rafmacalaba/data-use-mentions-extended huggingface dataset:
), and used a surveybased approach to assess the prevalence of AI usage within firms (Czarnitzki et al. 2023). Svanberg et al. (2024) estimate that firms will introduce AI gradually and at a much slower pace than originally anticipated given the introduction costs firms face. A synthesis of several academic studies estimates the overall impact of AI adoption on annual worker productivity growth within a firm to be between 1.7 and 2.7 percentage points (Hatzius et al. 2023).

There is some evidence that highly skilled workers will be particularly affected by recent AI developments (e.g., Felten et al. 2021, Felten et al. 2023). Some recent studies on generative AI have demonstrated that for a given task, productivity gains are primarily observed in lower-skilled, less experienced workers (Brynjolfsson et al. 2023, Noy and Zhang 2023).

In a study on the United States, Eloundou et al. (2023) used the O*NET database to gauge the impacts of large language models (LLMs) and calculate the potential for AI and automation. The authors conclude that approximately 20 percent of jobs are exposed to LLM in more than 50 percent of their tasks, suggesting that the impact of AI is pervasive and has significant effects on economic activities. A McKinsey study (2024) obtains similar results and estimates that about 27 percent of tasks in European countries and around 30 percent of tasks in the US will be AI-based

> 4 See Comunale and Mandera (2024) for an excellent review of the literature and an analysis of regulations that are currently ongoing.

6
<mark>demographic and employment attributes can produce accurate poverty predictions - consistently within the 95 percent confidence internal, and often within one standard error, of the observed “true” poverty rate.</mark><sup>2</sup> <mark>Finally, the last issue motivating our work is that existing studies that “validate” imputed poverty estimates were implemented in artificial settings. Specifically, these studies t</mark> ypically pursue validation by estimating an imputation model on an older, base consumption survey and applying the model to a more recent, target consumption survey, _pretending_ that there were no consumption data in the latter survey. These studies subsequently compare the resulting imputed estimate to the true poverty rate based on the actual consumption data in the target survey. The fact that the newer survey round serves both as the target survey and as the source of true poverty abstracts away from real-life differences in base versus target survey design that motivate our work in the first place. These traditional artificial settings also differ from many practical applications for survey-to-survey imputation where a new survey with a different design is implemented as the target survey (e.g., a survey that does not collect consumption data or that administers lighter consumption modules - as in the case of most proxy-means tests).

Against this background, we report on a unique randomized and nationally representative household survey experiment that was implemented in Tanzania in 2022 to systematically investigate the understudied topics that have a bearing on the operational/practical applications of survey-to-survey imputation to fill poverty data gaps. The experiment featured three treatment arms (TA) that sampled households were randomly assigned to and that differed in terms of questionnaire design. Treatment Arm 1 (TA 1) households were administered a questionnaire that collects comprehensive data on household consumption and allows for the computation of

> 2 We use the term “true” poverty rate to refer to the poverty rate that can be estimated using the actual household consumption data.

4
- Rutherford, Thomas F. and David G. Tarr (2002), "Trade Liberalization, Product Variety and Growth in a Small Open Economy: A Quantitative Assessment," _Journal of International Economics_ , Vol. 56, Issue 2, March.

- Shepotylo, Oleksandr and Volodymyr Vakhitov (2012), ―Impact of services liberalization on productivity of manufacturing firms: evidence from Ukrainian firm-level data,‖ World Bank Policy and Research Working Paper Number 5944, January, and Economics Education Research Consortium Final Report 12/01E. Available at http://www.eerc.ru/paperinfo/324

- Stankov, Petar (2012), ―Cross-Country Differences in Credit Market Liberalization Reform Outcomes,‖ Economics Education Research Consortium Final Report 12/04E. Available at http://www.eerc.ru/paperinfo/327

- Tarr, David G., editor, (forthcoming), _The Impact of Services Liberalization on Industry Productivity, Exports and Development,_ Moscow: Ves Mir Publishers for the World Bank.

- Triplett, Jack E. and Barry P. Bosworth (2004), _Productivity in the U.S. Services Sector: New Sources of Economic Growth_ , Washington D.C.: Brookings Institution.

- Vakulchuk, Roman, Farrukh Irnzarov and Alexander Libman (2012), ―Liberalization of Trade in Services in Kazakhstan and Uzbekistan: Analysis of Formal and Informal Barriers,‖ Economics Education Research Consortium Final Report 12/06E. Available at http://www.eerc.ru/paperinfo/329

28
**|||
|Camps|34|26,739|6,271|
|Blocks|1,953|465|109|
|Source: 2011 Ban|gladeshcensusandIOM NP|M Round12.Note:Averages|forCox’sBazardistrict|

On average, a union in Cox’s Bazar covers 10.5 villages and 3,758 native/host households. The IPA count asked a small set of union-level key informants<sup>14</sup> to provide village-level data on refugee counts, disaggregated by “old” and “new” Rohingya displaced. This approach was not likely to generate accurate numbers on prevalence. Moreover, the inaccuracy was most likely to pose problems for the key population of interest in this listing: Rohingya displaced living in villages outside camps.

Given these concerns, we implemented a full household listing in a random sub-sample of IPA count villages to validate the IPA counts. These villages were stratified for refugee prevalence based on refugee counts in the IPA listing and population counts from the 2011 census. Three types of prevalence were defined based on these data, to form the strata for the validation household listing (Table 2). The validation listing was done in 33 villages randomly chosen from the strata without refugee camps in November 2018. On average, one village was chosen from each mauza, across six out of the seven upazilas in Cox’s Bazar district.

> 14  The key informants were government officials in union level offices, which are the lowest local government administrative units in Bangladesh. IPA went to the respective union offices and asked for population counts.

7
- Self-employment: refer to primary job in past three months (from question 2122 of chapter 2, Job Characteristics). Includes self-employed not employing others and employers. Exclude agriculture, forestry and fishing using the activity category from question 2107 (“What is the economic activity of your main job during the last three months?”).

- Wage worker: as before, but including respondents classified as waged, monetary or in-kind workers in question 2122.

- Agricultural worker: Individuals working in agriculture, forestry or fishing according to question 2107.

- Income: sum of basic wage, supplementary payment, bonus, incentives, overtime, profits, and other earnings as detailed in question 4104 of chapter 4 (Earnings). Add average daily wage (x 20) for irregular workers (question 4109).

## First follow-up <u>(2006) and second follow-up (2012):</u>

Both follow-up surveys have similar structure. In what follows we refer to questions in the second follow-up questionnaire.

- Self-employment: refer to primary job in past three months (from question 5134 of chapter 5). Includes self-employed not employing others and employers. Exclude agriculture, forestry and fishing using the activity category from question 5103 (“What is the economic activity of the enterprise you work in or the job you do if not in an enterprise?”).

- Migration: the survey records each respondent’s governorate (variable gov ~~Y~~ Y), qism (constructed as gov ~~Y~~ Y + qism ~~Y~~ Y), and sheyakha (gov ~~Y~~ Y + qism ~~Y~~ Y + shyakha ~~Y~~ Y). We code governorate migration, qism migration, and sheyakha migration if a respondent experiences a move across relevant geographic units between two survey waves.

Notes: ELMPS tracks international migrants but it is not possible to directly link them to the baseline survey. Consequently, we decided against
To measure urban growth, we therefore rely on data that are not limited by India’s statistical infrastructure, namely night-time lights, as detected by satellite. These data are originally from the Defence Meteorological Satellite Program (DMSP) and are processed by the National Oceanic and Atmospheric Administration. They provide annual estimates over 1992-2012 at a one square kilometer resolution (in some years two satellites are in orbit so there are 33 satellite-year observations). Gibson et al (2015) use these data to estimate rates of area expansion over 1992-2012 for 47 Indian agglomerations with a population of at least one million in the 2011 census.<sup>12</sup> These agglomerations are shown in Figure 4, in terms of their lit area in 2012. We treat these 47 agglomerations as India’s “big cities” and contrast the effects of their growth with the effects of the growth of smaller towns.<sup>13</sup> The threshold of one million population is also used by Chatterjee et al (2015) and Li and Rama (2015) to demarcate between India’s large and small urban areas.

Figure 4 also shows the NSS regions; 39 of 59 have one or more of the big cities in them. For the others, the city effects come indirectly, with these spatial spillovers captured by our econometric model. We consider city growth on the extensive and intensive margins, since it is _a priori_ unclear which matters more to poverty reduction. For example, extensive margin growth may offer easier commuting from rural areas but more sprawling cities may be less efficient. Specifically, we use the night lights to both demarcate the edge of cities for examining effects of expanded areas (the extensive margin), and to calculate the intensity of light—the “digital number” (DN)— emanating from within these boundaries (the intensive margin).<sup>14</sup> There is also a measurement reason for distinguishing between lit area and light output within an area. Some
<!-- Start of picture text --> 14<br>12<br>10<br>China<br>8 India<br>World<br>6<br>USA<br>4 EU<br>2 Japan<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>-2<br><!-- End of picture text --> Panel A: Annual growth rates of GDP (%), 2000 to 2008 <!-- Start of picture text --> 15<br>10<br>China<br>5 India<br>USA<br>0 EU<br>2008 Q1 2008 Q2 2008 Q3 2008 Q4 2009 Q1 2009 Q2 Japan<br>-5<br>-10<br><!-- End of picture text --> Panel B: Quarterly growth rates of GDP (%) from Quarter 1, 2008 to Quarter 2, 2009.

Data source: IMF, 2009 Figure 1. Annual and monthly growth rates of GDP (%) in the selected countries, 2000 to 2009.

32
all the pollutants. The Chinese central govermment constructs a uniform fee schedule, however the implementation in different regions is not uniform<sup>6</sup> . The levy collected is used to finance environmental institutional development and administration and environmental projects and to subsidize firms' pollution control projects. If a firm, who pays levies to the levy fund, decides to invest in pollution abatement, a maximum of 80% of the levy paid by the firm can be used to subsidize the investment project proposed by the firm. To make the levy collection effective, a schedule of penalties is also specified<sup>7</sup> . Penalties can not be used to subsidize firm-level pollution control projects. Table 2 provides information on how the collected levies are used.

Although studies have been conducted to reform the levy system with most analysts recommending raising China's pollution charge rate (SEPA, 1998), few empirical analyses have actually investigated polluters' response to the existing charges. In Wang and Wheeler (1996), province-level data on water pollution was analyzed and it was determined that China's levy system had been working much better than previously thought. Provincial variations in the enforcement of the levy appear to reflect the local valuation of environmental damage and community capacity to enforce local norms. The results suggest that province-level pollution discharge intensities have been highly responsive to provincial levy variations.

While it is clear that the pollution intensities are responding to the charges, whether the charge has been strong enough to provide incentives for firms to invest in pollution abatement facilities remains unclear. Another policy instrument - the subsidies may be jointly playing an important role in firms' investment decisions.

- **6** For a detailed discussion, see Wang and Wheeler (1996) and SEPA (1998).

> 7 The penalty schedule is usually referred as "four small parts" in Chinese.

7
-

> 1The increased accessibility to Scandinavian registry data has led to an explosion of articles on the topic. Some of the most influential work includes the seminal article by S. E. Black, Devereux, and Salvanes (2005) and more recent iterations which exploit policy-induced variation in educational attainment among older generations such as Fischer, Karlsson, Nilsson, and Schwarz (2019) and Suhonen and Karhunen (2019). Chevalier (2004) and Dickson, Gregg, and Robinson (2016) conduct similar analysis using minimum school-age regulations in the U.K., while Maurin and McNally (2008) do so employing variation generated by the French student revolt of 1968. Work focused on the intergenerational impacts of education in the U.S. context using causal inference reasoning can be found in Currie and Moretti (2003), Oreopoulos, Page, and Stevens (2006), Carneiro, Meghir, and Parey (2013).

> 2A notable exception is Andrabi, Das, and Khwaja (2012), who focus on Pakistani women with very low levels of education and their children.

2
03|0.29|0.13|0.56|1|
|<br>Manufactured fertilizers|492|21,948|9,573|34,800|66,812|0.01|0.33|0.14|0.52|1|
|Petroleum and products|23,838|8,188|141,512|1,584,422|1,757,960|0.01|0.00|0.08|0.90|1|
|Rubber & plastic products|933|15,070|11,490|66,676|94,169|0.01|0.16|0.12|0.71|1|
|Glass & cement|7,415|11,754|3,474|61,536|84,179|0.09|0.14|0.04|0.73|1|
|Iron & metal products|15,508|40,123|136,733|211,008|403,372|0.04|0.10|0.34|0.52|1|
|Machinery & transport equipment|16,544|597,393|162,321|851,018|1,627,276|0.01|0.37|0.10|0.52|1|
|**Source: SITC Revision 3 data from UN**|**COMTRA**|**DE Statistic**|**s.**||||||||

**Appendix B: Documentation of the Calculation of Ownership Shares for Tanzania**

# **I. Telecommunications in Tanzania**

# **Company Market Shares**

The data source for market shares of the fixed line and mobile telephone companies in Tanzania was the website of the Tanzania Communications Regulatory Authority. It provides
2013).

Baker et al. (2016) provide a new measure of policy uncertainty based on newspaper coverage frequency and study the impact of this index on output, investment and employment. In a panel VAR setting for 12 large economies, they find that increases in policy uncertainty negatively affect these variables. In line with this finding, recent work at the World Bank documents the link between policy uncertainty and investment for emerging markets and developing economies (World Bank, 2017).

Hlatshwayo (2018) unpacks policy uncertainty by type of policy to distinguish generic, fiscal, monetary and trade policy uncertainty. Utilizing the latter index, Ebeke and Siminitz (2018) find an average decline of 0.8 percentage points in the investment-to-GDP ratio for five quarters following a one-standard-deviation increase in the level of trade uncertainty.

Ahir et al. (2018) develop a world uncertainty (WU) index that captures uncertainty related to economic and political developments in the short and long run. The WU index is compiled for a large set of developed and developing countries starting from 1996, by counting the occurrences of the word “uncertainty” in the quarterly Economist Intelligence Unit (EIU) country reports. Ahir et al. (2018) use this index to demonstrate that increases in uncertainty foreshadow significant 4
in wage inequality occurred within sectors-occupations or were associated with compositional changes, and assesses the potential contribution of labor formalization to changes in inequality. In line with recent studies, it emphasizes the role of firms (Card, Heining, and Kline 2013; Alvarez et al. 2018; Song et al. 2018). In particular, it uses detailed longitudinal matched employer-employee administrative data covering the formal sector in Brazil, Costa Rica, and Ecuador to identify the contribution to changes in inequality of changes in wage dispersion among workers, changes in pay across firms within sectors, and their interactions.

The paper builds on Messina and Silva (2018), which summarizes the main findings of a large research project on wage inequality in Latin America. It reports on these analyses and complement them in four important ways. First, the paper assesses trends in (overall,

> 1Unless otherwise noted, the analyses focus on the evolution of hourly earnings from labor of employees and self-employed workers. Hence, employers and unpaid family workers are excluded. This paper uses earnings and wages interchangeably to refer to this measure of remuneration.

2
If a group of countries find themselves on a fundamentally different development trajectory than the rest, as we argue is the case of countries of the bottom billion, then global aggregate analysis may struggle to discern this policy-relevant pattern. Indeed, when highlight populous countries such as China are not in such a group, they may get statistically ‘drowned out’ from such global analysis. The problem is especially acute when major economies such as China are below key averages, such as median income. This is due to the way global aggregates and trends are calculated; fast-growing China when below the average contributes to a better score for catch-up, meanwhile once they cross this important threshold, which is only occurring recently, the divergence picture will suddenly begin to look much gloomier.

We argue this outcome is arbitrary and one that researchers and policy makers can anticipate. We therefore favor heterogeneity analysis alongside global trend analysis to draw out these important distinctions. Even under global results of unconditional converge many counties continue to fall behind. Such average results risk being misinterpreted to imply these countries will catch up on current trends; we argue that on the contrary, without significant changes in policy approaches these countries will not just languish but fall ever further behind.

3. Variation in the availability of data on countries, years and time periods GDP per capita data can typically be tracked for a large sample of countries since 1960. Most convergence analysis begins here for this reason. Since that year, the world has gone through economic booms, crises, and recessions that have impacted growth rates. For example, after the early 1990 recession, when the global GDP per capita suffered a contraction of -0.2 percent, the world lived in a decade of growth until a new recession came in the early 2000s. A few years later, as the Golden Decade evolved, several countries, especially those relatively more dependent on natural resources, started seeing unprecedented growth rates.

But these years of abundance ended in 2015 and were followed instead by a period of slower
, with collection rates dipping to 97 percent in 2011-2012, and distribution losses at 10 percent of output. Together these losses were equivalent to 2 percent of BENECO’s revenue. An index of average tariffs (relative to the 1992 value) in local currency and in US $ is included as a reference.

**Figure 12: Evolution of MERALCO’s operational inefficiencies and average tariffs, 1992-2015**

> 13 There were no data on collection losses of MERALCO prior to 2005, and no data for either loss for BENECO prior to 2009.

35
# Trade and Local Labor Market Outcomes in Mexico:

Disentangling the Channels and the Role of Geography, Sectors, and Trade Types

## **Emmanuel Vazquez**<sup>**1**</sup> **and Deborah Winkler**<sup>**2**</sup>

**Keywords:** International trade, exports, imports, global value chains, local impacts, labor market, employment, income, migration, informality, flexibility, education spending, connectivity, Mexico.

**JEL Codes:** F14, F16, D3, I3, J61

> 1 Senior Researcher, Center for Distributive, Labor, and Social Studies, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata. E-mail: evazquez@cedlas.org.

> 2 Senior Economist, Global Trade and Regional Integration, World Bank Group. Email: dwinkler2@worldbank.org. The authors would like to thank Carlos Rodríguez-Castelán and Hernan Winkler for their permission to build on and expand their research in Rodríguez-Castelán et al. (2020). They also thank Luis Alejandro Aguilar Luna for his excellent research assistance in computing trade and global value chain measures drawing on Economic Census data. The authors are also grateful to Claudia Berg, Raymundo M. Campos-Vasquez, Sébastien Dessus, Christoph Lakner, Gladys C. Lopez-Acevedo, Maryla Maliszewska, Ambar Narayan, Israel Osorio-Rodarte, Raymond Robertson and Ben Shepherd for their helpful comments and suggestions, as well as to the participants of the World Bank authors’ workshops “Leveraging trade for more and better job opportunities in developing countries” in May and November 2022. They thank Natalia Volkow and Liliana Martinez of the Microdata Center of the National Institute of Statistics and Geography, Mexico, for their support for data use in compliance with the confidentiality requirements set by Mexican law. The work was carried out under the overall supervision of Sébastien Dessus and Antonio Nucifora and has been cleared by Mark Thomas (Country Director of Mexico). The
Policy Research Working Paper 10436

# **Abstract**

This paper analyzes the impact of changes in energy prices on the competitiveness of manufacturing firms in Chile. Using the Chilean Annual National Industrial Survey data, the paper illustrates that, _first,_ increases in energy prices generally do not hurt firm competitiveness. _Second,_ the impact of energy prices depends on the fuel type—while electricity price increases are negatively correlated with firm outcomes, fossil fuel price increases have a positive association with investment and firm productivity, a result that is consistent with the strong version of the Porter hypothesis. _Third,_ these effects are heterogeneous and vary by firm attributes such as size, ownership and location. _Fourth,_ investigating non-linear patterns in firm outcomes based on the level of energy prices, the findings show that the positive correlation between fossil fuel price increases and capital upgrading is particularly pronounced when energy prices are at relatively low levels.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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 authors may be contacted at agrover1@ifc.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