rafmacalaba/datause-encoder-data huggingface dataset:
Our study finds that at the country specific level counter-cyclicality of remittances is observed less often than pro-cyclicality, suggesting that, for the majority of the large number of countries examined, the portfolio motive of remitting is stronger than the altruist one.

# 2.3. Stabilization

The general perception arising from the literature that evaluates the cyclicality of remittances seems to be that counter-cyclicality automatically implies the ability to buffer macroeconomic shocks. Thus, Sayan (2006) states that the “countercyclicality enables remittances to serve as a stabilizer that helps smooth out large fluctuations,” while “in the case of procyclicality, remittances may act as a destabilizing force since this would increase the capacity of swings in remittance flows to produce additional fluctuations in output or current account balances, with serious macroeconomic effects.” In the same line, Lueth and Ruiz-Arranz (2006) conclude that remittances may not play a major role in limiting vulnerability to shocks, because they are found to be aligned with the business cycle in the home country.

One skeptical voice is that of Chami et al (2005) who despite finding remittances to be countercyclical question their role as a tool for economic development, because of what they consider the moral hazard accompanying the process of remitting money. This would be caused by the asymmetric altruism leading to free riding of remittance recipient on senders’ good will. In other words, recipients tend to work less when relying on money from abroad.<sup>3</sup> IMF (2005) and Acosta et al (2008) go one step further and examine empirically the direct impact of remittances on output growth volatility. The former study finds that “a 2.5 percentage point increase in the remittances/GDP ratio is on average associated with a one-sixth decline in aggregate output volatility.” In turn, Acosta et al (2008) obtain that “countries with larger remittances flows (as a percentage of GDP) tend to have less volatile real output fluctuations,” with one standard deviation increase in remittances reducing the standard deviation of growth in real output per capita by more than 10 percent.

> 3 However, the fact that they work less in response to remittances need not indicate the presence of moral hazard.  It may simply be an optimal response of recipients since the demand for leisure tends to rise with income.  A problem would arise if recipients behaved strategically and worked less in order to obtain more remittances from those who migrated.

7
||**Table 13: In**|**teraction term**|**s results for C**|**ontrol of Corru**|**ption**||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Dependent variable:<br>Employment Growth (log<br>difference,%,annual)|**Without G**|**DP per capita**<br>**term control**|**interaction**<br>|**With GDP**|**per capita inter**<br>**control**|**action term**|
|Informal Competition|**5.354**|**5.143**|**3.970**|**12.011*****|**15.115*****|**13.260*****|
|(Country level cell<br>average)*Control of<br>Corruption (lagged)|(3.657)|(4.545)|(3.738)|(3.732)|(4.604)|(3.761)|
|Informal Competition||||-9.207***|-12.296***|-11.414***|
|(Country level cell||||(3.174)|(2.918)|(2.958)|
|average)*GDP per capita<br>(lagged, logs)|||||||
|Informal Competition|-4.848|-5.041|-4.511*|83.655***|112.204***|104.357***|
|<br>(Country level cell|(3.710)|(3.207)|(2.531)|(29.255)|(27.172)|(27.912)|
|average)|||||||
|Control of Corruption|-2.746|-1.418|-1.469|-3.987**|-6.506***|-6.259***|
|<br>(lagged)|(1.797)|(2.377)|(1.787)|(1.948)|(2.457)|(1.982)|
|GDP per capita (lagged,||-1.662**|-1.433**|2.351|4.176***|4.004**|
|<br>logs)||(0.751)|(0.628)|(1.428)|(1.389)|(1.546)|
|Initial Employment|-3.765***|-3.891***|-4.153***|-3.836***|-3.884***|-4.160***|
|<br>(logs)|(0.687)|(0.637)|(0.577)|(0.647)|(0.647)|(0.587)|
|Age of Firm (logs)|||-3.398***|||-3.283***|
||||(1.025)|||(1.003)|
|Manager Experience|||0.062|||-0.002|
|(logs)|||||||
||||(0.348)|||(0.343)|
|Exports (proportion of<br>|||2.207**|||2.170*|
|sales)|||||||
||||(1.113)|||(1.121)|
|Female Top Manger Y:1<br>N:0|||-1.394|||-1.483|
||||(1.550)|||(1.536)|
|Multi-establishment firm Y|:1 N:0||3.447***|||3.483***|
||||(0.948)|||(0.954)|
|Crime losses Y:1 N:0|||<br>0.091|||<br>-0.086|
||||(1.148)|||(1.132)|
|Courts obstacle Y:1 N:0|||<br>-0.348|||<br>-0.410|
||||(0.654)|||(0.643)|
|Power outages (hours)|||-0.007*|||-0.007*|
||||(0.004)|||(0.004)|

60
the beginning of transition, Russians and other non-titular Slavs<sup>11</sup> comprised a substantial share of the population as can be seen in the table below. The table also shows that Russians and non-titular Slavs were overwhelmingly concentrated in urban areas. The share of Russians and non-titular Slavs living in urban areas ranged from 69 percent in the Kyrgyz Republic to over 90 percent in Azerbaijan, Estonia, Tajikistan, Turkmenistan and Uzbekistan.

**Table 3.1.  Urbanization rate for Russians and other non-titular Slavs**

(Ukrainians and Belarusans) in 1989

|Republic|Total Population|Urbanization rate for<br>total population (%)|Share of Russians &<br>other non-titular Slavs<br>(%)|Urbanization rate<br>for Russians/ non-<br>titularSlavs (%)|
|---|---|---|---|---|
|Armenia|3,304,776|67|2|86|
|Azerbaijan|7,021,178|54|6|93|
|Belarus|10,151,806|65|16|86|
|Estonia|1,565,662|71|35|92|
|Georgia|5,400,841|55|7|86|
|Kazakhstan|16,464,464|57|44|76|
|Kyrgyz|4,257,755|38|24|69|
|Latvia|2,666,567|71|42|85|
|Lithuania|3,674,802|68|12|89|
|Moldova|4,335,360|47|27|74|
|Tajikistan|5,092,603|33|9|93|
|Turkmenistan|3,522,717|45|11|94|
|Ukraine|51,452,034|67|23|87|
|Uzbekistan|19,810,077|41|9|94|

Source: 1989 Soviet Census.

In the aftermath of transition, industrial collapse and related economic shocks, as well as rapidly changing political situations, meant large numbers of Russians and other ethnic groups who were living outside their historic homelands opted to move. From 1989 to 1998, approximately 3 million ethnic Russians and 1 million ethnic Ukrainians returned to Russia or Ukraine.<sup>12</sup> The departure of large numbers of better off people meant that, on average, those who remained in the cities were poorer.

One of the results of the large international migration flows in the region can be seen in the table below. As the top half of the table shows, only 11 countries worldwide experienced absolute declines in urban populations from 1990-2002. And all 11 of those countries were transition countries.<sup>13</sup> From 1990-2002, of 187 countries for which data are available, only 18 experienced ruralization. Of these, 11 countries were found in the ECA region.<sup>14</sup> To a large extent, ruralization and absolute urban population decrease can be explained by high levels of emigration from urban areas, although in some cases population declines due to low fertility rates and conflict-related emigration are also important contributing factors.

> 11 _Slavs here refer to Belarusans and Ukrainians except for Belarusans in Belarus and Ukrainians in Ukraine, who are considered titular nationalities._

> 12 _United Nations. 2002.  “International Migration from Countries with Economies in Transition:  1980-1999.” Mimeo._

> 13 _Croatia, Czech Republic, Slovenia, Russian Federation, Ukraine, Lithuania, Bulgaria, Kazakhstan, Moldova, Estonia, and Latvia._

> 14 _Czech Republic, Russia, Kazakhstan, Slovenia, Estonia, Azerbaijan, Kyrgyz Republic, Uzbekistan, Tajikistan, Moldova and Latvia._

17
POLICY RESEARCH WORKING PAPER 1221

# **Summary findings**

Using UNESCO Aata for research and developnient (R&lD) expendirures and personnel, Birdsall and Rhee document international differer.ces in R&l) a-:tivities and assess the determinants of these differences and the link between R&I) and ecotiomic growth.

For a group of OECD cotiuitries, R&D activity and economic growthi are correlated for one of their two proxies. Contrary to the findings of Romer and Lichtenberg, however, they are not corielated acros, all (including developing) countries.

Moreover, even for OEC( countries, it appears likely thit economic activity affects R&D activity raither than vice versa. First, thtre is no evidence that R&D in the OFCD in the early years of 1970-85 contributed to growth durmng the whiiole period. Second, the anralysis of deteriniiianits of R&D activities suggests that level of income affects R&D activities; appairently R&D becomes important only after **a** coointry reaches a certain stage **ot** development.

For developing countries, the authors' results are consistent with the widespread view, first proposed by (Gershenkron. that countries ti.it are b hind grow by catching up technologic,ally, not by ad-ancing the technological frontier.

- - This paper a product of rhe Office of the Director, Policv Research Department is part of a larger effort in the department to assess the sources of growth across counitries. Copies of tthe paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sushma Rajan, room NI 1-045, extension 3,3747 (25 pages). November 1993.

_The Policy Research Working Paper Series disseminates the findings of uwork in progress to e:courage the exchange ol ideas about development issues. An objective of the series is to get the findings out quickly, even i. the presentations are less than fully polished. The papers carry the names of the authors and should be used and cited accordingly. The findings, interpretations. and conclusions are the authors'_ **_oum_** _and should not be attributed to the World Bank, its Executive Board of Directors, or any' of its member countries._ J Produced by the Policy Research Dissemination Center
Most of the results also hold when the regressions are re-run for the separate components of investment in distribution and transmission only (Tables 3 and 4).

**Table 4 Key Determinants of Introduction of PPP in Transmission**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**GOVER**<br>**SECTORALG**|**NANCE VARIAB**<br>**OVERNANCE VA**<br>|**LES**<br>**RIABLES**<br>||
|**Introduction of a regulatory agency**<br>_REG_|1.187***<br>(0.345)|1.358***<br>(0.404)|1.406***<br>(0.434)|
|**ECONOMYWIDE**|**GOVERNANCE **<br> <br>|**VARIABLES**<br> <br>||
|**Control of Corruption**_wgi_cc_|0.084<br>(0.174)<br> <br>|- 0.093<br> (0.240)<br>|-0.192<br>(0.223)|
|**Degree of democracy****_Polity2_**|0.076**<br>(0.030)|0.070**<br>(0.031)|0.074**<br>(0.031)|
|**LONG RUN ENVIRONM**|<br>**ENTALSUSTAIN **|<br>**ABILITY FACT**|<br>**ORS**<br>|
|**PPP Renewable Investment**|||0.896***<br>0231|
|**MACROE**|**CONOMIC CONT**<br>|**ROL**<br>|(.)<br>|
|**GDP**<br>_ln_GDP_||0.397**<br>(0.177)|0.533***<br>(0.204)|
|**Population**<br>_ln_Pop_||0.405<br>(0.078)|0.240<br>(0.082)|
|**Constant**|-3.251***<br>(0.401)|-13.860***<br>(2.382)<br>|-12.135***<br>(0.079)|
|**N**|1185|1143|1143|
|**Log likelihood**|-104.16|-85.20|-76.65|
|**χ**<sup>**2**</sup>|49.27***|85.20***|102.32***|
|**Pseudo R**<sup>**2**</sup>|19.13|33.34|40.03|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

**We can now move to determine which of the previous factors affect the private investor’s decision to enter the market of renewable-based energy and the amount of investment sunk in the sector.** Regressions (1) and (2) in Table 5 report the results of the two step Heckman model, introducing in the first stage as a dependent variable (reported in red) the likelihood of attracting private investment in T&D and as explanatory variables the key sectoral and economy wide governance variables. The second step of the model uses the logarithm of amount of private investment in T&D as the dependent variable, conditional on the decision to enter the renewable market, and as explanatory variables the broader model including the short run and long run variables described above. The final three regressions report the GLS model using as a dependent variable the logarithm of amount of private investment in T&D, which

14
# **The Impact of Financial Literacy Training for Migrants**

John Gibson, David McKenzie, and Bilal Zia<sup>*****</sup>

# **JEL Codes: F24, O12, C93.**

**Keywords:** Financial literacy; Remittances; Migration.

**Sector Board:** Financial Sector (FSE)

> * John Gibson is at the University of Waikato, and David McKenzie and Bilal Zia are both at the Finance and Private Sector Development Unit of the World Bank Development Research Group. The authors thank the Russian Trust Fund for Financial Literacy for financing this work; Wendy Li, Wasana Karunarathne and Halahingano Rohorua for leading the training sessions and survey interviews; Kim Hailwood and the New Zealand Ministry of Pacific Island Affairs for their work in helping to develop the training content with us; and all the survey participants and community groups that participated in this study.

1
The total value of non-tradable production would not differ if we equalized the intra-household allocation of time between men and women. Under this variant, the contribution of the female population would be USD 392 million (or 50.2 percent) compared to USD 388 million (or 49.8 percent) of men. Women’s aggregate contribution would continue to exceed men’s as the female population aged 15+ was larger than the male population aged 15+.

Alternatively, if we allowed the wage rate to depreciate as the traditional sector absorbed hours that were released from the non-tradable sector, the economic contribution of the non-tradables sector would be lower than the status quo. We translate the number of daily hours devoted to non-tradable production into the number of workers that they represent.<sup>28</sup> The additional workforce absorbed by the traditional sector would bring its wage down by 14.4 percent and by using this calibration, the non-tradable sector would be valued at USD 551.5 million instead of USD 780 million.

# **5.3 Women’s Potential Contribution to GDP with Multiple Convergence**

GDP would increase by 11.5 percent or reach USD 4.51 billion (Figure 6) if labor-relevant gender gaps were simultaneously closed. The analysis continues by considering the effect of equalizing the gender gaps in labor-force participation, wages and sub-sectors shares. The supply of labor would grow from 1.90 million to 2.04 million workers and the contribution of women would be USD 1.62 billion, or 36 percent of GDP and 50.1 percent of the aggregate production of labor. A 9.2 percent growth and 11.1 percent growth from the initial estimates, respectively.

The sectoral and gender decomposition indicated that most of the expansion would be driven by an increased allocation of the workforce to modern sector production which would grow by 86 percent for women alone. The aggregate output would grow by 9.6 percent (instead of 11.5 percent) if we would allow wages to change due to the displacement of the additional jobs created between sectors. Given the tight link between labor market earnings and poverty, the closure of the three labor-relevant gaps would reduce poverty by 8.4 percent and reach a headcount of 40.7 percent.<sup>29</sup>

# **Figure 6. Selected macro-economic changes due simultaneous convergence of labor-relevant gender gaps (in USD, growth in %)**

> 28 The number of workers is 543,000 and it is estimated by dividing the total amount of hours devoted by the population to non-tradable production by 8 which is the standard number of hours devoted to a job in a day.

> 29 Poverty in Liberia is widespread and 44 percent of the population lived under the international poverty line of $1.90 per day in 2016 (World Bank, 2021).

21
We next turn to the results that include location fixed effects, reported in the lower panel of Table 4. Including these fixed effects substantially reduces the difference in TVIP scores between richer and poorer households in Colombia, Ecuador and Nicaragua. This suggests that, in these three countries, a substantial portion of the socioeconomic gradients in cognitive development can be accounted for by residential sorting. Once we limit the comparisons to children who live in the same location, the gap between richer and poorer households is substantially smaller.

Finally, we use longitudinal data from rural Ecuador, rural Nicaragua, and rural and urban Peru to analyze possible changes in the wealth gradients as children age. For this analysis, we limit the sample to children who took the TVIP in all four survey waves in Ecuador (85 percent of children who took the TVIP at baseline), three survey waves in Nicaragua (92 percent), and two survey waves in Peru (96 percent). Figure 2 shows that in all three countries the wealth gradients that are apparent among 4-5 year old children are also apparent as these children age. In Ecuador, where the panel has the longest duration (7 years), differences in TVIP scores between wealthier and less wealthy children at 12-13 years of age, when children are of an age where they would be completing elementary school, are very similar to those found at 5-6 years of age. In all three countries, there is no evidence of catch-up. On the other hand, the poorest children do not appear to fall further behind either.<sup>8</sup>

# **4. Discussion and conclusions**

Early childhood development has long-lasting consequences for adult success. Long-term panels that have followed children from early ages into adulthood show that children with poor levels of nutrition, inadequate cognitive development, and low levels of socio-emotional development tend to do badly in school, have higher levels of unemployment, earn lower wages (even controlling for schooling attainment), have a higher incidence of teenage pregnancy, are more likely to use drugs, are more likely to be involved in criminal activities, and have children with worse nutritional status.

Evidence on the extent to which there are shortfalls and socioeconomic gradients in cognitive development among young children in developing countries is very scarce. In this paper we use data from five countries in Latin America to show that there are important differences in early language development between children in wealthier and poorer households. Latin America is

> 8 Of course, in the presence of noisy data, one must be cautious about interpreting these panel results for the same reason one must be cautious about interpreting age-patterns based on a single cross-section.

9
# **5.2. Non-income Inequality**

Non-income distribution is also uneven in a spatial dimension in favor of the leading coastal and urban areas. Overall, the average years of education of a household increased from 6.01 in 1989 to 6.75 in 2004 (Table 4). Human capital is better in coastal region than in inland region and in urban areas than in rural areas. The average increase in years of schooling is larger in coastal region than in inland region. However, unlike the distribution of income which worsened over time, the distribution of human capital stock tends to equalized – the segment of population with lower years of education gained more thanks to the implementation of nine-year compulsory education which has significantly improved the education level of the younger generation. Gini coefficient of years of formal education for all provinces decreased by 6.1 percent from 0.261 in 1989 to 0.245 in 2004. Gini coefficients of all subgroups also decreased over time to different extent.

**Table 4 - Average years of formal education of household adult members**

||1989|Meanvalue<br>2004|1989-<br>2004<br>variation<br>(%)|1989|IndexofGin<br>2004|i<br>1989-<br>2004<br>variation<br>(%)|
|---|---|---|---|---|---|---|
|All provinces|6.01|6.75|12.3|0.261|0.245|-6.1|
|Coastal provinces|6.36|7.24|13.8|0.248|0.235|-5.2|
|Urban areas|7.27|8.43|16.0|0.234|0.214|-8.5|
|Rural areas|5.88|6.63|12.8|0.247|0.235|-4.9|
|Interior provinces|5.80|6.47|11.6|0.266|0.248|-6.8|
|Urban areas|6.90|7.62|10.4|0.249|0.238|-4.4|
|Ruralareas|5.25|5.91|12.6|0.261|0.241|-7.7|

Not only household income level but also income gains are closely associated with education of household members. Households with better educated members tend to have higher income growth over time as the GICs of households with more years of education in general lie above those with less years of education (Figure 8). In particular, as the GIC of education of 10 years or above locates above all other curves for the entire population with a steeper slope, while the GICs of lower education levels somehow cluster together with a flatter slope, households with the highest education (with members in average completed primary middle school education or above) tend to have registered the biggest gains, especially those with higher initial income. As all GICs have a positive slope, which suggests that households with higher initial income have higher income growth, income distribution worsened within households of each category of education, and the widening of income gaps is the most acute between households with less than primary middle school education and those with better education, and within households with better education. In other words, there is a close relationship between income growth and education level. Households with a higher initial income and a better education level tend to have higher income growth due to the compound effects. This also gives support to the increase in overall inequality level for the entire population, and that in the higher end of the spectrum in particular.

12
# **3. The Input Sheet**

The input sheet includes all the debt, macroeconomic data and assumptions needed to use the template (Figure 2). Parameters and imported or copied data are stored only in the input sheet. If they are needed in another sheet, the values are transferred through formula linkages. Refer to Section 2. The Instruction Sheet for information on how to set parameters and import data. Once "input_fiscal" is filled in correctly, the baseline and stress test results are automatically calibrated and summarized in the output tables in sheets "Table_SR" (debt ratios) and "Table_GF" (gross financing need). If stochastic simulations are enabled the default values of the average and standard deviation of the random variables are defined. If the fiscal reaction function is enabled, the stochastic simulations and all stress tests are consistent with the automatic fiscal rule.

In general, the implementation of this model requires 10 years of historical data as well as 5 years of projections for key macroeconomic indicators that are used in the FS template. However, in environments with sparse data, a shorter period of actual data could be accommodated (e.g. 5 years). Regarding macroeconomic projections, this model can be used in conjunction with a macro projection framework, or alternatively, could be populated using the live data base of the Bank.

The implementation of this model requires projections for all variables listed in below, except for the public debt stock, which is derived from public debt dynamics used in the FSA template. However, since we recognize that for many market access countries foreign borrowing maybe rationed by the markets, we allow the user to enter country-specific assumptions for allowable foreign borrowing, derived from any macroprojection framework. Since the change in public debt is derived from the public debt dynamics equation and the external foreign currency debt path is predetermined, the time path for domestic public debt is derived as a residual.

For all variables the following information is to be provided:

- 1) Unit of measurement: national currency, U.S. dollar, index number in column A

- 2) Scale: billions, millions, percent in column B

- 3) Database: source of data (LDB, GDF, other sources) in column C

- 4) Series Code, in case data are from a database in column D

- 5) Descriptor: eg. “External debt stock”, in column E

- 6) Notes in column F.

Data requirements which need to be filled in the input sheet are divided in 6 main blocks (Figure 2):

- 1) Debt stock and debt service data 2) Fiscal data 3) GDP, prices and exchange rates 4) Monetary data

- 5) Other factors 6) Assumptions for baseline projections

For oil reach countries (ORCs), the FSA tool includes an additional block for oil revenues and prices.

13