rafmacalaba/datause-encoder-data huggingface dataset:
developed world’s rate of 94 percent (Figure 6). However, there is large drop off to 58 percent when individuals with secondary education are considered. This 42 percent fall-off is larger than any other change in the BRICS, ECA or the developed world. However the account use levels of Turkey for both individuals with secondary and primary education is still higher than their counterparts in the BRICS economies and other ECA countries.

Between 2011 and 2014, there is a fall in the account use of individuals with tertiary education in Turkey. Usage of accounts falls from 99 percent to 87 percent in 2014 amongst individuals with tertiary education. However, it is important to note that 87% is still a high level of bank usage when compared to the rest of ECA, in which 80 percent of individuals in the same cohort have an account at a financial institution.

Figure 6. Account use by Education <!-- Start of picture text --> 2011 2014<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Turkey BRIC Developing High Income Rest of ECA Turkey BRIC Developing High Income Rest of ECA<br>Primary Secondary Tertiary<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 Age also plays a role in account use across Turkey. Only 44 percent of 15-24 year olds have an account compared to 62 percent of 25-64 year olds and 67 percent of individuals over 65 (Figure 7). Interestingly, in none of the other compared regions do persons over 65 years old have the highest rate of account use while Turkey seems to have a direct correlation between age and account use.

Changes in account ownership between 2011 and 2014 diverge between different age groups in Turkey. Individuals over 65 years of age experience an increase while the two younger cohorts have a lower level of account usage in 2014. When compared to BRICS economies in 2014, younger cohorts use banks at a lower rate while the elderly are more likely to own accounts in Turkey.

<!-- Start of picture text --> Figure 7. Account use, by Age Groups<br>2011 2014<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Turkey BRIC Rest of High Income Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Developing<br>World World<br>15-24 25-64 65+ 15-24 25-64 65+<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 8
- 1.37 -

**Table** LC.5: **Calculation** of **Farm hkcoo** (Concept 2) **from the** National Accounts

||rTEM|1984|1985|1986|1987|
|---|---|---|---|---|---|
|1.|GDP, Agriculture & Uvestock (billions current cedis)|110.4|118.7|175.6|266.1|
|2.|GDP, Cocoa Production & Marketng (billions current cedis)|11.1|18.8|41.0|66.0|
|3.|Cocoa Farmers' Share, Cocoa Export Revenue|.311|<br>.320|.408|<br>.45 (est)|
|4.|Cocoa GOP Paid to Farmnrs (Row 2 x Row 3)|3.5|6.0|16.7|29.7|
|5.|Estmated Farm Income (Row I + Row 4)|113.9|124.7|192.3|295.8|
|6.|Rural CPI, Annual Average (1987=-100)|55.4|60.3|73.2|100.0|
|7.|Estmated Real Farrn Income (billions 1987 cedis)|205.6|206.8|262.7|295.8|
|8.|**Model**Estimate of Farm Income|209.3|223.4|230.2|227.9|
|9.|(GOP-Based Estmate/Model Estimate) x 100|98.2|92.6|114.1|129.8|

Source: The World Bank, 'Ghana Country Economic Memorandum, Report No.7515-GH-, (Green Cover), November 23, 1988, Statistical Appendix: Tabes 2.02, 7.01, 9.01.

generate both estimates of farm Income, the relative closeness of the two sets of farm Income estimates Is reassuring.

Finally, we note that the estimates of cocoa acreage In 1987 given In Table l.C.3 Imply total cocoa acreage of about 2,100,000 acres, or approximately 850,000 hectares. These estimates were derived first from the sample totals (by age class) of cocoa acreage obtained from the 1988 Agricultural Economics Survey for Cocoa Producer Pricing (by COCOBOD) and then projected to the natlonal level In the calibration of the Multiperiod Agricultural Sector Model. The exact calibratlon procedure Is given In Annex A, which lists the GAMS specificatlon for the model. The official COCOBOD estimate of cocoa acreage Is one millon hectares. There Is no precise way of resolving the difference between these two estimates of about 150,000 hectares short of a detailed census. However, we note that if the model estimate Is low, then model yield estimates (by tree ages) must also be too low. Again, only detailed measurements of cocoa yields (by age class) can answer this questlon.
(OD), introduced in Reijnders and de Vries (2018), and extended with a set of lower income Asian economies by Bertulfo et al. (2022), see Appendix Table C2 for the sources. The database provides information on the number of workers by occupation-industry pair as well as their labor earnings. For each country national data has been harmonized using mappings from national occupation classifications to an international classification of thirteen occupational groups based on 2-digit codes in the ISCO88 (International Standard Classification of Occupations), see Appendix table C3. While some countries have adopted the ISCO in their administrative statistics, most follow their own classification and bridge tables needed to be developed to have consistent coding. Furthermore, national industry classifications were mapped to a common set of 35 industries covering the overall economy. These are mostly 2-digit industries in the ISIC (International Standard Industrial Classification) revision 3.1, see Appendix table C4.

Previous studies have compiled large-scale data sets to compare changes in occupational structures across countries and over time. The _Jobs of the World Project_ is a prominent example (Bandiera et al., 2022). We elaborate by adding a cross-classification of occupations by industry. This is an important step to account for the variety in tasks required from workers of the same occupational group in different industries. Compare for example the so-called work activities performed by machine operators in the textile manufacturing industry and similar workers in the electronics manufacturing industry. Both occupations fall under ISCO 1-digit code 8. Data from O*NET indicates that “getting information” is a very important work activity in both industries, ranking first in electronics and second in textiles (out of forty-one work activities that are distinguished). But whereas “evaluating information to determine compliance with standards” and “making decisions and solving problems” are also very important activities in electronics (ranking third and second), they are much less important in textiles (11<sup>th</sup> and 10<sup>th</sup> ). Conversely, “repairing and maintaining mechanical equipment” is barely important in electronics (34<sup>th</sup> ) but above average important in textiles (17<sup>th</sup> ).<sup>3</sup> More generally, one can statistically test for the significance of adding an industry dimension to the occupational class category. To this end, we make use of O*NET’s importance indicators for the full set of industries, occupations and work activities.<sup>4</sup> More

> 3 Based on work activity data from O*NET for job codes 51-2022 - Electrical and Electronic Equipment Assemblers and 51-6062 - Textile Cutting Machine Setters, Operators, and Tenders (assessed 22 July 2022).

> 4 This is data for the U.S. only. Caunedo et al. (2021) document some cross-country differences

7
one in five, while in Haiti (which has a much larger population than the other three countries), the share of the population affected is lower at about three percent of the population. Hydrological events tend to affect a smaller share of the population, on average well-below 10 percent in each of the study countries. Importantly, the impacts of geophysical and climatological events in Haiti tend to be sizeable, affecting about a tenth of the population, or over a million people on average.

We can compare exposure to the major events listed in EM-DAT to reported exposure to disaster risk in the survey data. The vast majority of households in the five countries in our sample indicates being exposed to at least one of the twelve measured disaster risks (see Figure 2).<sup>11</sup> Even in the country with the lowest aggregate risk, Belize, over 75 percent of households perceived at least one natural hazard as a direct risk. In the other countries, exposure to at least one risk exceeds 80 percent and even 90 percent in Haiti. In line with the high number of households affected by meteorological events according to the EM-DAT data, meteorological risks are mentioned often. In the four countries located in the Caribbean hurricane belt (Belize, Dominica, Haiti, and Saint Lucia) over half of households indicates that they are exposed to tropical cyclones and hurricanes, and a large share of households is exposed to severe storms. Households in Haiti and Suriname indicate being exposed to extreme rainfall, the latter in accordance with several major floods hitting the country in recent years.

Moreover, the information from the household survey indicates that households perceive exposure to a wider range of risks, including those that are geophysical, climatological, and hydrological in nature. Earthquakes are the most commonly mentioned geophysical risk. In Dominica in particular, 70 percent of households indicates being exposed to earthquakes and 44 percent to volcanic eruptions. Households in Saint Lucia also commonly refer to the risk of earthquakes (62 percent). While these risks may not feature prominently in the EM-DAT, these results are in line with the monitoring of geophysical risks by the UWI Seismic Research Centre. According to the UWI Seismic Research Centre (2023), there were 186 earthquakes in the English-speaking islands of the Eastern Caribbean in the period from August 1, 2022, to July 31, 2023, alone. And the Volcanic Hazard Atlas of the Lesser Antilles<sup>12</sup> (Lindsay et al., undated)

> 11 Households were also asked about any ‘other’ perceived risks. In Haiti and Suriname, respectively, 4.5 and 10.7 percent of households reported this option. No households in other countries reported other risks.

> 12 See https://uwiseismic.com/downloads/volcanic-hazard-atlas-of-the-lesser-antilles/ (last accessed on May 8, 2024).

9
62%. The number of households without electricity supply in Chile decreased to 14% in 2002 from 62% in 1982 after reforms (Pollitt, 2004).

**Table 5: Reforms and Electricity Quality/Access**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts and_**<br>**_relevance_**|
|---|---|---|---|---|---|
|Cubbin and<br>Stern (2004)|econometric|global|panel data: 28<br>developing<br>countries; 1980-<br>2001|OLS, fixed<br>effects|regulatory law and<br>governance positively<br>related to higher per<br>capita electricity<br>generation and<br>capacity|
|Cubbin and<br>Stern (2006)|econometric|global|panel data: 28<br>developing<br>countries; 1980-<br>2001|fixed<br>effects,<br>error<br>correction<br>models|regulatory law and<br>governance positively<br>related to higher per<br>capita electricity<br>capacity controlling<br>for privatization and<br>competition|
|ESMAP<br>(2011)|econometric|global|panel data; 20<br>countries with<br>different system<br>sizes|fixed<br>effects;<br>random<br>effects|introduction of<br>independent<br>regulation escalated<br>access by 50 percent|
|Zhang,<br>Parker and<br>Kirkpatrick<br>(2005)|econometric|global|panel data: 25<br>developing<br>countries, 1985-<br>2001|fixed<br>effects|independent<br>regulation and<br>competition before<br>privatisation<br>important for higher<br>electricity generation<br>and capacity|
|Zhang,<br>Parker and<br>Kirkpatrick<br>(2008)|econometric|global|panel data: 51<br>developing<br>countries, 1985-<br>2000|fixed<br>effects|on their own<br>privatisation and<br>regulation do not lead<br>to obvious gains in<br>economic<br>performance|
|Erdogdu<br>(2014)|econometric|global|panel data: 55<br>developed and<br>developing<br>countries, 1975-<br>2010|fixed<br>effects,<br>random<br>effects|reform progress led to<br>higher levels of<br>electricity supply self-<br>sufficiency|
|Nagayama<br>(2010)|econometric|global|panel data: 86<br>developed and<br>developing|fixed<br>effects|IPPs, unbundling,<br>regulatory agency and<br>creatingwholesale|

22
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# BIiLIOGRAPHY

- Acharya, M. and L. Bennett. 1982. "Women and the Subsistence Sector: Economic Participation and Household Decision Making in Nepal." World Bank Staff Working Paper No. 526, Washington, D.C.

- Anker, R. 1983. "Female Labour Force Activity in Developing Countries: A Critique of Current Data Collection Techniques." Population and Labour Pclicies Papers No. 136, Geneva: ILO.

- Anker R. and C. Hein. 1986. "Sex Inequalities in Urban Employment in the Third World." International Labor Organisation, London: McMillan.

- Ausen, M. 1977. "Maternal Septicemia and Death After Circumcision." TroRical Doctor, October 1977.

- Belenky, M., B. Clinch, N. Goldberget, and J. Tanule. 1986. Women's WaYs of Knowing: The Development of Self. Voice and Mind, N.Y.: Basic Books.

- Belsey, M. 1976. "The Epidemiology of Infertility: A Review with Particular Reference to Sub-Sahara Africa." Bulletin of the WoIrld Health Organization 54 (3) Geneva.

- Berger, M. ed., 1988. "The Informal Sector, Microenterprise and Women's Work in Latin America." International Center for Research on Women, Washington, D.C.

- Berger, M. 1988. "Women's Responses to Recession in Latin America and the Caribbean: A Focus on Urban Labor Markets." International Center for Research on Women, Washington, D.C. Processed.

- Bevan, D.L., P. Collier, and J. Gunning. 1988. "Women, Public Services, and Income Generation: A Research Proposal." Oxford: Institute for the Study of African Economies, and World Bank: Women in Development Division, Washington, D.C.

- Birdsall, N. and W. McGreevey. 1983. "Women, Poverty and Development." In Women and Poverty in the Third Wcrld. Buvinic and others, eds. Baltimore: Johns Hopkins University Press.

- Bleiburg, F, and others 1980. "Duration of Activities and Energy Expenditure of Female Farmers in Dry and Rainy Seasons in Upper Volta." British Journal of Nutrition, 13.

- Blumberg, R. 1987. "Income under Female vs. Male Control." San Diego, C.A.: University of California, processed.

- Boserup, E. 1970. Women's Role in Economic Development. New York: St. Martin's Press.
Rossi, J. 2006. _Regulatory bargaining and public law._ New York: Cambridge University Press.

Shleifer and Vishny 1994. Politicians and Firms. _Quarterly Journal of Economics_ 109 (4): 9951025.

Shleifer, A. 2004. Does competition destroy ethical behavior? _American Economic Review_ 94(2): 414-418.

Shleifer, A. and R. Vishny. 1998. _The grabbing hand. Government pathologies and their cures._ Harvard University Press.

Smarzynska, B.K. and S.J. Wei, 2000. Corruption and Composition of Foreign Direct Investment: Firm-Level Evidence. NBER Working Papers 7969, National Bureau of Economic Research Søreide, T. 2005. Corruption in public procurement. In _Fighting Corruption and Promoting Integrity in Public Procurement._ Organisation for Economic Co-operation and Development (OECD), Paris.

Søreide, T. 2006a Corruption in international business transactions: the perspective of Norwegian firms. In S. Rose-Ackerman: _The International Handbook on the Economics of Corruption,_ Edward Elgar Publishing.

Søreide, T. 2006b. Procurement procedures and the size of firms in infrastructure contracts. The World Bank Annual Conference on Development Economics, ABCDE, Tokyo 2006, May 2930.

Søreide, T. 2007. Business corruption, uncertainty and risk aversion. CMI Working paper: Søreide, T. 2008. Beaten by bribery: Why not blow the whistle? _Journal of Institutional and Theoretical Economics_ 164(3).

Stainsbury, N. 2005. Exposing the foundations of corruption in construction in _Global Corruption Report 2005_ . Berlin: Transparency International.

Stigler, G. J. 1971. The Theory of Economic Regulation. _The Bell Journal of Economics and Management Science:_ 2(1): 3-21.

Svensson, J. 2003 Who Must Pay Bribes and How Much? Evidence from A Cross Section of Firms, _Quarterly Journal of Economics,_ 118(1):207-230.

Svensson, J. and R. Reinikka 2005. Fighting Corruption to Improve Schooling: Evidence from a Newspaper Campaign in Uganda. _Journal of the European Economic Association_ , 3(2-3): 259267.

Svensson, J. and R. Reinikka 2006. Using Micro-Surveys to Measure and Explain Corruption. _World Development_ 34(2): 359-370.

Tanzi, V. and H. Davoodi 1997. Corruption, public investment and growth. IMF Working Paper, WP/97/139,International Monetary Fund. Washington DC.

Tirole, J. 1986. Procurement and renegotiation. _Journal of Political Economy_ , 94(2): 235-239.

Tirole, J. 1994. The internal organization of government. Oxford Economic Papers.

Transparency International 2002. _Bribe Payers’ Survey_ . Berlin: Transparency International.

Treisman, D. 2000. The causes of corruption: a cross-national study. _Journal of Public Economics_ , 76(3):399-457. 0 23
- 11 -

is another important question for considering competitiveness (e.g., McAfee and McMillan, 1987; Levin and Smith, 1994; Bajari _et al_ , forthcoming). At least from the empirical point of view, however, a necessary question may be whether _N_ is common knowledge among bidders, whatever it represents. Our defined _N_ is the best proxy in this regard. In the practical ODA procurement circumstances, serious tenderers are likely to share a good sense of how many rivals are remaining for the final price competition, either through the formal prequalification result publication and informal business relationship. Therefore, the number of bidders that were prequalified and submitted price bids is considered the best information of _N_ known by tenderers.<sup>11</sup> Following the existing literature (e.g., Porter and Zona, 1993; Bajari _et al_ ., 2006; and Price, 2008), two instruments are used for a vector of selection determinants, _Z_ .<sup>12</sup> One measure is the total amount of contracts—in our sample—awarded to each firm in the three-year period before a particular contract is auctioned ( _BKLG_ ). If a bidder forms a joint venture, the average backlog among consortium members is used. Another variable to complement this is the amount of total transport ODA disbursed to each country in the three-year period prior to the auction ( _CAID_ ).<sup>13</sup> The basic idea behind these variables is that if prospective firms are already devoted to other development projects elsewhere, fewer bidders would be willing to bid on further new works because they might be too busy. An implicit assumption is that firms are resource-constrained, which will in fact be verified in the following analysis.

paper, the competition effect is specified in logarithmic form, because it is known that the mapping between winning bids and the number of bidders is never linear (Rezende, 2005) and the logarithmic models often better fit actual auction data (e.g., Porter and Zona, 1993; Gupta, 2001, 2002).

> 11 Technically, our _N_ is different from the sum of _d_ 2 _a_ and the sum of _d_ 2 _b_ as well. It represents the number of prequalified bidders bud may include those who are disqualified technically in the pre-stage of price bid evaluation. This is the case where the two-envelope procedure is adopted. In this system, all potential bidders are requested to submit both price and technical proposal and an auctioneer opens the price bids only if the submitted technical proposal meets the required standards. It will play the similar role as prequalification, but the order is difference and the scope of the examination may be more technical (see Estache and Iimi (2008b) for more discussion on this).

> 12 _X_ is also part of _Z_ except for _N_ , because at the preselection stage, participants may not be sure how many firms would finally decide to apply for it.

13 Our sample merely represents several percent of total ODA allocated to the transport sector all over the developing world. Therefore, _CAID_ is expected to capture the broader market conditions than _BKLG_ . The used aid data come from the OECD Creditor Reporting System database.
# **What explains current trend growth rates?**

Private investment, followed by productivity growth, are the largest drivers of current economic growth. Current growth rates can be decomposed using a log-linear approximation of the production function as in Devadas and Pennings (2019) (Equation 16). Private investment is the most important growth driver, with a net contribution of about 2ppts of the current 4.5 percent GDP growth (Figure 3.3, graph A). Private investment makes such a large contribution because of the current low private capital-to-output ratio – driven by many years of low private investment following the Asian Financial Crisis – which increases the current marginal product of private capital (Figure 3.3, graph B). Note, however, that both the marginal product and growth contributions change over time, which we discuss in detail below. After private investment, the next largest contribution is from TFP growth, with a contribution of 0.9ppts. Next, population growth and public investment each contribute around 0.65ppts, with human capital growth contributing 0.3ppts.<sup>11</sup>

## **Figure 3.3. Net Investment Contribution to GDP Growth and Marginal Product of Capital**

<!-- Start of picture text --> A. Net Investment Contribution to GDP Growth 1/ B. Marginal Product of Capital<br>Percentage points %<br>of GDP growth 35.0<br>2.50 30.0<br>2.00 25.0<br>20.0<br>1.50<br>15.0<br>1.00 10.0<br>5.0<br>0.50<br>0.0<br>0.00<br>Marginal product of measured public capital (MPK Gm ) 2/<br>Public investment Private investment<br>Marginal product of private capital (MPK P ) 3/<br>(I G /Y) (I P /Y)<br>1/ t �<br>Net public investment contribution to GDP growth ≈ G .<br>GG � �<br>HH YYtt<br>t �<br> Net private investment contribution to GDP growth ≈ φφ�KKtt YYtt −δδPP GG � �.<br>HH YYtt<br>2/ Marginal product of measured public capital, M G(1 −ββ−φφ) �= Gφ� is obtained by taking the derivative of Equation (16) in KKttPP YYtt −δδ PP<br>3/Appendix 1 with respect to Marginal product of private capital, IIttGG⁄YYtt. θθ NN =M θθtt, that is the efficiency of new investment remains the same as past investment. PP = 1−βKK�β−φ , is obtained by taking the derivative of Equation (16) in Appendix KKtt YYtt<br>1, with respect to ⁄ . KK KKttPP YYtt<br>PP<br>tt tt<br>II YY<br>2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br>2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text -->

> 11 Changes in the working-age to total population ratio contribute approximately zero as Malaysia in the middle of a transition between a demographic dividend and an aging population (see Figure 1.9). Labor force participation is assumed to be constant in the baseline, and so makes no contribution.

14