (RR)

39
66

# **A.I Night Lights**

For a model validation exercise in Section VII.F, we use data on night light intensity between January 2016 and December 2022 sourced from the Visible and Infrared Imaging Suite (VIIRS) Day Night Band (DNB) on board of satellites, prepared by the Colorado School of Mines. The key variable of interest is night light intensity at the year-month level for each 0.5 km x 0.5 km grid cell in Sri Lanka. For each month and district, we average the data across all grid cells that compose that district.<sup>103</sup>

# **A.J Weather Variables**

Since a wide array of weather variables have been shown to be important drivers of crop yields for many crops grown in Sri Lanka, it is important to control for weather in our analysis of the effects of the fertilizer import ban on yields (see Appendix C.D). Below, we describe our sources of weather data.

For rainfall data, we use the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, a quasi-global gridded rainfall time series data set based on a combination of high-resolution satellite imagery and in-situ station data covering latitudes between 50<sup>_◦_</sup> S and 50<sup>_◦_</sup> N, all longitudes, and all years from 1981 to the near-present.<sup>104</sup> For each agricultural growing season (Maha and Yala) and year between 1999 and 2022, we obtain three variables from CHIRPS: total rainfall, in millimeters (mm) per season; rainy days (defined as the number of days in the season receiving more than 2mm of rain); and dry spells (defined as the maximum number of consecutive days in the season receiving 1mm or less of rain). The Maha season is defined as starting on September 1 and ending on March 31. The Yala season is defined as starting on May 1 and ending on August 31. We aggregate the data up from the grid-cell level to the level of Sri Lankan districts.

For other weather variables, we use the ERA5-Land dataset, which provides high-resolution data on the evolution of the Earth’s land surface between 1950 and the present.<sup>105</sup> For each growing season and year, we obtain four variables from ERA5-Land: average incoming shortwave solar radiation, in watts per square meter (W/m2); total vapor pressure deficit, in hectopascals; and average minimum and maximum temperatures, in degrees Celsius. Vapor pressure deficit (VPD) is the difference between saturated vapor pressure (temperature dependent) and the actual vapor pressure. Higher VPD values indicate higher air dryness and therefore often negatively correlate with yields. VPD is computed daily and summed over the season. Minimum and maximum temperatures are defined as the mean of daily minimum and maximum temperatures for the entire season. As with rainfall, we aggregate grid-cell level data on these four variables up to the district level.

> 103 VIIRS data can be accessed at: https://eogdata.mines.edu/products/vnl/.

> 104 CHIRPS data can be accessed at: https://www.chc.ucsb.edu/data/chirps.

> 105 ERA5-Land data can be accessed at: https://www.ecmwf.int/en/era5-land.
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<!-- Start of picture text --> 4© ---* Other<br>©<br>><br><<br>8<br><!-- End of picture text --> <!-- Start of picture text --> 1980 1990 2000 2010<br>Year<br><!-- End of picture text -->
Second, we focus on the individual earnings of main household members. However, poverty is clearly a phenomenon that is experienced at the household level since, in most households, there is some pooling of resources. Thus, it is possible that the poverty transitions we observe may be due to changes in the incomes of household members that are not in our (or the IFLS) sample. However, although the IFLS sample did not interview every member of the household, the method used means that it is likely to have captured almost all the significant sources of income in the household. Moreover, the vast majority of individuals in our sample are the sole or main income earner for their household, so changes in their earnings are likely to have a much larger impact on poverty transitions than changes in the earnings of other household members. In fact, the individual earning used for the analysis account on average for 70 and 67 percent of their actual specific total household income in 1993 and 2000 respectively. The corresponding median is even higher at 74 and 77 percent. Thus, the individual earnings in our sample account on average for 70 percent of household income. With these caveats in mind, our sample seems adequate for exploring the questions asked.

# POVERTY LINE

The poverty line used for all the analysis below is derived from the 2000 BPS poverty line.<sup>8</sup> For 1993 BPS also calculated a poverty line, but they used a methodology which is rather different than that used for the calculation of the 2000 poverty line.<sup>9</sup> To ensure that the poverty line in 1993 represents the same purchasing power as that in 2000, the easiest approach would be to take the 2000 poverty line and deflate it using the provincial consumer price index (CPI) published by BPS. However, the published CPI uses a set of weights for its sub-indices which are heavily weighted in favor of wealthy consumers in urban areas.<sup>10</sup> This bias in the CPI is particularly inappropriate for our analysis. In particular, since food prices rose more quickly than other prices over the period and the poor have a much higher share of their expenditure on food than the better

> 8 This uses a variant of the Bidani and Ravallion (1993) methodology for calculating poverty lines

> 9 The BPS methodology for the calculation of the poverty line changed in 1996.

> 10 For example, the CPI share on food is 38% but the food share of the bottom quintile from Susenas is 66%. A detailed discussion of this general problem is to be found in Grimm and Günther (2006)

16
timates on firm-level control variables show expected signs. Sales, export, and import are positively associated with employment, while purchase is negatively associated with employment. Columns (4) through (6) repeat the same estimations as (1) - (3) with the PS matched sample and frequency-weighted regressions. All estimates do not change qualitatively.

Second, we apply an alternative definition of the non-MNC group. To the extent that labor market policy changes (such as an abrupt minimum wage hike) induce firms to conduct outward foreign direct investment (Fan et al., 2018), Korean firms without any foreign affiliates before the abrupt minimum wage announcement in 2017 may have decided to establish new foreign affiliates in response to rising labor costs (i.e., in 2017, 2018, and 2019). One could argue that classifying them as non-MNCs is problematic. To address this concern, we drop Korean firms that established new foreign affiliates after 2017 from the sample and redo the analysis. Column (1) of Table 6 presents the estimation result with the alternative definition of non-MNCs. Reassuringly, the core result is the same, which is not surprising given that non-MNCs account for much less than 5% Table 5: Additional Firm-Level Controls in Manufacturing, 2013~2019

|||Depend|ent Variable|: Logof Em|ployment||
|---|---|---|---|---|---|---|
|||Full Sample<br>||M<br>|atched Sam<br>|ple<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MNC_i ×_Post_t_|-0.0295***|-0.0159**|-0.0152**|-0.0507***|-0.0251***|-0.0244***|
||(0.0101)|(0.0066)|(0.0065)|(0.0124)|(0.0088)|(0.0086)|
|Log sales||0.3290***|0.3288***||0.3610***|0.3608***|
|||(0.0107)|(0.0107)||(0.0097)|(0.0096)|
|Log purchase||-0.0019|-0.0022||-0.0076|-0.0078|
|||(0.0056)|(0.0057)||(0.0050)|(0.0050)|
|Exporter|||0.0088<br>(0.0057)|||0.0178<br>(0.0116)|
|Importer|||0.0077<br>(0.0051)|||0.0027<br>(0.0059)|
|Observations|41,030|39,607|39,607|26,618|25,785|25,785|
|R-squared<br>Fixed Effects:|0.9541|0.9644|0.9644|0.9593|0.9691|0.9692|
|Firm|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry-Region-Year|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Columns (1)-(3) use the full sample, while columns (4)-(6) are based on the PS matched sample and frequency-weighted regressions. MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Sales and purchases are in real terms. Exporter (Importer) dummy indicates whether the firm exports (imports) in each year. Standard errors are clustered at the industry level. The number of observations differs across columns because some observations of firm-level control variables are missing or zero. *** p<0.01, ** p<0.05, * p<0.1.

21
# **Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security**

# **1. Introduction**

More than four months into the war that started with Russia’s attack on Ukraine on Feb. 24, 2022, the human and economic toll is already vast: 6 and 8 million individuals are estimated to have left the country or be internally displaced requiring support and GDP is estimated to contract by 45%. Implications for regional and global food security will be far-reaching as, with 41.5 million ha of highly fertile land–larger than the agricultural area of France (18. mn ha), Germany (12 mn. ha) and Poland (11 mn. ha) combined– Ukraine has traditionally been a breadbasket and a main exporter of wheat and sunflower oil. After the start of the war, grain prices rose well above the levels experienced in the 2007/08 food crisis, highlighting agriculture’s <mark>geostrategic role (von Cramon-Taubadel 2022).</mark> As the blockade of its Black Sea ports and the lack of alternatives with comparable capacity prevents Ukraine from exporting most of its 2021 harvest, countries such as Egypt, Indonesia, and Lebanon that are highly dependent on Ukrainian food imports already experienced shortages. The dearth of storage space for the 2022 harvest this creates, together with war-induced shortages of inputs such as fuel, fertilizer, seed, or labor, may affect farmers’ planting and crop management decisions in the 2022 season. This would depress not only local welfare but also imply that, instead of realizing its vast potential for intensification of wheat production (Swinnen _et al._ 2017), production losses in Ukraine will exacerbate global food insecurity.

To quantify war-induced direct and indirect impacts on area and expected yield of winter crops, this paper uses a 4-year (2019-22) panel of 10,125 village councils (VCs) for which reliable crop cover information based on a machine learning model using ground-truthed training data for each of these years is available. To generate a measure of war-related disturbances that is more relevant for agriculture than the measures normally used in the literature, we identify location and extent of damages to agricultural fields via burning, explosion of ordnance <mark>, rockets, or aircraft</mark> , and soil compaction from movement of tanks or heavy artillery from changes on repeat imagery by medium-resolution optical sensors (Sentinel-2 and Planet). Sample images are displayed in the appendix.

2
Within these data, there is a potential problem of reclassification, because the international Harmonized System of classifying traded products underwent various revisions during this time period, including in 1996 and 2000. Reclassification of exported products occurs when, in a particular year, a country’s customs body begins to report goods separately that were previously aggregated in a ‘not elsewhere specified’ (n.e.s.) group. This is particularly problematic from the point of view of identifying export discoveries, as any filter would falsely identify the newly disaggregated products as discoveries, even though in reality they are not new to the country’s export basket. In order to systematically identify cases of customs disaggregation, we apply a outlierdetection methodology based on the divergence from each country’s temporal trend during the time window used to count export discoveries by industries and countries. We identify 19 significant outliers that are likely cases of reclassification<sup>1</sup> , and eliminate these country/years from our sample, leaving a sample without of discovery activity by industry and country potentially influential outliers.

# **3. Results**

Our filter identifies 3089 cases of discovery during 1997-2002. These are listed by country and by Leamer’s (1984) commodity groups in the Appendix.<sup>2</sup> As mentioned, the empirical analyses proceed in stages, beginning with a brief exploration of the relationship between export diversification, export discoveries and the level of

> 1 See Appendix for a description of the filter and the identified cases of reclassification. It is noteworthy that many such episodes occurred around 1996-97 and 2000, when customs agencies were implementing the changes in nomenclature.

> 2 This metric of discovery is a revision of that developed in Klinger and Lederman (2004). It uses a more explicit definition of discovery (exports starting from 0 rather than from below a threshold), extends the length of the first period for a more stringent test of novelty of the export, and uses the reclassification filter discussed in the text.

6
Table A.1. Total number of households, villages and enumeration areas in sample frame (50 units).

||Total|Within 1 km<br>radius|Within 3 km radius<br>excl. those within<br>the 1 km radius|Within 5 km radius<br>excl. those within<br>the 3 km radius|
|---|---|---|---|---|
|Households|109,296|11,572|41,665|56,059|
|Villages|1,194|113|458|623|
|Enumeration areas|804||||

Source: UBOS maps and census data Table A.2. Number of households, villages and enumeration areas in sample frame (50 units)

||Mean|Median|Min|Max|
|---|---|---|---|---|
|Households in catchment area|2,483|2,728|490|3,938|
|Households within 1 km radius in CA|344|240|60|1014|
|Households within 3 km radius excl.|1096|991|127|2,357|
|those within the 1 km radius in CA|||||
|Households within 5 km radius excl.|1,303|1,231|173|2,428|
|those within the 1 and 3 km<br>radius in CA|||||
|Villages in catchment area|29|26|7|58|
|Villages within 1 km radius|3|3|1|8|
|Villages within 3 km radius excl.|13|11|2|30|
|those within the 1 km radius in CA|||||
|Villages within 5 km radius excl.|15|15|2|31|
|those within the 1 and 3 km<br>radius in CA|||||
|Enumeration areas in catchment area|20|19|4|35|
|Villages in enumeration area|1.9|2|0|6|

Source: UBOS maps and census data.

Table A.3. Village characteristics in sample frame (50 units).

||Mean|Median|Min|Max|
|---|---|---|---|---|
|Number of households in village|92|84|0|273|
|Distance to facility|3.9|5|1|5|
|Source: UBOS maps and census data|||||

34
# **Table A.3.8: Results of simulation 1 reported for the full sample with LAYS data and the subsample with PISA data**

|Changes in|Optimistic|Intermediate|Pessimistic|
|---|---|---|---|
|**Full sample (157 countries)**||||
|Learning-Adjusted Years of Schooling (LAYS)|-0.3|-0.6|-0.9|
|Per student average earning loss in annual terms ($)|-355|-872|-1,408|
|Per student average lifetime earning loss at present value ($)|-6,472|-15,901|-25,680|
|Aggregate economic cost of foregone earnings at present value ($)|-4.3 T|-10.0 T|-15.9 T|
|Aggregate economic cost as a share of total spending on basic<br>education|6.7%|15.5%|24.7%|
|**PISA subsample (92 countries)**||||
|Learning-Adjusted Years of Schooling (LAYS)|-0.3|-0.6|-1.0|
|Per student average earning loss in annual terms ($)|-497|-1,269|-2,072|
|Per student average lifetime earning loss at present value ($)|-9,061|-23,156|-37,806|
|Aggregate economic cost of foregone earnings at present value ($)|-3.9 T|-9.3 T|-14.9 T|
|Aggregate economic cost as a share of total spending on basic||||
|education|6.5%|15.2%|24.3%|

Source: Authors’ calculation. Simulation 1 results based on latest available LAYS of 157 countries, reported for the full sample and for the subsample of 92 countries for which PISA or PISA-D data is available (unweighted averages). All dollar figures are expressed in 2017 PPP dollars.

52