rafmacalaba/datause-encoder-data huggingface dataset:
Most of the Caribbean sugar industries will need to be restructured, and some will need to be closed. If this is not done, they face ongoing losses that will increase as preferences erode. Since most of the sugar industries of the region are state-owned enterprises, the governments must ultimately pay for the losses. Sugar industries in a few countries in the region, such as Guyana and Belize, are expected to survive with lower preferences. And some portion of other sugar industries in the region could be viable if properly restructured to include only the best sugar producing lands and factories. In Jamaica, for example, two private sector estates are profitable and could survive with lower preferences while much of the state-owned industry could not survive, even now, without government subsidies and would probably not be able to adjust to lower preferences. The Jamaican government has announced that it will close two of its stateowned sugar factories (F.O. Lichts 2005).

Sugar policies often adversely affect consumers. Many Caribbean countries have regulated sugar prices or high tariffs that increase the cost of sugar to consumers. For example, Jamaican consumers paid more than double the retail prices of consumers in Belize, Dominican Republic and Guyana (Figure 2), and slightly higher retail sugar prices than consumers in the European Union in 2002.

**Figure 2. Retail Sugar Prices, 2002, US$s per Lb., Selected Countries**

<!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Belize Dominican Rep. Guyana Jamaica Trinidad &<br>Tobago<br><!-- End of picture text -->

Source: International Sugar Organization 2003.

Government officials of Caribbean countries have been reluctant to close or rationalize their sugar industries because of the economic and social impact of such actions. Sugar is still a significant employer and export earner of many countries, and financial and technical assistance will be needed to help these countries make the transition from a sugar industry dependent on preferential access to the EU and U.S. sugar markets to a more diversified agricultural sector which produces high-valued products to meet the demands of the tourist industry or for export. The European Union has proposed a program of assistance. The international donor community as a whole needs to support these countries at this critical time to help meet the costs of transition.

3
consider only those variables capturing financial depth, such as the credit to GDP ratio or stock market capitalization, to assess the degree of financial development. Financial indicators assessing the degree of financial access, financial stability, or the efficiency of the financial industry have largely been ignored in cross-country studies due to data limitations. The newly developed Global Financial Development Database (GFDD) represents a significant improvement in this respect and provides a comprehensive set of financial indicators that reflect various functions and characteristics of the financial sector. In addition to financial depth, the GFDD provides measures of the efficiency and stability of and access to financial markets. Although data availability remains somewhat limited, we extend the existing literature by including these additional dimensions of the financial sector in our regression analysis to more completely evaluate the effect of finance on growth. Specifically, the indicators we use represent the depth, stability, and efficiency of the banking sector and stock markets as defined by Cihak et al. (2013).<sup>1</sup> In addition to the GFDD, we employ the widely used data set on the determinants of long-term growth developed by Fernandez et al. (2001), which encompasses over 40 explanatory variables capturing various economic, political, geographical, and institutional indicators.

While it is commonly assumed that causality goes from financial development to economic growth, some scholars argue that a growing financial sector merely follows the increasing needs of the real economy or may be determined simultaneously with growth due to other factors. The quantitative survey of the finance and growth literature by Valickova et al. (2015), for example,

> 1. We did not include financial access indicators because of data unavailability. In our sample, the data on the proxy variable recommended for financial access by Cihak et al. (2013), bank accounts per 1,000 adults, are missing for 36 out of 60 countries. Including financial access in the analysis would therefore severely limit our cross-section of countries resulting in the non-negligible loss in the degrees of freedom. Nevertheless, we have examined alternative (less than ideal) financial access indicators from the GFDD database (bank branches per 100,000 adults and ATMs per 100,000 adults), which are available for almost all countries in our sample. However, we fail to find these indicators to be decisive for the long-term growth.

4
|Table 1. Descriptive statistics of the skills and industrial emissions dataset|||||
|---|---|---|---|---|
|Variable|mean|SD|obs.|countries|
|Literacy skills (standardized)|0.16|0.95|81,265|22|
|Numeracy skills (standardized)|0.17|0.95|81,265|22|
|Problem solving skills (standardized)|0.02|0.97|55,146|18|
|Log CO2 per real value-added (industry of employment), log tonnes per million euros|4.05|1.67|81,278|22|
|Annual percent growth in CO2 per real value-added (industry of employment)|-3.25|5.57|81,278|22|
|Female|0.48|0.5|81,277|22|
|Age|40.51|12.31|73,635|20|
|Highest level of education:|||||
|None|0.01|0.1|75,107|21|
|Primary|0.03|0.18|75,107|21|
|Lower secondary|0.1|0.31|75,107|21|
|Upper secondary (vocational)|0.16|0.37|75,107|21|
|Upper secondary (general)|0.32|0.47|75,107|21|
|Post-secondary (technical)|0.11|0.31|75,107|21|
|Post-secondary (academic)|0.26|0.44|75,107|21|
|Means and standard deviations are weighted by sampling weights that have been adjusted to give<br>numeracy, and problem-solving skills, mean and SD are estimated using plausible values.|equal weight t|o each c|ountry; fo|r literacy,|

|Table 2. Descriptive statistics of the industry-level dataset<br>|||||
|---|---|---|---|---|
|Variable|mean|SD|obs.|countries|
|Cog. skills per worker (literacy skills, 2nd percentile set as zero)|127.98|18.55|373|22|
|Output per worker (euros 2012)|67,820|140,618|356|21|
|Capital per work (euros 2012)|471,920|1,661,381|319|19|
|Emissions per worker (tonnes)|24.80|81.74|356|21|

13
# Table I.4.

Turkey: Distribution of Labor *

# Population Census and SII

1960 and 1980 (percents)

||1960|||1980|||
|---|---|---|---|---|---|---|
||Census<br>Economicall|y SII|Ce<br>Economi%.|nsus<br>Employees|<br>SII|InsureL<br>las % o|
|Total, thousanas|11918.8|574.6|4,816.0|<br>1.2?8|1 947.||
|Mining and quarrying|<br>4.0|12.2|2.7|4.0|5.8|87,"|
|Manufacturing|46.1|56.6|41.0|46.3|52.6|68.2|
|Construction|15.1|20.3|15.9|21.9|24.6|67.7|
|Electricity<br>etc.|0.8|2.8|0.7|1.0|4.7|274.0|
|Trade, finance<br>etc.|21.0|2.0|28.6|18.3|7.3|?5* 7|
|Transport<br>and commun|. 12.9|6.1|11.0|8.5|5.1|n5.8|
|Total|100|100|100|100|100|-|

Source: Statistical Yearbook of Turkey, several issues.

Age 15 and above b Age 12 and above Excluding agriculture, forestry, hunting and fishing; community, social, and personal services; activities not adequately defined; altogether 85.2 percent of economically active in 1960, 74.1 percent in 1980.

-59-
to 3.30 in the 2004 ratings.<sup>34</sup> As shown in Table 6, most regions show modest improvement over time, but the increase for ECA was exceeded in magnitude only by the East Asia and Pacific region. Among all 134 countries in the CPIA in both 2002 and 2005, the average ranking for ECA countries was 64<sup>th</sup> in 2002, improving to 61<sup>st</sup> in 2005. In 2002, the mean rating for ECA was third-highest among regions, behind Latin America and South Asia. In 2004, ECA ranked behind only Latin America.

The **International Country Risk Guide** (ICRG) rated 140 countries, including 21 ECA countries, in June 2002 and March 2005. The ICRG is updated monthly, and data for those months were selected to coincide with the beginning of fieldwork for the BEEPS II and III. Unlike the CPIA, the ICRG sample includes most developed countries. Its corruption ratings range from a minimum value of 0 to a maximum of 6. The mean ECA rating increased from about 2.1 in 2002 to 2.2 in 2005. The average ranking for the 21 ECA countries also improved over the period, from 82<sup>nd</sup> to 76<sup>th</sup> .

The **World Economic Forum** data (WEF) included only 14 ECA countries among a total of 79 with available data for both 2002 and 2005. These 79 include many developed countries. Table 5 reports on nine WEF variables, all scaled from a low value of 1 to a high value of 7. Trends in these nine are highly mixed. The first four in the table, pertaining mostly to state capture, all show either stagnation or deterioration. In particular, the average rating for ECA countries on “business costs of corruption” – defined in terms of “other firms’ illegal payments to influence government policies, laws or regulations” – worsens from 4.5 to 4.1. The average ranking for ECA on this question fell from 45<sup>th</sup> in 2002 to 51<sup>st</sup> in 2005.

> 34 The 2004 ratings were produced in late 2004 and early 2005, so provide a better comparison than the 2005 ratings (finalized in early 2006) with the 2005 BEEPS.  The 2002 CPIA ratings were produced in mid and late 2002, so provide the best match with the 2002 BEEPS.

32
|**Table1 **|**:Percent s**|**hare ofs **|**omerelevantvariables**|||
|---|---|---|---|---|---|
|**Variables**|**1993**<br>|**1999**<br>|**Variables**<br>|**1993**<br>|**1999**<br>|
||**Percent**|**Percent**||**Percent**|**Percent**|
|**Gender**|||**Thefirm'sforeigncapitalismore**<br>**than50%offirm'stotalcapital**|||
|Female|28.97|34.87|o|85.12|80.92|
|Male|71.03|65.13|Yes|14.88|19.08|
|**Theworkerbelongstoa union?**|||**Firm'sexportsaremorethan**<br>**50%ofoutput**|||
|No|50.09|56.75|No|93.39|75.75|
|Yes|49.91|43.25|Yes|6.61|24.25|
|In-housetraining|||Thefirmhasqualitycontrols|||
|No|80.31|76.67|o|77.82|75.31|
|Yes|19.69|23.33|Yes|22.18|24.69|
|**Externaltraining**|||**ThefirminvestsinR&D**|||
|No|81.44|80.00|No|43.27|67.38|
|Yes|18.56|20.00|Yes|56.73|32.62|
|**Type ofworker**|||**Didthefirmadoptnew**<br>**technology?**|||
|Eventual|21.80|20.21|No|29.08|13.59|
|Permanent|78.20|79.79|Yes|70.92|86.41|
|Divisionofactivity|||Size|||
|Food,beverages, and tobacco|22.62|19.62|Micro|5.71|17.11|
|Textiles, clothingand leather|15.46|18.93|Small|20.25|17.86|
|Wood and woodproducts|6.94|4.39|Meduum|34.88|14.49|
|aper,paperproducts,printing,and<br>publishing|5.33|5.38|Large|39.16|50.54|
|Chemicals,oilderivatives,andcoal|11.01|11.64|Region|||
|Non-metallic mineral products|5.09|5.16|North|27.70|32.63|
|Basicmetallic industries|3.09|4.20|Center|43.24|45.93|
|Metallicproducts, machinery, and<br>equipment.|29.43|25.47|South|4.42|5.62|
|Othermanufacturingmdustnes|1.04|5.21|MexicoCity|24.64|15.81|
|<br>_Source_Author'scalculationsbased onENT|RAM-ENES|TYC.||||

Table 2 shows the increasing importance that human capital has had on firms; meaning that the effect has become more important since NAFTA took effect. All of the indicators increased from 1993 to 1999. A significant increase in all types of training can be seen between the two years. The average number of years of education increased as well by about 8 percent. The only indicator that decreased was seniority by 4 percent.

The analysis here will focus on formal training only, that is the training provided in-house in firm training programs or by external sources. Formal in-house or in-service 6
# **Appendix A – Search Procedures**

Electronic searches were conducted in the main international databases. Table A.1 provides the list of basic search terms used to construct complex search codes and identify studies to be included in the systematic review. Based on these terms, a detailed search strategy was set up to account for U.S. and U.K. English spelling, to seek for the most relevant studies and to restrict the search to LMICs. The search codes were developed by 3ie specialist John Eyres. The details of the search strategies are available in Piza et al. (2016) and on request. The base of the search strategy was developed using the Social Science Citation Index (ISI) and Econlit databases, two of the most important databases in economics. These codes were adapted for other databases that allow users to construct detailed codes for search terms. The databases searched are the following: 3ie database of impact evaluations, EconLit (Ovid), ABI/INFORM Global (ProQuest), PAIS International, Sociological Abst, Worldwide Political Science Abst (WPSA), ASSIA, Web of Science, that is, ‘Web of Science – Social Sciences Citation Index’, Business Source Premier (Ebsco), Academic Search Complete (Ebsco), Scopus, DAC (OECD), and Google Scholar.

# **Table A.1. Types of Intervention and Related Terms Used to Construct Complex Search Codes**

|**Types of Interventions Targeting SMEs**|**Related Search Terms**|
|---|---|
|Formalization/Business Environment<br>(Institutional Improvement)|SMEs and (formalization, business<br>environment, institutions, property<br>registration,regulatoryframeworks)|
|Exports/Access to External Markets|SMEs and (exports, certification, market<br>fairs)|
|Support for Innovation Policies|SMEs and (innovation, patents, trademarks,<br>research and development, technology<br>transfer)|
|Value Chain, Networks, and Cluster<br>Interventions|SMEs and (value chain, clusters, network,<br>localproductive systems, collective actions)|
|Training and Technical Assistance|SMEs and (training, technical assistance)|
|SME Financing/Credit Guarantee|SMEs and (finance, credit, guarantee),<br>matching grants|

36
# **From Indicators to Overlapping Deprivations**

The ability to analyze overlapping deprivations—the number of indicators and dimensions in which households are simultaneously deprived—is one of the main advantages of multidimensional poverty measures (Ferreira and Lugo 2013). Figure 14 illustrates the overlap of deprivations as defined by the indicators and shows the percentage of the population by number of deprivations. In 2015 only 2 percent of the population was completely free of deprivation. At the other end of the distribution, about 25 percent of households were deprived in eight or more indicators simultaneously.

**Figure 14. Armenian Experience of 0 to 12 or More Deprivations, 2015, Percent**

<!-- Start of picture text --> 12 plus<br>11 3% 0 1<br>10<br>3% 2% 7%<br>5%<br>9 2<br>5% 8%<br>8<br>9% 3<br>10%<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>11%<br>4<br>12%<br>6<br>5<br>13%<br>12%<br><!-- End of picture text --> _Source_ : ILCS data.

17
capital held by agents in the region to be 50 percent, but this percentage is not crucial to the solution of the model.

Regarding the capital of each region that is held outside of the region, it is convenient to think of a national mutual fund that holds the remaining capital in each region. For all three capital types ((i) mobile; (ii) specific capital in the energy sectors; and (iii) specific capital in the IRTS sectors) fifty percent of the capital used in the region is owned by this mutual fund and the balance is owned by the representative agent in the region. The national mutual fund invests in all regions and obtains an overall return. The representative agent in the region also holds shares in the national mutual fund.<sup>8</sup> .

For each region we report returns to capital as returns to the three types of regional capital held by the region’s representative agent. Plus the region’s representative agent obtains a share of the returns from the national mutual fund. The return to national capital is the region’s share of the return of the national mutual fund reported as a percentage of initial consumption of the region.

# **Key Data**

## **Ad Valorem Equivalence of Barriers to Foreign Direct Investment in Services Sectors**

Among the key restrictions against multinational service providers that have existed or exist in Russia are: the Rostelecom monopoly on long distance fixed-line telephone services (scheduled to be removed), affiliate branches of foreign banks are prohibited, and there is a quota on the multinational share of the insurance market.<sup>9</sup> Estimates of the ad valorem equivalence of these and other barriers to FDI in services are key to the results. Consequently, we commissioned 20 page surveys from Russian research institutes that specialize in these sectors and econometric estimates of these barriers based on these surveys.

> 8 Define M(r) as the representative agent’s share of the returns to the national mutual fund. We calculate M(r) as follows.  For each region r, the initial consumption income C( r) equals its endowment income E( r) minus the share of the trade surplus B( r) attributed to the region: C( r)  =  E( r) – B( r).  Define B( r) = [C(  r)/C]* B where C is aggregate consumption for Russia and B is the aggregate trade surplus. That is, the region’s share of the trade surplus is proportional to its share of aggregate consumption.

E( r) is defined above, but is also defined as: E( r) = L( r) + K( r) + M(r). That is, endowment income for each region is labor income L( r); plus capital income on regional capital held by the representative agent in the region K( r); plus the representative agent’s share of the returns to the national mutual fund M(r). E( r), L( r) and K( r) are all known. We calculate M( r) residually for each region r as: E( r) - L( r) - K( r) = C( r) - B( r) - L( r) - K( r) = M(r) .

9 The protocol on Russian accession signed between the European Union and Russia on May 21, 2004 calls for the termination of the Rostelekom monopoly by 2007 and allows for an increase in the upper limit on the multinational share of the Russian insurance market.

11
in the adoption of ISO certification and how changes in web-use by firms in ECA are related to changes in complementary inputs, in market incentives, and in access to international knowledge.

The estimated effects of complementary inputs on technology adoption confirm the results obtained using the 2002 and 2005 cross-sections. We find that firms increasing their share of professionals and firms gaining access to finance between 2002 and 2005 are significantly more likely to become ISO-certified or to start using the web. The effects of R&D intensity on ISO certification are weaker in the panel regressions but they are still positive and strong for web-use. Finally, our panel results show that in locations experiencing improvements in infrastructure between 2002 and 2005 (as indicated by an increase in the value of the index), the likelihood that firms use the web increases but their likelihood of being ISO-certified decreases.

The estimated effects of market incentives on technology adoption are weaker in the panel regressions than in the cross-sectional regressions. Firms that become privatized over the 2002-2005 period are less likely (though weakly) to engage in technology adoption than other firms. Firms whose ownership share of the largest shareholder increases between 2002 and 2005 are less likely to be ISOcertified and to use the web, significantly so for web-use. However, the results from regressions that include an interaction between the privatized dummy and the ownership share of the largest shareholder (in addition to the levels of the variables) show a positive coefficient interaction effect in the case of web use.<sup>18</sup> Thus, these findings are similar to those obtained for the 2002 and 2005 crosssections and indicate that firms experiencing an increase in ownership concentration face an incentive to adopt advanced technology only when there is a clear private owner with a profit incentive.

As in the cross-sectional regressions, Table 5 shows that firms reducing their price-cost margins between 2002 and 2005 are less likely to be ISO-certified or use the web, though the effects are weak. Firms that facing increased pressure from competitors to innovate and those facing increased pressure from consumers to innovate in 2005 are more likely to become ISO-certified and to use the web, but the coefficients are not significant. The results in Table 5 show that larger firms are significantly more likely to adopt advanced technology between 2002 and 2005. Overall, the panel findings provide some evidence that increased concentration is more conducive to technology adoption than increased competition and that there are demand pull reasons for the adoption of advanced technology in ECA countries, but the evidence is weak.

> 18 These results are available from the authors upon request.

- 16 -
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 13. Illustrative investment targets for infrastructure in Botswana**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber-optic links to neighboring capitals and<br>submarine cable.|Provide universal access to GSM signal and public<br>broadband facilities.|
|Irrigation|Develop additional 25,243 hectares of large-scale and<br>553 hectares of economically viable small-scale irrigation.|n.a.|
|Power|Develop 2,141 MW of new generation capacity and 2,120<br>MW interconnectors.|Increase electrification to 80 percent of the<br>population.|
|Transport|Achieve regional (national) connectivity with good-quality<br>2-lane (1-lane) paved road.|Provide rural road access to 79 percent of the<br>highest-value agricultural land, and urban road<br>access within 500 meters.|
|WSS|n.a.|Achieve Millennium Development Goals, clear sector<br>rehabilitation backlog.|

_Source:_ Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GSM = global system for mobile communications. n.a. = Not applicable.

**Table 14. Indicative infrastructure spending needs in Botswana, 2006–15**

|$ million per year||||
|---|---|---|---|
|Sector|Capital expenditure|Operations and<br>maintenance|Total needs|
|ICT|38|32|70|
|Irrigation|5|—|5|
|Power|261|201|462|
|Transport|57|50|107|
|Water supply and sanitation|70|71|141|
|Total|431|354|785|

_Source_ : Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others, AICD 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

Botswana’s infrastructure spending needs are comparatively low in GDP terms. Botswana’s burden of needs, at 7.5 percent of GDP, is among lowest in the region (figure 15) and seems very affordable for the country. Investment would absorb around 4 percent of GDP, less than a quarter of what China invested in its infrastructure during the mid-2000s.

Botswana already spends a sizable amount ($815 million per year) to meet its infrastructure needs (table 15). About 53 percent of the total is allocated to capital expenditure and 47 percent to operating expenditures. Operating expenditures are entirely covered from budgetary resources and payments by infrastructure users. Most of capital expenditure funding—around 93 percent—comes out of state coffers. The private sector accounts for another 4 percent of capital spending. Non-OECD finance at 2 percent and ODA flows at less than 1 percent also play a smaller, but nonetheless significant, role.

31