rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 11073

# **Abstract**

This paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.

This paper is a product of the Economic Policy Global Department. 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 pmandon@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
# **Appendix A: Trade Share Data and Tariff Rates for Kenya’s Trade Partners**

# **Trade Share Data**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database.

The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In this appendix, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. For COMESA, in order to avoid double counting, we took the COMESA countries less those in the East African Customs Union, i.e., Comoros, Congo, Djibuti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius Seychelles, Sudan, Swaziland, Zambia and Zimbabwe. Trade shares for the ―Africa‖ region in our model is the sum of East Africa Customs Union plus COMESA as defined above. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in the first table below.

We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in the subsequent three tables, by CRTS and IRTS goods in our model separately.

## **Tariff Rate Calculations**

**Tariff and Sales Tax Data** .  We started with MFN tariff rates at the eight digit level  taken from

the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php. These tariff</u> rates were then aggregated to the sectors of our model, using simple averages.

We obtained data on the total taxes on imports and the total value of imports and took the ratio to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent.<sup>30</sup> That is, on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production.

As we reported in Balestreri, Rutherford and Tarr (2009), the MFN tariff rates, multiplied times the trade flows, exceed the collected tariff rates. That is, using MFN tariff rates for all trade, the weighted average tariff rate exceeds the collected tariff rate of 8.4 percent for the economy as a whole. Thus, they exaggerate the protection received by Kenyan industry and agriculture. This is due to tariff preferences to regional partners and due to other preference items or tariff exemptions. We assume that zero tariffs apply 30 Economic Survey (2006, pp. 103, 115).

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**<u>C. By household labor market status (for inequality measured by consumption per capita)</u>**

||**Hun**|**gary**|**Pol**|**and**|**Rom**|**ania**||**Russia**||**Mol**|**dova**|**Tajik**|**istan**|**Geo**|**rgia**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1993|2002|1998|2002|1998|2002|1997|1999|2002|1998|2002|1999|2002|1999|2002|
|**Theil entropy measure**|**0.087**|**0.107**|**0.152**|**0.181**|**0.128**<br>**Decom**|**0.149**<br>**positio**|**0.218**<br>**n**|**0.214**|**0.193**|**0.24**|**0.209**|**0.149**|**0.187**|<br>**0.279**|**0.271**|
|**:**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|<br>**100%**|**100%**|
|of which between LM groups<br>of which within LM group of wage|2%|2%|1%|0%|4%|1%|0%|1%|5%|9%|8%|2%|0%|3%|1%|
|<br>earners<br>of which within LM group of self-|52%|53%|38%|40%|55%|69%|62%|63%|67%|43%|38%|13%|40%|17%|19%|
|<br>employed<br>of which within LM group of|16%|15%|43%|38%|18%|7%|5%|9%|14%|10%|18%|19%|39%|41%|39%|
|<br>subsistence farmers<br>of which within LM group of non-|2%|2%|3%|3%|11%|8%|26%|19%|8%|29%|19%|66%|14%|29%|28%|
|<br>working|27%|27%|15%|18%<br>**T**|13%<br>**heil entr**|14%<br>**opy mea**|6%<br>**sure**|8%|6%|8%|16%|1%|6%|11%|13%|
|within "formal" wage earners|0.084|0.104|0.145|0.171|0.118|0.142|0.194|0.207|0.184|0.239|0.208|0.171|0.21|0.228|0.205|
|within "informal" of self-employed|0.077|0.096|0.157|0.189|0.156|0.243|0.172|0.2|0.203|0.277|0.213|0.159|0.17|0.239|0.247|
|within "informal" subsistence farmers|0.086|0.089|0.146|0.202|0.121|0.133|0.339|0.233|0.156|0.187|0.134|0.138|0.16|0.339|0.354|
|within LM group of non-working|0.092|0.114|0.147|0.180|0.126<br>**Populat**|0.149<br>**ion sha**|0.196<br>**res**|0.221|0.18|0.196|0.243|0.433|0.24|0.368|0.325|
|"formal" wage earners|66%|56%|41%|43%|61%|56%|68%|62%|67%|36%|32%|11%|35%|20%|23%|
|"informal" self-employed|8%|15%|43%|37%|14%|18%|7%|9%|12%|7%|16%|15%|42%|50%|45%|
|"informal" subsistence farmers|1%|3%|2%|2%|13%|13%|17%|20%|14%|48%|38%|74%|17%|20%|20%|
|LM group of non-working|26%|27%|15%|17%|12%|13%|7%|8%|8%|9%|13%|0%|5%|10%|12%|
||**R**|**eal mea**|**ns, relat**|**ive to n**|**ational**|**mean pe**|**r capita**|**consum**|**ption =**|**1.00**||||||
|"formal" wage earners|1.022|0.97|0.988|0.992|1.059|1.077|1.02|1.042|1.055|1.222|1.193|1.071|1.011|<br>1.046|1.086|
|"informal" self-employed|1.089|1.172|0.967|0.988|0.824|0.816|0.929|1.037|1.116|1.152|1.095|1.17|1.018|<br>0.942|0.941|
|"informal" subsistence farmers|0.845|1.041|1.249|1.263|0.911|0.899|0.99|0.865|0.693|0.784|0.774|0.956|0.955|<br>1.212|1.081|
|LMgroupof non-working|0.921|0.968|1.092|1.01|1.006|1.023|0.903|0.963|0.891|1.161|1.071|1.05|0.931|<br>0.785|0.916|

_Source:_ authors’ calculations based on ECA regional data archive) 27
**Table 1: Summary Statistics (Project-level)**

||**Source**|**N**|**Mean**|**SD**|**P50**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|
|Disputes|PPI and UNCTAD|5237|0.080|0.272|0|0|1|
|**Micro variables**||||||||
|Duration (Contract age)|PPI|5237|9.569|5.999|9|0|28|
|IFI involvement|PPI|5156|0.131|0.338|0|0|1|
|Private share > 50%|PPI|4989|0.905|0.293|1|0|1|
|Private share > 80%|PPI|4989|0.755|0.430|1|0|1|
|Competitive bidding|PPI|1987|0.724|0.447|1|0|1|
|**Macro variables**||||||||
|Change in real per capita GDP (during contract)|WDI|5199|0.009|0.056|0.002|-0.360|0.563|
|Change in term of trade (TOT) (during contract)|WDI|4991|0.114|0.380|0|-0.684|5.139|
|Increase in input cost (average)|World Bank|4722|0.064|0.060|0.057|-0.272|0.446|
|Commodity windfall revenue (average)|World Bank, UN Comtrade|5191|0.081|0.053|0.091|-0.230|0.345|
|Democracy|Polity IV|5187|0.741|0.438|1|0|1|
|Right wing government (last year)|Database of political institution|3846|0.197|0.398|0|0|1|
|Log income per capita in 2000|Besley and Persson (2011)|5162|8.504|0.656|8.373|5.790|10.561|
|Primary commodity exporter|UN Comtrade|5237|0.258|0.437|0|0|1|
|Resource rents|WDI|5215|9.415|10.103|6.546|0|89.511|
|High state fragility|Marshall and Cole (2009)|5126|0.272|0.445|0|0|1|
|**Sector dummies**||||||||
|Oil, gas, and mining (OGM)|PPI and UNCTAD|5233|0.067|0.250|0|0|1|
|Energy|PPI and UNCTAD|5233|0.362|0.480|0|0|1|
|**Region dummies**||||||||
|East Asia and Pacific (EAP)|PPI and UNCTAD|5237|0.299|0.458|0|0|1|
|Europe and Central Asia (ECA)|PPI and UNCTAD|5237|0.144|0.351|0|0|1|
|Latin America and the Caribbean (LAC)|PPI and UNCTAD|5237|0.300|0.458|0|0|1|
|Middle East and North Africa (MENA)|PPI and UNCTAD|5237|0.027|0.164|0|0|1|
|South Asia|PPI and UNCTAD|5237|0.147|0.354|0|0|1|
|Sub-saharan Africa(AFR)|PPI and UNCTAD|5237|0.083|0.277|0|0|1|

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Table 3: Descriptive statistics on ethnicity and kinship links

||Mean|
|---|---|
|Ethnicity: Mandinka|54.1%|
|Ethnicity: Fula|18.6%|
|Ethnicity: Wollof|10.1%|
|Ethnicity: Jola|6.5%|
|Ethnicity: Sererr|5.6%|
|Household head has family links in the village|85.0%|
|Wife of household head has family links in the village|50.8%|
|Household have marriage links in the village|64.2%|
|Household has family links in the village|95.8%|
|Observations|1,625|

Source: Author’s own calculations on the Gambia Networks Data 2009.

structure may also be more amenable to policy interventions aimed at reducing discriminatory behavior. In contrast, it is unlikely that it is possible to ever totally eliminate idiosyncratic obstacles to trade due to, say, enmity between individuals.

The data contain information on three distinct aspects of pre-existing social structure: ethnicity, kinship (family ties), and geographical proximity. This data is used to construct two group-wise _Dij_ and two pairwise _Lij_ variables. The sample is representative of the ethnic diversity in The Gambia. The largest ethnic group (Mandinka) accounts for 54.1 percent of households in the sample (see table 3). Four other ethnic groups each account for at least 5 percent of the households.<sup>21</sup> Since, by definition, ethnicity is shared by all the members of a group, it is captured using a group-wise variable _Dij_<sup>_E_equalto1if</sup><sup>_i_and</sup><sup>_j_sharethesame</sup> ethnicity. In this case, each ethnic group forms a component.

Kinship, in contrast, defines a symmetric network in which a link _L_<sup>_K_</sup> _ij_<sup>between two households</sup> _i_ and _j_ is defined to exist if they are related, either through the household head, the wife(s) of the head, or through marriage. Unlike in other African contexts (e.g., Barr et al., 2012), kinship networks are relatively dense in the sample. 85 percent of household heads and 51 percent of their wives have relatives in the village (see table 3). Similarly, 64 percent of households have marriage ties in the village. In total, 95.8 percent of households have at least one of these kinds of links. This means that the largest component of the kinship network is often large: in the majority of villages, most (though not all) households are connected, directly or indirectly, through consanguinity and marriage, and thus belong to the same 20
Querubin, 2021; Mahmud and Riley, 2021). The notion that grants can be helpful in an emergency is a natural one.<sup>3</sup> Yet, it is relatively difficult to obtain rigorous causal evidence on the question given the logistical and political hurdles involved in implementing and measuring exogenous variation quickly in the midst of a crisis. There are a few papers that do so in the context of COVID-19, but to our knowledge only one other study focuses on entrepreneurship outcomes. Brooks et al. (2021) studies 753 female micro-entrepreneurs in Nairobi slums in a cash grants field experiment, and finds significant mitigation effects on profits, operational status, and household food expenditure. Specifically, average profits fell from about $2/day in January 2020 to $1/day in May, at which point a random subset received a $50 grant. By August, profits rebounded by 38%, businesses were 5 percentage points more likely to be open, and food expenditures increased. Our study involves both female and male entrepreneurs operating at a larger scale around the country, with a grant amount that is five times higher relative to monthly profits. Our analysis, therefore, complements the earlier findings.

It is important to note that grants may not necessarily have large impacts in times of crisis. Indeed, it is entirely plausible that very little can be accomplished via moderate grants when the entire system is under stress. Along these lines, Kimani et al. (2020) finds that even $1,000 grants given randomly to refugees in Uganda (in Feb-May 2020) did not lead to higher food consumption levels several months later. Banerjee et al. (2021) also finds only slightly higher well-being, as measured by food security, and physical and mental health, from a universal basic income program in Kenya. Most of the cash in their treatment arms was distributed prior to the pandemic, which resulted in improved outcomes at the time, but also led to more lost income after the pandemic.

In terms of business outcomes, a crisis is likely to endow a desire for flexibility and hence a preference for reversible investments and costs. This may imply that hiring and employment, which are often stickier than other costs, are slower to recover after a crash. Indeed, at a macro level, there is evidence for so-called ‘jobless growth’ in Nigeria (Osiobe and Oseghe, 2020), South Africa (Meyer, 2017), and Uganda (Bbaale, 2013). At a micro level, de Mel, McKenzie, and Woodruff (2012) studies the impact of the 2004 tsunami on Sri Lankan firms, along with randomized access to capital in the form of grants. The study finds that most firms recover their physical assets within three months of the disaster, but that profits take up to two years to return to normal – unless grants are provided, in which case recovery is almost immediate.

Combined, these studies show substantial variation in how impactful grants can be in times of crisis. Our study contributes to this young literature by offering field evidence from a large

> 3 For example, cash grants are a popular tool in times of humanitarian crises. See Doocy and Tappis (2017) for a review on cash grants in such settings.

5
response to appreciations or depreciations of the exchange rate. As shown in column 1, a 10 percent increase in the nominal exchange rate is associated with a 0.38 percent decrease of export prices in dollars. In column 2 we allow for differential impacts for depreciations and appreciations (by not restricting 6 = 0). Results show that the overall impact on export prices is indeed different depending on whether there is a depreciation or appreciation taking place. Export prices react φφ almost 15 percent more when the rupee depreciates than when it appreciates (the 15 percent is the additional 0.005 impact for depreciations divided by the baseline impact for appreciations, 0.034).

Table 8: Pricing to market in Pakistan (2003-2017)

|VARIABLES<br><br>|(1)|(2)|
|---|---|---|
|lNl<br>it<br><br> <br>|-0.038***<br>(0.017)|-0.034*<br>(0.018)|
|lNl<br>it<sup>× d</sup>it<br>||-0.005**<br>(0.002)|
|<br>llit|0.033***<br>(0.018)|0.037**<br>(0.018)|
|Time FE|Yes|Yes|
|Trading-partner FE|Yes|Yes|
|Product FE|Yes|Yes|
|Observations|369,916|369,916|
|R<sup>2</sup>|0.982|0.982|

***, **, * imply significance at the 1%, 5% and 10% respectively. SE, adjusted for clustering at the HS6-year level, are reported in brackets.

We further check whether this unveiled asymmetric pattern changes the size of NER changes. Table 10 presents results of estimating equation (6) interacting a set of dummies that identify large changes in bilateral nominal exchange rate (those in the top quartile of the distribution (column 2), those in the top decile (column 3), and those in the percentile 95 (column 4)). Estimates show a differential effect for the top 10 percent and top 5 percent NER changes that accentuates the asymmetric reaction of export prices. Asymmetry in PTM appears to be bigger when NER change 38
## **Contributory pensions**

In our analysis we do not include the study of contributory pensions, which represented 38.2 percent of total social spending in 2017, due to the complexity of accurately capturing effects of their elimination under an accounting framework. Contributory pensions are the main support for most of the retired. Thus, changes in the system would surely trigger strong individual behavioral responses, such as changes in labor market decisions and private savings, that cannot be accounted for when using the CEQ approach.

# **III. Methodology and data**

Fiscal policy contains several instruments that can help alleviate deprivation and close the gaps between different groups of the population. Certain policies can also promote economic mobility and break the intergenerational transmission of poverty through investments in public education and health that boost human capital accumulation and increase opportunities for all. An important piece of assessing how able governments are in achieving these goals is to look at how key wellbeing indicators of poverty and inequality behave in the application or absence of these fiscal instruments. Here we present an estimate of the impact of fiscal policy by allocating the elements of fiscal policy (such as programs, revenues collection and expenditures) to individuals and households for whom we can observe in the _Encuesta Continua de Hogares_ (ECH) 2017. The framework for the allocations and post-allocation analysis follows the methodology developed by the CEQ Institute to assess fiscal policy (Lustig, 2018).

# **A. Methodology**

The impact that fiscal policies have on income (or purchasing power or welfare) is quantified as follows. First, we estimate, a gross income without public transfers for each household (an income that would be experienced _before_ any transfers or payments generated by the fiscal system are received or imposed). This state, estimated through the concept of _pre-fiscal income_ ( h ), is obtained as the cumulative income received from wages and salaries (that is, from labor market II transactions including direct taxes and contributions to the Health System) plus the market value of consumption of own production; from capital (including direct taxes); and from private transfers (such as remittances from family members working abroad); and finally from pensions (whether private or public).<sup>12</sup>

> 12 One notable limitation of the current analysis is that it only incorporates the current contributions to the pension system, as well as the receipts from the pension system, but they are not part of the distributional analysis. Contributory pensions are only considered part of the market income. Contributory pensions are the main support for most of the retired individuals in Uruguay. Thus, we should expect an individual behavioral response if the contributory pensions program did not exist, such as an increase of private savings. Besides, given the important weight of contributory pensions, the assumption that they do not exist should lead to adjustments in macroeconomic and public accounts. Finally, the contributory pensions

8
<mark>Browning, M., & Chiappori, P. A. (1998). Efficient intra-household allocations: A general characterization and empirical tests.</mark> _<mark>Econometrica</mark>_ <mark>, 1241-1278.</mark> <mark>Carlson, G. J., Kordas, K., & Murray‐Kolb, L. E. (2015). Associations between women's autonomy and child nutritional status: a review of the literature.</mark> _<mark>Maternal & child nutrition</mark>_ <mark>,</mark> _<mark>11</mark>_ <mark>(4), 452-482.</mark> <mark>Connelly, R., Roberts, K., & Zheng, Z. (2010). The impact of circular migration on the position of married women in rural China.</mark> _<mark>Feminist Economics</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(1), 3-41.</mark> <mark>Gupta, M. D. (1996). Life course perspectives on women's autonomy and health outcomes.</mark> _<mark>Health Transition Review</mark>_ <mark>, 213-231.</mark> <mark>Diallo, S. A., & Voia, M. (2016). The threat of domestic violence and women empowerment: The case of West Africa.</mark> _<mark>African Development Review</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(1), 92-103.</mark> <mark>Doss, C., Grown, C., & Deere, C. D. (2008).</mark> _<mark>Gender and asset ownership: a guide to collecting individual-level data</mark>_ <mark>. The World Bank.</mark> <mark>Doss, C. (2013). Intrahousehold bargaining and resource allocation in developing countries.</mark> _<mark>The World Bank Research Observer</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(1), 52-78.</mark> <mark>Doyle, K., Levtov, R.G., Barker, G., Bastian, G.G., Bingenheimer, J.B., Kazimbaya, S., Nzabonimpa, A., Pulerwitz, J., Sayinzoga, F., Sharma, V. and Shattuck, D. (2018). Gender-</mark> 32
Figure 15. Direct and indirect value added exports of financial services as shares of GDP, 2011 <!-- Start of picture text --> 2.5<br>BGR<br>MYS<br>ISL<br>2 THA<br>CRI<br>ISR<br>1.5<br>MAR<br>DNK<br>CZE<br>KOR<br>HRVPRT ZAF BEL<br>HUN<br>SVN<br>1<br>CHN IND CAN NLD<br>RUS CHL VNM NZL FRA TUN ITA EST AUT<br>PER AUSPOL PHL NOR LVA<br>GRCSVK SWE GBR<br>BRN ROU DEU<br>0.5 TUR ESP<br>BRALTU FIN KHM<br>IDN<br>ARGCOL MEXJPN USA<br>SAU<br>0<br>0 0.5 1 1.5 2 2.5<br>Direct Value Added Exports/GDP (percent)<br>Indirect Value Added Exports/GDP (percent)<br><!-- End of picture text --> Source: OECD TiVA and authors’ calculations.

For business services, Figure 16 shows two interesting differences with developing East Asia:

- Given the greater cross‐border tradability or openness to trade of business services, there is less scope for an intermediate stage (for countries to populate the top left quadrant). When a country is not competitive in producing these services, it neither exports them nor do its goods sectors import them. When a country is competitive, it exports both directly and indirectly.

- Most countries in the region inhabit the bottom left hand quadrant, where both direct and indirect exports of business services are low, reflecting the relative weakness of this sector.

- In the bottom right quadrant are Philippines, and surprisingly, Malaysia, and along with the “premature deindustrializer,” India, where direct exports of business services are high but indirect exports are low.  In the case of the Philippines, the explanation is the relative strength of business services and the relative weakness of goods sectors, but the Malaysian case deserves closer inspection.

32