rafmacalaba/data-use-mentions-extended huggingface dataset:
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use of nontariff barriers in developed countries. 12/. Table 2 shows the 1966-86 changes in these indices as well as the actual value of the 1966 frequency index. With the exception of fuel imports, Table 2 shows that there have been major increases in the NTB frequency ratios over the 1966 to 1986 period. For all developed countries approximately 17 per cent of all fourdigit SITC products were affected by NTBs in 1966 and this ratio increased 37 percentage points (to an overall value of 54 per cent) by 1986. Food products recorded the highest overall 1966-86 increase in the frequency index (53 percentage) while the table shows that the percentage of affected products in this group rose by 40 points or more in Italy, Netherlands, Norway, United Kingdom, Switzerland and the United States. 13/ Analysis of the underlying trade statistics for the European Communities countries shows that the extension of variable import levies to additional products was a major factor accounting for the rise in the EC coverage ratios for foodstuffs (over 98 per

- 12/ In computing NTB indices, we have utilized a broader group of measures than has been previously employed by the World Bank for analyses of trade restrictions. Specifically, we have included several measures like health and sanitary regulations and safety and industrial standards in order to achieve comparability with data drawn for the 1960s. This would influence the levels of NTB indices in 1966 and 1986, but should not affect our analysis of trends in nontariff barrier use since the indices have been calculated for the same group of measures. As a result of this methodological change, the empirical results presented in this study are not directly comparable with those of other World Bank investigations.

- 13/ Over the 1966-86 period the United States extended agricultural trade barriers to a number of important product groups. In 1986, approximately 63 per cent of the tariff line items in SITC 02 (Dairy Products and Eggs were covered by quotas, while seasonal tariffs and tariff quotas were also applied extensively in this sector. Quotas were
retrospective questions about the values of such characteristics in the first survey round are asked in the second round.

Then let _yij_ represent household consumption or income in survey round _j_ , _j=_ 1 or 2. The linear projection of household consumption (or income) on household characteristics for each survey round is given by If _zj_ is the poverty line in period _j_ , then we are interested in finding out the percentage of households that are poor in the first (or previous) period but non-poor in the second (or current) period as well as the percentage of poor households in the first period that escape poverty in the second period For the average household, quantity (2a) provides the joint (unconditional) probabilities of household poverty status in both periods and quantity (2b) the conditional probabilities of household poverty status in the second period given their poverty status in the first period. Put differently, using panel data, quantities (2a) or (2b) provide the gross changes of poverty over time, which adds a dynamic and more nuanced picture to the net change of poverty that can simply be obtained by comparing the headcount poverty rates in two cross sections.

If true panel data are available, we can straightforwardly estimate the quantities in (2a) and (2b); but in the absence of such data, we can use synthetic panels to study mobility. To operationalize the framework, we make two standard assumptions. First, we assume that the each specific setting. Headcount poverty rates using this age restriction (Table 3) are very similar to those without the restriction (Table 1.1 in the Appendix). The Gallup Poll Survey collects individual data for people age 15 or older rather than household data, thus we restrict individuals to the age range 15-55 to keep reasonable sample sizes.

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Fargher S. and Hallegatte S. (2020), <u>Best investments for an economic recovery from Coronavirus: An illustration based on the Fiji Climate Vulnerability Assessment to pinpoint stimulus options.</u> World Bank Group, Washington, DC.

Giannakis, E., & Bruggeman, A. (2017). Economic crisis and regional resilience: Evidence from Greece. _Papers in Regional Science_ , 96(3), 451-476. https://doi.org/10.1111/pirs.12206 Greco, S., Ehrgott, M., Figueira, J.R. (Eds.), 2016. Multiple Criteria Decision Analysis: State of the Art Surveys, International Series in Operations Research & Management Science. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4939-3094-4

- Hammer S. and Hallegatte S., <u>Planning for the economic recovery from COVID 19: A sustainability checklist for policymakers, World Bank blog on Development and a Changing Climate, 14 April 2020.</u>

Helm D. (2020), <u>The environmental impacts of the coronavirus.</u> _Environmental and Resource Economics_ 76:21–38.

Hepburn C., O’Callaghan B., Stern N., Stiglitz J. and Zenghelis D. (2020), Will COVID-19 fiscal recovery <u>packages accelerate or retard progress on climate change?</u> _Oxf Rev Econ Policy_ 36(S1):1–48.

Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M., Roehrl, A., 2011. OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development. _Energy Policy_ , Sustainability of biofuels 39, 5850
# **5.1 DID Results**

The first set of results focus on three aggregate food insecurity measures constructed from FIES questions – prevalence of moderate or severe food insecurity, probability of being food insecure and raw food insecurity score (Table 2). The coefficients in the first row of Table 2 show that there was no significant change in the overall food security of households outside of Tigray after the outbreak of the war. This is not surprising, given the war was confined to the Tigray region in the study period. Our key coefficient of interest is the interaction term of wartime dummy and the dummy variable for Tigray region. The results in column 1 show, in contrast to the trend in the other regions, the prevalence of moderate and severe food insecurity increased dramatically in Tigray in the post-war-onset period. The conflict led to 38 percentage points increase in moderate or severe food insecurity in Tigray. This amounts to 106 percent increase, relative to the prevalence rate in the pre-war period. Column 2 presents similar results for the probability of being food insecure. Compared to the pre-war period, the incidence of food insecurity in Tigray is 26 percentage points higher, which is a 47 percent increase from the pre-war level. In column 3, we show regression results for the raw food insecurity index calculated from FIES questions. The results are consistent with those obtained for the other two aggregate food insecurity measures. In Tigray, the war has led to 1.6 units increase in the number of dimensions of food insecurity reported by households. These large effects are plausible given the massive humanitarian crises the war has caused in the region (see WFP, 2022).

**Table 2: The Impact of violent conflict on aggregate measures of food insecurity**

||(1)<br>Moderate or severe food<br>insecurity|(2)<br>Food insecure|(3)<br>Raw score|
|---|---|---|-
_Figure 2 - Focus group discussion with community members of Sabasaba on March 18, 2018_

# 2.2.4 Generalizing the results to the entire city

To generalize the results to a city level, sample weights were applied that had been calculated based on flood risk strata. Each household is assigned a sample weight corresponding to the number of households in the city that the sampled households represent, based on selected strata and population. By applying the weights, results can be scaled up to city level. Since we oversampled in areas affected by flood risk as per the methodology described above, we need to generate and use weights to “adjust” the sample to represent the city of Dar es Salaam in terms of flood risk. By using flood risk specific strata for weighting, we also generate more accurate results of the scaled-up results (number of affected people in the city and total losses from floods, etc.) by assuming that households within each stratum are facing similar risks and impacts of floods. Next, we will describe how we generated the flood risk weights.

The flood risk strata were identified using a machine learning (ML) approach, based on the reported occurrence of flood exposure by households in the collected data. The information from the Ramani Huria maps was complemented with elevation and population density by EA to more precisely and transparently categorize areas of Dar es Salaam according to their flood risk.<sup>8</sup> This information was used to build a risk model using an ensembled decision tree algorithm (Figure 3). The model identifies critical thresholds of the variables for different levels of risk of being affected. For example, “extra high risk” is defined as EAs for which area is covered by the Ramani Huria flood tag by 67% or more, located below 5.06 meters elevation and with a population density higher than 0.08 (people/square meter). Using these thresholds, four categories of risk level were identified – low risk, medium risk, high risk
of civic organizations may help spread popular views and/or government policies and encourage conformity. In Sierra Leone, for instance, more social capital is associated with a greater respect of their chiefs (Acemoglu et al. 2014), indicating ease in implementing leader policies. Local social networks in German towns had aided the rise of the Nazi party (Satyanath et al. 2017). These papers suggest that a high level of trust may speed up pandemic spread under wrong signals from the government, such as when facing official signals undermining the severity of the pandemic. When the government sends out signals that the pandemic risks are not that great—as in the case of a delay in restricting social mobility by the government—the strong trust in government may amplify this perception, and result in worse pandemic spread. _We thus expect that government policy delay in restricting mobility results in greater damage on pandemic outcomes where there is more trust_ .

Relatedly, when a high trust and a high vulnerability are combined in a country, the greater social interactions in such societies facilitate the spread of the pandemic, resulting in more infections and/or higher mortality from the pandemic. _We thus expect that high-trust countries experience higher pandemic damages when they also have a higher vulnerability_ .

# **III. Data and measurements**

Our key data source is the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at JHU, which contains the latest data including confirmed COVID-19 cases and deaths in 188 countries (or regions) from the beginning of COVID-19 outbreak. It is updated daily, and we use data up to November 19, 2020. We construct two key COVID-19 outcomes: the number of confirmed cases per 1,000 residents, to measure the pandemic spread; and the death rate among the COVID-19 infected, to measure the pandemic mortality rate. The pandemic mortality rate captures pandemic severity, and it sheds light on the amount of consumption a society is willing to give up to contain the pandemic (Hall, Jones
The same examination **was** used for admission to both types of general secondary schools (key and non-key) and to the SVS, with the admission cutoff score being highest for key general secondary (the best route to higher education) and lowest for general secondary; SVS scores fell in between. STS and SWS tests were, as discussed above, different and comparisons are not possible.

The data dccument excess demand for secondary places. They also indicate that students applying for SVS were better qualified than those applying to the general secondary schools.

Absent other data, further interpretation is speculative. We do not know what combination of factors led more than half of the applicant pool to select STS and SWS, although the prospect of a job assignment surely plays an important role. Conversations with Anshan educators indicate that the popularity of the SVS among parents has been growing in recent years as the ability of SVS to place students has been demonstrated.

While the planning and execution of skilled manpower requirements and supply are both based on a good deal of formal reporting, decision making, and instructions issued to lower levels, as described above, it is also important to note that all elements of manpower planning are carried on in the context of continuous consultation and discussion. District and municipal labor and education authorities visit schools and enterprises regularly to confer with principals and managers. The extended nature of the planning process requires many meetings, capped by formal provincial level conferences each year. The SVS, particularly, employ many mechanisms for consultation with employers -- labor surveys, formal meetings, shared faculty -- to interact with enterprises.

# Curriculum Planning

General education curricula are prescribed nationally by the State Education Co-mission, with very little local flexibility allowed. These curricula are followed for the general education courses in all three types of vocational schools.

Curricula in the vocational and technical specialties are set by the relevant technical ministries (e.g., by the Ministry of Machine Building for machining courses; the Ministry of Metallurgy for mining and
have a different economic structure, with a large share of workers in agriculture and low-skilled service jobs and fewer in knowledge-intensive sectors and occupations, which may be most affected by AI.

In this paper we analyze how differences in occupational composition may affect AI’s impact on jobs, with a focus on middle- and low-income countries and including the United States for comparison. We advance over the existing nascent literature in a number of ways. First, we apply the AI Occupational Exposure (AIOE) index developed by Felten et al. (2021) to labor force surveys covering a population of 3.5 billion people or around 45 percent of the world’s population. The AIOE relies on the O*NET classification of occupations originally developed for the US labor market. Second, we use detailed microdata from a larger set of low-, lower-middle-, and uppermiddle-income countries than employed in earlier studies like Cazzaniga et al. (2024) and Pizzinelli et al. (2023). The use of harmonized individual-level data for 25 countries across all income groups allows the identification of systematic differences and stylized facts. The dataset includes occupation-level data at the more granular four-digit International Standard Classification of Occupation (ISCO) level. Third, we exploit that microdata to explore how AI exposure varies by different socio-demographic groups in a multivariate framework. This allows us to provide a more differentiated view than earlier studies.

We find large differences in exposure to AI occupations by country income level. We normalize exposure at the individual worker level on a scale that ranges from 0 (no exposure) to 100 (full exposure). Mean exposure across workers is greatest in our high-income country (the United States) at 62. This is followed by upper-middle-income countries with an average exposure level 3
# **2.3 Regional differences in productivity**

In addition to difference in performance between SOEs and private firms, there is a large variation in returns across China’s provinces. Firms in many provinces in the west have considerably lower returns to capital than firms in the central and eastern provinces (Figure 13). This is partly the result of the government’s policies supporting regional development inland. Similarly, the returns to labor are on average lower in many western provinces.

**Figure 13 Average difference in returns on capital across provinces, 1998–2013**

(percentage points relative to Beijing, 1998–2013) <!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>Tibet Xinjiang Yunnan Qinghai Ningxia Hainan Shanxi Heilongjiang Jilin Liaoning Gansu Shaanxi Guizhou Tianjin Zhejiang Anhui Shanghai Sichuan Hebei Jiangxi Chongqing Guangdong Jiangsu Fujian Inner-Mongolia Guangxi Hubei Shandong Hunan Henan<br><!-- End of picture text --> Source: Authors’ estimates based on NBS’s Industrial Enterprise Survey. Note: The figure plots the coefficient on the on province dummies. Purple denotes a western province; green— an eastern one.

The difference in TFP growth performance between regions before and after 2007 is also notable. Remember the caveat that these relative comparisons may be biased if there were systematic differences between regions in the behavior of input and output prices. In 1998– 2007, productivity growth was robust, observed in all regions, averaging around 2.0 percent a year (Figure 14). In 2007–13, productivity growth fell sharply in all but one region. Productivity growth fell least in the south, the region of the economy most likely exposed to the shock of the global financial crisis.

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and other inc. (GHC)|-48|-64|-9|*|-58|-69||
|Total net household income(GHC)|4166|4952|2239|***|4032|5865|***|

_Source:_ Own computation from World Bank Kpong and Weta irrigation scheme survey. Asterisks denote significance of t-tests for equality of means between the preceding columns: *** p<0.01, ** p<0.05, * p<0.1.

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worldbank.org/X23UD9QUX0. 8 In banking systems characterized by a high degree of ownership by the government, banks‘ credit growth, portfolio quality, profitability, and productivity tend to suffer (Barth, Caprio, and Levine 2001; 2004; La Porta, Lopez-deSilanes, and Shleifer 2002). We would therefore expect the employment growth of firms to be slower in countries with state-dominated banking systems. Our data on the share of banking sector assets held by state-owned banks is taken from Micco, Panizza, and Yañez (2007). We do not have a strong prior about whether any pernicious effects of state ownership of banks would be stronger or weaker in countries in early development stages. The share of foreign bank ownership could also affect firms‘ employment growth. On the one hand, foreign banks tend to be more efficient than domestic banks in developing countries (see, e.g., Claessens, Demirguc-Kunt, and Huizinga, 2001). Their presence could therefore impose competitive pressure, resulting in lower interest rates on loans and, perhaps, a search for new borrowers, thus expanding the outreach of the banking system. All of these aspects of foreign bank participation could boost firms‘ labor growth. At the same time, there are longstanding concerns about the tendency of foreign banks to focus their lending on large, well-established firms. In banking sectors dominated by foreign banks, medium and small-sized borrowers might suffer in terms of access to credit, resulting in slower labor growth. Proponents of New Structural Economics would be especially concerned about the mismatch between foreign-owned banks and the large share of small businesses in developing countries. We test these ideas in our labor growth regressions using data on the share of banking sector assets held by majority-foreignowned banks from Claessens and Van Horen (2009).

8
first-stage estimator α�, we use the two-step bootstrap estimation to adjust standard errors for generated regressors.

To estimate Eq. (10), we consider the possibility that farmers’ expectations on the future price may differ depending on the marketing arrangement of the products in 2007. In Indonesia, there exist traditional contractual arrangements such as _Tebasan_ and _Ijon_<sup>8</sup> between farmers and traders, by which the purchasing prices of crops that traders offer differs from market prices at the harvest time. Experiencing a smaller price risk through an informal contract with traders, farmers may perceive future prices differently. For this reason, we do not include households who used either the _Tebasan_ or _Ijon_ system in our analysis.

# **4. Data and context**

# **4.1 Survey**

The data come from two rounds of household surveys conducted in rural areas of Indonesia in 2007 and 2010 for 98 villages in seven provinces: Lampung, Central Java, East Java, West Nusa Tenggara (NTB), South Sulawesi, North Sulawesi, and South Kalimantan. The locations of surveyed villages are shown in Figure 1. In 2010, we revisited all the 98 sample villages to reinterview sample households and their splits of the 2007 survey households.

> 8 _Tebasan_ is a harvesting practice in which standing crops, mainly paddy and maize, are sold on area basis just before harvesting at prices close to normal market rates. In this way, farmers and traders avoid transaction costs. While _Ijon_ is purchase of crops prior to the harvest at lower price, so that farmers can avoid harvest risks by paying risk premium to traders.

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. Column (2) models a 10% pass-through of taxes onto prices in the informal sector, following the share of formal inputs in informal firms in Mexico’s census. Column (3) allows for savings rate, which increases linearly from 0 for the bottom decile to 15% for the top decile, following evidence form consumer finance surveys. Columns (4) and (5) use the alternative assignment of store types to formality, by using either the probability of formality by store type from Mexico’s census, or by assigning specialized stores to the informal sector instead of the formal sector.

Table A7: Percent Change in Gini from Optimal Tax Policy

||**(1)**<br>**Baseline**<br>**Assignment**|**(2)**<br>**Baseline +**<br>**VAT on Input**|**(3)**<br>**Baseline + Distri-**<br>**butional Savings**|**(4)**<br>**Probabilistic**<br>**Assignment**|**(5)**<br>**Robust**<br>**Assignment**|**(6)**<br>**Cross**<br>**Elas**|**(7)**<br>**-variety**<br>**ticity**|
|---|---|---|---|---|---|---|---|
|**Tax policy**|||||| ̃_ε_<sup>_C_ </sup>= 1| ̃_ε_<sup>_C_ </sup>= 2|
|Uniform rate, only formal taxed
is generally consistent with the hypotheses just discussed; for instance, Ramachandran and Shah (1999) - using firm level data from Kenya, Tanzania, Zambia and

> 30 This is unfortunate since, as discussed in the introduction, ‘defensive and necessity entrepreneurs’ appear to make up the bulk of self-employment in DCs, with activities ranging from street vending and small retailing to traditional personal services.

> 31 One exception are the studies showing how in the US black-owned firms show lower survival rates and worse performances than white- or Asian-owned firms (see Fairlie and Robb, 2007; Jarmin and Krizan, 2010).

> 32 This mechanism can work up to a given threshold; indeed to belong to a socioeconomically excluded group may decrease the likelihood of successfully found a new firm (this is the case, for instance, of the caste system in India, see Monsen, Mahagaonkar and Dienes, 2012).

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