rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 9. Policy frameworks in EMDEs

A. EMDEs with fiscal rules <!-- Start of picture text --> Number Number of EMDEs Percent<br>70 Share of EMDE GDP 60<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10 10<br>0 0<br>1999 2015<br><!-- End of picture text -->

## C. EMDEs with flexible exchange rates

<!-- Start of picture text --> Number Percent<br>Number of EMDEs<br>30 80<br>Share of EMDE GDP (RHS)<br>20 60<br>10 40<br>0 20<br>1999 2017<br>E. Macroprudential policy in EMDEs<br>Index, 12=Most tools<br>4 Latest 2000 2010<br>3<br>2<br>1<br>0<br>EMDEs ECA EAP LAC MNA SAR SSA<br><!-- End of picture text -->

## B. EMDEs with inflation targeting

<!-- Start of picture text --> Number Percent<br>Number of EMDEs<br>70 60<br>Share of EMDE GDP (RHS)<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10 10<br>0 0<br>1999 2017<br><!-- End of picture text -->

## D. Central Bank transparency in EMDEs

<!-- Start of picture text --> Index<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1999 2014<br><!-- End of picture text --> F. Bankruptcy rights protection in EMDEs Distance to frontier score, 100=best <!-- Start of picture text --> 80 Advanced-economy average<br>60 2018 2010<br>40<br>20<br>0<br>ECA LAC EAP SAR MNA SSA<br><!-- End of picture text --> Source: Cerutti, Claessens and Laeven (2017); International Monetary Fund; Kose et al. (2017); Dincer and Eichengreen (2014); Ha, Kose, and Ohnsorge (2019); World Bank.

A. An economy is considered to be implementing a fiscal rule if it has one or more fiscal rules on expenditure, revenue, budget balance, or debt.

B. Inflation targeting as classified in the IMF’s Annual Report of Exchange Arrangements and Exchange Restrictions (AREAER).

C. An economy is considered to have a flexible exchange rate if it is classified as “floating” or “free floating” in the IMF AREAER.

D. Dincer and Eichengreen Transparency Index. The index ranges from 0 (least independent and transparent) to 15 (most independent and transparent).

E. Sample includes 123 EMDEs. Unweighted average of the macroprudential policy index of Cerutti, Claessens and Laeven (2017). The macroprudential policy index measures the number of tools used by authorities and is based on a simple sum of up to 12 including, but not limited to, the countercyclical capital buffer and loan-to-value ratios. F. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms that improve the business climate. EAP, ECA, LAC, MNA, SAR, and SSA include 22, 22, 32, 19, 8, and 46 economies, respectively. Advanced economies include 36 economies. Based on World Bank Doing Business report.

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<!-- Start of picture text --> Policy, Research, and External Affairs<br><!-- End of picture text -->

# "WORKING PAPERS

<!-- Start of picture text --> Socio-Economic Data<br>WPS 476<br><!-- End of picture text --> This paper - _a_ product of the Socio-Economic D3ta Division, International Economics<sup>Department-</sup> is part of a largereffort in PRE toward a more versatile approach<sup>to estimating conversion factorsfor the</sup><sup>_World_</sup> _Bank Atlas_ and operational purposes. Copies are available free from the World Bank,<sup>**1818**HStreet NW,</sup> Washington **DC** 20433. Please contact Estela Zamora, room **S7-136,** extension<sup>**33706 (59**pages, including</sup> tables).

The significant operational implications underlying the Bank's estimates of per capita **GNP** represent important considerations in systematizing the use of official and other exchange rates in determining the exchange rate to **be** used in the Bank's Atlas methodology. Hee explores the potential for a system of time series for various conversion factors and a corresponding set of time series for weights.

The framework is useful for countries with multiple exchange rates. It gives us a way to develop time series on parallel and black market exchange rates, purchasing-power parities, traderelated taxes and subsidies, and potentially more.

The starting premise is that a single official exchange rate has a weight of **1.0** in all years; all other rates (implicitly) have zero weights. **A** major component of this framework is the redistribution of weights among multiple exchange rates.

Such a matrix of conversion factors and corresponding weights could provide the mechanism **(1)** for a systematic approach to weighting alternative rates; (2) for estimating the effects of alternative weighting schemes; **(3)** for determining the effects of incorporating parallel or black market exchange rates; **(4)** for providing the basis for less erratic and unpredictable fluctuations in the data in the _World Bank Atlas_ and _World Tables; and_ **(5)** for improved and more transparent documentation of methods and "special" cases.

> The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy. Research, and External Affairs Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced **by** the PRE Dissemination Center
# **Figure 1 Employment Rates by Age Cohort of Older Workers and Elderly in Urban and Rural China**

Panel A. Employment Rates by Age Cohort: CHNS and the CHARLS (2008) Pilot <!-- Start of picture text --> Urban Male Urban Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>Age Age<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br><!-- End of picture text --> Panel B. Employment Rates by Age Cohort: CHNS (1991 – 2009) <!-- Start of picture text --> Urban Male Urban Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>age age<br>1991 1997 2000 2009 1991 1997 2000 2009<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>Age Age<br>1991 1997 2000 2009 1991 1997 2000 2009<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br><!-- End of picture text --> Notes: Employment rates by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. Panel A and B use data from the common provinces surveyed across waves of the China Health and Nutrition Survey (CHNS) conducted from 1991 to 2009. Panel A also includes data from the 2008 China Health and Retirement Longitudinal Study (CHARLS) Pilot.

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**Table 3.2 French Market Leaders: Life Insurance, Based on Premium Income**

|Insurer|Overall<br>Market<br>Share 2010(%)|Share<br>Direct<br>Business(%)|Equity-Linked<br>Bank in France|Nature of Model|
|---|---|---|---|---|
|CNP|18.2|16.7|BPCE, La Banque<br>Postale|JV/Partnership|
|Predica|14.8|14.6|Group<br>Credit<br>Agricole|Integrated|
|AXA|10.7|7.3||Partnership|
|Natio Vie, CARDIF,<br>Natio Insurance|8.6|8.7|BNP Paribas|Integrated<br>/<br>partnership|
|Generali France|8.0|7.2||Partnership|
|SOGECAP|7.4|6.6|Societe Generale|Integrated|
|Groupama–Gan Vie|6.0|3.3|Groupama<br>Banque|Partnership/<br>integrated|
|Alllianz France|5.6|4.3||Partnership|
|ACM|4.7|7.4|Credit Mutuel|Integrated/<br>partnership|

_Source:_ AXCO, FFSA.

# **Factors Supporting Bancassurance in France**

The success of Bancassurance in France may have been due to the fact that, aside from the liberalization of the banks’ scope of operations, various other factors have supported the Life Bancassurance model in France:

- The French taxation system encourages savings. In particular, Life insurance products held for a minimum period are exempted from tax on the proceeds. This benefit has been diluted in the last decade, but it continues to drive sales.

- Banks were able to distribute insurance products at marginal cost because their fixed overheads were covered by their large established deposit taking and lending businesses.

- Bank and Life insurer groups have effectively been able to extend the term of their liabilities through the creation of longer-term contracts.

- Banks have been able to retain distribution income in-house and generate asset management fees.

- The relatively weak Solvency I requirements have facilitated a degree of regulatory arbitrage, although this benefit has been diluted through the Financial Conglomerates Directive.

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# **Appendix: Construction of Port Congestion Index**

The index to compile is aimed to examining the effect of the physical investment of the ocean container-specialized port facilities on the trade costs. As stipulated in the fourth section in the main text, the capacity of the port directly affects the costs for its services in two aspects: the first is through the port tariffs and other charges for the unloading and loading services, and the second is the time costs due to the congestion. The expansion of the port capacity is accompanied by higher tariffs and charges, but lower degrees of congestion and waiting time for the movement of goods.

We have compiled an index of port turnover , defined as the sum of the loaded and unloaded containers in TEU (Twenty-foot Equivalent Unit) at the major container ports in the country _i_ in the year _t_ , divided by the sum of the estimated capacity of the major container ports in the country _i_ in the year _t._ Table below summarizes the ports referred to in the compilation of the index, together with the actual throughput and estimated port capacity of each port, and estimated port congestion turnover index for the country/economy. The numerator of the congestion index reflects the actual throughput of the major ports reported in the issues of _Containerization Yearbook_ . The same reference is used to estimate the capacity.

The estimate of the port capacity builds on only the physical magnitude. We put the following assumption on the full physical capacity of the port, based on the numbers and depths of the berths: The berths with 14 meters or deeper in depth can accommodate the vessel with 6000 TEU. The vessels use up 250 meters of the berth. The births with 13 meters in depth can accommodate the vessels with 3250 TEU, using up 200 meters of the berth. Those with 12 meters in depth, the vessels with 1750 TEU, using up 150 meters of berth. Those with less than 10 meters in depth, 500 TEU, using 100 meters and less of berth. Combination of various sizes of vessels are applied to maximize the estimated capacity the port can accommodate at once.

# **Table: Throughput, Port Capacity and Congestion Index of Major Ports in East Asia**

40
and Eichengreen (2002), and Noy (2004) similarly find that financial liberalisation can significantly increase bank fragility.

# _International shocks, exchange rate regime, and crises_

Another line of research investigates the impact of worldwide economic shocks and the exchange rate regime on bank fragility. A number of observers noticed the relationship between financial difficulties in emerging markets and tighter monetary conditions and 11 growth deceleration in the industrialised world. For instance, the Volcker disinflation in the US in 1979–81 has been blamed for contributing to the financial crises in Latin America in the early 1980s. Similarly, the monetary tightening in the United States in 1994 may have contributed to the Mexican crisis.

Eichengreen and Rose (1998) is the first empirical paper on the role of international shocks in banking crises. It finds a strong effect of OECD interest rates and, to a smaller extent, OECD GDP growth, on bank fragility in developing countries. Arteta and Eichengreen (2002) find that when the sample is extended to include more recent years, the evidence of an OECD effect becomes weaker. These authors conclude that the banking crises of the mid-1990s were different from earlier episodes, with external factors playing a much smaller role compared to domestic factors.

The impact of external factors on bank fragility might vary with the exchange rate regime. For instance, flexible exchange rates may have a stabilising effect on the financial system since the exchange rate can absorb some of the real shocks to the economy (Mundell, 1961). Flexible regimes may also curtail the tendency of countries to over-borrow in foreign currency and discourage banks from funding dangerous lending booms through external credit (Eichengreen and Hausmann, 1999). Further, with a fixed exchange rate (and even more so with a currency board), lender of last resort operations are severely limited, as 16
This is the first study to use firm-level data to investigate how well water infrastructure can cope with extreme weather variability. It is also the first study of the effect of water infrastructure quality on firm performance that is truly global in nature. Thus far, the handful of studies on water infrastructure quality and firm performance have tended to focus on a specific region such as SubSaharan Africa (Moyo, 2011; Escribano et al., 2010, Davis et.al, 2001) or Eastern Europe and Central Asia (Iimi, 2011; Bogetic and Olusi, 2013). Furthermore, most of these studies are based on a small number of countries (Moyo, 2011) or construct their own data for a specific country (Davis et.al, 2001). Finally, all previous studies based on World Bank Enterprise Surveys use data from early rounds that were not nationally representative (Iimi, 2011; Moyo, 2011; Escribano et al., 2010; Bogetic and Olusi, 2013).

The findings of these studies have not led to a definitive conclusion on whether and how much water infrastructure reliability impacts firm performance. On the one hand, deficiencies in water supply are found to be the largest impediment to firm productivity in the Russian Federation using Enterprise Surveys conducted between 2003 and 2008 (Bogetic and Olusi, 2013). The Enterprise Surveys conducted in 2005 also found that water shortages add to firms’ costs in the Eastern Europe and Central Asia region (Iimi, 2011). Similarly, Moyo (2011) using Enterprise Survey data for five economies - South Africa, Zambia, Uganda, Mauritius and Tanzania, between 2002 and 2005, finds poor water infrastructure quality has a negative effect on firm productivity. More recently Islam (forthcoming) has illustrated the extreme impact that water shortages can have on informal firms, which are an important source of employment in the developing world. On the other hand, Davis et al. (2001) find a small effect of water infrastructure improvement on expenditures of small enterprises. Escribano et al. (2010) use earlier Enterprise Surveys for 26 African countries conducted between 2002 and 2006 to explore the effect of infrastructure quality on firm productivity and only find an effect for slower growing African economies. Typically, studies using firm-level data have been relatively silent on the possibility of endogeneity of firmlevel infrastructure measures, pointing out instead that disaggregated infrastructure measures do not suffer several endogeneity concerns faced by aggregated public capital measures found in macro-level studies (Escribano et al., 2010; Moyo, 2011). Macro-level studies have not found robust results with regards to infrastructure and growth, although a few studies employing different tools to address endogeneity have found some positive and significant effects (Roller and Waverman, 2001; Shioji, 2001).

4
# **A.2.1 Comparison between experts’ and ChatGPT’s sophistication rankings**

We validate the industry experts’ technology sophistication rankings using AI-powered large language models. To start, we prepared files with the list of business functions and associated technologies in the grid, keeping the way they are described in the FAT survey. We generate separate files for GBFs and each specific sector functions. We then uploaded these files and asked ChatGPT to rank the sophistication of these technologies, providing the following prompts:

1. Based on the survey questions from the document, please create a report ranking the level of technological sophistication for each technology within business function on a scale of 1 to 5, where 1 represents the most basic technology and 5 represents the most sophisticated technology. You can use decimal points. Generate a report file with this information. Provide the data in an Excel sheet.

2. Please expand the previous exercise by providing the rank of technology sophistication for each of these definitions: 1) Functionality: The breadth and depth of features and capabilities that technology offers to perform various tasks and processes within a business function. Higher functionality indicates the ability to handle more complex tasks associated with the business function on a larger scale, faster, with more accuracy and reliability. 2) Integration: The ability of a technology to connect and interact seamlessly with other systems, platforms, or technologies within the business ecosystem. Higher integration means the technology can easily exchange data and coordinate processes with other systems. 3) Automation: The extent to which a technology can perform tasks automatically without human intervention. Higher levels of automation imply that the technology can execute processes, make decisions, and generate outcomes on its own.

3. For each business function, please expand the previous exercise by estimating the time each technology would take to perform a typical task in a company with 10 workers. Please describe this typical task in a separate column and explain the reason for these estimates in another column. Generate a report with the estimates and explanations in Excel.

Tables A.1 - A.13 summarize the main results from prompts 1 and 3. Figures A.12-A.16 show the strong positive association between ChatGPT-specific rankings of technology sophistication for functionality, integration, and automation with industry experts’ rankings. The rankings provided by ChatGPT are strongly correlated with our technology sophistication ranking, which is defined by human experts. The overall correlation across all functions and various definitions is above 90%. The results are similar across all business functions.

92
substantially more variation in trade and productivity differences across countries than previously feasible.

The second contribution is to provide panel estimates of the elasticity of export growth with respect to productivity development. Following the theoretical work of Costinot et al. (2012) that is discussed below, estimations include fixed effects for importer-industry-year and exporter-importer-year. The importer-industry-year fixed effects control, for example, for trade barriers in each importing country by industry segment while the exporter-importer-year fixed effects control for the overall levels of trade between countries (e.g., the gravity model), labor cost structures in the exporter, and similar. While these controls account for overall trade and technology levels by country, permanent differences in the levels of these variables across industries within a country are used for identification in most applications of this approach. This paper is the first to quantify Ricardian elasticities when further modeling cross-sectional fixed effects for exporter-importer-industry observations. This panel approach only exploits variation within industry-level bilateral trading routes, providing a substantially stronger empirical test of the theory.

The third and most important contribution is to provide instruments for the labor productivity development in exporting countries. Instruments are essential in this setting due to typical concerns: omitted variable biases for the labor productivity measure, reverse causality, and the potential for significant measurement error regarding the productivity differences across countries. The instruments exploit heterogeneous technology diffusion from past migrant communities in the United States for identification. These instruments are developed by combining panel variation on the development of new technologies across US cities during the 1975–2000 period with historical settlement patterns for migrants and their ancestors from countries that are recorded in the 1980 Census of Populations.

The foundation for these instruments is the modeling of Ricardian advantages through differences across countries in their access to the US technology frontier. Recent research emphasizes the importance of immigrants in frontier economies for the diffusion of technologies to their home countries (e.g., Saxenian 2002, 2006, Kerr 2008). These global connections and networks facilitate the transfer of both codified and tacit details of
18 Two final methodological notes are about the use of OLS for estimation and the time horizon for growth rate calculation. Since labor force growth is in part determined by income growth, estimates of ffie income growth-labor force growth relationship are potentially affected by their joint endogeneity. The estimates presented do not correct for this problem for thre reasons. First, since labor force growth is by and large predetermined by earlier population growth (although there are some participation effects) the endogeneity should be less severe in this case than in those studies that use contemporaneous population growth. Second, if more rapid income growth reduces population growth then whatever simultaneity bias there is should bias towards finding a more negative relationship. Since the primaxy puzzle is that the relationship is not as negative as people expect, correcting this bias would only reduce the already small population effect. Third, our experinentation with instumealvariables estimates (using eihr lagged labor force growth, lagged births or lagged child morality) gave similar results-but with significant added imprecision due to instrumenation.

Except for a smal seceon below, all the results reported are over the 30 year period from 1960-199019. There is a good reason. If one is interested in long-un relationships the use of high frequency data is unlikely to add much information. Earlier research (Easterly, et al, 1993) has shown that there is very litde persistence of economic growth rates across periods as long as a decade. In contrast, population growth rates change only quite slowly9<sup>0</sup> . Therefore, while moving to anmual data or _5_ year periods would add a large number of 19 Or as mach of that period was available. If data was only available 1962-1990 for instance, we just **used** the growth rate over that shorter penod.

20 Even in the ideal situation in which population was actually observed at high frequency. In most developing county cases much of the data about population is interpolated into to create complete series and so cannot represent real information useful for estimation in any case.