rafmacalaba/data-use-mentions-extended huggingface dataset:
is no significant difference in the performance of Models 8 and 9 when comparing deflation using quarterly or monthly CPI to deflation using the aggregated IMF CPI.

# _Alternative base survey_

The main results in Section 4.1 relied on base survey data that preceded the target survey round by less than two years. Had we relied on older base survey data, would that have compromised the performance of the promising imputation models? Figure 2 highlights a limited test of varying the timespan between the base and target survey rounds, by nearly doubling it from 1–2 years (in the case of using the TZNPS 2020/21 as the base survey) to 2–3 years (using the TZNPS 2019/20 as the base surveys). The full poverty estimates are shown in Appendix A, Tables A.8 to A.10. The results indicate that both base surveys perform similarly in terms of producing estimates close to the true poverty rates (except Model 4) with earlier round performs even better for Models 8-9 in TA 2 and TA 3.<sup>15</sup> That is despite the fact that the sample size of the more distant round, NPS 2019/20, is only a quarter of the sample in NPS 2020/21.

# _Varying poverty lines_

Finally, poverty as measured under the latest consumption data collected under TA 1 can vary by as much as more than two percentage points depending on whether we use the poverty line in the 2019/2020 TZNPS or in the 2020/2021 TZNPS. To examine sensitivity of our results to the poverty line, we re-estimate the main results shown in Tables 2 to 6 but using the poverty line in the 2019/2020 TZNPS instead of the 2020/2021 TZNPS.<sup>16</sup> The results, shown in Appendix A,

> 15 Figure 2 shows estimates that are obtained using the normal linear regression models. The estimates obtained using the empirical distribution of the error terms are similar (Appendix A, Figure A.
between June and August 2018. These were the worst floods in the state since 1924 and the third worst in India since 1900. A total of 504 people died and 23 million people were directly affected.<sup>2</sup> Since

> 1The publicly available Emergency Events Database (EM-DAT), maintained by the Center for Research on the Epidemiology of Disasters, is the most widely used database in economic research related to natural disasters. EM-DAT defines a disaster as a situation or event that overwhelms local capacity, necessitating national or international assistance and is often unforeseen and sudden. See https:www.emdat.be and Cavallo and Noy (2011) for more information.

> 2https://www.emdat.be/cred-crunch-53-flash-floods-sharing-field-experience-kerala

2
simple model, city growth could more effectively “trickle down” to the rural poor. It is an empirical question as to which type of urban growth is better for the rural poor.

Section III describes our data for addressing that question, in which we have formed a regional panel data set, combining results from household surveys with data on the extent of night light. Section IV explains our econometric model, which is calibrated to the panel data. Alternative models are also described, and shown to be testable restrictions on our preferred (encompassing) specification. Our results are then presented in Section V, which provide strong support for the hypothesis that economic growth in secondary towns has more impact on rural poverty than does growth of the big cities. Section VI concludes.

# **II. A Simple Theoretical Model**

The purpose of the following model is to illustrate one source of urban-rural linkage, namely through the labor market, for which urban economic growth emanating from cities brings different gains to the rural poor than growth in towns.

We suppose that the urban economy comprises two sectors, a town and a city. In the spirit of the classic Harris-Todaro (1970) model of rural-urban migration in the presence of an urban labor-market distortion, we assume that the wage rate in the city is fixed above the market clearing level, but the wages in the town and the rural economy are fully flexible, and come into parity. Technical progress in the town is assumed to increase the marginal product of labor there, which will lead to higher wages in the town, and also in the rural hinterland due to the integrated labor markets; indeed, in equilibrium the wage gains will be the same. Technical progress in the cities will increase employment there, which will attract workers out of unemployment and from both the town and rural areas. This will bring gains to wages in the latter sectors.

In the context of this model, we ask whether technical progress in the town has more impact on poverty than that in
(Patil et al. 2014; Dickinson et al. 2015; Pickering et al. 2015). Studies that do find any significant relationship either focus on CLTS programs accompanied by water-supply interventions (Godfrey et al. 2014) or find the reduction in only a specific subgroup of project beneficiaries (Borja-Vega 2014).

The empirical literature, however, indicates that CLTS interventions have a positive impact on nutritional outcomes in children under five. In their study of rural Orissa, India, Dickinson et al. (2015) find that mid-upper-arm circumference increased by two standard deviations in treatment villages relative to control villages. In Mali, children under five in treatment villages were taller and less likely to be severely underweight than their counterparts from control villages. The effect was more pronounced among children under two years (Pickering et al. 2015). A similar result is reported for Mali by Gertler et al. (2015), where CLTS interventions led to a 0.17 standard deviation increase in height-for-age Z scores (HAZ). The authors, however, find no significant effect on HAZ in India and Indonesia. Using pooled village-level data from India, Indonesia, and Mali, the study estimates that in a village where everyone practices open defecation, the complete eradication of open defecation would lead to a 0.44 standard deviation increase in HAZ. However, a modest reduction in open defecation or any further reduction where the rates of open defecation are already low, is unlikely to have any detectable impact on HAZ (Gertler et al. 2015).

The project interventions in Punjab have adopted a CLTS approach, with the BCC campaign as a central pillar. The BCC campaign was geared toward both changing personal behavior and creating demand for toilets. It intended to make open defecation repulsive by making people understand that by defecating in the open they end up eating each other’s feces and risking their lives as well as the lives of their children and neighbors. The campaign took a multipronged approach. Daily morning vigils were meant to prevent
h as a percentage of its initial consumption. To calculate the mean and standard deviation of the 55,098 values of 100 evh we need to take into account that the households in the sample are statistically representative of the Russian population, and each household in the Household Budget Survey has a weight wh that reflects the number of people it represents in the overall Russian population. We normalize the household weights by defining αh = wh /∑h wh The weights αh for h = 1, .,55,098 are each household‘s share of the total Russian population. Thus, our estimated weighted average increase in equivalent variation as a percent of initial household consumption is 100 ∑h αh evh = μ. Calculation of the weighted average variance of the increase in equivalent variation as a percent of initial consumption follows similar principles; it is defined as: ∑h [αh 100<sup>2</sup> {evh - μ }<sup>2</sup> ]. We report the positive square root of this value in table 4.1, i.e., the standard deviation. 46 These cross-country regression results are controversial, since trade liberalization is often accompanied by macro stabilization and other market reforms, and the open economy variable in the regressions can be picking up these other effects as well. Moreover, institutional reforms may also be rather important. But trade reform may compel complementary reforms, such as the elimination of state subsidies which are often unsustainable in an open economy. And WTO accession involves a number of institutional reforms. For example, liberalization of foreign direct investment in services will put great pressure on the regulatory authorities to improve the institutional environment.

69
**9 Tables & Figures**

<!-- Start of picture text --> Yucatán<br>65.40<br>Puebla<br>35.28<br>Guerrero<br>33.92<br>Percent Indigenous Population<br>Chiapas<br>Greater than 30% 36.15<br>20-29%<br>10-19%<br>Less than 10%<br><!-- End of picture text -->

**Figure 1:** States of Mexico

Note: The figure shows the percent of indigenous population in each state in Mexico. The parental involvement interventions were carried out in Guerrero, Puebla, Chiapas and Yucatan (labeled in white font). Data source: World Bank Open Data.
57 One such example of seemingly unintended redistributional consequences of grant allocations has been cited in India. Two different groups play important roles in the distribution of resources from the center to state governments--the Finance Commission and the Planning Commission. The former group recommends the method by which central government taxes are to be shared with the states as well as how grants-inaid as specified under Article 275 of the Constitution are to be allocated across states. The Planning Commission decides on the state-wise allocation of loans and grants carried out under the national plan and for central-government sponsored projects. A recent analysis considered the per capita allocations across states made by each group in 1960-61, 1970-71 and 1980-81 and compared them with per capita incomes. While none of the comparisorns Pe-vealed an exact correspondence of higher per capita grants flowing to higher income states, there were numerous instances where obviously wealthier jurisdictions were treated much mo-e favorably than were low income areas. In part this was attributed to the long-standing concern by the Finance Commission that one primary determinant of the allocation of tax shares be based upon the site of collection of the tax. This, as was noted in the Nigerian example above, results in higher income jurisdictions receiving above average shares.

# Empirical Evidence

The previously noted examples of differences between actual practices 4<sup>0</sup> P.K. Bhargava, "Transfers from the Center to the States in India," Asian Survey, Vol. XXIV (June 1984), pp. 665-687.
O G) O **C** z **0** r m **0** 0 I > a RX & a **0.** o R~~~~~~~~~~~ U-

|**Table B9. Informal Perceptions of**<br>EmploymentStatus<br>from Informal Sector Surveys conducted by the author|<25 hours per<br>week<br>Taxicabs|245 hours<br>per week<br>Taxicabs|Entire<br>Sample<br>Taxicabs|Entire<br>Sample<br>Kiosks|Entire<br>Sample<br>S<br>S<br>Markets|<br>Entire<br> <br>Sample<br>treet Services &<br>taeets Sendics &<br> <br>Vendors|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on the|99|101|308|245|137|<br>80|
|<br>question)|||||||
|Response to duureg census"Are you employed?"|||||||
|Yes|69.1|36.6|46.7|25.5|11.7|<br>25.0|
|Among those not employed, the reason provided to the duureg|||||||
|1-illness, looking after someone|3.3|6.5|8.1|14.6|10.7|<br>18.3|
|2-seasonal work|10.0|3.2|5.6|3.9|2.5|0.0|
|3-not finding professional work|26.7|30.7|
# 1. Introduction

Economic growth in the Latin America and Caribbean (LAC) region has slowed significantly since the start of the decade. While there is evidence of an uptick in growth since 2017, driven largely by external factors such as improved commodity prices (Vegh et al., 2018), recent projections forecast an annual growth rate of 1.9 percent for 2018, which is below that of the region’s major trading partners—the US and EU, and indeed is below the global average. If this pattern continues going forward, the region’s share in the global economy is forecast to decline. What is at the root of this problem? Low productivity growth in addition to low levels of capital accumulation have been highlighted as the principal explanations for the region’s poor economic progress (see, for example, Crespi and Zuniga (2012), Pagés (2010) and IDB (2018)). Indeed, the possible causes underlying low productivity in the region are numerous; a recent report by the Inter‐American Development Bank (IDB, 2018) notes that factors such as tax administration, labor regulations, and a lack of innovation may all contribute to the problem. What kind of policies could be implemented to boost productivity? While regulatory and institutional reform have an important role to play, in this research we consider policies at the firm level. In the past decade, a growing body of evidence has emerged, pointing to the strong impact that improved management practices can exert on firm‐level productivity. It is this link—that between management practices and productivity—that we focus on in this paper, using data from recent Enterprise Surveys of seven countries in the region, namely Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru and Uruguay.

While acknowledgment of the link between management practices and productivity has been gaining ground in the past decade or so, the concept is not new. Indeed, as far back as the mid‐1980s, Gershenberg (1986)
To obtain the index ln _AitR_ itself we estimated (a log transformation of ) equation (1) for each of the industries covered by the India sample, but on observations pooled from World Bank Investment Climate Survey data for seven countries including India.<sup>20</sup> Details of the estimates are given in Table 3. The first two columns of the table present _R_ OLS estimates which are, however, bound to biased. The reason is that TFP term, _ait_ , is a state variable that influences the choice of inputs. As a result it must be correlated with all other right hand side variable of the equation. There are at least three recent techniques of achieving identification in this setting. These are Olley and Pakes (1996), Blundell and Bond (2000), and Levinshon and Petrin (2003). We have used the Levinshon-Petrin estimator because of the relative ease of its application given our data. We have too few observations on investment to use the Olley-Pakes estimator, and our panel of observations over time is too short for effective use of the Blundell-Bond approach.<sup>21</sup> In the estimation of production functions labor input is often measured as the number of employees, _N it_ , of the producer. This, however, would entail ignoring the possible heterogeneity of the workforce in terms of both of skills and effort. One way of avoiding this is to take into account differences in labor productivity between producers by using the average plant-level wage rate, _wit_ , as a conversion or weight factor to define labor input as the wage bill at constant prices, that is, to set _Lit_ = _wit N it_ .

# **III. Econometric issues**

Let us assume, for a moment, that the population of manufacturing establishments in the 40 cities and the 11 industries from which our sample is drawn is all we care about when we model the relationship between establishment level productivity, ln _A_<sup>ˆ</sup> _ijtR_ , on
Sakijege, T., Lupala, J., Sheuya, S. (2012) “Flooding, flood risk and coping strategies in urban informal residential areas: The care of Keko Machungwa, Dar es Salaam, Tanzania” Jamba: Journal of Disaster Risk Studies 4(1): 46 Tanzania Urban Resilience Program (TURP) (2015). Partnership Review Note (FDID – World Bank) UN-HABITAT (2010). Informal Settlements and finance in Dar es Salaam, Tanzania. The Human Settlements Financing Tools and Best Practices Series. Nairobi: UN-HABITAT UN-HABITAT (2014). Retrieving Global Urban Indicators from 2009.

World Bank (2011). Urban Poverty and Climate Change in Dar es Salaam, Tanzania: A Case Study 31
https://doi.org/10.1111/j.1475-4932.2011.00747.x Del Boca, D., Pasqua, S., & Suardi, S. (2016). Child Care, Maternal Employment, and Children’s School Outcomes. An Analysis of Italian Data. _European Journal of Population_ , _32_ (2), 211–229. <u>https://doi.org/10.1007/s10680-015-9370-0</u> Dervisevic, E., Perova, E., & Lo Bue, M. C. (2021). _Maternal employment and children’s outcomes: Evidence from Indonesia_ . The World Bank.

Dunifon, R., Kalil, A., Crosby, D., & Su, J. H. (2013). Mothers’ Night Work and Children’s Behavior Problems. _Developmental Psychology_ , _49_ (10), 1874–1885. https://doi.org/10.1037/a0031241 Ermisch, J., & Francesconi, M. (2013). The Effect of Parental Employment on Child Schooling. _Journal of Applied Econometrics_ , _28_ (5), 796–822. https://doi.org/10.1002/jae.2260 Felfe, C., & Hsin, A. (2012). Maternal work conditions and child development. _Economics of Education Review_ , _31_ (6), 1037–1057. <u>https://doi.org/10.1016/j.econedurev.2012.06.008</u> Gennetian, L. A., Hill, H. D., London, A. S., & Lopoo, L. M. (2010). Maternal employment and the health of low-income young children. _Journal of Health Economics_ , _29_ (3), 353–363. <u>https://doi.org/10.1016/j.jhealeco.
the total number of randomly selected staff. Reasons for absence are also collected and are categorized as either authorized or unauthorized, to better understand the nature of health care provider absence from health facilities.

3.2.5 Diagnosis and treatment accuracy In recent years, quality of care has received more attention in the health research community, with an increasing recognition that good health outcomes depend not just on patient access to care but on the competence and skill of the health care provider. Accurate diagnosis and treatment are important for the health outcomes of patients and can also influence future patterns of health care utilization.<sup>15,16</sup> The SDI survey includes clinical vignettes which are administered to health care providers. This is an innovative addition which measures the quality of clinical care, rather than the inputs-focused perspective that earlier surveys had taken.<sup>17</sup> Clinical vignettes may be less reliable for assessing quality of care than other methods, such as the use of standardized patients, but are easier to implement, less expensive and less disruptive to health facility operations. Overall, clinical vignettes have been shown to be “a valid and comprehensive method that directly focuses on the process of care provided in actual clinical practice.”<sup>18</sup> The standardized clinical vignettes assess health care providers’ ability to diagnose and treat common outpatient conditions.<sup>19</sup> Vignettes are administered by two enumerators, who conduct the simulations privately with a clinician after walking the provider through an example case. The first enumerator imitates a patient presenting with an initial complaint and a basic description of the associated symptoms. The health care provider is encouraged to ask follow-up questions around symptoms and presentation of illness, and to request the results of various medical and laboratory tests. The first enumerator offers pre-specified responses to those questions and the second enumerator records how well the physician adheres to clinical guidelines in the questions asked and clinical actions undertaken. By the end of the vignette,
**Table 8 Age Distribution of Researchers in Latvia, 2000**

|Age|<35|36-45<br>46-55|56-65|>65|
|---|---|---|---|---|
|%|3.1|17.4<br>32.4|36.5|10.6|
|_Source:_Dat|a of the Mi|nistry of Education and Sc|ience of Latv|ia|

- As the two charts below indicate, most university students (70%) prefer to study liberal arts, business and law rather than science and engineering (less than 15%). Therefore, the system is not generating enough new science and engineering students to renew the rapidly ageing existing cadre.  Furthermore, Latvia is generating new science and engineering graduates at a rate that is less than half of the rate of the EU as a whole and less than a third of the rate of Ireland and Finland.  This is not the least bit surprising.  Why study science and engineering if there is no demand in Latvia for these skills?<sup>36</sup> Nevertheless, these trends do not bode well for Latvia’s future scientific and innovative potential.

**Figure 28 New S&E Graduates in 2000**

<!-- Start of picture text --> Turkey<br>Slovenia<br>Poland<br>Latvia<br>Lithuania<br>Hungary New S&E graduates (‰ of 20 -<br>Estonia 29 years age class) in 2000 or the<br>Czech Rep. closest year<br>Bulgaria<br>EU<br>Ireland<br>Finland<br>Denmark<br>0,0 5,0 10,0 15,0 20,0 25,0<br><!-- End of picture text --> _Source_ : Eurostat, Trendchart S&
# **7 Early Impacts of Generative AI on Website Traffic**

In this section, we explore the early impacts of generative AI on individuals’ online activities and the implications on the digital ecosystem. Generative AI tools can disrupt the current traffic patterns and advertising-based business models through multiple channels: First, chatbots can provide direct answers, potentially reducing the need for users to click through to multiple websites and aggregate information by themselves. This may divert traffic from traditional search engines and information websites. Second, AI-generated content may compete with human content creators, reducing traffic to blogs, news sites, and other content platforms. However, high-quality, curated content will become more valuable, benefiting some content creators that offer authoritative, novel, and unique content. Third, AI assistants could act as intermediaries, potentially reducing direct traffic to e-commerce websites. If traffic is diverted, it could significantly affect the current attention-advertising based business model. Fewer page views and clicks could shrink ad revenue for many websites. However, traditional websites and platforms will integrate generative AI features to remain competitive. New monetization models will also emerge to leverage AI capabilities. Eventually, a new balance will be struck between AI-mediated and direct web interactions.

Analysis of monthly traffic patterns across various websites reveals distinct impacts potentially correlated with the emergence and updates of ChatGPT. Figure17 compares the normalized traffic trends of three popular information-acquiring websites: Google, Wikipedia, and Quora. Despite ongoing speculation about whether Bing or ChatGPT could supplant Google as the go-to portal for internet users seeking information, we observe no clear trend in Google’s traffic before and after the launch of GPT 4. As of March 2024, Google’s traffic remains 80 times higher than ChatGPT’s (Figure 3). This substantial gap suggests that the fluctuations in Google’s traffic are more likely attributable to seasonality rather than ChatGPT’s influence. Similarly, Quora’s traffic exhibits high volatility without clear correlations to ChatGPT’s trajectory. Wikipedia’s traffic, however, was quite