rafmacalaba/data-use-mentions-extended huggingface dataset:
Evidence is also indicative of the differential impacts of robot usage on jobs in advanced and developing economies. De Vries et al. (2020), using data for 37 economies, conclude that a rise in robot adoption relates significantly to a fall in the employment share of routine manual taskintensive jobs in advanced economies but not in developing economies. This reflects the fact that higher wages for low-skilled workers provide larger incentives for automation in advanced economies. It is also possible for automation in advanced economies to displace jobs in developing economies through cross-border effects. Kugler et al. (2020), for example, find that robot use in the United States has lowered employment and earnings in Colombia among workers in sectors characterized by a high level of automation in the U.S. labor market. Similarly, Faber (2020) finds negative spillovers from robot adoption in the United States on employment in Mexico.

The literature on the effects of AI on jobs is nascent. Using data for the United States for 2010-18, Acemoglu et al. (2022) find no detectable effect on employment and wages in occupations and industries exposed to AI. Felten, Raj, and Seamans (2019)—also using U.S. data—find that, on average, employment increases and wages decline in industries where AI is used. In general, there has been an acceleration in the displacement effects of automation and a deceleration in reinstatement effects during the past 40 years in the United States compared to the 40 years before (Autor et al. 2024).

Importantly, none of these studies captures data from very recent AI developments—in particular, the rapid uptake of generative AI. It is possible that the economic effects of AI will take time to fully discern in the data, just as the effects of the first wave of digitalization took time to measure. There is consensus that the advent of AI is poised to automate a wider range of cognitive tasks relative to ICT which made it feasible for machines to replace labor in routine tasks (
% of Bolivian rice output (output of rice per capita is 115 kilograms), while only 5.2 and 6% of national output is produced in Cochabamba (11 kilograms per capita) and La Paz (7.7 kilograms per capita) respectively. Population differences between Cochabamba and La Paz (see table (14)), together with the fact that Cochabamba is about 400km closer to Santa Cruz than La Paz (see table (15)) explain that rice is cheaper in 16 Because we do not have data on consumption, we can only approximate whether a region is a surplus or deficit area by calculating the ratio of output to population, and taking the average as a benchmark.

17 Data on output are available for all Bolivian departments. Data on prices are only available for La Paz/El Alto, Cochabamba and Santa Cruz, therefore, all other regions are in grey in the RHS maps, indicating no data are available.

14
access and quality remain unequally distributed. These challenges are aggravated in the context of diminishing financing after 2014. Healthcare outcomes, such as maternal and child health, remain relatively low in comparison to other countries in the region. For instance, malnutrition rates remain higher than in Peru.<sup>9</sup>

# **4 Data**

We merge data from two sources: Ecuador’s Quarterly Labor Force Survey (known as ENEMDU), which provides the data for the labor market outcomes at the regional level, and novel data on the geographical distribution of the Venezuelan population in Ecuador based on mobile phone records by _Telefonica Ecuador_ .

## **4.1 Household Survey Data: ENEMDU**

ENEMDU is the official labor market survey for Ecuador.<sup>10</sup> It is a nationally representative household survey that collects information on individual characteristics, employment status and sources of income for individuals living in Ecuador.

Our analysis uses data for the fourth quarter of the ENEMDU for years 2008-2019, which we aggregate to the _canton_ level.<sup>11</sup> But our regression analysis focuses on the period 2013-2018, which provides a tighter window around the onset of the arrival of Venezuelans into Ecuador.

Our analysis is based on the working-age population, defined as all individuals age 15 or above.<sup>12</sup> We analyze a wide range of canton-level labor market indicators: the participation rate, the employment-to-population ratio, and two indicators of the quality of employment (the rate of adequate employment and the rate of Informality). In addition, we also examine effects on monthly earnings and on hourly wages.<sup>13</sup>

> 9Likewise, social assistance in Ecuador remains below the 2012 level. To this date more than 50% of the population considered to be in extreme poverty lacks coverage by the system and social integration of the poor population is still very limited.

> 10 _ENEMDU_ stands for _Encuesta Nacional de Empleo,
equal groups. We then rolled out a subsidy scheme sequentially, each group receiving a 50 percent discount on the price of piped water for six months, followed by a 75 percent discount for another six months after that. At any point in time, therefore, at least one group did not receive a subsidy and could thus serve as a control. To make the changes in price throughout the experiment immediately salient, we undertook a personalized communication campaign, both at the home and at the time of monthly bill payment, which was always done in-person at a central location. Meanwhile, we collected data from the utility on monthly piped water usage and, at a quarterly frequency, from households on, among other things, the number and duration of stay of residents (including visitors) and on water-sharing practices.

In addition to a modest response to the current price, the experimental data reveal persistence: households that faced a lower water price during months in the recent past (and thus had higher usage in those months) also consumed more in the current month, _conditional_ on the current month’s price. We take this as evidence that households were building consumption capital, either in the form of habits or learning through experience.<sup>1</sup> Alternative explanations can be plausibly ruled out: lags in adjustment to new prices were obviated by our proactive information campaign; loss aversion or reference dependence, which could lead to apparent demand persistence, is inconsistent with the symmetric demand responses to upward and downward price changes observed in the data and cannot account for demand growth prior to the experiment; finally, household investments in piped water infrastructure (such as plumbing and washing machines) cannot be the source of the experimental findings given the size and duration of the price subsidies.

To quantify the welfare gains from utility pricing strategies that leverage such persistence, we next use our experimental data to structurally estimate the dynamics of piped water demand. Under our maintained assumption that, in deciding on their current piped water usage
text for definitions of the variables. *, **, and *** indicate significance at the 10%, 5% and 1% levels respectively.

These results should be interpreted with some caution—certainly they are still relatively imprecise and almost certainly lack broader external validity—but they have several important implications. First, the conclusion that the long-run rural wage-rice price elasticity is approximately equal to unity is similar to the conclusions of Jacoby (2013) for India, Lasco et al. (2008) for Bangladesh, and Ivanic and Martin (2014) for a broader range of countries. Second, the short-run food price elasticity is quite low (0.14 to 0.19), though our use of higher frequency data reveals that the response is relatively quick: wages start to change in the next quarter. Third, consistent with Headey et al.’s (2011) results for Ethiopia, urban wages might adjust to higher food prices quite strongly in the long run but they show no substantial adjustment in the short run. This further suggests that rural and urban labor markets may be quite weakly integrated, perhaps because of low levels of short-term mobility between rural and urban areas.<sup>17</sup> In summary, wage adjustments seem to provide some compensation for rural laborers—and relatively quickly at that—but it would appear that agricultural supply responses are likely to be the main explanation of the poverty-reducing effects of higher food prices.

> 17. This is in contrast to the assumption in Ivanic and Martin’s (2014) model, in which rural and urban labor markets are perfectly integrated.

18
value both proximity and quality but that has not prevented them from traveling abroad for a range of treatments, such as cosmetic surgeries, rehabilitative care, alternative medicine, and in some cases, even eye and cardiac surgery. Estimates suggest that in 2003, over 350,000 patients traveled to Cuba, India, Jordan, Malaysia, Singapore and Thailand, specifically to consume health care services. A significant number were patients from industrial countries, traveling to a growing number of overseas medical centers to obtain “first-world treatments at thirdworld prices”.<sup>4</sup> 1 The rapid advances in medical technology, increased market power in the hospital sector, higher prescription drug costs, and an increasingly aging population are often cited as the main cost-drivers fueling the rise in US health spending (see e.g., Alliance for Health Reform, 2004). To a lesser extent, generous malpractice awards and higher administrative costs for insurers also contribute to rising health care costs.

2 A typical employer pays for 73 percent of premiums with the employee paying the rest.

3 The prices outside the US include the cost of travel from the US. The price differentials are even greater for more complicated surgeries such as Coronary Artery Bypass Grafts (CABG), which costs over $35,000 in the US, but under $ 9,000 (inclusive of travel costs) in the best cardiac surgery centers in India and Thailand. Source: CMS hospital reimbursement data and Apollo Hospital, Delhi.

4 In 2003, an estimated 50,000 “medical tourists” traveled overseas from the UK for a variety of check-ups, treatments and surgeries, to Thailand, South Africa, India and Cuba (The Guardian, May 11, 2004).

2
to these reporting differences.

We classify households according to whether or not they had at least one member aged 15 and over who had migrated to the United States prior to 1 January 1994. We then are able to look at the impact of previous migrant experience on subsequent child health measures. This timing allows us to avoid concerns that child health outcomes and the migration decision are both the result of contemporaneous shocks, such as poor weather or disease outbreaks. The ENADID survey asks whether each member of the household has ever been to the United States in search of work, and whether they have ever lived in the U.S. These questions are asked of all household

- 5 -
and women 15 to 49 years in these households were administered the anthropometry, hemoglobin, and blood pressure measurements. I limit the analytical sample to those that are born before or were in utero during the start of the conict in February 1996 so that they are old enough to be sampled in the ever-married NDHS 2016 women sample and have children that can be incorporated in the second-generation analysis. The nal analytical sample size is 4,421 women ages 20 to 49 at the time of the survey (0 to 29 at the start of the conict) and their 2,168 under age 5 children.

Table 2 summarizes women's health outcomes and their exposure to conict and demographic characteristics. I divide women into two cohorts: ages 0 to 15 (treatment) and 16 to 29 (control) at the time of the start of the conict. While the women in the former cohort would have still been in 11
sanitation for non-poor households that didn’t have access to any sanitation at baseline. This group also showed improvements in the disposal of children’s feces.<sup>21</sup>

# _9.2 Program Effects on Child Health Outcomes_

Table 17 reports the ITT effects for child health outcomes. Consistent with the data presented in Table 8b, we find a statistically significant reduction in diarrhea prevalence (seven- and two-day prevalence). Specifically, being in a treatment community is associated with diarrhea prevalence being lower by approximately 1.3 percentage points from a 4.6 percent base. This is the case across all specifications on the panel observations (columns 3 – 6) and for seven- and two-day prevalence. This is a large effect—about a 30 percent (44 percent) reduction in seven-day (twoday) diarrhea prevalence. The fact that the estimated program effect changes only slightly in magnitude as we add controls is unsurprising given that the TSSM survey provides a balanced sample.<sup>22</sup> Columns 7 and 8 show that this is being driven by decreases in diarrhea among households that did not have sanitation at baseline.

We also find some evidence that parasitic infection intensity for both _Ascaris_ and _Trichuris_ , as proxied by number of egg worms per milligram, is lower in treatment communities. However, this result is not statistically significant over the whole sample.<sup>23</sup> Having blood or mucus in the stool is also significantly lower in treatment communities by about 1 percentage point (57 percent) as well as refusal to eat for children in treatment communities.

> 21 Additional one-tailed tests of significance (not reported in Table 16) show a significant decrease at the 10 percent level in the practice of open defecation for the treated non-poor households with no sanitation at baseline.

> 22 The estimate in column 2 uses the entire endline sample (not restricted to panel households) and although the point estimate is negative, it is weakly significant for seven-day prevalence (
workers, to foster firm fidelity (Zwick 2009).

> 95 https://iuslaboris.com/insights/comparing-social-security-contributions-rates-across-latin-america/

> 96 OECD 2019b.

> 97 Auer and Fortenu 2000.

98 Estimates based on Employing Workers Database, https://www.worldbank.org/en/research/employingworkers 99 Bussolo et al. 2015.

100 Jin and Baumgartner 2019.

101 Generation 2021.

19
Policy Research Working Paper 9882

# **Abstract**

Electrifying the transportation sector is key to reaching the goal of carbon neutrality. This paper provides a comprehensive analysis of the diffusion of passenger electric vehicles based on detailed data on model-level electrical vehicle sales across the world from 2013 to 2020. The analysis shows that the highly uneven electrical vehicle penetration across countries is partly driven by cross-country variation in incentives and especially in the availability of charging infrastructure. Investment in charging infrastructure would have been much more cost-effective than consumer purchase subsidies in promoting electrical vehicle adoption. This finding highlights the importance of expanding charging infrastructure in the next phase of deeper electrical vehicle diffusion.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Dai, H., T. Masui, Y. Matsuoka and S. Fujimori (2011). Assessment of China's climate commitment and non-fossil energy plan towards 2020 using hybrid AIM/CGE model. Energy Policy, Vol. 39, No. 5. pp. 2875-2887.

- Dai, H. and P. Mischke (2014). Future energy consumption and emissions in East-, Centraland, West-China: Insights from soft-linking two global models. Energy Procedia 61: 2584 – 2587.

- Dai, H., P. Mischke, X. Xie, Y. Xie and T. Masui (2016). Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions, Applied Energy, Volume 162, Pages 1355-1373.

- De la Chesnaye, F. and J.P. Weyant (2006). Multi-Greenhouse Gas Mitigation and Climate Policy. The Energy Journal, Special Issue.

- Drouet, L., Haurie, A., Labriet, M., Thalmann, P., Vielle, M., Vigier, L. (2005).  “A coupled bottom-up/top-down model for GHG abatement scenarios in the Swiss housing sector.”  In: Loulou, R., Waaub, J. P., Saccour, G. (Eds.), Energy and Environment, Cambridge, pp. 27-62.

- Edenhofer, O., C. Carraro, J. Kohler and M. Grubb (Ed.) (2006). Endogenous Technological Change and the Economics of Atmospheric Stabilization. The Energy Journal, Special Issue.

- National Bureau of Statistics (NBS). China input-output tables 2012.〈http://data.stats.gov.cn/informal.htm?u=/files/HTML/quick search/trcc/trcc01.html & h=740〉. (in Chinese).

- OECD. 2012. Looking to 2060: Long-term global growth prospects. OECD economic policy papers.
As can be seen in Annex Table 1, for each sector we have attempted to select appropriate tariff levels rather than averages over many, disparate tariff lines. That is, for a specific industry we have tended to choose a specific tariff, or an average over a few appropriate tariffs lines associated with more-processed products. For the tradable inputs used by the sector, we have tended to choose tariff lines associated with raw products or lower processed products. Note that Annex Table 1 also includes, for each industry, the share of total tradable input costs associated with specific inputs. The list of inputs is specific to each industry.

# **5. Estimating Non-tariff Barriers for Selected Activities**

By their nature, non-tariff barriers are not established by policy makers in terms of ad valorem equivalents. They involve, therefore, some detective work to estimate them. The case of purely quantitative restrictions (QRs) is a special case, because with some information of relevant supply and demand elasticities, the NTB can be extracted in terms of a price wedge. Unfortunately many NTBs, however, are not so explicit as QRs. One source of NTBs, for example, might simply be administrative delays that raise the local component of the costs of imported goods. Other NTBs can be import licenses to be negotiated with trade authorities, testing protocols for food and drugs,and sanitary and phyto-sanitary restrictions, which – while in themselves might not be formal trade restrictions – can be used as instruments for protection against import competition. Annex 3 presents some NTBs that are noteworthy to market participants in Egypt.

For this present report, Dr. Khaled Hanafy prepared estimates of price wedges between domestic prices of imported goods and their border prices, adjusting for applied tariffs, transport costs and, where appropriate, marketing margins. Annex 3 discusses the details of the NTB tariff-equivalent estimation for selected industries for which we have more detailed cost-share estimate from the Nathan study from 1999. It is important to point out that although the price wedge approach used here gives
|**Table 6: Comparison of Characteristics of High an**|**d Low Profi**|**t Women**<br>Low|High||
|---|---|---|---|---|
||Men|Initial Profit<br>Women|Initial Profits<br>Women|Sri Lankan<br>Women|
|Monthly profits in January 2009<sup>a</sup>|||||
|Mean|130|38|173***|28|
|Median|91|37|137***|20|
|Monthly sales in January 2009|||||
|Mean|502|187|822***|87|
|Median|240|120|500***|50|
|Total Capital Stock in January 2009|||||
|Mean|611|251|456***|207|
|Median|255|102|162***|100|
|Age of Owner|35.4|35.9|37.0|41.1|
|Age of Firm|9.1|6.0|7.4**|9.5|
|Ever had a formal loan|0.07|0.08|0.15**|0.23|
|Keeps accounts|0.45|0.31|0.44**|0.29|
|Years of Education|10.04|7.80|8.63**|9.44|
|Digitspan Recall|5.70|4.59|4.80|5.68|
|Chose sector as it had low capital requirements|0.17|0.40|0.32*|n.a.|
|Chose sector for profit potential|0.18|0.
where MPK and MPL are the aggregate marginal products of capital and labour, i.e. the derivatives of total production with respect to K and L. The third term in Equation (17) is the contribution of within-sector TFP growth to aggregate TFP growth. The first two terms are the contributions of reallocations of factors across sectors to aggregate TFP growth. These expressions are very intuitive. If the growth rate of capital (labour) input is higher than average in a sector where the marginal product of capital (labour) is higher than average, this will contribute positively to higher aggregate productivity growth.

We first use this framework to provide estimates of the contribution of intersectoral factor reallocation to growth in China between 1984 and 1999, following the calculations in Kraay (1996) and World Bank (1996). We separate the Chinese economy into agriculture, industry, and services, and within industry and services distinguish between state-owned firms, collectively-owned firms, and other ownership firms consisting primarily of foreign investment, and private firms. Using published Chinese data and a number of assumptions detailed in Appendix 1 of World Bank (1996) we are able to construct estimates of the distribution of real value added and the distribution of employment across these seven sectors. Assuming a Cobb Douglas production function and using output elasticities of capital and labour as described in World Bank (1996), we are able to calculate the second line of Equation (17) which captures the contribution of labour reallocation to aggregate productivity growth. The results of these updated calculations through 1999 are reported in Table 3.<sup>9</sup> Factor reallocation has contributed about 1.3 percentage points of growth between 1984 and 1999, and the pace of this factor reallocation has remained fairly stable. What has

> 9 Unfortunately we are not able to update these calculations past 1999 due to a break in the series on the distribution of industrial production by ownership.  Up to 1999, the China Statistical Yearbooks report this distribution for all firms, but after 1999
comparative studies show in diverse settings that those with a PhD or high productivity levels are less likely to return migrate than other skilled workers (Finn 2007; Gibson and McKenzie 2009; Harvey 2009; Gaule 2014).

> 10 Germany loses about 50 percent and Netherlands as many as 60 percent of foreign students coming to complete a degree within five years of graduation (Bijwaard 2010). Gundel and Peters (2008) find that high‐skilled immigrants are more likely to re‐emigrate from Germany. Pohl (2006) argues that a large share of high‐skilled migrants exhibits short staying patterns, but once controlling for a number of other factors the average high‐skilled migrant is actually less likely to leave. Dustmann and Weiss (2007) use the British household panel and find that outmigration causes the remaining immigrant pool in the United Kingdom to become less skilled over time. In Canada, all immigrant groups display high re‐emigration rates, but those arriving in the skilled class and business class were most likely to leave again (Aydemir and Robinson, 2008). Dustmann et al. (1996) review policies related to return migration in Europe.

13