rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 3.7. Base: Real annual sector export growth 2023-2030 (%)

|-2.0<br>-1.0<br>**Figure 3.7. Base: Real ann**|0.0<br>**ual sector e**|1.0<br>**xport gro**|2.0<br>**wth 2023**|3.0<br>**-2030 (%**|4.0<br>**)**||||
|---|---|---|---|---|---|---|---|---|
|Source: Authors' calculations bas<br>-0.9<br>Agriculture<br>Mining<br>Manufacturing<br>Private services<br>Total<br>**Table 3.1. Macro indicators in 20**|ed on simu<br><br>**22 and by **|lation res<br>1.0<br><br>**scenario**|ults.<br><br> **(% annu**|3.0<br>**algrow**|3.2<br>3.4<br>**th 2023-2**|**030)**|||
||**2022**<br>|**base**<br>|**limbo**<br>|**rec-t**<br>|**rec-ta**<br>|**rec+t**<br>|**rec+ta r**<br>|**ec+ta+**<br>|
|**Absorption**|1,848.5|2.0|2.7|3.6|3.6|5.2|5.2|6.0|
|**Consumption - private**|1,460.5|2.0|2.6|3.2|3.2|4.2|4.2|5.1|
|**Consumption - government**|268.7|2.0|3.1|2.1|2.1|3.8|3.8|3.8|
|**Fixed investment - private**|109.4|2.2|3.1|8.6|8.6|13.6|13.6|14.7|
|**Fixed investment - government**|9.9|2.0|2.8|22.0|22.0|30.9|30.9|30.9|
|**Exports**|95.1|1.0|2.3|4.2|4.2|6.9|6.9|9.0|
|**Imports**|561.4|1.8|2.0|2.9|2.9|4.0|4.0|4.4|
|**GDP at factor cost**|1,361.5|2.0|3.1|4.0|4.0|6.0|6.0|7.1|
|**Total factor employment (index**|Eps|1.2|1.5|2.1|2.1|3.0|3.0|3.3|
|**Total factor productivity (index)**|Eps|0.8|1.6|1.9|1.9|3.0|3.0|3.8|
|**GNI**|1,382.8|2.0|3.0|3.7|3.8|5.5|5.5|6.6|
|**GNDI**|1,849.8|2.0|2.8|3.4|3.4|4.8|4.8|5.7|
|**GNI per capita**|0.4|0.0|1.1|1.7|1.8|3.4|3.5|4.5|
|**GNDI per capita**|0.6|0.0|0.8|1.4|1.4|2.8|2.8|3.6|
|**Real exchange rate (index)**|Eps|0.61|1.66|1.90|1.92|2.81|2.83|3.76|
|**Unemployment rate (%)**|25.4|31.9|29.8|29.1|29.0|25.5|25.3|23.3|
|**Male (%)**|24.4|31.0|29.0|28.2|28.1|24.6|24.5|22.4|
|**Female (%)**|36.8|42.3|40.0|39.5|39.4|35.8|35.8|33.7|
|**Headcountpoverty rate(%)**|74.4|74.4|72.0|70.0|69.8|66.0|65.8|62.4|

**Note:**

**1. Unless otherwise noted, column for initial year shows data in bn 2020 YER.**

**2. For the unemployment and poverty rates, the base-year and simulation columns show base-year rates and simulation-specific final-year rates, respectively.**

17
# **References** :

Acemoglu Daron, Simon Johnson and James A. Robinson (2001) “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review, 91:13691401.

Akyuz Yilmaz and Charles Gore (2001). “African economic development in a comparative perspective”, Cambridge Journal of Economics, Vol. 25, 265-288.

Amsden A. (1991). “Diffusion and Development: The Late-Industrializing Model and Greater East Asia,” AEA Papers and Proceedings, 81:282-286 Andreosso-O’Callaghan Bernadette (2002). “Human capital accumulation and economic growth in Asia”, Working Paper No. 30, Antweiler Werner and Daniel Trefler (2000). “Increasing returns and all that.” NBER Working Paper, Vol. 7941.

Artadi V. Elsa and Xavier Sala-i-Martin (2003). “The Economic Tragedy of the 20<sup>th</sup> Century: Growth in Africa”, National Bureau of Economic Research Working Paper No. 9865.

Aw Bee-Yan (2003). “Technology Acquisition and the Development of Taiwan” WBI Working Papers No. 37222 Azariadis Costas and Allan Drazen (1990). Threshold externalities in economic development. The Quarterly Journal of Economics 105 (2), 501-526.

Azariadis Costas and John Stachurski (2004). Poverty Traps. In: Aghion, Durlauf (Eds.), Handbook of Economic Growth.

Baark E. (1991). “The Accumulation of Technology: Capital Goods Production in Developing Countries Revisited.” World Development, 19:903-914.

Barro Robert (1990). “Government Spending in a Simple Model of Endogenous Growth”. Journal of Political Economy 98 (5), S103-S126.

Battese G. and T. Coelli (1995). “A Model for Technical Efficiency Effects in a Stochastic Frontier Production Function for Panel Data”, Empirical Economics 20, 325-332.

Bayraktar Nihal and Hippolyte Fofack (2007), “Specification of Investment Functions in SubSaharan Africa”, World Bank Policy Research Working Paper No. 4171, The World Bank Group.

Berg Andy and Zia Qureshi (2005). “The MDGs: Building a Momentum”, Finance and Development, The International Monetary Fund. Vol. 42. Number 3.

Bloom David E., Canning D., Chan K. (2005). “Higher Education and Economic Development in Africa” Havard University.

Bloom David E., David Canning and Jaypee Sevilla (2003), “Geography and Poverty Traps”, Journal of Economic Growth 8(4), 355-378.

Bu Yisheng (2006). “Fixed Capital Stock Depreciation in Developing Countries: Some Evidence from Firm Level Data”, Journal of Development Studies, Vol. 42, No. 5, 881-901.

Chen Shaohua and Martin Ravallion (2004). “How Have the World’s Poorest Fared since the Early 1980s?”, World Bank Research Observer, Vol. 19, No. 2, 141-169.

Chenery Holis, Sherman Robinson and Moshe Syrquin (1986). Industrialization and Growth: A Comparative study, New York, Oxford University Press.

Easterlin Richard A. (1981). “Why isn’t the whole world developed?” Journal of Economic History, 41:1, pp. 1-19.

24
Table A.7: Excess female mortality across child ages

|||**Outco**<br>|**me: Death **<br>|**before**<br>||
|---|---|---|---|---|---|
||1 year|2 years|3 years|4 years|5 years|
||(1)|(2)|(3)|(4)|(5)|
|_Firstborn girl_ * _Female_|1.829***|2.330***|2.546***|2.781***|2.768***|
||(0.476)|(0.578)|(0.581)|(0.610)|(0.588)|
|_Firstborn girl_ * _Female_ * _Post1_|-0.691|-0.827|-0.968|-1.212|-1.525*|
||(0.607)|(0.731)|(0.723)|(0.726)|(0.768)|
|_Firstborn girl_ * _Female_ * _Post2_|-0.550|-1.095|-1.521*|-1.650**|-1.996**|
||(0.424)|(0.710)|(0.750)|(0.712)|(0.744)|
|N|309,860|289,705|269,495|248,807|227,129|
|Baseline mean|9.65|11.07|11.75|12.12|12.38|
|_Xijt_|x|x|x|x|x|
|_Firstborn Girl_ x Birth year FE|x|x|x|x|x|
|_Female_ x Birth year FE|x|x|x|x|x|
|_Female_ x State FE|x|x|x|x|x|
|_Female_ x Birth order FE|x|x|x|x|x|
|Birth order x Birth year FE|x|x|x|x|x|
|Birth order x State FE|x|x|x|x|x|
|State x Birth year FE|x|x|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression. The outcome is an indicator for death before age 1, 2, 3, 4, 5 for column (1), (2), (3), (4), and (5) respectively. For each column, we drop children that do not have full exposure to the risk of mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Data: NFHS. *** 1%, ** 5%, * 10%.

54
# **Table A.9. Income Inequality, Reclassification: Top Performers Are Firms in the Top 40 Percent in Capital Productivity among the Top 40 Percent of the Capital Distribution**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Top-pe|rformers|Constrain|ed gazelles|Sur|vivalists|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.00112**<br>(0.000504)|1.001|0.000899***<br>(0.000339)|1.001|0.00183*** <br>(0.000384)|1.002|
|Age owner|0.0775|1.081|-0.0465|0.955|-0.0291**|0.971|
||(0.0914)||(0.0369)||(0.0143)||
|Age squared owner|-0.000784<br>|0.999|0.000512<br>|1.001|0.000364** <br>|1.000|
||(0.00110)||(0.000470)||(0.000162)||
|Male(=1)|-0.974**<br>(0.484)|0.377|-0.167<br>(0.145)|0.846|-0.394***<br>(0.128)|0.674|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|1.011|2.749|0.277|1.319|-0.0498|0.951|
||(1.012)||(0.435)||(0.164)||
|Some secondary|0.835|2.306|0.947**|2.579|0.300**|1.350|
||(0.930)||(0.419)||(0.151)||
|Post secondary|2.143**|8.529|1.663***|5.273|0.876***|2.400|
||(1.051)||(0.451)||(0.194)||
|Other post primary|2.089*|8.073|0.946*|2.575|0.861**|2.366|
||(1.241)||(0.505)||(0.400)||
|Can read|-0.943<br>(0.887)|0.389|0.656**<br>(0.286)|1.928|0.509***<br>(0.126)|1.664|
|No opportunity in small firms|1.104*|3.017|-0.156|0.856|0.0233|1.024|
||(0.604)||(0.156)||(0.133)||
|Firm size|-0.00573|0.994|-0.0889|0.915|-0.102|0.903|
||(0.228)||(0.0900)||(0.0969)||
|Trade register|-|-|1.196***|3.306|1.207***|3.343|
||||(0.385)||(0.464)||
|Financial knowledge|-0.369|0.691|0.341**|1.406|0.397***|1.487|
||(0.505)||(0.150)||(0.129)||
|Business association|0.228|1.256|-0.248|0.780|0.00599|1.006|
||(0.470)||(0.207)||(0.176)||
|Lack of customers|-0.00695|0.993|0.141|1.151|-0.00625|0.994|
||(0.385)||(0.113)||(0.0906)||
|Lack of credit access|-0.342|0.710|-0.295**|0.744|-0.217**|0.805|
||(0.384)||(0.122)||(0.0955)||
|Management problems|0.106|1.111|0.292**|1.338|0.0610|1.063|
||(0.583)||(0.148)||(0.116)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|Pseudo-R2|0.1813||0.1205||0.0970||
|Observations|159|159|1,132|1,132|1,849|1,849|
|Standard errors in parentheses ***|p<0.01, ** p<0.|05, * p<0.1|||||
|<br>Note: Discipline problems, no opp|<br>ortunity in large|<br>firms, firms ag|e and lack of qualif|ied workforce a|re non significa|nt additional|
|control variables which are include<br>because of collinearity and limited|d in the regressi<br>observations.|ons but not pres|ented in this table|. Trade register i|s dropped from|the regression|

37
**Table 9B - Robustness Checks: Effect of Equality of Opportunity on Institutional Quality Dissimilarity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Depend|ent Variable: Institutions,Av|erage WBGI 2009|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-high)|OLS<br>-44.247***|OLS<br>-4.193|IV<br>-7.488|
||(6.76)|(9.35)|(13.41)|
|Dissimilarity Index*||-1.794***|-2.069**|
|||(0.26)|(0.61)|
|constant|67.794***|90.581***|93.098***|
||(3.44)|(3.57)|(4.32)|
|R-sqr|0.188|0.541||
|Number of Observations|187|60|48|
|Wald F-statistics from first stage|||11.346|
|**Controlling for Legal Origin**<br>Regression|Depend|ent Variable: Institutions,Av|erage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability<br>Dissimilarity Index*|OLS|OLS<br>-1.744***|IV<br>-2.573***|
|||(0.23)|(0.55)|
|British Legal System|2.821|10.711*|5.979|
||(5.75)|(5.10)|(5.98)|
|French Legal System|-6.042|7.527|9.748|
||(5.14)|(4.19)|(6.76)|
|Germany Legal System|42.788***|15.047*|9.146|
||(11.44)|(6.50)|(8.12)|
|Scandinavian Legal System|51.321***|22.728**|17.319**|
||(11.44)|(6.53)|(5.42)|
|constant|44.444***|79.935***|89.522***|
||(4.21)|(4.08)|(6.41)|
|R-sqr|0.239|0.703||
|Number of Observations|144|55|48|
|Wald F-statistics from first stage|||12.814|
|**Controlling for Share of Tropical Land**<br>Regression|Depend|ent Variable: InstitutionsAv|erage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS<br>|OLS<br> ,|IV<br>|
|% land area in geographical tropics|-26.318***|-3.104|12.987|
||(3.92)|(6.55)|(17.30)|
|Dissimilarity Index*||-1.792***|-2.815**|
|||(0.26)|(1.04)|
|constant|58.603***|89.347***|96.860***|
||(2.65)|(3.14)|(8.55)|
|R-sqr|0.219|0.538||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage<br>|||2.313|
|**Controlling for Latitude**<br>Reression|Deen|ent Variable: InstitutionsA|erae WBGI 2009|
|g<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>p|OLS<br> ,|IV<br> g|
|latitude of country centroid|0.376***|-0.041|-0.102|
||(0.08)|(0.08)|(0.11)|
|Dissimilarity Index*||-1.901***|-2.392***|
|||(0.25)|(0.58)|
|constant|37.923***|91.473***|97.848***|
||(2.63)|(4.83)|(8.69)|
|R-sqr|0.12|0.539||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage|||11.074|
|**Controlling for Longitud**<br>||||
|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Depen|OLS<br>ent Variable: Institutions,A|IV<br>erage WBGI 2009|
|longitude of country centroid|-0.043|-0.048|-0.087*|
||(004)|(003)|(004)|
|Dissimilarity Index*|.|.<br>-1.893***|.<br>-2.267***|
|||(0.23)|(0.42)|
|constant|46.779***|90.692***|93.971***|
||(2.21)<br>|(3.17)<br>|(4.34)|
|R-sqr|0.008|0.554||
|Number of Observations|163|59|49|
|<br>Wald F-statistics from first stage|||14.576|

* p<0.05, ** p<0.01, *** p<0.001

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

31
# **_E. Evidence on Returns to High School in Urban Areas_**

Low marginal returns to high school education, relative to middle school and university, is consistent with segmentation of the urban labor market between urban and rural _hukou_ holders (Meng and Zhang 2001), and further supported by Li et al. (2012), who use twins data from 2002-3 to show that returns to high school education in urban China were close to zero at this time, while returns to a year of academic and vocational technical tertiary education are 10 and 5 percent, respectively. In Appendix A.3, we use a Heckman selection model and data from the 2003 RCRE survey round to present supporting evidence of the low return to high school education for migrants in our sample. For the 2003 survey round, questions regarding the daily earnings of migrants were included in the regular annual survey, allowing us to examine returns for different levels of schooling. We introduce a piecewise linear spline for years of schooling between 0 and 9, 10 to 12 years and more than 12 years. Consistent with other work examining the returns to education in urban areas over this period, one observes positive and significant returns to education for individuals with less than nine years of education, returns near zero for migrants with 9 to 12 years, and positive but insignificant returns for migrants with educational attainment above 12 years.<sup>10</sup>

# **_F. Direct Evidence on the Activities of High School Age Children_**

With the growth of migrant networks from the village, the local labor market may also experience general equilibrium effects that increase the opportunity cost of high school enrollment. As migrants leave the village, the local labor force decreases in size, which in turn may increase the return to labor in home production (agricultural or non-agricultural family 10As we use average daily wages reported by household members on behalf of the migrant worker, one might expect that these are low estimates because migrants were unlikely to have been working for the full number of days that they were outside the household for work. Further, unlike the follow-up survey which asks about all children of the household head and spouse, the 2003 survey on migrant incomes only asks about earnings of migrants still registered as household members. Because migrants with more than secondary education are more likely to convert to urban _hukou_ , there are relatively few observations with more than secondary education and point estimates of returns are well below what we would expect for this level of educational attainment. The main analysis sample, based on the follow-up supplemental survey conducted in 2004, includes information on the educational attainment of all current and former household residents, but did not enumerate estimates of current or past earnings of these individuals.

26
**Climate Change Impacts on Rural Poverty in Low-Elevation Coastal Zones**

Edward B. Barbier Department of Economics & Finance, University of Wyoming Keywords: coastal zone, rural poverty, climate changes, rural development, coastal countries JEL: I30, Q54, Q54, Q57

**Acknowledgements.** I am grateful to the guidance and comments provided by Richard Damania, Marianne Fay, Stephane Hallegatte, Kirk Hamilton, Ulf Narloch, and Breje van Wiejbergen.  I am also grateful to Stuart Hamilton for providing his data on 2000-2012 mangrove extent and loss and to Jacob P. Hochard for technical assistance in conducting the spatial analysis of poverty among coastal and low elevation coastal zone populations of this study.
1995 of 34.7 million in the OECD's _Employmrent Outlook_ [OECD 1994].

# **3.2.** Results

Table 5 - A presents the aggregated data. The totals (and averages) of the working age population, the unemployment rates, the sectoral shares and the within sector shares in wage employment are the only data input. All the other data Tables 5-A through 5-F are derived from these. World totals are aggregated from the estimates for low-, middle-, and high-income economies.

For ease of use, Tables 5-B through 5-F present the estimates in various forms: Table 5 - B presents the estimated number of individuals in each category.

- Table 5 - C presents the share in each category as a percentage of the working age population in each income group or region.

- Table 5 - D presents the share in each category as a percentage of the labor force in each income group or region.

- Table 5 - E presents the share in each category as a percentage of the world's working age population

Table 5 - F presents the share in each category as a percentage of the world's labor force.

Figures 1 through 7 present these data in graphical form 9
# Y� �αT� �βT� ∗M� �γX� �π�� �ε�                   �2�

For this specification, Mi captures the dimension of heterogeneity, Xi is a set of control variables (also including Mi), πis are fixed effects for each randomization strata (by gender and study subject) and εi is a mean-zero error term. We estimate this equation separately for several dimensions of heterogeneity: gender, social capital (as proxied by whether individuals work on a project in pairs), family socio-economic background, and skills (above or below average grades in second year at university). α captures the ITT estimates when Mi=0, α�β the ITT estimates when Mi=1, and β is the difference in program impacts between the two subgroups. Robust standard errors are again clustered by gender and study subjects.

# _Compliance and “local average treatment effects” (LATE) estimates_

In addition to ITT estimates, we also present and briefly discuss “local average treatment effects” (LATE) estimates for each of the two specifications. Local average treatment effects account for noncompliance in the treatment group (drop-out from the entrepreneurship track), as well as in the control group (take-up of entrepreneurship training past graduation). In particular, the CEFE entrepreneurship training is offered by the employment offices, so that some students may have taken it after graduation. LATE estimates can be considered as a test of the robustness of the results to noncompliance, including to rule out that the estimated impacts are underestimated due to individuals in the control group taking the training.

Of the 856 students who applied and were randomly assigned to the entrepreneurship track, 67% completed the CEFE business training, and 59% completed both business training and coaching.<sup>13</sup> A few students from the treatment groups completed CEFE training between graduation and the midline survey. Therefore, the actual take-up of CEFE training among the treatment group included in the endline sample is 71 percent.

In the context of the medium-term analysis, some control group individuals may have taken the CEFE entrepreneurship training after graduation. Administrative data shows that take-up of CEFE training was low, with only approximately 3.4% of the control group completing it between graduation and the midline survey. Administrative data also show that only approximately 10 percent of the control group took CEFE training between midline and endline.

> 13 More details on drop-out patterns can be found in World Bank and MFPE (2012).

12
top-left graph. Such configuration is rare in our sample, as we shall further discuss in Section V.<sup>14</sup> At the other extreme, in economies with very low poverty rates (bottom-right graph), the effect of raising inequality on poverty is virtually negligible, and its impact on growth, which is shown to be slightly negative, occurs only through the decreasing returns to scale in the production function.

In the intermediate case of poor (but not extremely poor) economies shown in the top-right and bottom-left graphs, the impact of inequality on poverty is always positive, and hence its indirect contribution to growth is negative, more so the higher the prevailing poverty rate. In turn, the direct effect of inequality on growth (i.e., the second term in (11)) is almost zero, due to the mutually opposing effects discussed above. As a consequence, the overall impact of inequality on growth is negative, due to the indirect effect of inequality that accrues through poverty. Finally, it is worth noting that these conclusions hold regardless of the prevailing level of inequality, at least for values of the Gini coefficient in the range between 0.2 and 0.8 – which covers 99% of our empirical sample, as shown in Section III.

# **III.- Growth, inequality and poverty: Empirical implementation**

We turn to the empirical strategy for analyzing the relationship between growth, inequality and poverty. First, we describe the data we use. Second, we present the baseline growth equation and examine the correlation between the key variables in the model.

# **III.1. Data**

We use an extended version of the poverty database constructed by López and Servén (2009). Details are given in the Appendix. In brief, given the limited availability of survey-based poverty data, we construct the headcount ratio, P0, the poverty gap, P1, and the squared poverty gap, P2, using a lognormal approximation on the basis of the observed per capita income levels and Gini coefficients, which are available much more widely (López and Servén, 2006). Similar approaches have been used by Dollar and Kraay (2002), Sala-i-Martín (2006) or Pinkovsky and Sala-i-Martin (2013; 2014). We experiment with alternative and widely-used poverty lines: US$ 1.25, US$ 2 and US$ 4 per person and per day, in 2005 PPP US$.

Our final database is an unbalanced panel of non-overlapping five-year periods, containing 804 observations covering 158 countries and spanning the years 1960-2010. This sample is fairly large

> 14 Our sample mainly contains economies with headcount poverty rates between 0.10 and 0.60, as well as rich economies with negligible poverty, as discussed in the next section.

9