rafmacalaba/data-use-mentions-extended huggingface dataset:
over future nominal devaluations partly stems from the lack of central bank transparency and credibility surrounding the course of exchange rate policy.

The country could cushion the negative effects of policy uncertainty by pursuing a systematic approach to exchange rate policy (Stone _et al_ ., 2009). An institutional reorientation towards greater central bank credibility underpinned by improved policy coherence and consistency, transparency in exchange rate management, and more independent operations is crucial in tackling uncertainty for investors and ensuring macroeconomic stability (IMF, 2008; Stone _et al_ ., 2009). Accordingly, the course of future actions has to be consistent with any exchange rate changes that Ethiopia may undertake and there needs to be a more proactive communication about policy intentions and decisions. Provided that these policy and institutional frameworks are put in place, it is less likely that devaluation will give rise to significant uncertainty about the policy direction for the currency.

# **<mark>7.    Conclusions and Policy Implications</mark>**

<mark>A competitive real exchange rate would help provide an environment conducive to manufacturing-led structural transformation, sustained growth acceleration, and improved external balance. Ethiopia’s real exchange rate has witnessed rapid appreciation over the past year and the overvaluation of the birr currently stands at about 32 percent. The overvalued currency has manifested itself in, among others, the acute foreign exchange crunch, currency rationing, persistent trade and current account deficits, and slow pace of structural change. Empirical analysis using Ethiopian data provides fairly strong evidence to support the proposition that a real devaluation would be able to stimulate exports, improve the external balance, and bolster economic growth as well as promote industrialization-driven structural transformation.</mark> <mark>In other words, a birr devaluation will play an important role by fostering expenditure-switching effects and long-term development (i.e. the growth of efficient export-oriented and import-substituting industries). Nominal exchange rate devaluation will not necessarily cause inflation. However, devaluation would involve key macroeconomic trade-offs including the higher cost of imported capital equipment and
method to deal with the potential endogeneity of our financial structure variables.

We also apply the regression methodology pioneered by Rajan and Zingales (1998) (henceforth RZ) to test whether firms in industries that rely heavily on external financing have higher labor growth rates in countries with relatively well developed financial sectors. We find evidence consistent with that proposition, but only for firms in low-income countries with

> 4  For example, job growth is the topic of the upcoming World Development Report for 2013, and a key concern of the current U.S. administration.

> 5 Not only the firm-level survey information, but also a steady expansion in the number of countries that have financial structure data available enables us to undertake this analysis. For comparison, the original financial structure indicators presented in Levine (2002) were available for only 48 countries.

5
Figure 10. Alternative Estimates for Age-Stratified Fatality Ratios <!-- Start of picture text --> China Italy<br>30<br>20<br>10<br>0<br>Spain South Korea<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80+ 0 10 20 30 40 50 60 70 80+<br>Age cohorts<br>Fatality ratio (percent)<br><!-- End of picture text --> Source: Istituto Superiore di Sanità (2020; March 30), Korean Ministry of Health and Welfare (2020; May 6) and Spanish Ministry of Health (2020; May 4).

As noted, the results are sensitive to the age gradient present in the base epidemiological parameters. While according to Verity and others (2020) the assumption of age-neutral infection prevalence is robust, the J-shaped gradient implicit in the age-stratified infection fatality data by the same authors will matter. For these reasons it useful to conduct a robustness check to ensure that the variation seen across other estimates does not fundamentally change the results.

To verify robustness, we recalculated the results based on age-stratified case fatality ratios from Italy, Korea and Spain (Figure 10). These case fatality ratios do not correct for ascertainment and censoring biases and hence show a steeper age gradient since ascertainment bias is more significant for older cohorts (Verity and others, 2020). Relative to the baseline of Figure 9, the case fatality rates of Italy would boost the HIC share by 0.1 ppts; the Korean data would raise it by 3.1 ppts; and the Spanish data would raise it by 0.2 ppts. The fact that these deviations are small suggests that the first-order effect of demographic structure is robust and outweighs reasonable variations one might see in underlying epidemiological assumptions providing that we apply the same assumptions across all countries.

# **Comorbidities and Environmental Factors May
The pattern of heterogeneous effects with respect to plant size and fuel type further bolsters the case for the shift in generation to combustion power plants as the primary mechanism. Specifically, we find that the effects on excess PM2 _._ 5 are largest for smaller power plants and those that use oil or biomass and more muted among larger power plants and those that use coal. These results are consistent with the idea that smaller power plants are more able to respond during a drought as they tend to be air-cooled, while larger power plants tend to be water-cooled. Another possible reason the effect is more muted among larger power plants (mainly coal) is their limited flexibility (i.e., they provide baseload generation).

The third set of results quantifies the number of lives lost building on our estimates of drought-induced excess PM2 _._ 5, demographic data on the population exposed, and well-established concentration-response functions from the literature that documents the adverse health effects of PM2 _._ 5 (e.g., Deryugina et al. (2019); Liu et al. (2019)). Our preferred counterfactual calculation shows that the region experiences as many as 10,000 premature deaths per year. The monetized losses from the lives lost using conservative estimates of the value of a statistical life indicate an associated cost in the order of $12 billion (constant 2019 USD) per year.

3
knowledge declined. However, the analysis does not find that student test scores declined in a statistically significant way in response to the fee removal. Also using SACMEQ, Atuhurra (2016) finds that FPE in Kenya was associated with large achievement declines (in public schools) and argues that these are linked to lower teacher effort and disengagement of communities. Bold and others (2011) and Bold, Kimenyi, and Sandefur (2013) find that the decline in average quality in public Kenyan schools is mostly the result of selection—with higher socio-economic-status, and potentially higher-achieving, students switching to private schools after FPE—and not a decline in value-added.<sup>5</sup> It is unclear whether this pattern of switching to private schools occurred similarly in other countries where the number of private schools is often much lower than in Kenya.

While not linking changes to FPE _per se_ , Taylor and Spaull (2015) analyze the changes in “access to learning” between 2000 and 2007 in 10 African countries using SACMEQ data on test scores and household surveys (e.g. Demographic and Health Surveys—DHS) on enrollment. The study defines “access to literacy” as the product of the grade 6 completion rate and the proportion of grade 6 students who reach a basic level of literacy, with a corresponding measure of “access to numeracy.” The analysis finds that access to learning increased over this period in all the countries studied—despite the fact that the proportion of students who reached the basic literacy/numeracy threshold actually fell in 3 of the countries.

Le Nestour, Moscovitz, and Sandefur (2022) use data from the DHS and Multiple Indicator Cluster Surveys (MICS) to estimate literacy rates for birth cohorts ranging from the 1950s to the 1990s. The analysis shows that while average literacy has increased in all regions, including in Sub-Saharan Africa, “education quality” (defined as the expected literacy acquired after 5 years of primary schooling in a country at a particular time) has not
unique visitors, average session duration, and further disaggregates these metrics by countries using users’ IP addresses. For each website, Semrush also infers user profiles based on patterns of online behaviors.

We accessed the monthly traffic data for various tools from January 2022 to March 2024 using the Semrush API. The data includes total visits and unique users per domain from both desktop and mobile devices across geographies. It also encompasses traffic originating from mobile applications. However, it is important to note that this measure of consumer usage does not reflect professional usage through APIs or indirect access via third-party apps. Nonetheless, since most generative AI tools are designed for productivity and work-related tasks for individual usage, our website usage 8
from the existing literature based on advanced (Graetz and Michaels (2018); Acemoglu et al. (2020); Acemoglu and Restrepo (2019); Koch et al. (2019); Dauth et al. (2017)) and emerging economies (Giuntella and Wang (2019); Artuc et al. (2019)), which document a negative impact of robots on employment. Unlike this literature, our results suggest that for labor the productivity-enhancing effect of robot adoption prevails over the replacement effect. As a further departure from the evidence in advanced economies, we also find that automation generates positive employment spillovers from down-stream robots-adopting plants, which help extend the benefits of automation to non-adopting plants as well. We discuss potential underlying reasons for the difference in findings between Indonesia and other more advanced economies in the concluding section.

By increasing revenues and the demand for domestic inputs, robots generate positive employment spillovers from downstream automating industries. Such spillovers are not limited to manufacturing, but extend to services and construction, thus boosting the demand for labor across sectors. This finding is in line with the increasingly important role of services inputs in manufacturing production, including in Indonesia (HallwardDriemeier and Nayyar (2017); Duggan et al. (2013)).

Our second contribution is to examine in depth the plant level impacts of robot adoption. We provide evidence that plants more exposed to robots lowered real marginal costs, but they also increased markups, exports, and import. This suggests that robot penetration in Indonesia allowed firms to produce higher quality products and upgrade in global value chains (GVCs).<sup>2</sup> We focus the analysis on the impact of robots at two levels of aggregation: plant and local labor market. The local labor market analysis exploits labor force survey data to calculate the shares of workers at high risk of automation in each industry of a local market in the base year. The shares are then used to aggregate robot import by industry and year within the local labor market
of compliance with policy reform objectives.<sup>59</sup> Meanwhile, adjustment lending rapidly increased from 7 percent of the volume of lending in 1980-82 to 26 percent in 1987-90.<sup>60</sup> In later decades, this modality of lending would surpass 50 percent of the volume of lending, depending upon recipient country demands.

In the 1990s and 2000s, country ownership and country specificity came to the forefront, as well as a renewed focus on governance and enhancing country level understanding of development. Sweeping policy formulas were largely swept aside. On the intellectual front, both within the Bank and beyond, institutional economics and historical analysis became more prominent with the awarding of the Nobel Prize to Douglas North and Robert Fogel in 1993. In the 2000s, there were efforts to improve country level analysis of both firm and household data to better understand the dynamics of productivity growth, including the ―investment climate‖<sup>61</sup> , as well as the determinants of poverty. The concept of ―equity‖ or equality of opportunity extended traditional analysis of income inequality, via the 2006 World Development Report. Another trend in research is the use of quasi-experimental techniques to understand micro interventions for development. The Poverty Lab at MIT has been a leading proponent; however, researchers at the Bank and many universities have also contributed actively to this research area. In addition, country teams are currently using impact evaluation techniques to improve the quality of ―results‖ measurement for World Bank supported projects. Currently, the Bank‘s Chief Economist is proposing a ―new structural‖ approach to rethinking development.<sup>62</sup> A number of country teams in the World Bank are beginning to apply new structural approach to analyze policies for improving economic growth prospects.

Country specificity and country ownership entered into the operational units of the Bank at the country level. For example, ―Country Assistance Strategies‖ became ―Country Partnership Strategies.‖ ―Best practice‖ for policy based
the US$1.90 and US$3.20 poverty line, by GHS wave

||Poverty rate US$1.90|95% Confide|nce Interval|
|---|---|---|---|
|2010/11 GHS|43.54%|40.97%|46.11%|
|2012/13 GHS|42.49%|39.77%|45.20%|
|2015/16 GHS|40.75%|37.30%|44.20%|
|2018/19 GHS|41.88%|38.31%|45.44%|
||Poverty rate US$3.20|95% Confide|nce Interval|
|2010/11 GHS|72.88%|70.45%|75.32%|
|2012/13 GHS|72.12%|69.58%|74.67%|
|2015/16 GHS|70.44%|67.00%|73.89%|
|2018/19 GHS|72.28%|69.22%|75.34%|

Note: the table shows imputed poverty estimates for each wave of the GHS. We develop a consumption model using data on 23 non-monetary indicators and household consumption available in the 2018/19 NLSS. Using the estimated parameters, we then impute in each round of the GHS using the same non-monetary indicators and 100 imputations. The imputed consumption vector is then converted to 2011PPP and poverty estimates are reported at the US$1.90 and US$3.20 poverty lines. Poverty estimates are reported with the respective 95 percent CI.

34
>0 17.2<br>-20.1<br>0.6 -1.5<br>-12.6<br>-50 -40 -23.1<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of per capita disposable income Quintiles of per capita disposable income<br>Direct Transfers Direct Taxes<br>Indirect Taxes Health Net Cash Fiscal Position<br>Education<br>% of total expenditure/tax % of total expenditure/tax<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

20
100.00|100.00|100.00|100.00|

Source: Own elaboration based on INDEC Biodiesel is a small sector according to 2006 data, but is growing steadily. Though there were many investment projects ongoing, there was no production of bioethanol yet for that year<sup>5</sup> . Hence, to perform simulations considering future production of this product, we have included a “latent” industry of bioethanol (see Table 1) in which the proportions of value added, labor and capital costs, and intermediate costs are coincident with those of the current available bioethanol technology. This “virtual” or latent sector is ready to grow if the simulations give the proper incentives.

Table 2 shows the intermediate transactions as percentage of total intermediate costs for the sector included for the specific purposes of this analysis. The biodiesel and bioethanol cost structures are based on own estimates.

Regarding the demand side, domestic consumer groups are divided by income decile, the government, one foreign consumer and one foreign producer. The small open economy assumption is adopted, implying that Argentina is a price taker in the international markets.

> 5 As of 2006; there were some bioethanol plants already in operation and several announced investments.

7
and generating statistics on green jobs. Albania. Intenrational Labour Organization (ILO)

# **Task approach**

Dierdorff, E.; Norton, J.; Drewes, D. W.; Kroustalis, C.; Rivkin, D.; and Lewis, P. (2009) Greening of the World of Work: Implications for O*NET® -SOC and New and Emerging Occupations. The National Center for O*NET Development.

Dierdorff, E.; Norton, J.; Gregory, C.; Rivkin, D.; and Lewis, P. (2011) Greening of the World of Work: Revisiting Occupational Consequences. The National Center for O*NET Development.

Excel shared by Janser with green jobs terms Janser, Markus (2018) The greening of jobs in Germany: First evidence from a text mining based index and employment register data, IAB-Discussion Paper, No. 14/2018, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg Janser, Markus. (2018b) The greening of jobs in Germany: Online Appendix ‘text Mining & Descriptives’ Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg 41
From an economy-wide perspective, Gort and Klepper (1982) postulate that rapid product innovation yields a surge in entry, a period of significant experimentation followed by a shakeout period when unsuccessful enterprises wither and exit while successful developers and firms grow. Pioneering a new methodology, Foster et al. (2018) provide some empirical support to this hypothesis for the U.S.<sup>3</sup> 2 In a 1987 interview by the New York Times, Solow is quoted saying: “[...] what everyone feels to have been a technological revolution, a drastic change in our productive lives, has been accompanied everywhere, including Japan, by a slowing-down of productivity growth, not by a step up. You can see the computer age everywhere but in the productivity statistics.” (New York Times, July 12, 1987, p. 36)

> 3 The authors use low-frequency firm-level data for the entire U.S. private sector and infer whether innovation took place by looking at firms’ entry dynamics. Building on Gort and Klepper (1982), spikes in entry could be interpreted as proxy of innovation in the previous period. Foster et al. (2018) document that a surge in productivity in high-tech sectors in the late 1990s is a strong contributor of the increased within-industry covariance between market share and productivity (i.e., firms whose productivity has grown in previous period gain market share). This surge in productivity is associated with higher entry and within-industry productivity dispersion, and high productivity growth in high-tech sectors, all elements that could be reconciled with an increase in innovation. In line with the Gort and Klepper (1982) hypothesis, they also find that the post-2000s period has seen a decline in entry and a high-tech productivity slowdown driven by lower within-firm productivity growth and smaller covariance between market share and productivity. In contrast with Gort and Klepper (1982), they also find increased within-industry productivity dispersion, which is even greater for young (instead of mature) firms
correlations between monthly international price and domestic prices at each of the different coffee producing centers are above 0.9.

Figure 1: Monthly international coffee price index <!-- Start of picture text --> 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Price index (adjusted for nominal exchange rate)<br>International price index<br>5000 300<br>4000 250<br>3000 200<br>International price index<br>2000 150<br>Price index (adjusted for nominal exchange rate)<br>1000 100<br><!-- End of picture text --> Note: This figure plots coffee price index for the variety of coffee known as _Brazilian Naturals_ , which is Arabica species that is dried inside the fruit rather than after the fruit has been removed. It is one of the most widely traded coffee type, with Ethiopia as one of the major suppliers. In addition to the international price index, I also plot the index adjusted for nominal exchange rate of Birr/USD to facilitate comparison with domestic coffee prices below. The gap between the international price index and the index adjusted for nominal exchange rate is expanding after 2012 due to depreciation of Birr against USD. The vertical red lines indicate the survey windows. Data Source: International Coffee Organization.

# **2.2 Descriptive statistics**

In this subsection, I provide descriptive statistics of key variables from my dataset. Table 1 provides descriptive statistics of most of the variables used in analysis. I provide descriptive statistics for the whole sample and for two different groups of households: households that allocate at least 10% of their farmland to coffee and those who allocate less than 10% of their farmland to coffee. While the 10% threshold is arbitrary, it can be argued that farmers who allocate at least 10% of their farmland to coffee are likely to be significantly affected by coffee price fluctuation, and can be considered coffee producers. These households account for 11% of
down to prevent overestimation bias.<sup>16</sup> Regarding indirect taxes, it is assumed that they are paid entirely by the consumers, and their estimation is based on the EPF which is used to calculate, by consumption decile, the shares of consumption spent on indirect taxes. Since these shares must be imputed to each individual’s disposable income in the corresponding consumption decile, it is necessary to rank individuals in the CASEN by consumption decile which requires both the CASEN and EPF surveys to interlock.

The estimation of indirect taxes in the EPF and the survey-to-survey imputation follows the hot-deck procedure used by Larrañaga et al. (2012) in their tax-benefit microsimulation model for Chile. In order to avoid a potential overestimation of the actual VAT rate paid and to be consistent with the CEQ methodology, a distinct feature in the treatment of the VAT between that microsimulation model and the incidence analysis presented in this paper is that the latter does not use the statutory rate (19 percent), but the effective rate (14.3 percent) which is based on the estimate of evasion (24.5 percent) in 2013.<sup>17</sup> For the estimation of the VAT, the analysis considers fiscal exemptions, being the most important those on health, education, insurance and financial operations, gambling, and cultural services. It also considers special sale taxes such as those on alcoholic and nonalcoholic beverages, and excise taxes such as those on tobacco and fuel.

The last income concept, _final income_ , is constructed by adding the monetized value of in-kind transfers on education and health and by subtracting the corresponding copayments and fees for the use of such services to/from consumable income. For both education and health, the allocation method is imputation. In the first case, the CASEN allows to identify who attends an educational institution, the educational level attended, and the financing scheme of the institution —i.e., public, subsidized or private—, so that it is