rafmacalaba/datause-encoder-data huggingface dataset:
binding when compared to richer economies. In low income countries, 10.2% of all jobs are telecommutable. However, only 3.8% of those jobs can be effectively performed from home. The DN2020 measure thus overestimates the number of telecommutable jobs by a factor of almost 3. In contrast, internet access constraints in high income countries only prevent 1 of every 12 telecommutable jobs (3.3 percent of 38.8 percent) from being performed from home. Upper and lower-middle income countries are intermediate cases where internet access limitations reduce the number of telecommutable jobs by around 22 and 41 percent, respectively.

Accounting for residential internet access limitations thus leads to the largest reductions in the share of jobs that can feasibly be performed from home in countries where telecommutable jobs are relatively scarce to start with. As a result, the correlation between GDP per capita and the feasibility of home-based work strengthens when internet connectivity is taken into consideration. This in turn implies that conventional measures of labor market exposure to COVID-19 may underestimate its impact on inequalities in job vulnerability across countries, a theme we will return to below.

# **4 Robustness tests and extensions**

The construction of the home-based work indices based on O*NET surveys and the use of the Gallup World Poll data to determine internet access requires certain assumptions which might bias our results. The goal of this section is to use alternative assumptions and assess the robustness of our main findings as represented in Figures 1a and 1b. Figure 2 displays the share of jobs amenable to telework using alternative assumptions. As was the case in Figure 1b, countries are grouped according to their average income levels.

The DN2020 index is, in essence, a weighted average of different types of tasks and embodies ICT requirement via inclusion of email dependence in the calculation. We first remove this condition to expand the set of jobs that can be feasibly performed from home without internet usage and recalculate the DN2020 index. This modification guarantees that our results are not an artifact of constraining the set of jobs that can theoretically be performed from home to be ICT dependent by assumption. As expected, removing the constraint that jobs must require frequent e-mail use to be performed from home indeed raises the share of jobs that can be done at home, but only marginally.

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Table 4: SNAP participation and effect of SNAP-benefit changes on prices and real sales

|Specifcation|(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|
|---|---|---|---|---|
|VARIABLES|Log pri|ce index|Log re|al sales|
|Log benefts p.p.|0.0165|0.0738***|0.0466|-0.0108|
||(0.0107)|(0.0250)|(0.0335)|(0.104)|
|x Participation rate|0.0774**|0.130***|0.254**|0.265**|
||(0.0329)|(0.0371)|(0.115)|(0.128)|
|Observations|382524|382524|382524|382524|
|R-squared|0.904|0.899|0.963|0.963|
|Prob _>_ F|0.000|0.000|0.000|0.000|
|Number of units|7970|7970|7970|7970|
|Number of clusters|48|48|48|48|
|First stage F-stat||10.439||10.439|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and period fixed effects are included. Log benefits p.p. refers to log benefits per population. Participation rate refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2006.

Table 5: Concentration measures and effect of SNAP-benefit changes on prices

|VARIABLES|(1)|(2)<br>Log pri|(3)<br>ce index|(4)|
|---|---|---|---|---|
|Log benefts p.p.|0.0726***|0.0338|0.0662***|0.0264|
|x HHI|(0.0232)<br>0.0399***|(0.0288)|(0.0246)<br>0.0360***|(0.0295)|
||(0.00730)||(0.00741)||
|x Log est. per pop.||-0.00407***<br>(0.000959)||-0.00397***<br>(0.000937)|
|x Participation rate|||0.116***|0.129***|
||||(0.0370)|(0.0374)|
|Observations|382524|381984|382524|381984|
|R-squared|0.900|0.900|0.901|0.901|
|Prob _>_ F|0.000|0.000|0.000|0.000|
|Number of units|7970|7958|7970|7958|
|Number of clusters|48|47|48|47|
|First stage F-stat|10.098|10.017|6.950|6.956|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and period fixed effects are included. Log benefits p.p. refers to log benefits per population. Participation rate refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2006. HHI refers to the county-level measure of HHI for the grocery industry fixed to the pre-period of 2006. Log est. per pop. refers to log of the number of grocery establishments per population in each county fixed to the pre-period of 2006.

41
|**Model**|**Investment**|**Factors of Production**|**Assumptions about Factors**|**Sectoral Detail**|**Household Detail**|**Coverage**<br>**and**<br>**Regional**<br>**Detail**|
|---|---|---|---|---|---|---|
|**Kohn, 2003**|-|Labor,Water|-|2 products, water and non-<br>water|-|Middle East two countries<br>simulation|
|**Briand, 2004**|-|Primarywater,Labor,Capital|-|3 Sectors: Drinking water<br>production, distribution and<br>agriculture|3 Types, Dakar, other urban,<br>rural|<br>Senegal,3 regions|
|**Finoff, 2004**||||5 Sectors including brine cyst<br>harvesters,<br>mining,<br>agriculture,<br>recreation,<br>industry|1 type|Great Salt Lake ecosystem|
|**Boccanfuso****_et. al.,_**<br>**2005**|<br>Determined byagent savings|Qualified<br>and<br>Unqualified<br>Labor,Capital,Water|Labor is mobile|18 Sectors|3,278 households modeled<br>explicitly,no HHgroups|<br>Senegal,no regions|
|**Diao ****_et. al., _2005**||Rural Labor, Urban Labor,<br>Capital,and Land|Labor<br>is<br>mobile<br>within<br>agriculture. Capital and Labor<br>are mobile within regions<br>among agricultural sectors,<br>water<br>is<br>mobile<br>within<br>irrigation districts|88 activities: 82 Agricultural,<br>66 crops, 5 livestock, 11<br>processing<br>49 Products: 23 Agricultural|2<br>types:<br>Rural<br>and<br>agricultural|<br>Morocco,20 Regions|
|**Roe****_et. al._, 2005**||Labor,Capital,Land||88 activities: 82 Agricultural,<br>66 crops, 5 livestock, 11<br>processing<br>49 Products: 23 Agricultural|5 Household types|Morocco,20 Regions|
|**Diao et. al., 2008**||||71 Agricultural; 66 in crop<br>production, captured spatially<br>as irrigated and non-irrigated,<br>33 crop production activities<br>within the water irrigation<br>authority<br>perimeters, of which, 21 are<br>irrigated crop production and<br>11 are rain-fed||Morocco,21 Regions|

43
29

**Figure 3-4. Whom were teachers addressing in class?**

**Figure 3-5, Use of Instructional Materials by Country**

<!-- Start of picture text --> 100 45<br>90 40<br>80 35<br>70 30<br>60 25<br>50 20<br>40 15<br>10<br>30<br>20 5<br>0<br>10<br>0<br>Pernambuco Morocco Ghana Tunisia<br>1 student small group everyone<br>Pernambuco Morocco Ghana Tunisia<br>Nothing Books Notebook Blackboard Manipulatives Audiovisual CalculatorsCooperative Learning<br>%<br><!-- End of picture text --> The non-instructional time was taken up with organization/management activities, some of which are necessary, but also included socialization and the teacher being out of the room. In Ghana and Pernambuco, 28% of classroom time was taken up by such activities. Possibly due to poorly organized learning activities, 19.1% students in Ghana and 21.3% in Pernambuco were on average “off task,” uninvolved with class activities. By comparison, only about 9.9% of Tunisian students and 9.2% of Moroccan students were “off task” during class (Table 3-4). This observational inference suggests that students might not be learning when distracted. As a result, Tunisia and Morocco offered not only more class time in learning tasks, they also engaged a higher percentage of students in learning than Brazil and Ghana. It can be argued that the former countries teach their students more efficiently than the latter.

_Grade-wise changes._ The frequency of various classroom activities predictably changes with age. Teachers of lower grades spent more time on organization, and younger children had higher off-task rates, given a briefer attention span. With the passage of years, reading aloud and drill tend to diminish while lecturing and discussion increases. For example, in Morocco reading activities took up about 17% of the time in grades 1 and 4, but only 2.5% of the time in grade 5 (and 15.1% in grade 6, as students perhaps prepared for examinations). In Brazil 8<sup>th</sup> graders spend proportionately more time copying because they do not have textbooks at the secondary level. Tunisian 6<sup>th</sup> graders and classes with a larger percentage of male students were slightly more likely to be off task and less likely to be engaged in interactive instruction. Gradewise time on task indicators can be compared in grade 4, which was measured for all countries (Table 3-5).<sup>108</sup> 108. A large-scale US study found that fifth graders spent most of their time (91.2%) working in wholegroup or individual-seatwork settings and only 7% of the time in small-group instruction (two to five students). In the same grade, 37% of instruction was in literacy and 25% was in math, while in first and third grades, more than 50% of instruction was in literacy and less than 10% was in math. Science and social studies activities occurred in 11 and 13% of intervals in fifth grade, respectively. In the fifth grade spent 17%of their time instructing students on managing materials or time (Pianta et al. 2007)
that all changes affect household consumption. This is a standard approach in public finance analysis because it provides the welfare results of tax policy in isolation of other macroeconomic adjustments, such as changes in investment or government expenditure. Domestic savings (savings by institutions or households) are assumed to adjust and the economic and welfare effects are driven primarily by changes in net household income and consumption as the changes in energy taxes filter through the economy. In addition, revenue is fixed in the government budget, allowing the substitution of energy related taxes with lump-sum taxes (transfers) on household income or with pre-existing distortionary taxes such as sales and excise taxes, or import tariffs. The consumer price index (CPI) is the price numéraire.

One feature that characterizes the labor market in South Africa is the high rate of unemployment among low-skilled and semi-skilled workers. The unemployment rate was as high as 29.4 percent in 2001 but has declined to 26.7 percent in 2005 (Statssa, 2006). We have taken the interpretation due to Banerjee, Galiani, Levinsohn, and Woolard (2007) that this high unemployment is structural but in equilibrium – that is, the nonparticipation of the less-skilled who are jobless is caused by structural problems in South Africa as well as various vestiges of the apartheid system in the past, and not a voluntary choice. Accordingly, we model all the labor market categories in the reference case with aggregate employment fixed exogenously, and wages varying to clear the labor market with that employment level.

We consider how sensitive our results about the marginal cost of abatement are to various economic distortions prevalent in developing countries, particularly distortions in the labor market. To do so, we modify the labor market closures as described under set 2 of the simulations below.

The experiments are comparative statics with capital mobile across all sectors, except in coal, gold, and other mining, to mimic long-run equilibrium. No dynamics are introduced so that the model cannot look at the implications of adjustment costs of investment. Since the model is fairly neoclassical, the differences between the welfare results of a fully intertemporal model and a static one are minimal when the simulations are comparable - see, for example, Devarajan and Go (1998).

# **III. Scenarios and Results**

30. Given a target reduction of CO2 emissions, the economic cost or impact of various tax instruments depends on several sets of factors: i) the relative substitutability of energy inputs with capital or other intermediate inputs; ii) the relative substitutability among energy inputs; iii) various tax and non-tax related distortions in the economy. We devise four sets of simulations to test the sensitivity of the effects to these key factors.

13
4 when attempting to undertake such a measurement task in developing countries. This approach is being used in a new research project convened by the World Bank that is applying it in more than 40 developing countries and in Europe’s transition economies that, together with the OECD countries, account for around 90 percent of global agricultural production, employment and trade (see <u>www.worldbank.org/agdistortions).</u> The focus is on those border and domestic measures that are due exclusively to governments’ actions, and as such can be altered by a political decision and have an immediate effect on consumer choices, producer resource allocation, and net farm incomes. Most commonly these are import or export taxes, subsidies and quantitative restrictions for farm and non-farm products, supplemented by direct domestic taxes or subsidies for farm outputs or inputs and food consumer subsidies or taxes.

The incentives faced by farmers are affected not only by direct protection or taxation of primary agricultural industries but also indirectly via policies assisting nonagricultural industries, since the latter can have an offsetting effect by drawing resources away from farming. While those non-agricultural measures may be of only minor importance in most OECD countries today, and therefore have been ignored by the OECD Secretariat in generating their PSEs and CSEs for those countries,<sup>3</sup> they have been too important in developing countries over the past half-century to ignore. Another difference between the OECD’s methodology (see OECD 2007c) and what is required for examining developing country policies, especially prior to the 1990s, has to do with exchange rate policies: they cannot be ignored as they have had a substantial distortionary impact on agricultural incentives in many developing countries. However, we suggest they be treated somewhat differently than in Krueger, Schiff and Valdes (1988, 1991), focusing on the differential impact across commodities of dual or multiple exchange rates rather than on overall real exchange rate misalignment. This ensures the indicators we provide for distortions to prices of individual commodities can be direct price-wedge inputs into the database of computable general equilibrium (CGE) models. Such models can then be used to estimate the effects of those distortions on such things as resource

> 3 Australia, like New Zealand, is an exception, and both have been more like developing countries in that until recently their high manufacturing tariffs have heavily discouraged agricultural production (Anderson, Lloyd and MacLaren 2007).
shocks to equity inflows by measuring shifts in foreign investor interest unrelated to changes in domestic firms’ prospects. In particular, we instrument equity inflows using various measures of the attractiveness of _other_ countries’ equity markets to foreign investors, which we argue are plausibly exogenous to demand-side shocks to the subject country. The idea is that for a given amount of total capital inflows to emerging markets as a whole, positive shocks to other countries’ attractiveness to foreign investors constitute negative shocks to the subject country’s supply of funds.<sup>3</sup> We provide regression results using three alternative instruments, all of which are strongly correlated with equity capital inflows. As our first instrument, we use the lagged weight of a country in the MSCI Emerging Markets stock index. The MSCI weight of a country is a function of both the relative market capitalization of that country’s stock market (in relation to the global market capitalization of 25 emerging economies) and sporadic decisions by MSCI on the index constitution. When institutional investors receive funds from their underlying investors, they tend to invest those funds into emerging economies’ equity markets according to the weights of those economies in the MSCI index (Raddatz et al., 2017). The time variation of each country’s MSCI weight should primarily reflect shocks to the market values of the other 24 countries’ stock markets.<sup>4</sup>

> 3 As we discuss in Section 3, other facts about the association between equity inflows and issuances are suggestive of supply-driven causality. The fact that the response of issuances to equity inflows is greater among large firms suggests a supply-side channel. If inflows were simply responding to improved economic conditions in the country, one might expect all firms, not just large ones, to issue more equity at times of large capital inflows (our country-year fixed effects control for all shocks that affect all firms equally in a country). The finding that inflows tend to prompt issuances particularly by large firms suggests that those firms may be especially attractive to foreign equity investors. However, one cannot rule out that demand-side shocks are heterogeneous across firms. The fact that lagged inflows predict equity issuances is also suggestive of supply-driven causality, but here too, it is conceivable that serially correlated demand shocks could account for the correlation between lagged equity inflows and equity issuances.

> 4 Our results remain unchanged when we exclude the largest countries in the MSCI index from our analysis.

4
price fluctuations and facing more variable demand for their products. Micro firms, on the contrary, rather produce subsistence goods or deliver essential services and agro borrowers mostly grow fruit and vegetables or raise sheep for the local market so that both are considerably less affected by economic fluctuations. These varying levels of affectedness seem to influence the bank’s risk assessment of firms and to finally translate into accordingly varying levels of credit availability.

# **4.4 Geographical variation in the crisis shock**

To confirm that the two shocks are distinctly different, we use the location of the branch that granted the loan, which is also a proxy for the location of the firm, to study exogenous variation in the severity of the crisis shock in the following analysis. Given that Azerbaijan’s economy is weakly diversified and highly dependent on the oil and gas sector, we distinguish between _Petroleum locations_ , i.e. locations in which the oil and gas industry is most important according to the State Statistical Office of the Republic of Azerbaijan<sup>21</sup> and the location of the major oil and gas fields, and the rest of the country. These petroleum locations are the capital city of Baku and the close-by next biggest city Sumqayit. We then analyze the interaction effects _Funding shock_ * _Petroleum location_ and _Real economy shock_ * _Petroleum location_ . We expect the latter interaction term to be significantly negative as the petroleum locations should be affected more severely by the economic downturn. In contrast, the funding shock led to short funding which should have restricted lending in all branches over the country. Therefore, we expect the interaction term _Funding shock_ * _Petroleum location_ to be statistically insignificant.

Table 6 displays results for LPM regressions on the bank’s decision whether to approve a loan application or not for the full sample and the three subsamples based on our loan type categories. Columns (1-4) present the results for the effect of the funding shock, while columns (5-8) contain the respective results for the real economy shock.

# [Insert Table 6 here]

As expected, the interaction term _Funding shock_ * _Petroleum location_ is not statistically significant in the full sample as well as for agro and micro loans. Together with the significantly positive main effect _Funding shock_ this indicates that for loans made in nonpetroleum locations approval rates for those borrowers who actually apply for a loan are

> 21 See www.azstat.org/statinfo/industry/en/index.shtml#.

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