rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12.** LPM regressions- effects of norms and personality traits on wage employment  (vs. self-employment)- women sample

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Norms_|_Norms+_<br>_Controls_|_Norms+_<br>_Controls+_<br>_Personalit_<br>_y_|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychroni_<br>_city_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achievem_<br>_ent_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls+_<br>_Optimism_|
|Traditional|-0.003*|0.001|0.000|-0.014***|0.001|-0.001|0.005|0.002|0.014|0.009|0.014*|0.001|
||(-1.73)|(0.59)|(0.04)|(-2.81)|(0.09)|(-0.09)|(1.00)|(0.24)|(1.48)|(1.50)|(1.67)|(0.07)|
|Age||-0.002|-0.003|-0.003|-0.002|-0.001|-0.001|-0.002|-0.001|-0.000|-0.002|-0.002|
|||(-0.18)|(-0.29)|(-0.32)|(-0.18)|(-0.16)|(-0.13)|(-0.25)|(-0.11)|(-0.05)|(-0.25)|(-0.25)|
|Age_sq||-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
|||(-0.69)|(-0.59)|(-0.55)|(-0.70)|(-0.71)|(-0.75)|(-0.62)|(-0.78)|(-0.82)|(-0.63)|(-0.63)|
|Married||-0.018|-0.018|-0.012|-0.018|-0.019|-0.022|-0.016|-0.020|-0.022|-0.013|-0.016|
|||(-0.42)|(-0.42)|(-0.29)|(-0.43)|(-0.45)|(-0.51)|(-0.37)|(-0.46)|(-0.53)|(-0.30)|(-0.37)|
|At least secondary education||0.125**<br>*|0.131***|0.126***|0.126***|0.127***|0.127***|0.129***|0.125***|0.127***|0.116***|0.126***|
|||<br>(3.30)|(3.49)|(3.36)|(3.30)|(3.42)|(3.33)|(3.40)|(3.32)|(3.39)|(3.05)|(3.31)|
|Gender of head(male=1)||0.049|0.056|0.048|0.047|0.053|0.052|0.046|0.040|0.056|0.060|0.039|
|||(0.56)|(0.62)|(0.56)|(0.53)|(0.60)|(0.58)|(0.51)|(0.45)|(0.61)|(0.67)|(0.44)|
|Muslim(Christian is base)||-0.131**|-0.127**|-0.127**|-0.133**|-0.128**|-0.129**|-0.131**|-0.126**|-0.125**|-0.138**|-0.133**|
|||(-2.37)|(-2.31)|(-2.31)|(-2.40)|(-2.32)|(-2.31)|(-2.35)|(-2.29)|(-2.27)|(-2.48)|(-2.36)|
|Spousepresent in household||-0.036|-0.044|-0.035|-0.034|-0.039|-0.039|-0.038|-0.022|-0.037|-0.046|-0.027|
|||(-0.39)|(-0.47)|(-0.39)|(-0.37)|(-0.42)|(-0.43)|(-0.40)|(-0.24)|(-0.40)|(-0.51)|(-0.29)|
|Household size||-0.009|-0.009|-0.009|-0.009|-0.009|-0.008|-0.009|-0.009|-0.009*|-0.009|-0.009|
|||(-1.58)|(-1.60)|(-1.65)|(-1.53)|(-1.60)|(-1.52)|(-1.60)|(-1.63)|(-1.70)|(-1.61)|(-1.60)|
|Poor||-0.019|-0.013|-0.021|-0.018|-0.019|-0.020|-0.019|-0.022|-0.020|-0.020|-0.014|
|||(-0.52)|(-0.34)|(-0.54)|(-0.48)|(-0.50)|(-0.53)|(-0.52)|(-0.59)|(-0.52)|(-0.52)|(-0.36)|
|Urban||0.056*|0.051|0.055*|0.054*|0.056*|0.057*|0.054*|0.057*|0.056*|0.058*|0.052|
|||(1.72)|(1.52)|(1.67)|(1.71)|(1.71)|(1.73)|(1.66)|(1.77)|(1.70)|(1.77)|(1.64)|
|Ashanti(Northern is base)||-0.029|-0.041|-0.022|-0.031|-0.028|-0.030|-0.032|-0.021|-0.028|-0.031|-0.032|
|||(-0.55)|(-0.77)|(-0.43)|(-0.59)|(-0.54)|(-0.57)|(-0.60)|(-0.41)|(-0.54)|(-0.59)|(-0.61)|
|Impulsive|||-0.020|-0.280***|||||||||
||||(-0.81)|(-2.98)|||||||||
|Work centrality|||0.012||0.008||||||||
||||(0.61)||(0.12)||||||||

45
regulations than non-exporters are (see Table 5). This makes intuitive since excessive trade regulations will impose greater constraints for enterprises involved in exporting than for enterprises that only operate in domestic markets.

We attempt to control for this in two ways. First, rather than using the enterprises’ own assessment of the obstacle, we substitute this with the average assessment of enterprises involved in exporting in the same sector and region as the enterprise. In addition to reducing concerns about endogeneity, using average perceptions should also clean out white noise associated with the perceptions of the individual manager. Second, we implement a Generalized Least Squares procedure for Tobit models allowing these variables to be determined endogenously.<sup>15</sup> Two sets of instruments are used in this part of the analysis: (i) the average assessment of trade and customs regulation for enterprises in the same sector and region that are involved in exporting and (ii) the average and maximum times for imports and exports to clear customs for firms in the same sector and region. The second set of instruments is particularly interesting. Since it is not perceptions based (i.e., it is based upon actual experience with customs administration), it is not subject to the same concerns as perception-based data.

In addition to the variables representing trade and customs regulations, the regressions also include a series of additional control variables. First, all regressions include a set of sector dummies (γj) to control for differences between manufacturing sub-sectors with respect to export behavior. For example, some products might be more difficult to transport than other products, limiting export potential. Second, the regressions include a series of enterprise-level controls (Xijk) including enterprise size (proxied by number of employees), age of the enterprise, dummies indicating whether the enterprise in state or foreign-owned and a dummy variable indicating whether the enterprise manager has a university education. Finally, the regressions also include a series of country dummies to control for differences between countries that might

> 15 The procedure that we use is the IVTOBIT routine written for STATA by Joe Harkness at John Hopkins University.  It implements Amemiya’s GLS estimated using formulas from Newey (1987)

13
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||Crossover|Crossover|Crossover|Crossover|Crossover|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Digitspan Score (Score 0-8)|0.0200*||||0.0206*|
||(2.25)||||(2.16)|
|RavenTestScore(Score0-12)|-0.000529||||-0.00650|
||(0.07)||||(0.99<br>)|
|Education:Completedmorethansecondary(1=Yes;0=No)|0.00637||||-0.00204|
||(0.13)||||(-0.04)|
|Preference(1=Yes;0=No)||0.0112|||0.00458|
|||(0.35)|||(0.17)|
|Sawmarketopportunitywhenstarting(1=Yes;0=No)||0.197"|||0.181**|
|||(3.38)|||(3.00 )|
|BusinessKnowledgeIndex(Score0-7)||0.0269*|||0.0174|
|||(2.15)|||(1.38 )|
|Married(1=Yes;0=No)|||-0.0177||-0.0146|
||||(0.71)||(-0.59 )|
|Spouse works in a business (1=Yes; 0=No)|||0.176"**||0.167***|
||||(4.93)<br>||(4.62)<br>|
|HadaMalerolemodel(1=Yes;0=No)|||0.0494||0.0409|
||||(1.47)||(1.08)|
|Self Efficacy (Score 0-1)||||0.188|0.117|
|||||(1.23)|(0.65)|
|Impulsiveness(Score0-1)||||0.115|0.246|
|||||(0.87)|(1.85)|
|OrganizedPerson(Score0-1)||||0.0373|0.0362|
|<br>||||(0.52)<br>|(0.45)<br>|
|Observations|790|790|790|790|790|

Notes: (1) Columns (1), (2), (3), (4) and (5) report the regression coefficients with t statistics in parentheses (2) Column (5) includes all the independent variables in one regression to predict crossing over

- (3) In all regressions, standard errors are clustered by subcity.

- “Significant at the 1 percent level

- **Significant at the 5 percent level

- *Significant at the 10 percent level
Table S1.2: COMPARISON OF SOCIO-ECONOMIC CLASSES

||Ultra poor (UP)|Other poor (OP)|Middle class (M)|Upper class (R)|UP vs. OP|UP vs. M|UP vs. R|OP vs. M|OP vs. R|M vs. R|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|_A. Socio-economic Status:_|||||||||||
|Wealth (BDT)|5850.98|14544.02|152293.69|841129.07|.000|.000|.000|.000|.000|.000|
||(30655.60)|(72425.83)|(315650.26)|(964643.22)|||||||
|Consumption (BDT)|9829.35|10127.50|12205.72|20664.38|.000|.000|.000|.000|.000|.000|
||(4518.74)|(4771.70)|(7112.22)|(36441.19)|||||||
|Literacy|0.07|0.17|0.27|0.51|.000|.000|.000|.000|.000|.000|
||(0.26)|(0.37)|(0.44)|(0.50)|||||||
|Height (cm)|148.71|149.13|149.82|150.13|.000|.000|.000|.000|.000|.018|
||(5.37)|(5.32)|(5.14)|(5.07)|||||||
|_B. Informal Transfers Received:_|||||||||||
|Received any transfers during last year|0.22|0.22|0.16|0.22|.945|.000|.723|.000|.684|.000|
||(0.41)|(0.41)|(0.37)|(0.41)|||||||
|Value of transfers received (BDT)|270.08|328.30|513.29|3097.03|.137|.000|.000|.001|.000|.000|
||(1798.85)|(2708.87)|(4081.52)|(29555.99)|||||||
|Ever receives food|0.92|0.92|0.83|0.42|.389|.000|.000|.000|.000|.000|
||(0.27)|(0.26)|(0.38)|(0.49)|||||||
|Number of food transfer sources|2.17|2.24|1.94|0.90|.000|.000|.000|.000|.000|.000|
||(0.95)|(0.95)|(1.11)|(1.17)|||||||
|Fraction of transfer sources within village|0.88|0.88|0.88|0.70|.769|.441|.000|.608|.000|.000|
||(0.24)|(0.24)|(0.26)|(0.41)|||||||
|_C. Informal Transfers Given:_|||||||||||
|Gave any transfers during last year|0.02|0.03|0.09|0.21|.000|.000|.000|.000|.000|.000|
||(0.14)|(0.18)|(0.28)|(0.41)|||||||
|Value of transfers given (BDT)|45.20|62.69|251.40|691.21|.387|.000|.000|.000|.000|.000|
||(1044.62)|(1323.43)|(2993.95)|(4273.22)|||||||
|Ever gives food|0.44|0.54|0.73|0.81|.000|.000|.000|.000|.000|.000|
||(0.52)|(0.50)|(0.45)|(0.39)|||||||
|Number of food transfer recipients|1.00|1.27|1.70|1.91|.000|.000|.000|.000|.000|.000|
||(1.24)|(1.30)|(1.21)|(1.13)|||||||
|Fraction of transfer recipients within village|0.95|0.94|0.92|0.87|.054|.000|.000|.000|.000|.000|
||(0.18)|(0.19)|(0.67)|(0.26)|||||||
|_D. Reciprocity:_|||||||||||
|<br>Reciprocity of transfers within-village|0.42|0.50|0.67|0.80|.000|.000|.000|.000|.000|.000|
||(0.44)|(0.44)|(0.42)|(0.35)|||||||
|Received transfer from an employer|0.10|0.08|0.03|0.00|.001|.000|.000|.000|.000|.000|
||(0.30)|(0.28)|(0.16)|(0.00)|||||||
|Observations|6,732|7,340|6,742|2,215|||||||

**Source:** Author’s analysis based on original survey data. **Notes:** The sample includes observations from the baseline survey. The sample is restricted to ultra-poor households in column 1, other poor households in column 2, middle class households in column 3 and upper class households in column 4. For variable definitions, see section 8. All monetary values are in Bangladeshi TAKAs. In 2007, 1USD=69BDT. Columns 5-10 report p-values for the equality of means across the four wealth classes (based on one-sided t-tests).
Benavente, J. M., Crespi, G., & Maffioli, A. (2007). Public Support to Firm-Level Innovation: An Evaluation of the FONTEC Program. OVE Working Papers 0507, Inter-American Development Bank, Office of Evaluation and Oversight (OVE).

Bruhn, M. (2011). License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico. The Review of Economics and Statistics, MIT Press, 93(1), 382-386.

Bruhn, M., Karlan, D., & Schoar, A. (2012). The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico, Working Papers 1010, Economic Growth Center, Yale University.

Cassano, F., & Jõeveer, K., & Svejnar,J. (2013). Cash flow vs. collateral-based credit. The Economics of Transition, The European Bank for Reconstruction and Development, 21(2), 269300.

Castillo, V., Maffioli, A., Monsalvo, A. P., Rojo, S., & Stucchi, R. (2010). Can SME Policies Improve Firm Performance? Evidence from an Impact Evaluation in Argentina. OVE Working Papers 0710, Inter-American Development Bank, Office of Evaluation and Oversight (OVE).

Castillo, V., Maffioli, A., Rojo, S., & Stucchi, R. (2011). Innovation Policy and Employment: Evidence from an Impact Evaluation in Argentina. Inter-American Development Bank Science and Technology Division, Social Sector. TECHNICAL NOTES, IDBTN-341.

Cho, Y., Honorati, M., 2014. Entrepreneurship programs around the developing world: a meta analysis. Labour Economics, 28, 110–130.

Chudnovsky, D., López, A., Rossi, M., & Ubfal, D. (2006). Evaluating A Program of Public Funding of Private Innovation Activities. An Econometric Study of FONTAR in Argentina. OVE Working Papers, Inter-American Development Bank, Office of Evaluation and Oversight (OVE) 1606.

Corseuil, L. C. H., & Moura, R. L. (2011). O Impacto do Simples Federal no Nível de Emprego da Industria Brasileira. Discussion Papers 1643, Instituto de Pesquisa Econômica Aplicada - IPEA. Crespi, G., Maffioli, A., & Melendez, M. (2011). Public Support to Innovation: the Colombian COLCIENCIAS’ Experience. Inter-American Development Bank, Social Sector Science and Technology Division, TECHNICAL NOTES No. IDB-TN-264, April.

31
# **2.1.1 The Household’s Behavior**

It is assumed that all allocative decisions lie in the parents’ hands, as long as they are alive. We rule out any bequests at death, so that the whole of current income, as given by (5), is consumed. Concerning the allocation of consumption within the family, let the husband and wife enjoy equality as partners, and let each child obtain a fraction β _∈_ (0 _,_ 1) of an adult’s consumption if at least one adult survives. Full orphans ( _st_ = 4) do not attend school, and consume what they produce as child laborers.

From (2), the budget sets of single-mother and single-father households with the same endowments of human capital and the same number of children are identical. In the absence of any taxes or subsidies, the household’s budget line may therefore be written as where _ct_ ( _st_ ) is the level of each adult’s consumption. The expression on the LHS represents the costs of consumption and the opportunity costs of the children’s schooling. The expression on the RHS is the family’s so-called full income<sup>2</sup> in state _st_ = 1 _,_ 2 _,_ 3 _,_ whereby assumption 1 ensures that states 2 and 3 are identical where the budget set is concerned. Observe that single-parent households not only have lower levels of full income than their otherwise identical two-parent counterparts, but that they also face a higher relative price of education, defined as α _nt_ γ _/_ [(3 _− st_ )+ _nt_ β ].

In keeping with the rather imperfect state of knowledge about the relationship between AIDS and fertility, we make no attempt to model fertility in a sophisticated way. Let all mortality among children occur in infancy, and suppose that so-called ‘replacement fertility’ behavior is unhindered by premature adult mortality. Then: 2A household’s full income is the scalar product of its endowment vector and the vector of market prices. Here, output is taken as the num ́eraire.

14
EUROPE AND U.S.S.R.

# '. **.stiuates** and Projections

|**T1w**|**PauIst2i.**|Im.<br>**ratio**|<br>**Perlod**|**Iraof**<br>rate|-|m|**TFC**<br>**rtem**|b|**Famt**|INN|x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Ii<br>1990<br>1995<br>2000<br>**2005**<br>2010<br>2015<br>2020<br>2025<br>2050<br>2075<br>2100<br>212S<br>2150|769036<br> <br>766717<br>803251<br>18717S<br>U4101<br>67392<br>659005<br>669232<br>876243<br>6967S4<br>69740<br>903W<br>914173<br>918117|51.3<br>51.1<br>51.9<br>52.1<br>53.2<br>52.9<br>54.5<br>57.7<br>61.5<br>71.4<br>71.4<br>72.?<br>73.9<br>74.5|19M-89<br>1990-94<br>1995-99<br>2000-04<br>200s-09<br>2010-14<br> 2014-19<br>2020-24<br>2025-49<br>20S0-74<br>2073-9I<br> 2100-24<br>2125-49|0.45<br>0.42<br>0.36<br>0.37<br>0.32<br>0.27<br>0.24<br> 0.21<br>0.06<br>o.o<br>0.04<br>0.03<br> 0.02|14.9<br>14.0<br> <br>13.2<br> <br>13.0<br>12.6<br>12.7<br>12.5<br>12.3<br> <br>12.0<br>11.9<br> <br>11.9<br>11.7<br>11.7|10.5<br>9.8<br> <br>9.4<br> <br>9.2<br>9.6<br>10.0<br>10.1<br>10.2<br> <br>11.2<br> <br>11.9<br> <br>11.4<br>11.4<br> <br>11.5|1.97<br>1.90<br>1.66<br>1.91<br>1.95<br>1.99<br>2.01<br>2.04<br> <br>2.07<br> <br>2.07<br> <br>2.06<br>2.06<br>2.06|69.1<br>70.4<br> <br>71.7<br> <br>73.0<br>73.6<br>74.6<br>75.5<br>76.4<br> <br>78.4<br> <br>60.3<br>11.4<br> <br>62.2<br> <br>62.5|76.5<br>78.1<br>M9.S<br>60.6<br>1.5<br>82.3<br>63.0<br>63.9<br>65.6<br>67.4<br>6.5<br>69.2<br>89.5|la<br>15<br>13<br>11<br>iO<br>a<br>7<br>5<br>3<br>3<br>2<br>2<br>2|22<br>19<br>16<br> <br>14<br>12<br>10<br>9<br>7<br>5<br>4<br>3<br>3<br>3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 756+<br>70.74<br>65-6B<br>60-64<br>55.5,<br>50-54<br>4549<br>40-4<br>35.39<br>30.34<br>25-20<br>20.24<br>15.19<br>10-14<br>5.,<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> oaiiiiiIiilcA C13 -s:* APOICA (12 ><br>('4t~~~~~~~~~~~~~~~~~~~~~~~~~~~A~C<br><!-- End of picture text --> <!-- Start of picture text --> MMICA CI2 SI<br>*' CoX^O 4U) c\<br>ASIA CS7 CU<br><!-- End of picture text --> 1 6
-25-

# _Selgnlorage_

The cash-flow that is generated by an expansion of the monetary base dH is equal If the data on dM and P were available at e-ery instant, the computation of revenues would be easy. However, the values of dM and P are reported only at discrete times. Moreover, the price index (often the CPI) may not be reflect the composition of government expenditures.

For this reason, most people use a measure such as in Fischer (1982], where the amount of revenues is estimated by the formula: The term AM is the increment over a discrete period, and Y is the value of nominal GDP over the same period. The value of R' measures the ratio of real income from money creation ovet the real value of GDP for the period. For most developing countries the minimum period for the computation of GDP is one year. This formula uses standard national income data with no price index or deflator to produce a measure of real income and is thus very convenient. It gives an exact measure of real income when

- the velocity of money is constant, and

- the rate of growth of money are constant
|Kiln|Total cost|Total cost<br>|due to chan|ge in coalpr<br>|ice<br>|Total co|st due to|carbon t|ax<sup>*</sup>|
|---|---|---|---|---|---|---|---|---|---|
|<br>||25%||25% high|50% high|||||
|technology|(current)|<br>low|50% low|||CT 10|CT 30|CT 50|CT100|
|CK|27|22|18|31|36|27.7|29.7|31.7|36.7|
|FC BTK|18|16|14|20|22|18.3|19.2|20.1|22.4|
|ZZK|17|16|14|19|21|17.8|18.6|19.4|21.5|
|VSBK|15|14|13|17|18|15.9|16.5|17.0|18.4|
|HK|19|17|15|21|23|19.9|20.7|21.6|23.9|
|TK|44|42|40|46|49|44.7|45.7|46.7|49.3|

_Notes_ : * Four carbon taxes (US$ 10, 30, 50, and 100 per ton of CO2 emissions) are considered. This translates into NRs 1.3, 3.9, 6.5, and 13 per kg of CO2 emissions, assuming the current exchange rate of 1 US$ = NRs 130.

<!-- Start of picture text --> 282<br>166<br>115<br>103 100<br>71<br>33 33<br>CK TK FC BTK ZZ FC HK VSBK CSEB HCB<br>BTK<br><!-- End of picture text -->

# **Figure 6. Average production-based emission intensity for coal-based brick kilns by technology type (g/kg of fired brick)**

Although the government has set emission performance standards, they do not provide any incentive for brick industries to reduce emissions above and beyond the minimum that is required, even if the marginal costs of additional emission abatement are very small in comparison with the social costs they impose. However, economic instruments, such as the carbon tax on coal, incentivize brick industries to reduce CO2 emissions and to innovate using cleaner fuels and processes. At the same time, imposing a carbon tax on coal used for brick production increases the total production cost. For example, if the carbon tax of US$ 10 per ton of CO2 is imposed on coal, the total cost of production increases from a high 4% for the least energy-efficient CK to a low 1% 17
mechanism appears to be access to vaccines, in particular against Tuberculosis, Polio, and Tetanus (given to pregnant mothers), as improvements in all these indicator come at Decision Point. Results are robust to various country-specific confounders and other sensitivity tests.

# **References**

- Alderman, H., Hoddinott, J., & Kinsey, B. (2006). Long Term Consequences of Early Childhood Malnutrition. _Oxford Economic Papers_ , _58_ (3), 450-474.

- Birdsall, N., & Williamson, J. (2002). _Delivering on Debt Relief: From IMF Gold to a New Aid Architecture_ (No. 337). Peterson Institute for International Economics.

- Cameron, A. C., & Trivedi, P. K. (2005). _Microeconometrics: Methods and Applications_ . New York: Cambridge University Press.

- Cassimon, D., Essers, D., & Verbeke, K. (2015, April). _What to Do After the Clean Slate? post-Relief Public Debt Sustainability and Management_ (Working Paper No. 3). Belgian Policy Research Group on Financing for Development (BeFinD).

- Cassimon, D., Van Campenhout, B., Ferry, M., & Raffinot, M. (2015). Africa: Out of Debt, Into Fiscal Space? Dynamic Fiscal Impact of the Debt Relief Initiatives on African Heavily Indebted Poor Countries (HIPCs). _International Economics_ , _144_ , 29-52.

- Chauvin, D. N., & Kraay, A. (2005). _What Has 100 Billion Dollars Worth of Debt Relief Done for LowIncome Countries?_ (International Finance No. 0510001). EconWPA.

- Clement, B., Bhattacharya, R., & Nguyen, T. Q. (2005). _Can Debt Relief Boots Growth in Poor Countries?_ (IMF Economic Issues No. 34). Washington, D.C.: International Monetary Fund.

- Cooper, W. O., Boyce, T. G., Wright, P. F., & Griffin, M. R. (2003). Do Childhood Vaccines Have Non-Specific Effects on Mortality? _Bulletin of the World Health Organization_ , _81_ (11), 821-826.

- Coppedge, M., Gerring, J., Lindberg, S. I., Skaaning, S.-E., Teorell, J., Altman, D., ... Farhad, M. (2015). _Varieties of Democracy (V-Dem) Project Dataset v5._ Gothenburg: The V-Dem Institute.

- Crespo Cuaresma, J., & Vincelette, G. A. (2009). Debt Relief and Education in HIPCs. In C. A. P. Braga & D. D ̈omeland (Eds.), _Debt Relief and Beyond - Lessons Learned and Challanges Ahead_ (p. 3548). Washington, D.C.: The World Bank.

- DHS Program. (2015). _Demographic and Health Surveys._ Rockville, MD: ICF International.

- Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The Next Generation of the Penn World Table. _American Economic Review_ , _105_ (10), 3150-82.

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**Table 8: Number of registered foreigners by sending region (as of the end of the year)**

|||||||**2004**||
|---|---|---|---|---|---|---|---|
|**Region**|**2000**|**2001**|**2002**|**2003**<br>||**Share (%)**|**Percentage change**<br>**from the end of the**<br>**previous year (%)**|
|**Total**|1,686,444|1,778,462|1,851,758|1,915,030|1,973,747|100.0|3.1|
|**Asia**|1,244,629|1,311,449|1,371,171|1,422,979|1,464,360|74.2|2.9|
|**South America**|312,921|329,510|334,602|343,635|358,211|18.2|4.2|
|**North America**|58,100|60,492|63,201|63,271|64,471|3.3|1.9|
|**Europe**|47,730|51,497|55,288|57,163|58,429|3.0|2.2|
|**Oceania**|12,839|14,697|15,898|16,076|16,131|0.8|0.3|
|**Africa**|8,214|8,876|9,694|10,060|10,319|0.5|2.6|
|**Stateless**|2,011|1,941|1,904|1,846|1,826|0.1|-1.1|

Source: Japanese Ministry of Justice.

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