rafmacalaba/data-use-mentions-extended huggingface dataset:
increase in robot density in developed economies is associated with a 6-percentage point increase in imports from developing economies, and a 12-percentage point increase in exports to these countries. Exploiting differences across countries and industries, Hallward-Driemeier and Nayyar (2019) find that, past a threshold level, the increasing number of robots per 1,000 employees in high-income countries (HICs) is negatively associated with the growth rate of the stock of outbound foreign direct investment (FDI) from HICs to lower-middle-income countries. However, Freund, Mulabdic, and Ruta (2022) find that 3D printing is, on average, supportive of existing trade patterns.

Systematic documentation of how the use of AI affects productivity is still limited, even for advanced economies. AI usage positively contributes to firm productivity in studies using data from Germany and Denmark (Czarnitzki, Fernández, and Rammer 2022; Warzynski forthcoming). There is evidence of a positive correlation between wages and exposure to AI language modeling in the United States (Felten, Raj, and Seamans 2023). At the sector level, machine learning has been found to enhance judicial output and labor productivity (Kleinberg et al. 2018), and to improve doctors’ prediction capacity (Mullainathan and Obermeyer 2022). AI-based digital technologies may allow larger segments of the labor market to improve their productivity if policies support the necessary shift in occupational demand (Ernst, Merola, and Samaan 2018).

As the use of AI is still very limited in most developing economies, empirical work on the productivity impacts of the second wave of digitalization on productivity has hardly begun. One recent study of customer service agents, working predominantly in the Philippines, finds that the use of an AI-based conversational assistant increased the number of customer issues resolved per hour by an average of 14 percent (Brynjolfsson, Li, and Raymond 2023).

# **_2.3. Does digitalization increase capital accumulation?_**

The literature identifies ICT investment as a key factor behind productivity growth in the United States (Jorgenson, Ho,
br>0<br>0 20 40 60 80 100<br>Phycological Physical Sexual<br>Percentage<br>Source: Own elaboration based on 2020 ENVIF-VCM. Source: Own elaboration based on 2020 ENVIF-VCM<br>Percentage<br><!-- End of picture text --> The percentages of informal complaints of the different types of violence, such as asking relatives, friends, or others for help when an episode of violence occurs, are relatively higher than the rates of formal complaints. Of the total number of women who report having been victims of physical violence perpetrated by their partner in the past 12 months, 50 percent state that they asked for help, either from their family, friends, neighbors, or others. This percentage is 30 percent for victims of psychological violence and 18 percent for victims of sexual violence. Additionally, of the total number of victims of intimate partner violence who report having asked for help, between 60 and 68 percent asked for help mainly from their family (Appendix C provides details).

The reasons victims of intimate partner violence do not file a formal complaint vary according to the type of violence. However, some reasons are common to all kinds of violence. Nearly 40 percent of the victims of psychological violence who did not file a formal complaint said that they did not consider it severe or did not believe it necessary. Among victims of physical violence who did not file a complaint,

> 14 In the 2020 ENVIF-VCM, all women who report having experienced any type of violence are asked if they are familiar with the Domestic Violence Law.

> 15 Economic violence is not included because the survey does not collect information on complaints of this type of violence.

8
, women|56.1|57.0|57.9|55.1|55.6|56.1|
|Unemployment rate,men|14.7|13.1|10.3|6.5|7.1|7.4|
|Unemployment rate,women|12.1|11.0|10.7|8.6|8.9|9.0|

_Source_ : Central Statistical Bureau of Latvia and OECD.

# **C. LABOR FORCE PARTICIPATION AND EMPLOYMENT**

**Activity patterns differ from the EU-15.** The “Joint Assessment of Employment Policy Priorities in Latvia” (Stake and Diamantopoulou, 6 February 2003) notes that participation in Latvia is relatively low for young people of both sexes and for prime age males.   Participation is high relative to the EU-15 among women aged 25-49. However, it should be noted that youth activity rates can be misleading. In fact, only amongst teenagers is labor force participation low (see Appendix Figure 3). Moreover, in comparison with Central European countries, only the Czech Republic, Romania and Slovakia have higher activity rates in the 15-24 age group.

Youth participation and employment rates have sharply declined in 1998-2001 for men and in 1998-2000 for women; youth unemployment rates have also declined over these periods, although there were temporary increases for men in 1999 and for women in 1998 and 2000. One potential explanation for these developments is an almost 80 percent increase in number of students in higher education institutions per 10,000 population between 1997 and 2001; a 16 percent real wage growth and introduction of students’ loans enabled more families to finance their children’s education. On the other hand, lack of jobs suitable for students can discourage them from job seeking.

Youth and the elderly are becoming more active. After declining for several years, the overall activity rate for women increased in 2001 by one percentage point due to increased participation in age groups 20-24 and
penalties were thus the keys to inducing compliance.<sup>8</sup> Over time these models came to be complemented by an expanded focus on facilitating tax compliance, recognizing that taxpayers were more likely to comply when doing so was straightforward and low cost, thus shifting the rational economic calculus in favor of compliance.<sup>9</sup> This focus on enforcement and facilitation has underpinned a range of desirable reforms across the developing world, along with modest, but consistent, revenue gains. Efforts to strengthen enforcement have included investments in building expanded assessment and audit capacities, new IT systems designed to reduce the scope for avoidance, improved collection methods, efforts to expand access to third-party information, and policy changes designed to reduce the scope for evasion.<sup>10</sup> Meanwhile, efforts to facilitate compliance – and emphasize “customer service” – have correspondingly sought, among other aims: (a) to simplify tax systems, and related reporting requirements; (b) to provide easy access to information about tax liabilities; (c) to provide access to support and advice; (d) to provide simple methods for making tax payments, including online, at banks and via SMS; and (e) to reduce face-to-face interactions with tax collectors, which have historically caused delays and increased the risk of abuses.<sup>11</sup> The priority given to these issues is reflected, for example, in the World Bank’s Doing Business surveys, which track the difficulty of tax compliance for businesses.<sup>12</sup> Finally, reform efforts have not only targeted tax _payers_ , but also sought to improve the effort and performance of tax _officials_ , in order to reduce collusion, corruption and simple shirking. These have included pay increases, performance contracts, increased monitoring, efforts to reduce opportunities for corruption and collusion, and a broad emphasis on strengthening the “customer orientation” of tax administrations.<sup>13</
Since there are only four Latin American countries in PIAAC, we extrapolate the measures of computer use at work from PIAAC to the full set of countries in the SEDLAC database. In particular, we estimate a predictive model for the probability of computer use at the individual level using the full set of countries in PIAAC<sup>19</sup> and independent variables that are available both in the PIAAC and SEDLAC databases.<sup>20</sup> We then use the estimated model and the set of independent variables to predict the probability of computer use in the SEDLAC database. More specifically, we first estimate the following Logit model in PIAAC: specifically, we first estimate the following Logit model in PIAAC: Pr (c c c c I , a , GDP , i c ) (1) oo aa cc,ii cc,ii cc,ii cc,ii cc,ii cc cc cc Where c c c c = 1) = ff(I is a binary variable equal to 1 if individual cc , ffc aaffcc , cceecc in country cciic , bbc uses a computer at aaeebbaaiiee work; I I is a vector of 39 dummy variables for each 2-digit ISCO08 occupation21; a is cc,ii a vector of 4 dummy variables indicating age groups; oo is a dummy variable equal to one for ii cc aa cc,ii cc,ii High School graduates; GDP is the log of GDP per capita in 2017 US$ PPP; i c is the rate cc cc,ii of internet users per 100 people, and; is the number of fixed broadband subscriptions cceecc cc cc per 100 people. Since the reference year of the PIAAC surveys varies by country, we use the cciic cc corresponding year of the country-level variables (i.e. GDP, internet and broadband). These bbc aaeebbaaiiee country-level variables are helpful to capture the link between the economy-wide level of digital and economic development with the level of computer use at work.

In the next step, we use
WPS 3875

# **The Poverty Impact of Rural Roads: Evidence from Bangladesh**<sup>**1**</sup>

## **Shahidur R. Khandker World Bank**

## **Zaid Bakht Bangladesh Institute of Development Studies**

## **Gayatri B. Koolwal National Economic Research Associates**

### **Abstract**

The rationale for public investment in rural roads is that households can better exploit agricultural and non-agricultural opportunities to employ labor and capital more efficiently. But significant knowledge gaps remain as to how opportunities provided by roads actually filter back into household outcomes and their distributional consequences. This paper examines the impacts of rural road projects using household-level panel data from Bangladesh. Rural road investments are found to reduce poverty significantly through higher agricultural production, higher wages, lower input and transportation costs, and higher output prices. Rural roads also lead to higher girls’ and boys’ schooling. Road investments are pro-poor, meaning the gains are proportionately higher for the poor than for the non-poor.

World Bank Policy Research Working Paper 3875, April 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 We would like to thank Gershon Feder and Robert Floyd for valuable comments and suggestions, as well as Hussain Samad for research assistance.

1
competition index in 2000 and a 0.35 increase in 2001. The standard deviation of the competition index is 3.92 so these increases amount to roughly a 0.1 standard deviation increase in the competition index. These positive pro-competitive effects fade somewhat over time but rise again between 2006 and 2009.<sup>22</sup>

> 21 The point estimates on individual competition indicators, however, are broadly suggestive of entry rates (column 1), the number of firms (column 7) and prices falling somewhat faster in industries where firms owned by the Suharto family had greater market shares. By contrast, price costs margins (column 3), the Hirschman-Herfindahl index of concentration (column 5) and the market share of the 4 largest firms (column 6) seemed to be rising somewhat faster in these industries. This helps explain why point estimates from regressions in which the summary competition indicator – the competition index Z – is the dependent variable (column 9) are suggestive of deteriorating competition in industries in which connected firms had higher market share during the Suharto era, though none of the individual coefficients is statistically significant.

22 These results could also in part be driven by the increase in sample coverage in 2006 which is a census year; every 10 years the Indonesian bureau of statistics (BPS) conducts an economic census, which typically leads to an improvement in coverage of the manufacturing survey.

22
29</mark>_ <mark>(2), 238-249.</mark>

- Celhay, P. A., P. J. Gertler, P. Giovagnoli and C. M. J. Vermeersch (2015). Long-run effects of temporary incentives on medical care productivity. The World Bank, Policy Research Working Paper Series, 7348.

- Das, Jishnu, Jeffrey Hammer and Kenneth Leonard. 2008. “The Quality of Medical Advice in Low Income Countries”. Journal of Economic Perspectives, 22(2): 93–114

- <mark>Das, J., and J. Hammer. 2014. “Quality of Primary Care in Low-Income Countries: Facts and Economics.”</mark> _<mark>Annual Review of Economics</mark>_ <mark>6 (1): 525–53.</mark>

- de Walque, D., P. J. Gertler, S. Bautista-Arredondo, A. Kwan, C. Vermeersch, J. de Dieu Bizimana, A. Binagwaho and J. Condo (2015). "Using provider performance incentives to increase HIV testing and counseling services in Rwanda." Journal of  Health Economics: 40: 1-9.

- <mark>de Walque, Damien, Paul Jacob Robyn, Hamadou Saidou, Gaston Sorgho and Maria Steenland. 2017. Looking into the Performance-Based Financing Black Box. Evidence from an Impact Evaluation in the Health Sector in Cameroon. World Bank Policy Research Working Paper # 8162.</mark>

19
Policy Research Working Paper 5941

# **Abstract**

The world financial crisis that started in the US housing market in 2008 brought into evidence deep failures of prudential oversight, linked for the most part to a failure to comprehend and handle systemic risk in a way that could prevent systemic crises. This paper summarizes the responses to the joint World Bank-ASBA survey o the state of systemic oversight in the Latin American and Caribbean financial sectors and reflects on some of the challenges identified by respondents. The authors found that there is broad consensus among regional financial authorities on the need to enhance the current systemic oversight framework. Improving consolidated supervision to mitigate risk-shifting in conglomerates, adjusting prudential regulations to account for the accumulation of systemic risks, redefining the role of the supervisor to make it more proactive, and improving coordination among local supervisors as well as with foreign supervisors figure preeminently in the regional reform agenda.

This paper is a product of the Financial and Private Sector Department, Latin America and the Caribbean Region. 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://econ.worldbank.org. The authors may be contacted at egutierrez2@worldbank.org and pcaraballo@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
of the evidence and fully liberalize the official economy.

28. Figures calculated on the basis of data from: Ekonomika Ukrayiny v 1994 rotsu; Annual Report of the President of Ukraine to the Verkhovna Rada of Statistics of Ukraine, March 1995; **Statvstychny Schovichnyk Ukrayiny a 1994 rik,** Ministry of Statistics of Ukraine (Kiev: Tekhnika, 1995); data of the Ministry of Finance of Ukraine.

29. These general points on optimal tax rates would require further empirical elaboration; no precise recommendations on tax rates or tax reform measures are intended here. More generally, however, even the entrepreneurs' responses indicate their willingness to return to the official economy if taxes were moderate and stable (and nonretroactive). But the return to the official economy would be gradual, which would need to be factored into the detailed tax calculations.

30. A clear distinction would need to be made between leniency regarding the past and the strict penalties expected from underreporting in the future. In fact, a clear signal from the time of the announcement in the form of stronger enforcement of tough penalties for concurrent illegal activities, would be warranted.

31. See Johnson, Kaufmann, and Ustenko, "Winner, Losers and Survival Strategies," for a formalized and empirical elaboration on these issues.

32. Ibid.
banking sectors). In addition, import restrictions can hurt businesses that need imported inputs. Sanchez-Martin et al (2014) find expropriation risk and trade openness have an effect on FDI flows in Latin America. Actions that target particular sectors (export taxes) or firms (nationalization) reduce microeconomic credibility, as defined in the introduction of this paper. Brunetti et al (1998) use a survey of entrepreneurs to develop a measure of policy credibility and find a significant correlation with economic growth and investment.

# _Business climate_

One summary measure of the business climate is the Ease of Doing Business aggregate from the annual Doing Business report.<sup>51</sup> As in the macroeconomic performance section, a useful set of comparator countries is the emerging market members of the G20. Figure 3 below reports the “distance to the frontier” measure rather than the ranking. The distance to the frontier is how close the country’s performance is to the leading country in that indicator. In other words, Mexico’s score in figure 4 is a little over 70 percent of the leading country. This becomes particular interesting when one looks at subcategories of doing business (See Annex 2). It should be noted that many of the subcategories are based on the analysis of legislation in the particular area; i.e., the _de jure_ regulations. One issue related to government credibility is the degree and consistency of _de facto_ enforcement of _de jure_ regulations.

**Figure 4: Doing Business 2015, Comparison to G20 Emerging Markets**

<!-- Start of picture text --> Ease of Doing Business 2015<br>Distance to the Frontier<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: Doing Business database, 100 represents the score of the leading countries.

Figure
Cowan, and Cowan 1990). This scale can be used to characterize parenting in terms of authoritarian, authoritative, and permissive (see Baumrind (1968) and Maccoby et al. 1983).

Finally, we measure expectations about the benefits of a better home environment and quality time spent with children by adapting a scale to elicit these beliefs developed by Cunha, Elo, and Culhane (2013). This instrument asks parents about the age at which they would expect children to achieve developmental milestones in language and socio-emotional development, under different scenarios concerning home environments and parental investments. Our adaptation confronts parents with two scenarios: a high-investment home, in which parents spend quality time cognitively stimulating their children and do not use harsh disciplinary strategies to manage their behaviors; and a lowinvestment home, in which parents do not cognitively stimulate their children often and they rely on harsh discipline. Our hypothesis is that parents in the treatment group may report earlier ages in the high-investment home than those in the control group, because as a result of NEP they are more aware of the impact of home investments on child development.

# 2.3.2 Parental investments in children

To measure parenting behaviors and home environments we combine self-reported and directly observed variables. We use two sub-scales of the Parent Behavior Checklist (Fox 1994), where parents were asked to indicate how frequently they engaged in different activities with their child over the past couple of weeks. A first sub-scale measures Nurturing practices, associated to positive parental socio-emotional interactions with the child. A second sub-scale measures Discipline practices, a mixture of positive and harsh disciplinary practices (an exploratory factor analysis of the Discipline subscale confirmed that these are indeed capture two separate underlying constructs).

In addition, in the baseline survey we administered the Family Care Indicators (FCI) (Hamadani et al. 2010), which measures the frequency of learning and play activities with children, as well as the amount and variety of play and learning materials available at home. In the endline survey we used a
Times from 1950 onwards. For more recent decades, the data comes from the Central Statistical Office of the British government for the years 1939-1988 and from Eurostat from 1989 until today.

## <u>UK inflation/CPI series, monthly</u>

The monthly UK CPI series comes from the Bank of England’s (BoE) historical dataset sheet called “A Millennium of UK Data”, Version 3.1, by Thomas and Dimsdale (2017). From 1914 onwards, we use the spliced monthly consumer price index. For the period before WW1, we use the spliced monthly wholesale/producer price index. These series are those recommended by the BoE.

## <u>US CPI series, monthly</u>

The monthly “Historical Consumer Price Index for All Urban Consumers” is from the US Bureau of Labor Statistics and starts in 1913. For the period before that we rely on series gathered and cited by Reinhart and Rogoff (2009), in particular from the Historical Statistics of the United States. Note that we only have ten USD bonds in our sample that were issued before 1913.

## <u>US Corporate bond total return index, monthly, since 1900</u>

The US corporate bond return series is spliced by combining the returns on the S&P 500 AAA Investment Grade Corporate Bond Index from GFD until 1984 with data from the Bank of America Merrill Lynch US Corp AAA Total Return Index thereafter, which we retrieved from the Federal Reserve Bank of St. Louis database.

# **References Appendix A** (those not cited in the main paper):

British Central Statistical Office (various issues) _Annual Abstract of Statistics_ . London: CSO. Hayek, Friedrich A. (1935) _Prices and Production._ London: Routledge.

A2