rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 Selected Infrastructure Indicators of Caribbean Countries <u>And Comparable Developing Countries 2004-06</u>

||**Fixed line and**<br>**mobile phone**<br>**subscribers (per**<br>**100 people)**|**Improved sanitation**<br>**facilities (% of**<br>**population with**<br>**access)**|**Improved water**<br>**source (% of**<br>**population with**<br>**access)**|**Internet**<br>**users (per**<br>**100**<br>**people)**|**Price basket**<br>**for Internet**<br>**(US$ per**<br>**month)**|**Roads, paved**<br>**(% of total**<br>**roads)**|
|---|---|---|---|---|---|---|
|Antigua and Barbuda|168.9|95.0|91.0|38.1|22.0|33.0|
|Bahamas,The|111.6|100.0|97.0|31.9|25.0|57.4|
|Barbados|116.8|100.0|100.0|54.8|25.6|100.0|
|Belize|51.1|47.0|91.0|11.4|12.7|17.0|
|Dominica|87.9|84.0|97.0|36.1|16.5|50.4|
|Dominican Republic|57.2|78.0|95.0|20.8|12.3|49.4|
|Grenada|68.3|96.0|95.0|18.2|22.0|61.0|
|Guyana|52.9|70.0|83.0|21.6|12.5|7.4|
|Haiti|6.9|30.0|54.0|6.9|12.0|24.3|
|Jamaica|117.7|80.0|93.0|46.4|26.5|73.9|
|St. Kitts and Nevis|74.5|95.0|100.0|21.4|22.0|..|
|St. Lucia|..|89.0|98.0|33.9|22.0|..|
|St. Vincent and the<br>Grenadines|92.1|..|..|8.4|22.0|70.0|
|Suriname|88.2|94.0|92.0|7.1|16.2|26.3|
|Trinidad and Tobago|149.1|100.0|91.0|12.3|12.6|..|
|**Caribbean**|**88.8**|**82.7 **|**91.2 **|**24.6**|**18.8**|**47.5**|
|East Asia & Pacific|57.8|50.6|78.5|11.1|5.8|11.4|
|Latin America &<br>Caribbean|72.8|77.0|90.9|18.4|12.2|24.3|
|**Comparators**|||||||
|Benin|209.0|13.0|33.0|67.0|8.0|11.0|
|Korea,Rep.|138.5|..|92.0|70.5|34.6|76.8|
|Malaysia|91.2|94.0|99.0|43.2|2.7|81.3|
|Mauritius|90.1|94.0|100.0|14.5|16.2|100.0|
|Singapore|148.1|100.0|100.0|38.3|13.2|100.0|
|Taiwan,China|165.4|..|..|63.6|1.5|95.5|
|Thailand|75.5|99.0|99.0|13.3|5.8|98.5|

Sources: World Bank (2008).

The EPA recently concluded with the European Union recognizes these needs. In Part I, Article 8 states that development co-operation shall be primarily focused on the following areas: (i) The provision of technical assistance to build human, legal and institutional capacity in the CARIFORUM States so as to facilitate their ability to comply with the commitments set out in the Agreement; (ii) The provision of assistance for capacity and institution building for 19
**When NTMs are included to the calculation of the overall trade restrictiveness index (OTRI),**<sup>**15**</sup> **the MENA region comes across as the most restrictive region in the world, driven by their high NTMs on agriculture goods** (Figure 7). According to the firms surveys mentioned above, transport-related infrastructure and real trade costs (i.e., trade facilitation) have become the most important constraint to trade in the region. This confirms recent analytical studies that conclude that the magnitude of Arab trade flows is significantly lower than it would otherwise be as a result of high real trade costs (Dennis 2006, Harb 2007, and Peridy 2007).

**_Figure 7.  Overall Trade Restrictiveness Index by region, 2008_**

<!-- Start of picture text --> OTRI<br>Percent<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>East Asia & Europe & Central Latin America & Middle East & South Asia Sub-Saharan<br>Pacific Asia Caribbean North Africa Africa<br>Total trade Agrigulture Manufacturing<br><!-- End of picture text --> _Source:_ World Bank and UNCTAD staff estimates.

**Limited access to Arab markets for world exporters tends to insulate the region.** The level of trade policy restrictions confronting exporters from a country in other markets can be measured by the Market Access Overall Trade Restrictiveness Index (MA-OTRI). This index measures the uniform tariff faced by exporters in all the external markets that is consistent with the current aggregate export of a country. Given trade preferences and the composition of goods exported, most countries have good market access in the market of high-income countries (Figure 8).  Market access in the MENA region is quite restrictive, particularly among exporters from the Latin American and Sub-Sahara Africa countries.  In fact the market access of Sub-Sahara African exporters in the MENA region has deteriorated in 2008.  It was about 17 percent in 2007, and it jumped to more than 40 percent in 2008.  Likewise, MA-OTRI for Latin American exporters to the Middle East and North Africa increased from 40 percent to 57 percent.

> 15 The OTRI adds nontariff measures to the TTRI to calculate an overall trade restrictiveness index.

15
# **S8.4 Second Stage Results**

Tables S6-S9 show results from the second stage of IV models using different versions of the MSTs. Across different versions of the MSTS, results show road upgrades associated with growth in NTL and urban area, particularly in areas with medium-to-high baseline nighttime lights.

**Table S6:** Long Difference, IV Results using Least Euclidean Distance MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|N|TL|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._03<sup>_∗_</sup>|0_._01|0_._001|0_._0000|0_._004|0_._004|0_._01|_−_0_._005|0_._04|0_._04|_−_0_._05|_−_0_._06|
||(0_._02)|(0_._01)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._06)|(0_._05)|(0_._04)|(0_._04)|(0_._07)|(0_._07)|
|Imp Rd._×NTL_96 Low||_−_0_._02<sup>_∗∗_</sup>||0_._0001||_−_0_._01||0_._01||_−_0_._01||_−_0_._06|
|||(0_._01)||(0_._0003)||(0_._01)||(0_._03)||(0_._01)||(0_._09)|
|Imp Rd._×NTL_96 Med||0_._13<sup>_∗∗_</sup>||0_._004<sup>_∗∗_</sup>||0_._001||0_._19<sup>_∗∗_</sup>||0_._06||0_._06|
|||(0_._06)||(0_._002)||(0_._01)||(0_._10)||(0_._05)||(0_._05)|
|Imp Rd._×NTL_96 High||0_._25<sup>_∗∗_</sup>||0_._03<sup>_∗_</sup>||0_._01||0_._22<sup>_∗_</sup>||0_._50<sup>_∗_</sup>||_−_0_._06|
|||(0_._11)||(0_._01)||(0_._03)||(0_._13)||(0_._27)||(0_._05)|
|Constant|0_._04<sup>_∗∗_</sup>|0_._03<sup>_∗_</sup>|0_._001|0_._0003|0_._001|0_._0004|0_._03|_−_0_._005|_−_0_._20|_−_0_._28|0_._18|0_._19|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._20)|(0_._21)|(0_._13)|(0_._17)|(0_._25)|(0_._25)|
|Unit|1_km_|1_km_<br>|1_km_|1_km_<br>|1_km_|1_km_<br>|_Keb._|_Keb._|_Keb._|_Keb._<br>|_Keb._|_Keb._|
|1st Stage F-Stat|213_._31|58_._81|213_._32|58_._81|213_._32|58_._81|31_._34|8_._19|31_._34|8_._19|31_._34|8_._19|
|Observations<br><br>|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.004|0.01|0.0001|-0.001|-0.0000|-0.0000|0.002|0.02|-0.003|-0.04|-0.01|-0.01|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S14
<!-- Start of picture text --> Figure 1: GDP per capita<br>2800<br>2400<br>100<br>2000<br>0<br>-100<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: GDP per capita growth<br>8<br>4<br>0<br>8<br>-4<br>4<br>0<br>-4<br>-8<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita growth<br>Trend<br>Cycle<br><!-- End of picture text --> At the aggregate level, growth increased substantially during 1995-2005 and was accompanied by a sharp reduction in the coefficient of variation. The per capita growth rate rose to 1.88% in this period from -0.23% of the previous decade, and the coefficient of variation fell to 3.2% from -25.5%. This shift implies an increase of 2% in growth, which is about three-times the long term growth rate of 0.7%. Income per capita went up to $2,486 in 1995-2005, which represents an increase of about $300, or 11%, as compared to previous periods. Recursive residual estimations, Chow breakpoint tests, and Chow forecast tests, do not reject the hypothesis that a structural break in the growth series occurred between 1995 and 1997 (Arbache and Page, 2007b).

4
**Table 2:** Data density by sector and country-income groups

||**2000-20**|**03**|**2004-2**|**007**|**2008-2**|**011**|**2012-2**|**016**|
|---|---|---|---|---|---|---|---|---|
||#<br>observations|%<br>coverage<br>|#<br>observations|%<br>coverage|#<br>observations|%<br>coverage|#<br>observations|%<br>coverage|
|||**Gove**|**rnment spen**|**ding on heal**|**th**||||
|Overall|729|96%|737|97%|742|98%|920|97%|
|High income|212|100%|212|100%|212|100%|265|100%|
|Upper middle income|209|97%|213|99%|216|100%|265|98%|
|Lower middle income|202|95%|204|96%|204|96%|250|94%|
|Low income|106|88%|108|90%|110|92%|140|93%|
|||**Gover**|**nment spendi**|**ng on educa**|**tion**||||
|Overall|492|65%|464|61%|506|67%|583|61%|
|High income|186|88%|172|81%|168|79%|189|71%|
|Upper middle income|129|60%|113|52%|129|60%|139|51%|
|Lower middle income|117|55%|121|57%|132|62%|148|56%|
|Low income|60|50%|58|48%|77|64%|107|71%|
|||**Governme**|**nt spending**|**on social pro**|**tection**||||
|Overall|||||21|3%|102|11%|
|High income|||||1|0%|11|4%|
|Upper middle income|||||5|2%|33|12%|
|Lower middle income|||||10|5%|37|14%|
|Low income|||||5|4%|21|14%|

When we look at the disaggregation by region, for health we do find coverage that is close to universal for all the regions (Table 3). This may be related to decades-long international efforts to collect health expenditure data (Bui et al. 2015). That is not the case for education, where the trend in data availability is particularly worrying for Latin America (LAC) and Middle East and North Africa (MENA). It is encouraging, however, to observe a significant positive trend in data availability for South Asia and SubSaharan Africa. Importantly, for education, the shifts in data availability are so large that comparisons of mean spending values are not particularly informative since data availability might be systematically related to the amount of spending.

7
al., 2006; Wirsenius, 2003, 2010). For instance, using data for the Netherlands in 1990, GerbensLeenes et al. (2002a) estimate the land requirement for beef to be 20.9 m<sup>2</sup> year per kilogram of meat which is more than twice that for pork (8.9 m<sup>2</sup> year per kilogram of meat) whereas their land requirement estimate for cereals turns out to be relatively small (1.4 m<sup>2</sup> year per kilogram). For China, land requirement estimates obtained by Li et al. (2013) and Zhen et al. (2010) are broadly comparable with those from other studies.<sup>14</sup> The implications for environmental burdens implied by the CE coefficients<sup>15</sup> are also generally in line with the findings of other studies.<sup>16</sup> Researchers tend to find that beef production has the most severe GHG impact per kilogram of meat, followed by pork and chicken production (e.g., Fiala, 2008; Gerber et al., 2013; Steinfeld et al., 2006).

Finally, the cereal equivalent measure used in this study does not consider varying feed requirements for animal production depending on technology (e.g., feed mix and efficiencies), production systems (Robinson, Thornton, Franceschini, Kruska, Chiozza, Notenbaert and You, 2011) and local resource availabilities. Differentiating CE coefficients by regions and by production systems would be a potential subject of future research. Further, our analytical technique in the empirical section does not take into account distortions from agricultural incentives, food price policy, or farm gate pricing differences related to product self-sufficiency.

> 14 For instance, the arable land requirement for beef of 2.5 m2 per kilogram of meat estimated by Li et al. (2013) is much smaller than that of Zhen et al. (2010) of 16.7 m<sup>2</sup> per kilogram, largely reflecting the fact that the former study considers only the arable land area for growing “refined” livestock feed (e.g., maize etc.) whereas the latter study includes grassland used to grow grass fodder.

> 15 For instance, Williams et al. (2006) find the global warming potential to be about 20 times higher for beef production than for wheat production (Rask and Rask, 2011; Williams et al., 2006).

> 16 Livestock production is believed to be one of the largest sources of greenhouse gases (GHG), contributing about 18 percent of the environmental pressures that are believed to be causing global warming (Gerber et al., 2013; Steinfeld et al., 2006). Feed production and processing, and enteric fermentation from ruminants are reported to be the two main sources of emissions, representing 45 and 39 percent of the sector emissions respectively, followed by manure storage and processing (10 percent) (Gerber et al., 2013). For instance, it is reported that, in the Brazilian Amazonian region, a significant amount of carbon dioxide is released when forest is converted into grazing for cattle ranching or into arable land for soy production (Garnett, 2009; Steinfeld et al., 2006). Brazil has committed to a range of mitigation targets to reduce deforestation in the Amazon and in the Cerrado (Gerber et al., 2013).

11
**Figure 6: Growth and changing shares of income in 27 countries, using household survey data**

<!-- Start of picture text --> 10 y�<br>8<br>6<br>4<br>2<br>s�<br>0<br>-10 -5 0 5 10<br>-2<br>-4<br>-6<br>-8<br>-10<br><!-- End of picture text --> Source: Author’s calculations based on data from Povcalnet.

# **V. Discussion**

The introduction made note of the fact that many of the bottom 40 percent in developing countries have income levels below the poverty line in rich countries. Some numbers make the point more explicit: with the exception of the Russian Federation, in all of the other “BRICs”, the people in the bottom 40 percent have incomes below the average incomes of the bottom 5 percent of the population in the United States (Milanovic, 2012, Figure 7). Certainly, then, promoting income growth of the bottom 40 percent is a worthwhile pursuit in even the more advanced developing countries.

As noted above, the “simplest” algebraic decomposition should not be taken too literally. There is a vast literature on the theory and evidence of the links between inequality and economic growth. In addition, a constant negative value for ŝ is socially “unsustainable” in the most brutal sense: if it persists, incomes of the bottom 40 percent shrink to zero. On the other hand, the simple decomposition highlights the role of economic growth in the long-run. On the positive end, the “sharing” runs into the limit of perfect equality and only growth can then drive ever higher levels of prosperity. The simulations above illustrate how long-run compounding of 13
- De Mesa, A. and C. Mesa-Lago, 2006. The Structural Pension Reform in Chile: Effects, Comparisons with Other Latin American Reforms, and Lessons _, Oxford Review of Economic Policy_ 22(1), 149-167.

- Didier, T., R. Rigobon, and S.L. Schmukler, 2008. Unexploited Gains from International Diversification, MIT, mimeo.

- Disney, R. and C. Emmerson, 2005. Public Pension Reform in the United Kingdom: What Effect on the Financial Well Being of Current and Future Pensioners? _Fiscal Studies_ 26(1), 55-82.

- Edwards, S., 1996. The Chilean Pension Reform: A Pioneering Program, NBER Working Paper 5811.

- Fama, E.F. and J.D. MacBeth, 1973. Risk, Return, and Equilibrium: Empirical Tests, _Journal of Political Economy_ 81(3), 607-636.

- Ferson, W. and K. Khang, 2002. Conditional Performance Measurement Using Portfolio Weights: Evidence for Pension Funds, _Journal of Financial Economics_ 65(2), 249-282.

- Gill, I., T. Packard, and J. Yermo, 2005. Keeping the Promise of Social Security in Latin <u>America, Stanford University Press and World Bank.</u>

- Grinblatt, M., S. Titman, and R. Wermers, 1995. Momentum Investment Strategies, Portfolio Performance, and Herding: A Study of Mutual Fund Behavior, _American Economic Review_ 85(5), 1088-1105.

- Holzmann, R. and R. Hinz, 2005. <u>Old-Age Income Support in the 21st Century: An International Perspective on Pension Systems and Reform, World Bank,</u> Washington, D.C.

- IMF and World Bank, 2004. Financial Sector Assessment Program. Chile: The Pension Fund Sector, IMF Country Report 04/269.

- Impavido, G. and A.R. Musalem, 2000. Contractual Savings, Stock, and Asset Markets, World Bank Policy Research Paper 2490.

- Impavido, G., A.R. Musalem, and T. Tressel, 2003. The Impact of Contractual Savings Institutions on Securities Markets, World Bank Policy Research Paper 2948.

- Kaminsky, G., R. Lyons, and S.L. Schmukler, 2004. Managers, Investors, and Crises: Mutual Fund Strategies in Emerging Markets, _Journal of International Economics_ 64(1), 113-134.

- Lakonishok, J., A. Shleifer, and R.W. Vishny, 1992. The Impact of Institutional Trading on Stock Prices, _Journal of Financial Economics_ 32(1), 23-43.

- Larraín, L., 1993. Social Security Reform, in The Chilean Experience: Private Solutions <u>to Public Problems, ed. C. Larroulet, Center for International Private Enterprise,</u> Santiago, Chile.

- Lefort, F. and E. Walker, 2000. The Effects of Economic and Political Shocks on Corporate Governance Systems in Chile, Revista ABANTE  2(2), 183-206.

- Lefort, F. and E. Walker, 2002a. Premios por Plazo, Tasas Reales y Catástrofes: Evidencia de Chile, _El Trimestre Económico_ 69(2), 191-225.

- Lefort, F. and E. Walker, 2002b. Pension Reform and Capital Markets: Are There Any (Hard) Links? World Bank Social Protection Discussion Paper 0201.

- Levine, R., 1997. Financial Development and Economic Growth: Views and Agenda, _Journal of Economic Literature_ 35(2), 688-726.

43
**Figure 10. The share of adults who saved formally was low in most developing economies in Europe and Central Asia**

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>Moldova<br>Montenegro<br>Republic of North Macedonia<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 10% 20% 30% 40% 50% 60%<br>Percent of adults who saved formally in 2016<br>Formal savings Semi‐formal savings Other<br><!-- End of picture text -->

# _Source_ : Global Findex database.

_Note:_ The length of the bars represents the percentage of adults (aged 15 and above) who reported saving in any way in the previous 12 months.

In the euro area, 67 percent of all adults reported having saved in the past year. Seventy‐three percent of savers (about half of all adults) saved formally. These shares are slightly lower than in an average high‐income economy.

Globally, saving patterns vary by gender and income level. In developing economies, men are more likely than women to save formally. The average gender gap in the share of adults who saved formally was 6 percentage points in 2017. In the developing economies of ECA, the gender gap widened slightly, from 2 percentage points in 2014 to 3 percentage points in 2017. In the euro area, it remained stable, at about 6–9 percentage points, between 2011 and 2017.

Wealthier adults are more likely than poorer adults to save formally. Globally, the income gap between the top 60 percent and the bottom 40 percent was 23 percentage points in high‐income economies and 15 percentage points in developing economies in 2017. In the developing economies of ECA, the income 21
# **Appendix B. Additional System GMM regression**

Table B.1: Additional System GMM regression

|Dependent variable|CPIA cluster D|
|---|---|
|number of cumulative conditions|.006|
||(.003)<sup>_∗_</sup>|
|number of cumulative conditions, squared|-.00003<br>(.00001)<sup>_∗∗_</sup>|
|log GDP per capita (PPP)|.24<br>(.056)<sup>_∗∗∗_</sup>|
|aid over GDP|-.301|
||(.545)|
|Political Rights|-.135|
||(.028)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|
|_year fxed efects_|_yes_|
|Observations|1690|
|Number of instruments|30|
|Wald statistic|199.8|
|_p-value_|0.0001|
|Hansen J-test|11.13|
|_p-value_|0.517|
|Dif-in-Hansen test|0.10|
|_p-value_|0.951|

Note: cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. Collapsed instrument matrix. Lags 5 to 10 used . * significance at 10%; ** significance at 5%; *** significance at 1%.

43
3

**Table 1: Balance of Payments**

<!-- Start of picture text --> A. Current Account (IMF BoPY* line 1 .44),<br>which includes<br>Al. Travel: credit (IMF BoPY line 9)<br>A2. Reinvested earnings on direct investment abroad (IMF BoPY line<br>11)<br>A3. Reinvested earnings on direct investment domestically (IMF<br>BoPY line 12)<br>A4. Other investment income: credit (IMF BoPY line 19)<br>B. Net Equity Flows<br>of which:<br>B1. Net foreign direct investmnent (IMF BoPY line 45..52)<br>B2. Portfolio Investment: Corporate equities (IMF BoPY line 59..61)<br>C. Other short-term capital of other sectors: net (IMF BoPY line 93 .97)<br>of r.'hich:<br>Cl Other assets (IMF BoPY line 94)<br>D. Portfolio investment, other bonds (IMF BoPY line 56-58)<br>E. Change in deposit money banks' foreign assets (IFS line 7a.dzf)<br>F. R,..erves (IMF BoPY line 98.. 111)<br>G. Net errors and omissions (IMF BoPY line 112)<br>H Other long-term capital of resident official sector (IMF BoPY line 62-<br>68)<br>or<br>H'. Change in external debt (World Debt Tables - see further section 4)<br>* IMF Balance of Payments Yearbook.<br><!-- End of picture text --> Since all capital flight measures attempt to estimate private capital flows, this would imply that capital flight can be simply measured as the sum of identified outflows (C + D + E). And, if all unidentified capital flows were private capital outflows, then net errors and omissions (G) would also be included.