rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

The flow of workers’ remittances to developing countries has increased dramatically in recent years. While in 1996 developing countries received $58 billion dollars in remittances, in 2003 remittance inflows amounted to $96 billion dollars (see Annex 2 for detailed data on remittances). It is expected that the volume of workers’ remittances will surpass $110 billion dollars in 2004. Workers’ remittances constitute an increasingly important source of income for many families, especially poor families, whose relatives have emigrated to other countries in search of better employment opportunities. Moreover, after foreign direct investment, workers’ remittances constitute the largest source of external financing for developing countries. For 25 developing countries, remittance flows represented more than 5% of their respective GDP in 2003.

In an effort to improve the developmental impact of remittances in recipient countries, the World Bank is currently conducting a series of studies to better understand the characteristics of remittances, as well as the financial infrastructure underpinning money transfers in both sending and recipient countries. In particular, the World Bank is talking to central banks, money transfer companies, and senders and recipients of remittances in a number of developing and developed economies.

The purpose of this paper is to present the findings of a survey conducted by the World Bank with central banks in 40 developing countries (other World Bank surveys with market participants in the remittance industry and senders and recipients in selected countries are currently under preparation). This survey focused on the following four topics: (i) coverage of national statistics on remittances, (ii) cost of transferring and delivering remittances, (iii) regulatory regime for remittance transactions, and (iv) recent initiatives in developing countries to channel remittance flows through formal financial institutions.<sup>1</sup> Most of the information contained in this paper was kindly provided to the World Bank by the central banks of participating countries between July and December 2004. The list of 40 developing countries in Asia, Europe and Central Asia, Latin America and the Middle East and Africa that have participated in this survey is presented in Table 1. Although more countries may participate in this survey in the future, the author believes that the preliminary findings of this survey are sufficiently robust to be presented to the general public to generate discussions and stimulate the debate about the survey’s main findings.

> 1 In this paper, the term “remittances” refers to the money that migrant workers send to their relatives in their countries of origin. Remittances in kind are not analyzed in this paper.

4
**Table 9 – Bivariate Logit Regressions for Negative and Positive Results**

||**Em**<br>|**ploym**<br>|**ent**<br>|||**Wages**<br>|<br>||
|---|---|---|---|---|---|---|---|---|
|**t-stat> 1.3 in bold**|**n**|**g**|**p**|**os**|**n**|**g**|**p**|**os**|
||**Unw**|**Wei**|**Unw**|**Wei**|**Unw**|**Wei**|**Unw**|**Wei**|
||**eg.**|**gh.**|**eg.**|**gh.**|**eg.**|**gh.**|**eg.**|**gh.**|
|Time of evaluation since crisis|**-1.38**|0.22|0.20|**2.62**|**-3.22**|**-3.06**|**2.94**|**4.03**|
|Refugee share|-0.21|-0.54|0.78|1.29|**1.79**|0.14|-0.85|**-2.85**|
|Males|**2.75**|**2.60**|-0.29|-1.23|0.94|0.93|-1.52|**-2.57**|
|Females|0.85|**3.07**|-0.88|-0.95|**2.59**|**4.90**|-1.13|**-1.93**|
|Young|**2.56**|**4.73**|.|.|.|.|.|.|
|Old|0.42|**1.33**|**1.40**|**-1.45**|.|.|.|.|
|Formal|**-1.42**|**.**|**4.68**|**2.10**|.|.|0.65|-0.11|
|Informal|**3.36**|**2.17**|.|.|.|.|.|.|
|Lowskilled|-0.83|**-1.84**|**2.62**|**6.81**|-0.84|-1.24|-0.87|-0.50|
|Highskilled|-0.62|-0.82|-0.39|**2.07**|**-1.91**|**-2.82**|0.73|0.69|
|HIC|0.43|0.06|0.00|0.39|**-1.39**|**-4.43**|**3.08**|**3.71**|
|MIC|-0.15|-0.79|-0.54|-0.62|**1.55**|**4.73**|**-3.30**|**-3.79**|
|LIC|-0.32|1.11|0.66|0.26|.|.|1.34|1.21|
|Burundian and Rwandan refugees in|||||||||
|Tanzania|-0.32|1.11|0.66|0.26|.|.|.|.|
|Congolese refugees in Rwanda and|||||||||
|Uganda|.|.|.|.|.|.|.|.|
|Cuban refugees in Miami|.|.|.|.|-0.68|**1.61**|0.68|-0.41|
|Ethnic Germans from EE and FSU to<br>Germany|**2.58**|**5.20**|.|.|1.18|**2.71**|.|.|
|Ethnic Greeks from Turkey to Greece|.|.|.|.|.|.|.|.|
|Expellees from Algeria to France|.|.|.|.|0.68|0.61|.|.|
|Expellees from East Europe to West|||||||||
|Germany|**3.97**|1.21|1.17|**3.57**|.|.|.|.|
|FSU escapees to Israel|**-2.09**|**-2.56**|-0.30|**-1.42**|0.11|**-3.80**|1.24|-0.06|
|FY refugees to Europe|**2.77**|**5.28**|.|.|.|.|.|.|
|IDPs in Colombia|.|.|.|.|**4.45**|**4.32**|.|.|
|IDPs in Sudan (Darfur)|.|.|.|.|.|.|.|.|
|Palestinians from West Bank to Israel|.|.|.|.|0.96|0.14|.|.|
|Refugees in Denmark|**-2.20**|**-4.11**|**1.84**|**3.06**|.|.|**4.39**|**6.55**|
|Refugees in Kenya (Turkana)|.|.|.|.|.|.|1.34|1.21|
|Refugees in the USA|.|.|.|.|.|.|.|.|
|Returnees from Angola and|||||||||
|Mozambique to Portugal|.|.|.|.|**1.69**|**3.00**|0.37|-1.14|
|Syrian refugees in Jordan and Turkey|0.09|-0.62|-0.39|-0.47|.|.|-1.23|-0.76|

36
Gujarat Figure 3a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 3b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Gujarat<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>% Annual Change<br><!-- End of picture text --> <!-- Start of picture text --> Gujarat<br>60000 12%<br>50000 10%<br>40000 8%<br>30000 6%<br>20000 4%<br>10000 2%<br>0 0%<br>Year<br>Tax Revenue Stamp Rate<br>2517 4002 10347 30077 47783<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text --> Kerala Figure 4a: Annual Percentage Change in Stamp Duty Revenues and Rates

# **Kerala**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>Year<br>Tax Rev Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % change<br><!-- End of picture text --> Figure 4b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Kerala<br>35000 13.6%<br>30000 13.4%<br>25000 13.2%<br>13.0%<br>20000<br>12.8%<br>15000<br>12.6%<br>10000 12.4%<br>5000 12.2%<br>0 12.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-811985-861990-911995-962000-01<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text --> 73
18 Figures 4a: Global Productivity Growth and Vertical Specialization by Industrial Sector, 19862014 <!-- Start of picture text --> 32<br>34<br>26 33 29 31<br>33<br>26 28<br>36 29 32<br>36 25 34<br>25<br>35 31<br>28 30<br>35<br>30<br>0 20 40 60<br>Vertical specialization (world imports of parts and components<br>as a share of world total exports, average 1986-2014, percent)<br>200<br>150<br>100<br>Productivity Growth<br>(average by period, percent) 50<br>0<br><!-- End of picture text -->

# 1986-2000 2001-2014

_Source:_ UNIDO (INDSTAT2), UN Comtrade, and authors’ calculations. _Notes:_ ISIC3 industry codes are used; productivity is normalized using average industry productivity; weights are represented by value added.

ISIC3 codes: 25 Manufacture of rubber and plastics products; 26 Manufacture of other non‐metallic mineral products; 28 Manufacture of fabricated metal products, except machinery and equipment; 29 Manufacture of machinery and equipment n.e.c.; 30 Manufacture of office, accounting and computing machinery; 31 Manufacture of electrical machinery and aparatus n.e.c.; 32 Manufacture of radio, television and communication equipment and apparatus ; 33 Manufacture of medical, precision and optical instruments, watches and clocks ; 34 Manufacture of motor vehicles, trailers and semi‐trailers; 35 Manufacture of other transport equipment; 36 Manufacture of furniture; manufacturing n.e.c.
service indicators (good health outcomes) implies a pro-rich inequality. These socioeconomic differences are expected. However, the trends in the differences are relevant for interventions that could be adopted. Therefore, our interest is to show if the absolute gaps were widening or narrowing.

The results, in Table 3a, show that there has been a widening absolute pro-rich inequality between the poor and the rich in child nutritional outcomes. The pro-rich inequality was observed in all the three child nutrition status indicators. It is significant in stunting and underweight cases. In DHS 2000, the level of child stunting for the poor was higher by about 4.2 percentage points. In DHS 2014, the difference between the poor and the rich was 10 percentage points. Similarly, the gap in underweight increased from 5 to 8 percentage points. In both cases (stunting and underweight), the differences at the earlier survey and the latest survey were significantly different [p<0.05], implying a widening pro-rich inequality. Similarly, the declines in child mortality have been sharper /faster for the rich. For both IMR and U5MR, the differences at the earlier survey were actually more pro-poor and they crossed the line of equality in favor of the better off.

On the services side, there has been a larger increase in modern contraceptive use by married women among the better off. On all others, however, either the improvement has been pro-poor (e.g., measles vaccination [p<0.5]) or no indication of significant widening in inequality. Given the overall improvement in the mean, this suggests that both the poor and the rich benefited from the expansions in MNCH services.

19
Table 14: Other programs (survey round 2)

|Dependent variable:|(1)<br>Indicator:<br>any free food<br>or maize|(2)<br>Indicator:<br>any school<br>feeding|(3)<br>Indicator:<br>any<br>scholarship|(4)<br>Indicator:<br>any cash<br>transfer|(5)<br>Indicator<br>any fertilizer<br>coupon|
|---|---|---|---|---|---|
|PWP|0.002|0.004|-0.002|-0.000|0.047|
||(0.025)|(0.030)|(0.004)|(0.005)|(0.032)|
|PWP * Top-up|-0.013|-0.009|-0.002|0.004|0.018|
||(0.013)|(0.017)|(0.003)|(0.005)|(0.021)|
|R-squared|0.24|0.24|0.01|0.02|0.15|
|P-value for non-zero efect on top-ups|0.67|0.84|0.19|0.51|0.04|
|Upper bound (total efect)|0.037|0.053|0.002|0.015|0.124|
|Lower bound (total efect)|-0.058|-0.065|-0.010|-0.007|0.005|
|Observations|2840|2840|2840|2840|2840|
|Mean of dep. var.|0.14|0.22|0.01|0.01|0.50|
|in control group||||||
|OLS estimates. Standard errors cluster<br>Estimates include district and week-of-<br>_∗p <_0_._10_, ∗∗p <_0_._05_, ∗∗∗p <_0_._001|ed at the EA le<br>interview fxed|vel.<br>efects.||||

Table 15: Transfers and loans (survey round 2)

|Dependent variable:|(1)<br>Indicator:<br>any transfers<br>received|(2)<br>Indicator:<br>any transfers<br>given|(3)<br>Indicator:<br>any loans<br>received|(4)<br>Indicator:<br>any loan<br>payments|
|---|---|---|---|---|
|PWP|0.005<br>(0.025)|-0.053**<br>(0.020)|-0.032<br>(0.024)|0.003<br>(0.013)|
|PWP * Top-up|-0.028<br>(0.020)|0.007<br>(0.016)|-0.002<br>(0.016)|0.001<br>(0.011)|
|R-squared|0.11|0.18|0.04|0.03|
|P-value for non-zero efect on top-ups|0.33|0.02|0.13|0.74|
|Upper bound (total efect)|0.023|-0.009|0.010|0.027|
|Lower bound (total efect)|-0.070|-0.084|-0.079|-0.019|
|Observations|2840|2840|2840|2840|
|Mean of dep. var.|0.24|0.22|0.20|0.06|
|in control group|||||
|OLS estimates. Standard errors cluster<br>Estimates include district and week-of-<br>_∗p <_0_._10_, ∗∗p <_0_._05_, ∗∗∗p <_0_._001|ed at the EA le<br>interview fxed|vel.<br> efects.|||

46
# **3 SERVICE EXPORT SOPHISTICATION**

## **3.1 The Service Export Sophistication Methodology**

We build upon the Hausmann, Hwang and Rodrik framework, which uses a goods export sophistication measure _Goods EXPY_ as a proxy for the most productive set of goods the country can produce at a given time. They argue that export data is the best way to reveal this production frontier as we can expect countries to export those goods in which it is most productive. Along the same reasoning and as described below we will construct a _Service EXPY_ to proxy the service production frontier of a country, by and large following the methodology developed by HHR.

We start by constructing so-called _PRODY_ ‘s for each category of goods exports, reflecting the income/productivity level associated with each good, and we do the same for each category of services. _PRODYj_ is the income value associated with the service _j_ , and is constructed by using the service export ( _x_ ) share of a country _i_ in world‘s export of service _j_ , divided by the sum of shares of _j_ in world exports of _j_ across all countries exporting that service.<sup>5</sup> These ratios are multiplied by the exporting countries‘ respective per capita income level ( _Y_ ) and the result is summed up across all countries. In other words, the _PRODY_ becomes the weighted average of per capita GDPs, where the weights represent the revealed comparative advantage in service _j_ for each country.<sup>6</sup> _PRODY_ s are constructed for each service category, for each year of available data, and are by construction the same for all countries.

_EXPY_ is then the weighted income value of services exported by a country, computed as the sum of _PRODY_ s using as weights the share of the particular service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics, which has data available for over 190 countries from 1990 to 2007. Due to data being reported inconsistently on service exports, we end up with a sample of approximately 100 countries 1990 to 2007. GDP per capita data is taken from the World Bank Indicators database. Before describing the _service EXPY_ we would like to discuss two of its characteristics that differ from preexisting goods index – the need for _dynamic PRODYs_ and the high level of aggregation.

First, the _Goods EXPY_ was constructed using _static PRODY_ s (the _PRODY_ for each good is held constant at the average value 1999-2001). This means that any increase in _Goods EXPY_

> _xij_ 5 _X i_ is hence the value-added share of commodity j in the country‘s overall export basket.

> 6 The rational for using revealed comparative advantages as weights is to control for country size when ranking the services.

11
Two messages are embedded in the above: first, the back-to-the-wall effect of a drastic reduction in aid starting in 1992 stimulated substantial reform. Second, it took the successful December 2002 elections and smooth transfer of power from President Arap Moi to President Kibaki for visible results to materialize. This suggests two interacting effects: long lags before payoffs to reform appear; and threshold events (such as a successful election) which trigger these payoffs. Not surprisingly, we concluded (prior to the December 2007 elections) that the maintenance and acceleration of these positive trends depended crucially on continued success in entrenching political stability, improving governance and strengthening fiscal and financial institutions; and relaxing the infrastructure constraint on private investment. The unfortunate December 2007 election fiasco has reinforced the key insight of this paper: that sustained growth and poverty alleviation will eventually depend upon Kenya’s ability to engender political stability and social harmony for a prolonged period; experience from countries as disparate as China, India and Vietnam suggests that such stability is needed for a minimum 10-15 years for positive trends to take hold.<sup>8</sup> The reasoning is that irrespective of the political system, good governance and stability in the rules of the game for a long period are needed for having well-managed public finances, investor confidence and hence long-run growth.

Section 2 sketches out an analytical framework which underpins the subsequent presentation of empirical evidence. Section 3 contains a snapshot of Kenya’s economic liberalization dating to 1993. Section 4 analyses government debt dynamics, discussing key facets such as revenue mobilization and expenditure composition. While impressive strides have been made towards an efficient revenue system based on broad-based taxes, the composition of expenditure remains a serious constraint on growth. Specifically, spending on infrastructure is inadequate. Section 5 presents evidence on declining country and political risk based on a

> 8 Rodrik (1999) contains a seminal analysis of the importance of social cohesion in responding efficiently to exogenous shocks and nurturing long-run growth.

5
# **9. Appendix**

**<u>Table A.1.</u>** <u>Descriptive statistics and differences between those in household roster and sample</u>

||Women in the|Women’s||Men in the|Men’s||
|---|---|---|---|---|---|---|
||householdroster|Sample|Diff|householdroster|Sample|Diff|
|Age|39.88|40.49|0.61|38.35|39.40|1.05|
||(18.18)|(17.34)|(0.75)|(17.35)|(18.07)|(0.83)|
|Marital status|||||||
|Never married|0.21|0.16|-0.05***|0.36|0.34|-0.02|
||(0.40)|(0.36)|(0.02)|(0.48)|(0.47)|(0.02)|
|Monogamous union|0.49|0.53|0.04*|0.53|0.53|0.00|
||(0.50)|(0.50)|(0.02)|(0.50)|(0.50)|(0.02)|
|Polygamous union|0.06|0.06|0.00|0.04|0.05|0.00|
||(0.24)|(0.24)|(0.01)|(0.20)|(0.21)|(0.01)|
|Widow/er, divorced,|0.24|0.25|0.01|0.07|0.08|0.01|
|separated|(0.43)|(0.43)|(0.02)|(0.26)|(0.27)|(0.01)|
|<br>Currently attending|0.06|0.07|0.01|0.13|0.14|0.00|
|School|(0.25)|(0.26)|(0.01)|(0.34)|(0.34)|(0.02)|
|Educational attainment|||||||
|No schooling|0.43|0.43|-0.00|0.10|0.10|0.00|
||(0.50)|(0.50)|(0.02)|(0.30)|(0.30)|(0.01)|
|At least some|0.41|0.41|0.00|0.61|0.60|-0.00|
|primary school|(0.49)|(0.49)|(0.02)|(0.49)|(0.49)|(0.02)|
|Some secondary|0.16|0.15|-0.00|0.30|0.30|0.00|
|school (or higher)|(0.36)|(0.36)|(0.02)|(0.46)|(0.46)|(0.02)|
|Lives with parents|0.21|0.21|-0.00|0.49|0.50|0.01|
||(0.41)|(0.41)|(0.02)|(0.50)|(0.50)|(0.02)|
|Lives with in-laws|0.19|0.19|-0.00|0.01|0.00|-0.00|
||(0.39)|(0.39)|(0.02)|(0.09)|(0.06)|(0.00)|
|Lives in single adult|0.02|0.03|0.00|0.02|0.03|0.01|
|<br>household|(0.16)|(0.17)|(0.01)|(0.15)|(0.17)|(0.01)|
|Lives in household with a|0.14|0.15|0.01|0.15|0.15|0.01|
|couple and no other|(0.34)|(0.35)|(0.01)|(0.36)|(0.36)|(0.02)|
|adults|||||||
|Lives in extended family|0.62|0.62|-0.00|0.59|0.58|-0.01|
|household|(0.49)|(0.49)|(0.02)|(0.49)|(0.49)|(0.02)|
|Lives in a household with|0.07|0.06|-0.00|0.04|0.04|-0.00|
|co-wives|(0.25)|(0.24)|(0.01)|(0.20)|(0.20)|(0.01)|
|Ganze|0.21|0.24|0.03*|0.19|0.24|0.05**|
||(0.40)|(0.43)|(0.02)|(0.39)|(0.43)|(0.02)|
|Kaloleni|<br>0.38|<br>0.44|<br>0.06***|<br>0.30|<br>0.39|<br>0.08***|
||(0.49)|(0.50)|(0.02)|(0.46)|(0.49)|(0.02)|
|Magarini|<br>0.28|<br>0.33|<br>0.05**|<br>0.29|<br>0.38|<br>0.09***|
||(0.45)|(0.47)|(0.02)|(0.45)|(0.48)|(0.02)|
||1,214|1,059|2,273|1,054|808|1,862|

_Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All estimations are computed using survey weights to address sampling. There is less than one percent of men who live with their in-laws._ 3
GDP. While public resources finance social pensions, capital income finances the contributory scheme in about 41%.

Under a traditional approach to social security finance, the expenditure rate is relatively low. However, the rapid increase of total expenditures observed during the last years, which not only relates to an effort to increase system coverage, requires certain attention. If this trend continues, the pressure on the system may be excessive, which in turn could exert pressure towards a reduction in expenditures which, in general, take place through access or benefit restrictions, that is to say, through a reduction in coverage.

# 4. Demographic transition and challenges in the medium and long terms

Ecuador is undergoing a demographic transition process towards an older population structure. That is the result of a simultaneous reduction of the fertility and mortality rates from the beginning of the last century. Based on that, there is a growth projection both on the dependency ratio as well as on the older people rate for the coming decades (Figure 14). In addition, in 2018, the total demographical dependency ratio, that is to say the coefficient between the population that depends, in theory, of third party ́s income (people younger than 15 years and older than 65 years) and working age (between 15 and 64 years), was 54.7 and would reach its lowest value (53%) in year 2035. This term in which the lowest levels in dependency rate are recorded, known as demographic bonus, would end by year 2060, when the demographic dependency would exceed 60%.

On its part, the older people dependency rate presents an increasing trend from the beginning of the 21st century. While the relation between the population older than 65 years and the working population is 11.4% in 2019, this coefficient would reach 24.9% by 2050 and 53.2% by 2100.

The increase in number and proportion of population older than 65 years of age in many countries of the world is interesting due to the impact of this trend on social security systems. The apparent implication of this phenomenon puts pressure on sustainability of expenditures targeted to finance pension benefits at the same time that income for contributions reduces, due to a lower number of working-age population.

22
|PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPS98 Diversification<br>In Rural Asia|Agriculture<br>and Rural<br>Development<br>Staff|October 1988|S. Barghouti<br>38408|
|WPS99 Trade Policies and the Debt Crisis|Sam Laird<br>Julio Nogues|September 1988|<br>S. Torrijos<br>33709|
|WPS100 Public Infrastructure<br>and Private<br>Sector Profitability<br>and Productivity||||
|<br> <br>In Mexico|Anwar Shah|September 1988|<br>A. Bhalla<br>60359|
|WPS1IO Measuring the Impact of Minimum Wage<br>Policies on the iconomy|Luis A. Riveros<br>Ricardo Paredes|October 1988|R. Luz<br>61762|
|WPS102 Effects of the Multifibre Arrangement<br>(MFA) on Developing<br>Countries:||||
|A Survey|Junichi Goto|October 1988|J. Epps<br>33710|
|WPS103 Industrial<br>Portfolio Responses<br>to Macroeconomic<br>Shocks: An||||
|Econometric<br>Model for LDCs|James Tybout<br>Taeho Bark|October 1988|C. Cabana<br>61539|
|WPS104 Economic Effects of Financial Crises|Manuel Hinds|October 1988|L. Horsepian<br>32979|
|WPS105 Securing International<br>Market Access|Richard H. Snape|October 1988|J. Sweeney<br>31021|
|WPS106 Energy Issues in the Developing World|Mohan Munasinghe<br>Robert J. Saunders|||
|WPS107 A Review of World Bank Lending for<br>Electric Power|Mohan Munasinghe<br>Joseph Gilling<br>Melody Mason|||
|WPS108 Some Considerations<br>in Collecting<br>Data<br>on Household Energy Consumption|L. Leitmann|||
|WPS109 Improving<br>Power System Efficiency in<br>the Developing<br>Countries Through<br>Performance<br>Contracting|Philip Yates|||
|WPS11O Impact of Lower Oil Prices on||||
|Renewable Energy Technologies|E. Terrado|||
||M. Mendis<br>K. Fitzgerald|||