rafmacalaba/datause-encoder-data huggingface dataset:
and the quality of services in different infrastructure sectors, particularly in developing countries.

Calderon and Serven (2008) and Calderon (2008) analyze the impact of infrastructure on economic performance of African countries. Using panel data for a large sample of countries for the period 1960-2005, they employ growth regressions estimated through a Generalized Method of Moments estimator and evaluate the impact of several types of infrastructure assets, as well as measures of quality of their services. Their findings suggest that both infrastructure stock and quality are positively and significantly related to real GDP per capita growth. In addition, the latter study evaluates the impact of a higher infrastructure development in African countries over the last 15 years (comparing 2001-05 to 1991-1995). At the country level, Egypt has attained the largest contribution of infrastructure development to growth (1.51%) among Northern African countries, with a rate higher than the average of the Africa region (0.99%).

Finally, infrastructure also affects economic performance through an indirect channel related to income distribution. Higher access to infrastructure services often helps reduce income inequality by lowering logistics costs or raising the value of human capital or land (Estache, Foster and Wodon, 2002, Estache (2003), Calderon and Chong, 2004, Calderon and Serven, 2004a, 2008, Galiani et al., 2005).

# **B. The Impact of Infrastructure in Egypt**

The share of public investment to GDP in the Middle East and North Africa (MENA) region exceeds other regions in the developing world. In particular, historically Egypt has had a high share of public investment in infrastructure even among MENA countries. Over the last few decades, however, public infrastructure investment in Egypt has been falling, and the decline in public investment has not been compensated by a rise in private investment.<sup>5</sup> Reflecting the specific situation of Egypt, the impact of infrastructure in the country has been discussed from the following perspectives in the literature. 1)

> 5 IFC (2003) reports that private participation in infrastructure investment in the MENA region declined in the 2000s compared to the 1990s and in fact its cumulative investment for 1990-2001 is smaller than other regions, even smaller than Sub-Saharan Africa. The World Bank (2003) concludes that the MENA region especially suffers from an unfavorable investment environment that prevents private participation in the last decade.

5
- Related to the previous point about units, we disagree with K's argument that since each underlying data source measures a somewhat different concept of corruption, the implicit definition of corruption is different when we compare two countries with different sets of underlying data sources.  Again, it is useful to think about our aggregation method as a way of putting different data sources in common units.  Suppose one data source measures corruption in procurement, while another measures corruption in the judiciary, and suppose once again we want to compare one country that appears only in the one indicator with another that appears only in the other indicator.  Our aggregate indicator extracts the common component of these (and all our other data sources), which we label as overall "Control of Corruption".  That is, we have just one implicit definition of corruption, which comes from the aggregation of these many data sources across many countries.  Using the aggregate indicator we of course cannot distinguish between these particular dimensions of corruption, and for policy purposes in a particular country this distinction may be useful.<sup>6</sup> But what our indicator does do is allow us to compare the extent of overall corruption in the two countries, based on the imperfect information both particular indicators provide about overall corruption.

- Whether this criticism is practically important or not depends a lot on whether (a) different data sources successfully distinguish between different dimensions of corruption, and (b) the different nuances of corruption measured by different sources really differ a lot across countries.  If for example some countries have very "clean" judiciaries but high administrative corruption, while other countries are the other way around, and if data sources were able to sharply distinguish between the two, then a measure of overall corruption based on measures of administrative and judicial corruption would not be very informative.  We note first that several of the individual data sources in fact have quite general questions about corruption.  And in cases where a single data source distinguishes between alternative forms of corruption, we in fact average together the different

> 6 This is something we have long acknowledged.  For example, in our very first paper, Kaufmann, Kraay and Zoido-Lobatón (1999a) we write in the conclusion that "There is therefore a need to improve the quality and quantity of governance data, both by improving and extending crosscountry survey work of governance perceptions, as well as employing country-specific in-depth governance diagnostics", and similar statements can be found in our subsequent updates of the governance indicators.

7
<!-- Start of picture text --> Benin Burkina Faso Cameroon Chad<br>Côte d ’ Ivoire Kenya Malawi Mali<br>Mozambique Nigeria Senegal Tanzania<br>Togo Uganda Zambia Zimbabwe<br>1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020<br>Year<br> Monopsony Regulation<br> Low Compet. Strong Compet.<br>2000<br>1000<br>0<br>2000<br>1000<br>0<br>2000<br>1000<br>Yield (FAO, Kg/Ha) 0<br>2000<br>1000<br>0<br><!-- End of picture text --> Figure 1: Productivity and market organisation in African cotton sectors after the independances
9

# Table 2. Tax Revenues

# (2a) A Comparison with Other Low Income Countries (1986-88 average)

||Tax<br>Revenue<br>(IR)/GDP|Income Tax/<br>TR|Domestic<br>Tax/TR|International<br>Tax/TR|
|---|---|---|---|---|
|Low income<br>countriese|16.3|22.4|27.3|30.9|
|Madagascar|11.8|14.3|28.3b|_55.6_|
||(2b) M<br>Foreign<br>Trade|adagascar: Budg<br>Budgetary<br>Tax on Goods|etary Revenu<br>Income<br>& Profits|e (1988)9<br>Other|
||200.5|96.8d'|51.49|11.8|

- a. Low-income countries: sample of 36 countries with 1980 income per capita below $500. Average values for 1986-88. Source:<sup>Faini and de Melo (1991) table 2.</sup>

- b. Includes taxes on goods and services and taxes on property.

- c. Billion FMG. Source: Guillaumont et al. (1990) and<sup>authors' calculations.</sup>

- d. Includes monopoly profits tax (24.8 billion).

- e. Includes profits (22.7 billion) and wage tax (14.4 billion).
# **Table of Contents**

|1. Introduction ......................................................................................... 1|
|---|
|2. Domestic Public Debt Management .............................................................. 2|
|2.1. Fiscal deficit financing .......................................................................... 2|
|2.2. Domestic financing in LICs .................................................................... 3|
|3. Domestic Public Debt in LICs: A New Dataset ............................................... 8|
|4. Characteristic of Domestic Public Debt in LICs ............................................. 10|
|4.1. Evolution of domestic debt ..................................................................... 10|
|4.2. Financial cost and burden ...................................................................... 11|
|4.3. Instruments ....................................................................................... 13|
|4.4. Maturity .......................................................................................... 15|
|4.5. Investor base ..................................................................................... 16|
|4.6. Relationships between cost of domestic debt, maturity, and investor base .............. 17|
|5. Conclusions ......................................................................................... 21|
|References ............................................................................................. 25|

# **Figures**

|Figure 1: Domestic and External Debt ............................................................. 11|
|---|
|Figure 2: Cost of Domestic and External Borrowing ............................................. 13|
|Figure 3: Domestic Debt by Type of Instrument ................................................... 14|
|Figure 4: Domestic Debt by Maturity ............................................................... 16|
|Figure 5: Domestic Debt by Holder ................................................................. 17|
|Figure 6: Implicit interest rate and maturity ....................................................... 20|
|Figure 7: Implicit interest rate, maturity, and investor base ..................................... 20|
|Figure 8: Implicit interest rate, maturity, and investor base........................................ 20|
|Figure 9: Domestic Debt Level and Structure in 2007 and 2011 ................................. 21|

# **Tables**

|Table 1: Databases on LIC public debt ............................................................... 7|
|---|
|Table A1. LIC Domestic Public Debt Dataset ....................................................... 23|
|Table A2. LIC Domestic Public Debt Dataset – Debt Stock and Debt Structure Samples ..... 24|
- **Shu, Catherine.** 2021. “Chaldal, Bangladesh’s largest grocery platform, raises 10M Series C.” _TechCrunch website www.techcrunch.com/2021/09/14/chaldal-bangladeshs-largest-grocerydelivery-platform-raises-10m-series-c/_ .

- **Ulyssea, Gabriel.** 2020. “Informality: Causes and consequences for development.” _Annual Review of Economics_ , 12: 525–546.

- **World Bank, The.** 2018. “World development report 2019: The changing nature of work.”

- **World Bank, The.** 2020. “South Asia Economic Focus, Fall 2020: Beaten or Broken? Informality and COVID-19.”

29
7 An important implication is that any subsidy programme administered via IDAAN will only reach a minority of the poor people in Panama, and a corollary is that the existing subsidy schemes administered by IDAAN are largely going to benefit people who cannot be regarded as poor. This follows from the fact that (according to the Living Standards Measurement Survey) only 16% of the IDAAN customer base lives either in poverty or extreme poverty; whereas, at least two-thirds of IDAAN's customers benefit from the subsidy programmes reviewed in the preceding section.

Thus, if it is to have any real impact on poverty, any future subsidy scheme directed towards IDAAN's customers would have to be both very small in its scope and very carefully targeted towards the small group of customers who are genuinely poor.

# **The Case for Use of Service Subsidies**

In order to establish the affordability of IDAAN's current water bills to its poorest clients, two different methods were used.

- The first and simplest approach entailed calculating the proportion of income that poor households were actually spending on water and sewerage services. This percentage can then be compared against international benchmarks, which suggest that water bills amounting to between 3% and 5% of income are generally affordable for the poorest households. The data for Panama indicated that a household on the extreme poverty line would on average be assigning less than 3% of its budget to water charges.

- The second and more sophisticated approach entailed asking customers their willingness to pay by means of a household survey conducted as part of the subsidy study. The advantage of a survey is that it is possible to consider the affordability of tariffs above the current level that might be needed in order to finance new investments. However, the survey methodology also brings considerable methodological problems of its own. These include (among others) the possibility that respondents may overstate their willingness to pay, potential biases generated by the structure and ordering of the questionnaire, as well as the sensitivity of the resulting estimates to the statistical specifications used in the data analysis.<sup>2</sup>

The results of the survey are presented in Table 3.2 below. They indicate the proportion of respondents who were willing to pay a range of different water tariffs, _all of which lie above the current basic tariff of US$0.80 per thousand gallons_ (equivalent to US$0.21 per cubic metre). The data refer only to households in the first three income deciles, who are therefore below the official poverty line.<sup>3</sup> The data show that more than half of this group is willing to

**2** For a fuller discussion of the methodological pitfalls of the contingent valuation approach, see the accompanying technical paper.

3 To be precise, the poverty line lies in the third income decile, while the extreme poverty line lies in the second income decile.
they appear to be quite similar to upper-caste Hindus in that they are less likely to experience the death of a child (particularly a girl), have higher female-male sex ratios among children alive as well as among children ever-born, are less likely to use contraception and have preferences for greater number of girls as well as boys, as evidenced by the greater numbers of girls and boys that they regard as “ideal”.<sup>10</sup><sup>_,_11</sup> In all these cases, the differences are statistically significant. Age-specific fertility also confirms that Muslim women bear larger numbers of children at earlier ages than women from other religious groups (Figure 1).

# **3.1 Fertility Differences**

To explore the often-cited fact about higher Muslim fertility, we construct from the female sample the total number of children the woman has had (those born alive as well as those who have died, but excluding miscarriages and stillborns). We first examine the determinants of fertility on the entire sample of women, and then restrict the sample to women over the ages of 30 and 40. Estimation on the restricted sample permits us to examine the relationships for women who have most likely completed their child-bearing.

We control for a variety of other individual, family and regional characteristics. First, we control for a woman’s age because of the well-known fact that children born to mothers at very young or very old ages are more likely to die in infancy than children born to mothers in prime childbearing ages. A second set of controls includes variables on whether a woman (the child’s mother) and her husband (the child’s father) had completed primary school. We also include a dummy variable indicating whether they had never attended school. Third, we control for economic status using a wealth index. This index has been developed and tested in a large number of countries and has been shown to be consistent

> <u>10</u> Sex-ratios are measured at the level of the cluster/village rather than at the level of a woman. Since a sex-ratio is defined as the number of females relative to the number of males, it can only be constructed for a woman who has had at least one male birth. When aggregating at the level of the village or cluster (which was the NFHS primary sampling unit) however, this problem is alleviated, since it is an average of female and male deaths for a broad group of women.

> 11The low female-male ratio at birth may be attributed to the fact that the practice of sex selective abortion is higher among upper-caste Hindus. This group has been shown to engage in this practice more than other groups (Arnold, Kishor and Roy, 2002). However, since the NFHS data do not contain information on the prevalence and practice of abortion, this is one aspect that we are not able to investigate comprehensively. However, we do acknowledge, readily, the importance of these practices for the subject of this research.

8
generation by 0.6 percentage point. Regarding fossil fuels, share of coal-fired electricity generation decreased by 1.6 percentage point, largely substituted by natural gas.

Further investigation shows that the share of nuclear power generation did not change much during the pandemic as it normally provides the baseload. Countries that relied heavily on natural gas saw a decrease in natural gas generation in the mix in response to the drop in total generation. The analysis also shows a widespread reduction in the wind energy mix across multiple European countries, likely due to the low wind speed in Europe during the study period. However, many countries expedited their solar development, and we see a drastic increase in solar generation during the pandemic to compensate for the wind loss as well as counterbalance the natural gas shortage.

Two simple simulation exercises further show renewables can well withstand the pandemic and accompanying lockdowns. If Sweden were to adopt a stricter lockdown policy that was similar to that of France, it would see a 0.8% decrease in the total daily generation since the pandemic hit, a slight decrease in nuclear and wind as a share of the energy mix, but an increase in hydro share. If the United States were to adopt a stricter lockdown policy that was similar to that of Italy, it would see a 0.4% decrease in the total daily generation since the pandemic hit, a decrease in natural gas as a share of the energy mix, and a slight increase in hydro and wind share.

Caution is, as always, needed when interpreting the results of this study. We have examined electricity _generation_ on a daily basis. But countries often meet demand through imports and/or exports of their excess generation. The US, for example, while a major energy exporter in 2020 (see <u>https://www.eia.gov/energyexplained/us-energy-facts/imports-and-exports.php), at times</u> imports electricity from its neighbors Canada and Mexico. Unfortunately, we do not have highfrequency electricity imports and exports data, nor is it possible for us to compute “net imports” as the difference between generation and final consumption, as we lack data about the latter.

Imports and exports of electricity are commonplace within EU countries, and both Norway and Switzerland export electricity to EU countries. Only a small share of the total electricity consumption is however imported into the EU from non-EU countries (e.g., Russia<sup>9</sup> ), suggesting that EU aggregate generation serves as a good approximation to final consumption of electricity in the EU.

> 9 See https://view.news.eu.nasdaq.com/view?id=b62aadb50d4d3bd34550a8d2b09b7f6df&lang=en

20
# **1. Introduction**

The rapid growth in migrants’ remittances has triggered considerable attention in policy circles (e.g. GCIM 2005, World Bank 2005) and has led to renewed research attention to the development impacts of remittances.<sup>1</sup> Several recent empirical studies have emphasized the potential for remittance transfers to alleviate credit constraints and thereby increase educational attainment of children in migrant families.<sup>2</sup> The recent theoretical literature on the “beneficial brain drain” or “brain gain” suggests a second channel through which migration can increase educational attainment. The basic idea of such theories is that education has a high return when migrating, and so the prospect of migrating in the future raises the expected return to education, inducing higher domestic enrollment in schools.<sup>3</sup> However, Chiquiar and Hanson (2005) have shown that the return to education is higher in Mexico than for Mexican migrants in the United States. This arises due to higher inequality in Mexico than in the U.S., and additionally from the fact that most first-time migration from rural Mexico is illegal, leading only to job opportunities with low formal educational requirements. As a result, the incentive effect of the prospect of future migration for children growing up in migrant households in Mexico may actually lower the incentive to invest in education, counteracting the remittances effect.

The implicit assumption in most of the existing studies of remittances is that migration only affects educational outcomes through remittances, and not through any

> 1 See the recent survey of Rapoport and Docquier (2006), which provides an extensive discussion of different motives for remitting.

> 2 See Cox Edwards and Ureta (2003) for Nicaragua, Lopez-Cordoba (2004) for Mexico and Yang (2004) for the Philippines.

> 3 See for example Commander, Kangasniemi and Winters (2004) for a review of the various channels through which a beneficial brain drain can be obtained.

2
**Table 4: Determinants** **<u>of net land leased-in: Maximum likelihood estimates</u>**

||**Simple fric**<br>|**tion model**<br>|**Ability i**<br>|**ncluded**<br>|
|---|---|---|---|---|
|**Variable**|**Leased-out**|**Leased-in**|**Leased-out**|**Leased-in**|
|Owned area (ha)|0.305***|-0.208***|0.303***|-0.208***|
||(5.14)|(3.84)|(4.95)|(3.75)|
|Proportion of good soil quality|-0.052|-0.007|-0.058|-0.011|
||(0.83)|(0.11)|(0.83)|(0.18)|
|Number of dependents|-0.057***|0.067***|-0.044**|0.066***|
||(2.79)|(3.56)|(2.03)|(3.40)|
|Number of adult male|-0.090***|0.071**|-0.091***|0.070**|
||(2.76)|(2.40)|(2.59)|(2.32)|
|Number of adult female|-0.004|-0.039|0.009|-0.043|
||(0.13)|(1.11)|(0.25)|(1.19)|
|Number of bulls and oxen|-0.641***|0.292***|-0.629***|0.286***|
||(9.36)|(4.32)|(8.91)|(4.25)|
|Value of other livestock owned × 10<sup>-3</sup>(Birr)|-0.096***|0.036**|-0.083**|0.035**|
||(3.00)|(1.99)|(2.57)|(1.98)|
|Number of rooms of the household|<br>-0.030|<br>0.079***|<br>-0.043|<br>0.084***|
||(1.03)|(2.79)|(1.30)|(2.90)|
|Age of household head (years)|0.011***|-0.011***|0.012***|-0.011***|
||(5.93)|(5.26)|(5.52)|(5.04)|
|Female headed household|0.619***|-0.654***|0.611***|-0.634***|
||(7.41)|(5.85)|(6.81)|(5.48)|
|Household head can read and write|0.021|-0.000|0.008|-0.000|
||(0.35)|(0.01)|(0.12)|(0.01)|
|Farm ability|||-0.058|0.111*|
||||(0.94)|(1.68)|
|**Threshold effect and constant parameters**|||||
|Woreda2|-0.270**|0.222**|-0.294***|0.232**|
||(2.50)|(2.41)|(2.62)|(2.44)|
|Woreda3|-0.265**|-0.186*|-0.253**|-0.139|
||(2.53)|(1.89)|(2.16)|(1.29)|
|Woreda4|-0.762***|0.340***|-0.840***|0.334***|
||(5.93)|(2.79)|(5.98)|(2.66)|
|Woreda5|-0.958***|0.333***|-1.026***|0.355***|
||(7.30)|(2.87)|(7.19)|(2.98)|
|Woreda6|-0.439***|-0.053|-0.436***|-0.003|
||(3.76)|(0.48)|(3.44)|(0.03)|
|Time trend|0.101***|0.138***|0.097***|0.142***|
||(3.50)|(4.58)|(3.03)|(4.58)|
|Constant|-0.630***|0.369***|-0.735***|0.360**|
||(3.60)|(2.58)|(3.81)|(2.43)|
|σ|1.025***||1.058***||
||(9.78)||(9.58)||
|Log likelihood|<br>-4680.15||<br>-4466.07||
|Wald chi2(23)|156.65***||140.08***||
|Observations|4268||4106||

**Note.** Robust z statistics adjusted for clustering in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Coefficients for leasing-out (except constant and dummies) are multiplied by -1 for ease of interpretation. The discrepancy in the total number of observations is mainly because about 137 households leasing-out land were pure landlords over the three periods, and the rest is due to missing values in the value of crop output or its determinants. This leads to some missing values in the household fixed effects (a proxy variable for farming ability) obtained from the regression estimates given in appendix table 2.

23