rafmacalaba/datause-encoder-data huggingface dataset:
development (R&D) expenditures. However, few firms in developing countries categorize any expenditure as R&D, meaning we must look for altemative measures. One determinant is market structure (discussed in the following section), since it directly impacts technological progress.<sup>7</sup> Other aspects of technology can be proxied by equipment vintage, the number of technical personnel and their wages, and purchased foreign inputs and equipment. Sources and vintage of equipment proxy for technology under the assumptions that newer equipment represents a higher level of technology, and that imported equipment is more advanced. In addition, technological know-how is embodied in technical personnel. A higher percentage of the work force with technical knowledge-engineers and employees with college degrees, for example-represents a more advanced state of technology. Finally, the development literature sometimes considers the relationship between purchased foreign inputs and productivity to be an indicator of the firm's level of technology (Tybout, 1998).

Few Bank surveys have attempted to measure technology. In addition, most of these data sets are only cross-sectional, making it difficult to measure technological change. Some surveys contain a minimum amount of information about technology, including questions on the percentage of inputs imported from different foreign countries. The _RPED_ and the _ICS_ surveys serve as nice guides for future firm surveys on technology. These surveys gather data on licensing fees, the presence and amount of foreign technical assistance, the number of expatriates, equipment vintage, level of new investment, imported equipment, the number of scientists and engineers, R&D expenditures, types of R&D, new product or process adoption, and whether the firm sells any technology.

# **D. Market Structure**

Market structure is one of most important determinant of firm behavior and performance. It thus is useful to compare the market structure across countries in the _same_ industries to explore whether competition leads to faster innovation and superior performance. In addition to the 7Scherer and Ross (1990, p. 660) note that "What is needed for rapid technological progress is a subtle blend of competition and monopoly, with more emphasis on the former than the latter, and with the role of monopolistic elements diminishing when rich technological opportunities exist." 11
Policy Research Working Paper 9221

# **Abstract**

The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading.

This paper is a product of the Strategy, Analytics, Financing Solutions and Knowledge Unit. 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 bandree@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
Table 1: **Summary statistics**

# **Panel A: Full Sample (Obs: 2,048,164)**

||**Mean**|**SD**|
|---|---|---|
||(1)|(2)|
|Child’s Edu|8.84|4.33|
|Child’s Primary Completion(=1)|0.90|0.30|
|Father’s Edu|5.28|4.52|
|Normalized Inpres Intensity|0.22|0.11|
|Enrollment Rate in 1971|0.17|0.08|
|Number of Children in 1971 (in thousands)|194.29|124.18|
|Allocation of Water and Sanitation 1973–1978|0.48|0.27|

# **Panel B: Exposed and Comparison Cohort**

||**Mean**|**SD**|
|---|---|---|
||(1)|(2)|
|Exposed Cohort(Born Between 1968-1972),Obs= 1,796,198|||
|Child’s Edu|9.12|4.20|
|Child’s Primary Completion(=1)|0.92|0.28|
|Father’s Edu|5.43|4.54|
|Comparison Cohort(Born Between 1957 to 1962),Obs= 251,966|||
|Child’s Edu|6.79|4.68|
|Child’s Primary Completion(=1)|0.77|0.42|
|Father’s Edu|4.20|4.26|

Notes: The variable _Normalized Inpres Intensity_ measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The full sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Edu represents years of schooling, which was calculated based on the education level completed. Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

38
||**Table 4-**<br>|**Impact of**<br>|**Standards**<br>|**on Trade**<br>|<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|VARIABLES|selection|intensity|selection|intensity|selection|intensity|
|number of MRLs|-0.001<br>[0.000]***|-0.001<br>[0.001]|||||
|average MRL|||0.000<br>[0.000]|0.008<br>[0.004]*|||
|Restrictiveness Index|||||-0.011<br>[0.004]***|-0.223<br>[0.916]|
|Ln(1+tariff)|-0.001|-0.382|-0.001|-0.375|-0.001|-0.382|
||[0.000]***|[0.067]***|[0.000]***|[0.067]***|[0.000]***|[0.067]***|
|Contiguity<sup>d</sup>|0.006|0.563|0.006|0.571|0.006|0.564|
||[0.001]***|[0.173]***|[0.001]***|[0.175]***|[0.001]***|[0.173]***|
|Colony<sup>d</sup>|0.004<br>[0.001]***|0.445<br>[0.161]***|0.004<br>[0.001]***|0.424<br>[0.163]***|0.004<br>[0.001]***|0.439<br>[0.161]***|
|Ln(distance)<br>|-0.007<br>[0.000]***|-0.913<br>[0.278]***|-0.007<br>[0.000]***|-0.914<br>[0.279]***|-0.007<br>[0.000]***|-0.909<br>[0.278]***|
|Common language<sup>d</sup>|0.002<br>[0.000]***||0.002<br>[0.000]***||0.002<br>[0.000]***||
|zeta||3.683||3.835||3.700|
|||[1.553]**||[1.556]**||[1.565]**|
|zeta_sq||-0.339||-0.407||-0.356|
|||[0.837]||[0.837]||[0.843]|
|zeta_cb||-0.051||-0.042||-0.048|
|||[0.155]||[0.154]||[0.155]|
|mills||1.462||1.482||1.455|
|||[0.476]***||[0.471]***||[0.476]***|
|Observations|2,109,457|98,065|2,055,256|95,047|2,109,457|98,065|
|R-squared||0.320||0.322||0.320|
|Robust standard errors in<br>equation equals one if the<br>in the intensity equation i<br>product-time effects also<br>groups. Superscript<sup>d</sup>is fo|brackets *** p<br>re is positive t<br>s the natural lo<br>included in all<br>r dummyvaria|<0.01, ** p<<br>rade between<br>garithm of th<br>regressions.<br>bles.|0.05, * p<0.1.<br>countries or z<br>e value of trad<br>Error terms cl|Dependent v<br>ero otherwise<br>e. Importer-t<br>ustered by im|ariable in the<br>. The depende<br>ime, exporter-<br>porter-produc|selection<br>nt variable<br>time, and<br>t-year|

25
The problem with this approach is that aggregate data fails to capture heterogeneity across firms and non-linearities in investment decisions recently highlighted by a new streamn of works (Doms and Dunne, 1994; Caballero, et al, 1995; Cooper, et al, 1995).

The problems of an "aggregate" approach to the study of investment decisions are even more evident by comparing two recent studies on investment productivity and growth in Africa. The first - Devarajan, et al (1999) - focuses on investment productivity using cross-country data. Contrary to the aforementioned theoretical predictions, the authors find that neither public nor private investment significantly impacted growth and output in Africa over the past three decades. To reconcile the empirical findings with the theoretical predictions, the authors integrate cross-country exploration with firn-level data on investment in the mianufacturing sector in Tanzania. This analysis suggests that the low level of investment can be partially attributed to the low productivity of capital for African firms. The low level of productivity of capital then explains the absence of a cross-country correlation between growth performance and investment.

The second study - Gunning and Mengistae (1999) - on the other hand reaches the opposite conclusion: the productivity of capital for African firms has increasecl over the past ten years. In particular, the authors explore the rate and the productivity of investment using firmlevel panel data collected by the RPED in eight African countries. Their detailed data set contains information on capital stock and market structure and helps document low levels of investment as well as heterogeneity in firm and investment productivity in the African manufacturing industries. They conclude that manufacturing firms are profitable and that investment productivity has improved in the past ten years.<sup>1</sup> These results, in contrast with the findings of Devarajan et al. (1999), suggest that solving the puzzle of the low level of investment and the weak link between investment and growth in Africa may require exploring more closely other determinants of investment, such as the degree of reversibility or risk.

Micro panel data on characteristics of capital stock and firm productivity could improve our understanding of the link between investment and growth. Unfortunately, this type of data is not readily available. Bank coverage has improved recently with the implementation of the three

> 1 l This result is derived by looking directly at the productivity of the firms, and indirectly at the degree of tolerance of the markets toward inefficient investment.

20
_Africa’s Macroeconomic Story_ Aggregate measures of real GDP per capita growth for the region (and for the various country classifications) are generated using weights based on population. Countries with larger populations therefore play a more prominent role in the aggregates. This is especially important for the oil exporting country grouping where Nigeria dominates.<sup>6</sup> In 2012, trend growth for the oil exporters grouping is close to four percent, yet only Angola and Nigeria had growth rates above four percent. Four of the seven countries in the group have growth rates under two percent (Figure 6). Median growth rates (a measure that is independent of population size) show a different pattern (Figure 5, right panel). By this measure, the sharp increase in commodity prices in the mid-2000s appears to have had a transitory impact on trend growth in oil exporting countries.

**Figure 6: Trend Real GDP per Capita Growth – Resource-Rich Countries, 2012**

_Percent_ <!-- Start of picture text --> Oil exporters Non-oil resource exporters<br>6<br>4<br>2<br>0<br>Angola Nigeria Congo, Rep. Gabon Cameroon Chad Sierra Leone Ghana Tanzania Botswana Zambia Namibia Niger Cote d'Ivoire Guinea Mali Swaziland<br>Central African...<br>Equatorial Guinea Congo, Dem. Rep.<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013.

> 6 Nigeria accounts for almost three quarters of the total population in the oil exporting countries group in 2012. Similarly, three countries—Ghana, the DRC and Tanzania-- account for 60 percent of the population in the nonoil resource-rich country group.

14
# **Table 5**

# **Climate Regressions for India**

|Independent<br>Variables|<br> Wea|Dependen<br>ther Stati|t Variable: Cr<br>on<br>|opland Value|in 1997<br>Satellite|
|---|---|---|---|---|---|
||Temp|Climate|All|Temp|Climate All|
|Constant|-37000|-82500|-53700|3680|3964 8470|
||(2.35)|(6.11)|(4.24)|(0.39)|(0.57) (1.22)|
|January Temp|39.|–1160|-1240|-390.|180. –238.|
||(0.10)|(3.27)|(3.50)|(0.84)|(0.50) (0.63)|
|April Temp|965.|2030|1160|504.|-1130 -1300|
||(1.08)|(2.63)|(1.60)|(0.55)|(1.74) (2.04)|
|July Temp|1810.|1800|976.|-446.|-160. –230.|
||(1.97)|(2.10)|(1.25)|(0.40)|(0.22) (0.32)|
|October Temp|-177.|2190|1880|137.|1260 1540|
||(0.21)|(2.27)|(2.14)|(0.10)|(1.50) (1.87)|
|Jan Temp Squared|3.70|27.3|25.7|7.6|-9.3 1.0|
||(0.40)|(3.26)|(3.17)|(0.53)|(0.82) (0.09)|
|Apr Temp Squared|-21.3|-34.7|-17.6|-13.6|24.7 29.3|
||(1.39)|(2.62)|(1.42)|(0.76)|(1.95) (2.34)|
|Jul Temp Squared|-28.2|-28.8|-15.9|4.1|3.5 4.0|
||(1.87)|(2.08)|(1.26)|(0.20)|(0.26) (0.31)|
|Oct Temp Squared|4.23|-33.9|-30.6|8.3|-34.6 -41.9|
||(0.31)|(2.17)|(2.16)|(0.28)|(1.79) (2.18)|
|January Prec|...|-57.6|-25.1|...|-59.0 -49.0|
|||(2.82)|(1.17)||(0.43) (0.36)|
|April Prec|...<br>|91.8|50.4|...|1320 1230|
|||(3.68)|(2.13)||(6.83) (6.41)|
|July Prec|...<br>|7.2|6.0|...|-39.1 -1.8|
|||(2.15)|(1.99)||(0.40) (0.02)|

52
determine if they were systematically linked to several possible explanatory factors. To evaluate the influence of geographic concentration of imports by each African country -- a variable that would reflect a situation that could lead to the abuse of monopoly power -- variables were tested which measured the share of iron and steel supplies originating in the largest, and three largest exporting countries. 8/ While these measures rallel the concentration ratios used in structure-performance studies of domeslic markets, there is a special problem in that similar (equal) ratios can meek different d tributions of competing firms. In support of the country ratios, however, is the fact Lhat firms headquartered in the same exporting nation may have a tendency to participate in cartel arrangements or collusive oligopoly decisions on foreign prices. Also, iron and steel production is generally among the most concentrated of industries in developed countries so the potential number of expoiting firms is limited. During the 1962-1987 period which is the focus of this study there appear to have been only 3 or 4 firms producing the (sampled) French steel products for export, and during various sub-intervals the links between these companies were reinforced by nationalization.

Two variables were employed to test the relation between the size of the export shipments, or the export market, and the pattern of relative prices. First, the relative y dntity (tons) of each country's iron and steel

- 8/ Concentration ratios, such as the share of imports received from the largest or three largest suppliers are statistical measures which show the per cent of sales controlled by a given number of the largest firms. For a discussion of the use, and problems with measures of market concentration see Adelman (1951), Prais (1958) or Scherer (1970). Numercus studies of domestic markets in developed and developing countries have consistently shown that firm prices and profits are (positively) linked to the level of market concentration. Since the country ratios are employed as proxies for the geographic concentration of suppliers, this variable tests whether the normal structure-performance relation also holds for international markets.
an annual allocation of 46.1 million for survey implementation and 16.9 million for technical assistance.

The paper is organized as follows. Section II discusses data gaps that need to be filled for effective monitoring of poverty and shared prosperity. Section III details the nature of the surveys to be implemented for filling the data gaps as well as the assumptions that underlie our costing exercise. Section IV provides the estimates of costs based on several scenarios and Section V concludes.

# **II. Defining data gaps: Why focus on surveys in IDA countries, every three years?**

The starting point for monitoring progress in poverty reduction and enhancing shared prosperity is to have household consumption survey data that are not only available at reasonably frequent intervals, but that are also comparable over time. Alongside household consumption survey data, complementary data are needed for tracking poverty, especially when comparing poverty across countries in a given year (World Bank, 2015a). This includes data from population censuses, cost of living adjustments across countries or Purchasing Power Parities (PPPs), inflation, and economic growth. However, the main bottleneck for monitoring poverty and shared prosperity is typically lack of household surveys (Serajuddin et al., 2015).

To assess a country’s ability to monitor poverty and shared prosperity, the frequency of poverty data matters; however, setting a standard on the ideal interval is not straightforward. Though valuable for decision making, surveys typically impose significant demands on financial and human resources. The General Data Dissemination System (GDDS), developed as an attempt to improve the quality of statistics, recommends that poverty statistics be updated in at least 3 to 5 year intervals.<sup>9</sup> This can serve as a useful benchmark of household survey periodicity. For the 2002-2011 time period, Serajuddin et al. (2015) note that 63 of 155 countries had three or more data points during those ten years, and another 15 countries met the GDDS minimum requirement for data needs with two data points with 5 year intervals (Figure 1). Over time, the number of countries with three or more data points in 10 years clearly increased, while countries with no data points declined (e.g., from 50 in 1990-1999 to 29 in 2002-2011). Nonetheless, challenges remain, in that approximately half of the countries (77 of 155) do not meet the GDDS minimum requirement, and in 57 countries, it is not possible to monitor poverty or shared prosperity at all.

Additionally, two data points with 5 year intervals may not be sufficient for poverty monitoring purposes, as poverty can be volatile. For instance, according to PovcalNet (as of October 2014), the poverty headcount rate declined in India by 10 percentage points in rural areas and by 7

> likely due to the provisions for a resident advisor, our total survey implementation cost estimate is also 23 percent lower.

> 9 The Enhanced General Data Dissemination System is available at <u>http://dsbb.imf.org/pages/GDDS/TableB.aspx.</u>

5
standardizing the AI sector (PRC MIIT, 2024).<sup>22</sup> Furthermore, in December 2024, China's Ministry of Industry and Information Technology (MIIT) established an artificial intelligence standardization technical committee. This committee is tasked with developing industry standards in areas such as large language models (LLMs) and AI risk assessment. Lastly, DeepSeek-R1, an open-source reasoning model developed by the Chinese AI startup DeepSeek (or 深度求索 ) was publicly released on January 20, 2025. A notable aspect of this LLM is its cost-effectiveness. The model was developed with an investment of under US$6 million, a fraction of the expenditure—estimated to be multiple billions—reportedly associated with training models like OpenAI's o1. It also relies on inferior chips.<sup>23</sup> By contrast, certain UMICs with relatively high observed AIPI scores, such as Brazil, Mexico and Türkiye, appear to underperform relative to their multidimensional ECI.

Last but not least, Figure 5, also weighted by population size, highlights the relationship between AIPI and multidimensional economic complexity for the subgroup of 43 LICs and LMICs, with an value of 0.70 and of 0.83 (statically significant at 0.1 percent). This stronger correlation aligns with the RR<sup>2</sup> earlier explanation regarding fiscal buffers. Unlike certain UMICs, these countries, particularly LICs, oo face severe fiscal constraints, including notably low levels of tax mobilization, which significantly limit their policy options (Choudhary, Ruch, and Skrok, 2024). As a result, they are compelled to prioritize cost-effective digital and AI-specific strategies whenever feasible. This also highlights that, similar to HICs at the opposite end of the spectrum, their AI preparedness is on average very closely linked to their trade and research complexity. However, this relationship functions at a much lower equilibrium, constrained by their critical multidimensional development challenges (UNSD, 2024).

> 22 These guidelines propose the development of over 50 national and industry-wide AI standards by 2026 and contributions to more than 20 international AI standards, focusing on key technologies and practical applications. This comprehensive approach underscores China's strategic commitment to AI dominance.

> 23 “In terms of performance, DeepSeek-R1 has demonstrated capabilities comparable to leading models like OpenAI's o1. For instance, on the MATH-500 benchmark, which assesses high-school-level mathematical problem-solving, DeepSeek-R1 achieved a 97.3 percent accuracy rate, slightly outperforming OpenAI o1's 96.4 percent. In coding tasks, it scored 49.2 percent on the SWE-bench Verified benchmark, edging out OpenAI o1's 48.9 percent.” <u>[https://venturebeat.com/ai/calm-down-deepseek-r1-is-great-but-chatgpts-product-advantage-is-farfrom-over].</u>

11
that the young cohort in 2000 is less likely to have completed a given grade. The magnitude of the negative impact increases from grade 1 to grade 4 (for which the estimate is -0.157) and then decreases for grades 5 and 6. This suggests that the largest impact of the genocide on schooling achievements seems to have occurred for children in grades 3 and 4.

The empirical identification strategy, illustrated by these cross-tabulations and in Figure 2a, relies on the comparison between the schooling achievement of similarly aged individuals in 1992 (prior to the genocide) and 2000 (after the genocide). We examine whether school-aged children in 2000 who had been exposed to the consequences of the 1994 genocide have lower educational attainment than similar school-aged children in 1992, relative to the older cohorts of individuals who had completed their schooling. The change in schooling attainment for the older cohort of children acts as a control group for what the change in the younger cohort’s education rates should have looked like in the absence of the conflict. Building on these cross-tabulations and figures, we estimate a baseline regression in which the key variable of interest is the interaction of a dummy variable for being in the young cohort and a dummy variable for being in the 2000 DHS data. The regressions also include fixed effects for province and children’s age, as well as an indicator for being in the 2000 DHS data.<sup>6</sup> Despite the two datasets bracketing the time period of the genocide (one dataset two years prior and one dataset six year post), there could have been other changes or events occurring during this time interval that might be correlated with changes in schooling, and we would incorrectly be attributing the observed decline in educational attainment to the genocide. To address this issue, we provide two robustness checks to argue that the decline in schooling is directly caused by the conflict. First, we develop three distinct measures of province-level war

> 6 Correlation among the error terms for children in a given province experiencing the same shock might bias the OLS standard errors downward, so in all regressions we cluster the standard errors by province (Moulton, 1986; Bertrand, Duflo, and Mullainathan, 2004).

9