rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 10 Examples of Countries With Inferior Fit Due to Non-Labor Income**

<!-- Start of picture text --> A) Chile 2017<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>-1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>Upper bound Actual growth<br>Lower bound Old Model<br>New - Rescaling New - No Rescaling<br>A) Uruguay 2019<br>1<br>0<br>6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>-1<br>-2<br>-3<br>-4<br>Lower bound Upper bound<br>Actual growth Old Model<br>New - Rescaling New - No Rescaling<br><!-- End of picture text --> <!-- Start of picture text --> B) Chile 2017 – Labor Income<br>8<br>6<br>4<br>2<br>0<br>6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>-2<br>Upper bound Actual growth<br>Lower bound Old Model<br>New - Rescaling New - No Rescaling<br>B) Uruguay 2019 – Labor Income<br>4<br>2<br>0<br>-2 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>-4<br>-6<br>-8<br>-10<br>-12<br>Lower bound Upper bound<br>Actual growth Old Model<br>New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

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<!-- Start of picture text --> ©<br>a<br>a=]aor<br>a<br>oO<br>2<br>=<br>oe<br>vo@<br>=—<br>0 10 20 30 40 50<br>x<br>——— 2002 ——— 2015<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>a<br>a<br>no]aoe<br>a<br>oO<br>2<br>=<br>en<br>vo@<br>ol ——-———_—~<br>0 10 20 30 40 50<br>x<br>——— 2002 ——— 2015<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>S<br>se<br>@<br>2<br>3”<br>=<br>e<br>a<br>En<br>0 5 10 15 20<br>x<br>——— 2002 ——— 2015<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>ae<br>s<br>@<br>fa<br>E<br>=<br>e<br>a<br>ZS<br>05 10 15 20<br>x<br>——— 2002 ——— 2015<br><!-- End of picture text --> <!-- Start of picture text --> @<br>a]age \<br>2<br>= |<br>7ac<br>o \ \. Fal<br>0 10 20 30<br>x<br>——— 1998 ——— 2015<br><!-- End of picture text -->

<!-- Start of picture text --> a=]a=<br>s2<br>a<br>c<br>o f aae——<br>0 10 20 30 40 50<br>x<br>—— 1998 ——— 2015<br><!-- End of picture text -->
While it is not entirely clear-cut which type of freight forwarder experiences longer clearance time, the findings suggest that medium/large freight forwarders are able to clear their typical import shipment faster, even though they also face a higher probability of having their import shipments spend a night at the border post or terminal for clearance.

# **_Although imports of agricultural products and foodstuffs are usually more subject to inspections, most freight forwarders clear these in less than a day_**

For freight forwarders importing agricultural products or food, beverages, and tobacco 80% or more have their typical import shipments cleared within a day in Figure 7. In fact, 12% of freight forwarders importing agricultural products clear their shipments in less than 1 hour. In contrast, about a third of freight forwarders that import raw materials or manufactures spend a night at the border post or terminal or at least 24 hours to clear their typical shipments. While agricultural goods often require more inspections than other goods, the faster reported clearance times likely reflect the existence of expedited procedures for goods that are time-sensitive, because they are more sensitive to spoilage, for example.<sup>13</sup> The survey provides some additional evidence that this is the case: the majority of freight forwarders report that cold storage shipments are always or frequently given priority for clearance at the border.

**Figure 7. Time Spent at Border/Terminal for Clearance by Sector**

<!-- Start of picture text --> Share of freight forwarders<br>Agricultural 12% 28% 28% 16% 4% 12% 0%<br>Food,<br>Tobacco, 6% 29% 29% 26% 3% 6% 0%<br>Beverages<br>Raw<br>4% 25% 27% 16% 11% 12% 4%2%<br>Materials<br>Manufactures 7% 20% 20% 18% 11% 17% 6% 2%<br>Less than one hour 1‐2 hours 3‐5 hours 5‐10 hours<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

13 According to the Revised Kyoto Conventions live animals and perishable goods are supposed to be given preference in scheduling examinations.

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# **Table A3: Moderate poverty**

|||201|4|||201|5|||201|6|||201|7||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Place o|f reside|nce|||||||||
|Minsk|10.7|7.5|5.7|5.3|5.5|8.7|9.3|8.7|11.8|11.0|14.8|12.3|15.5|14.9|18.2|16.9|
|Large cities|19.9|17.8|20.1|19.2|21.0|23.9|23.9|23.7|28.0|30.4|33.2|35.8|35.5|33.9|34.8|35.7|
|Small cities|19.6|20.9|19.1|18.8|25.5|25.4|26.7|26.3|32.4|33.2|36.3|39.4|40.1|38.1|41.1|39.4|
|Rural area|24.8|25.2|26.3|26.4|34.7|35.3|32.7|33.8|38.0|40.1|41.0|41.9|44.0|39.8|40.0|41.7|
||||||||Age||||||||||
|Child|33.0|32.3|31.1|31.8|38.0|37.5|37.8|38.1|46.6|47.0|48.9|51.6|54.0|50.1|49.8|50.3|
|working age|19.1|17.3|18.2|17.7|22.4|24.6|23.5|24.1|28.7|29.5|32.2|33.2|35.2|32.9|33.8|34.0|
|Retiree|9.5|10.1|10.7|9.8|11.3|13.6|15.1|13.9|14.6|18.2|21.0|22.2|21.1|20.4|24.3|24.5|
||||E|mploy|ment s|tatus f|or work|ing age|popul|ation|||||||
|Employed|12.6|12.1|12.6|12.1|16.0|18.2|17.1|17.5|21.5|22.3|24.9|25.4|26.2|24.9|26.7|26.8|
|Partly employed|27.1|22.8|23.4|23.7|30.0|36.9|24.3|37.4|39.2|40.1|31.5|42.7|55.7|49.1|37.4|42.6|
|Nonemployed|32.0|28.7|30.7|31.1|35.3|36.2|37.6|37.1|43.0|44.7|49.6|49.3|51.8|48.0|47.6|47.5|
|||||Educa|tion le|vel for|workin|g age p|opulati|on|||||||
|high education<br>secondary|9.9|7.1|8.0|7.8|10.2|11.3|11.2|11.9|14.0|13.8|17.3|18.0|19.4|16.9|19.1|18.4|
|specialized|17.5|16.3|17.3|16.9|21.8|24.8|23.7|24.3|31.0|31.6|33.3|34.0|37.8|35.1|35.1|36.7|
|other|26.1|24.6|25.4|24.6|31.6|33.6|32.3|33.1|37.3|38.7|42.0|43.1|45.0|43.0|43.7|42.9|
||||||Ty|pe of h|ouseho|ld (HH)|||||||||
|HH without children|9.2|8.5|9.4|8.5|10.5|13.6|13.6|13.0|13.5|15.5|19.1|19.1|18.4|19.0|21.2|21.0|
|Retiree HH|4.4|6.3|7.2|6.0|6.0|9.2|11.9|10.6|10.7|14.1|17.7|19.0|16.2|16.9|21.1|19.4|
|HH with children|29.4|28.2|28.2|28.3|34.6|34.4|34.5|35.2|43.1|44.2|46.0|48.5|50.9|46.1|47.1|47.2|
|1 child|18.2|16.8|19.0|18.1|23.9|25.5|24.3|26.5|32.7|34.6|37.6|39.3|41.7|35.7|38.3|38.2|
|2 children|41.8|35.7|35.4|35.5|48.0|43.8|46.1|44.2|54.6|56.1|54.6|58.6|61.5|58.7|56.9|57.6|
|3 and more|||||||||||||||||
|children|59.5|65.9|53.2|56.5|64.5|64.1|66.3|65.9|77.0|69.8|76.8|77.4|79.7|76.1|74.6|76.8|
|singleparent|34.5|32.9|36.1|36.8|36.0|36.7|36.9|39.2|46.0|43.9|47.1|52.7|51.0|50.3|45.9|47.0|

Source: Authors’ estimates based on HSS data.

30
insensitive with respect to the field shape. They also show that field area measurement errors can be limited if an appropriate combination of operator speed and GPS acquisition rate is selected.” Fasbender and Lucau (2012) also find that plot shape as well as plot size affects GNSS area measurement error. In their synthetic simulation of four distinct parcel shapes (square, rectangular, elongated narrow rectangle, and irregular polygon), with simulations for areas from 1 m<sup>2</sup> to 10 ha, they find that the variance of the measurement on the elongated rectangle and irregularly shaped polygon are the most amplified. Specifically, they suggest that the error on such irregular parcels, which are common amongst low-income regions ’ small-holder farmers, is primarily attributable to operator (enumerator) error (Fasbender and Lucau, 2012).

One concern with GPS measures in large scale surveys, which does not apply to self-reported measures, is the rate of missingness in the data. Missingness rates of 20-30 percent are not uncommon in existing datasets, and the pattern of missingness is not random but tends to be correlated with both plot and respondent characteristics. Kilic et al. (2013) show, with national data for Uganda and Tanzania, how plot distance from the interviewed household is the main factor determining what plots get measured, as field protocols normally include a provision not to measure plots beyond a given distance. Partly due to that, the plots that are not measured also differ systematically from those for which a GPS measure is taken in a number of (self-reported) characteristics, such as self-reported plot size, level of input use, and titling. Furthermore, some respondent characteristics are associated with higher missingness rates: plots belonging to older, less educated, poorer household heads, owning fewer plots are more likely to be measured than other plots. This is a drawback of GPS data that raises concerns about possible biases introduced by relying on observed GPS plot measures alone.

In presence of rates of missingness this high, imputations are often necessary for analysts to be able to work with complete case datasets. Self-reported land areas measures have been shown to be an important predictor of GPS area measure (Kilic et al., 2013). For that reason it is recommended that GPS measures be taken in addition to, not instead of, self-reported ones.

# **_C. The ‘Gold-Standard’: Compass and Rope Measurement_**

Traversing, also known as the compass and rope method, is widely used in farm surveys and is often considered to be the gold standard (FAO, 1982). When properly implemented, traversing returns highly accurate measures that can also provide a benchmark against which to judge the precision of other 10
almost 33 percent of the rural households in 2010/11. One important aspect of microcredit participation is the overlapping or participation in multiple programs, which was almost nonexistent in the early 1990s but has grown significantly since then. In fact in 2010/11, among the Grameen Bank members in our study, almost 61 percent were also members of other programs (Khandker and Samad 2013).

The overall program participation rate increased from 26.3 percent in 1991/92 to 48.6 percent in 1998/99 and to 68.5 percent in 2010/11. Table 1 also shows that not all microcredit participants are borrowers. This may happen because new members of many programs have to wait for some time before they can borrow, and some programs also have non-borrowing membership plans which allow individuals to save money with microcredit programs. That said, a great majority of microcredit members are borrowers, as shown in Table 1. For example, in 2010/11, about 69 percent of the rural households were microcredit members, while microcredit borrowers constitute about 56 percent of the households, implying that 13 percent of the rural households are non-borrowing members.

Figure 1 presents the breakdown of original 1,509 households from 1991/92 to 2010/11 by program participation status. In 1991/92, only 26.3 percent of 1,509 households were microcredit program participants. By 1998/99, there was a 2.8 percentage point drop in the share of participants while there was an increase in participation of 26.4 percent from the original non-participants. Similar transitions continued as we can see in the 2010/11 survey data. A trend is clear from such transitions – at each stage over time, a very high proportion of the participants remained with the programs, and also a good proportion of non-participating households eventually joined microcredit program, resulting in substantial growth in 11
**_Figure 1. Definition of CEQ Income Concepts and Fiscal Interventions in Jordan_**

<!-- Start of picture text --> Contributory social insurance<br>old-age pensions (net of<br>contributions)<br><!-- End of picture text --> <!-- Start of picture text --> Direct cash transfers: NAF;<br>bread compensation scheme,<br>Zakat, non-contributory<br>pensions, others.<br>Indirect subsidies (direct and<br>indirect impacts): electricity;<br>water<br><!-- End of picture text --> <!-- Start of picture text --> In-kind transfers net of user<br>fees: health; education<br><!-- End of picture text --> <!-- Start of picture text --> MARKET<br>INCOME<br>(PGT scenario)<br><!-- End of picture text --> <!-- Start of picture text --> MARKET INCOME<br>PLUS PENSIONS<br>(PRE-FISCAL INCOME)<br><!-- End of picture text --> <!-- Start of picture text --> NET MARKET<br>INCOME<br><!-- End of picture text --> <!-- Start of picture text --> DISPOSABLE<br>INCOME<br><!-- End of picture text --> <!-- Start of picture text --> CONSUMABLE<br>INCOME<br><!-- End of picture text --> <!-- Start of picture text --> FINAL<br>INCOME<br><!-- End of picture text --> <!-- Start of picture text --> Direct taxes: PIT, property tax,<br>border tax<br><!-- End of picture text --> <!-- Start of picture text --> Indirect taxes: GST; SST<br>(tobacco, alcohol, sodas,<br>mobile phones)<br><!-- End of picture text --> Source: Adapted from Lustig (2018).

# Allocating direct taxes: Personal income tax (PIT)

We compare two methods to estimate the PIT paid by households. The first method simply takes selfreported information on PIT payments. The second method imputes the expected household PIT burden for the corresponding incomes using a recursive methodology (World Bank 2020b).<sup>20</sup> This imputation works as follows: to start, we estimate the PIT that each household would pay under the statutory rates and the self-reported household incomes. A difficulty arises because the net-market income calculated from the survey is post-PIT, rendering an inaccurate estimate of the value of the tax paid. To solve this simultaneity problem between income and PIT, we calculate the household rate of statutory PIT and use it to estimate what the pre-PIT market income would be (net market income*). Then, we calculate the statutory PIT paid (in JOD) based on the pre-PIT market income.<sup>21</sup> Both the direct measure and the imputation of PIT paid underestimate the government income receipts from PIT, although the underestimation is lower in the self-reported case. We use the self-reported PIT paid for those households who report it, and the estimated statutory PIT for households that should be 20 This mirrors the method used in Indonesia (Jellema, Wai-Poi and Afkar, 2017). 21 P p = (N N )⁄(1 – P ) ; where N N p PP p NN mmppm ppi iimmNN ∗ P PPmmppN PPmmppN NN mmppm ppi iimmNN = ddppiiddppd NN ppi iimmNN – p mmNNiiNN NNmmppiiddttNNmmdd.
homogeneous price growth among the other consumption groups. The country with the second highest dispersion is Moldova, where the RMSD was almost 18 percentage points. The large dispersion in price changes in Moldova is evident when looking at the price change by consumption group (Figure 3, left panel). While on average, the 12-month price change was 30.2 percent, the price of housing rose by 75.9 percent in the same period, and the price of communication services increased by 5.6 percent – a difference of about 70 percentage points.

Switzerland experienced the narrowest dispersion in price changes in 2022, with an RMSD of just 2.4 percentage points. Prices in Switzerland grew by 2.8 percent across consumption groups in 2022 (Figure 3, right panel). Prices of house equipment saw an increase of 5.7 percent during the same period, while prices of communication services _decreased_ by 3.3 percent, –– a difference of about 9 percentage points.

Across countries, price changes across consumption groups were heterogeneous. The highest median inflation in ECA was for food and beverages (17.8 percent). It was followed by housing (which includes electricity and gas), with a median inflation rate of 15.6 percent (Figure 4). The lowest median inflation across ECA was communication services at 0.9 percent, followed by education at 3.2 percent.

The heterogeneity across countries can be measured by the standard deviation of the 12-month price change for each consumption group (Figure 5). The group with the largest standard deviation is housing, which reflects the heterogeneity of the energy price shock across ECA (World Bank, <u>2022b). Some countries in the region -particularly in Central Asia and the South Caucasus-</u> experienced no substantial price shock in electricity and gas because their fossil fuel supply was unaffected. In contrast, countries in the Western Balkans and throughout the European Union faced a surge in energy prices as natural gas supplies from Russia were disrupted. The price changes in transport also showed significant cross-country variation. On the other hand, price changes in communication services and clothing were similar across countries.

# _5.2 Differences in household spending on broad consumption goods_

Differences in households’ exposure to inflation depend on how inflation rates differ across the goods and services they consume and how different their consumption baskets are. In countries in which households spend approximately the same proportions of their budget on each consumption group, household-specific inflation will not vary significantly across households. The opposite will 10
permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued.<sup>14</sup> The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $6 billion (with total outside contributions $300 million).<sup>15</sup> Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey.

# **2.2 Data Sources**

We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available).<sup>16</sup> By design the LFS does not contain any information on Syrian refugees. The Disaster and Emergency Management Presidency of Turkey (AFAD) provides information on the number of Syrian refugees. The numbers used in this paper are taken from Erdogan (2014), who draws on information from AFAD and the Ministry of Interior and reports the number of refugees by NUTS 2 subregion. To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war). Finally, Google Maps was used to derive the travel distance between each governorate in Syria and the most populous city in each NUTS 2 subregion in Turkey.

> 14 Most recently, in January 2016, labor market access for Syrian refugees in Turkey was eased considerably. Importantly, they now can benefit from vocational training under the Turkish Employment Agency, employers will be able have to Syrians comprise up to 10 percent of their staff, and seasonal workers are exempted from the work permit, see http://www.resmigazete.gov.tr/eskiler/2016/01/20160115-23.pdf. It is of course too early to evaluate the impact of these legislative changes.

> 15 Hurriyet Daily News (February 2015) http://www.hurriyetdailynews.com/turkey-urges-worlds-help-onsyrian-refugees-as-spending-reaches-6-billion.aspx?pageID=238&nID=78951&NewsCatID=359.

> 16 Starting with 2014 there was a change in the design of the Household Labour Force Survey to ensure full compliance with European Union standards. This has caused some difficulty in making comparisons across years. However, our identification strategy does not use aggregate variation across years for identification and should hence be unaffected by the changes to the design of the survey.

8
**Figure 2.2: Left: Seasonal frequency of severe convective local storms (SLCS) from 1990 to 2005. Right: Average monthly number of lightning flashes from 1998 to 2014**

<!-- Start of picture text --> 900 200<br>800 180<br>700 160<br>140<br>600<br>120<br>500<br>100<br>400<br>80<br>300<br>60<br>200 40<br>100 20<br>0 0<br>Number of SLCS lightning flashes<br>Average number of<br>January February March April May June July August September October November December January February March April May June July August September October November December<br><!-- End of picture text --> _Source_ **_:_** _Left : Yamane et al. (2010) ; Right: NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) and Dewan et al. (2017)_ In order to better capture SLCS, we use lightning data as a proxy. Lightning data are constructed by Dewan et al. (2017), who use data NASA’s Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) from 1998 to 2014. This dataset provides estimated counts of lightning strikes. The LIS is a measurement device launched in 1997 to record lightning flashes between 35° N and 35° S latitude. With a spatial resolution of 4–7 km, LIS captures total flash counts, including both cloud-to-cloud and cloud-to-ground lightning flashes. The LIS is also well suited for identifying the spatial distribution of lightning, recording the time of events and measuring the radiant energy of lightning flashes (Dewan et al., 2017). They analyze lightning flash counts to examine annual, monthly and diurnal patterns of flash activity in Bangladesh over the study period (1998–2014). Overall, a total of 83,641 flashes were estimated to have occurred within the territory of Bangladesh, of which 86% occurred over land and the rest was over the Bay of Bengal. Figure 2.2 demonstrates that the seasonality of SLCS and lightning strikes is indeed strongly correlated.

## 2.3. Control variables

In addition to the climate and outage data, the analysis also includes auxiliary state-level control variables, in particular GDP per capita, annual power generation, the number of electricity customer accounts, surface area, and forest coverage (as a share of the state’s total area). In Bangladesh electrification rates are also taken into account. The data is obtained from the World Bank’s World Development Indicators (2018) and Hidden Dimensions of Poverty databases (Hansen _et al._ , 2013).

# 3. Outage patterns

Data from 28 countries enable an analysis of the trends and main causes of power outages. This section shows that the vulnerability of electricity systems to natural shocks varies significantly across countries. The share of overall outages that is caused by natural shocks typically lies anywhere between 10% and 70%, though subnational disaggregation (e.g. by federal states of the USA) yields an even wider range. Figure 3.2 summarizes these results and shows that natural shocks are indeed a significant (and often 8