rafmacalaba/datause-encoder-data huggingface dataset:
# **V. Infrastructure Investment and Growth in Egypt**

## **A. Trends in Infrastructure Investment**

We now turn to reviewing the long-term trends in infrastructure investment in Egypt. The investment data are disaggregated by sector of origin: public and private, and by destination of industry. Infrastructure investment includes both capital expenditures (the construction of new infrastructure) and current expenditures (operations and maintenance spending).

There are two kinds of time series data available for Egypt’s infrastructure investment. The first data range from 1960 through 2007 with the disaggregation of two infrastructure sectors: transportation (including Suez Canal) and communications, and electricity. The second data cover the more recent period: 2003-2007, with a higher degree of disaggregation, that is, five infrastructure sectors: transportation, communications, electricity, water, and Suez Canal.

Figure 4 offers a comprehensive view of infrastructure investment in Egypt relative to GDP from 1960 through 2007. Panel (a) of Figure 4 illustrates the time path of total infrastructure investment that consists of investment in two infrastructure sectors: transportation and communications and electricity, with a disaggregation of public and private investment. Total investment rose until the late 1980s, and then it declined until the mid 2000s, when it stabilized. Total investment in recent years has returned to its level in the early 1960s at roughly 5 percentage of GDP. It is clear that public investment has been a dominant force for more than four decades in Egypt. In contrast, after having stagnated for more than two decades since the 1960s, private investment finally took off in the mid-1980s. The magnitude of private investment has been growing and is roughly two-thirds of the amount of public investment in recent years. Though considerable, rising private investment in the last two decades has not fully offset the decline in public investment.

From Panels (b) and (c) of Figure 4, there is an apparent downward trend in total investment which originated from a decline in public investment in both the transportation/communications and electricity sectors. Public investment in the former has been declining since the early 1980s, while that in the later since the late 1980s.

31
management policies and what are the common debt management failings that may easily be avoided.

# **_Picking the low hanging fruit_**

One important finding from the DeMPA is that not all weaknesses in debt management capacity are equal. In some instances failure to meet the minimum standard of a dimension of debt management is because that aspect of debt management is simply not addressed, as for example, the widespread absence of internal and external audits in small states. However, the DeMPA also show that frequently the gap between meeting the minimum standard in a given dimension of debt management and failure to do so is in fact relatively small. To give specific examples, a large number of countries failed to meet the minimum requirement with regard to assessing the most beneficial and cost effective borrowing terms from external sources. In most instances this was not the result of absence of capacity to make such as assessment but because borrowing was on concessional terms and the exercise regarded as superfluous, although concessional loans are not all equal, or national authorities did not believe they had borrowing choices. Many countries were found to compile comprehensive information on public debt obligations yet failed to meet statutory and contractual reporting requirements because this information was not available within the required timeframe. Similarly others produced a debt publication or statistical bulletin but with a long time lag. In small states failure to produce a debt management strategy could be rapidly rectified by capitalizing on their proven capacity to coordinate between fiscal and monetary policy.

What this suggests is that small states should, in the first instance, look to areas where instituting relatively small changes to current practice may bring a rapid payoff. In this regard the DeMPA is an important input that can serve as a guide not only to help formulate more robust debt management practices but also to pinpoint areas where weaknesses in debt management can most quickly be rectified. Picking the low hanging fruit will enable small states to enhance the overall assessment of their debt management capacity and clarify the aspects of debt management requiring the greatest amount of attention.

# **_Pooling resources through regional cooperation_**

Small states should exploit more fully the commonality of their legal and institutional approaches to debt management, particular within each region, and the track record of cooperation already established in common regional monetary and custom unions. These offer considerable potential for cost effective improvements in debt management capacity through cross-country coordination. The launch of the Caribbean Debt Management Forum in October with participation from 15 countries suggests this proposal is already coming to fruition.

Multi-state or regional borrowing instruments, while an attractive idea, are likely to be impeded by issues of sovereignty and common currency. However, nothing precludes small states from 20
Table A5: Data Filling Summary

|||||Annua|l||Quarter|ly|
|---|---|---|---|---|---|---|---|---|
|Flow|Sect.|Group|BOP|Int. Fill|Ext. Fill|BOP|Int. Fill|Ext. Fill|
|PD|GG|Adv.|80.6|0.0|19.4|79.4|0.0|20.6|
|PD|GG|Em.|82.4|0.3|17.3|74.2|0.8|25.0|
|PD|GG|Dev.|40.2|0.7|59.1|25.0|0.1|74.9|
|PD|CB|Adv.|9.5|58.3|32.2|7.5|60.5|32.0|
|PD|CB|Em.|23.5|40.6|35.9|19.5|35.6|44.9|
|PD|CB|Dev.|11.2|8.2|80.5|2.6|4.8|92.7|
|PD|DC|Adv.|67.6|3.6|28.8|67.7|3.4|28.8|
|PD|DC|Em.|61.7|4.1|34.3|55.6|3.5|40.9|
|PD|DC|Dev.|18.6|1.6|79.8|10.3|0.7|89.0|
|PD|OS|Adv.|75.4|0.0|24.6|74.7|0.0|25.3|
|PD|OS|Em.|69.8|2.3|28.0|64.4|1.9|33.6|
|PD|OS|Dev.|29.3|0.5|70.2|13.3|0.3|86.5|
|OID|GG|Adv.|80.0|2.1|17.9|78.4|3.2|18.4|
|OID|GG|Em.|93.7|0.8|5.6|88.1|0.9|11.0|
|OID|GG|Dev.|87.7|0.0|12.3|49.7|0.0|50.3|
|OID|CB|Adv.|68.2|13.9|17.9|65.8|15.4|18.7|
|OID|CB|Em.|87.4|6.6|6.0|79.2|9.8|11.0|
|OID|CB|Dev.|74.6|13.3|12.1|46.0|6.7|47.3|
|OID|DC|Adv.|81.9|0.0|18.1|81.4|0.0|18.6|
|OID|DC|Em.|94.0|0.0|6.0|89.0|0.0|11.0|
|OID|DC|Dev.|77.7|6.1|16.1|48.0|1.8|50.2|
|OID|OS|Adv.|84.0|0.4|15.6|82.8|0.1|17.2|
|OID|OS|Em.|94.4|0.0|5.6|89.0|0.0|11.0|
|OID|OS|Dev.|88.4|1.1|10.5|52.5|0.7|46.8|
|Bala|nced S|ample|12|16|89|0|10|85|

This table displays the percentage of total observations in our final sample of Advanced (Adv.), Emerging (Em.) and Developing (Dev.) countries (89 for annual, 85 for quarterly) that is derived from each step of our data construction. BOP = Percent coverage of sample from raw BOP data; Int. Fill = Percent coverage of sample from Internal Filling exercise; Ext. Fill = Percent coverage of sample from non BOP data sources. OID = other investment debt; PD = portfolio debt; GG = General Government; CB = Central Bank; DC = Banks; OS = Corporates. The last line indicates the number of countries in our balanced sample 1996 to 2014 that we have data for each sector non-missing.

77
# **Table 4 Returns to Education IV Models**

**Panel A: Returns to Middle School, High School and College Attainment**

|Model #|1|2|3|
|---|---|---|---|
|Model|OLS|IV|IV|
|Dependent Variable|ln(wage)|ln(wage)|ln(wage)|
|College*|0.420|0.362|0.346|
||(0.030)|(0.160)|(0.161)|
|High School*|0.209|0.294|0.283|
||(0.029)|(0.116)|(0.115)|
|Middle School|0.234|0.192|0.203|
||(0.064)|(0.089)|(0.086)|
|Significance of Instruments||||
|College: F-Test|-|72.32|35.18|
|F-Probability|-|0.000|0.000|
|High School: F-Test|-|131.52|59.19|
|F-Probability|-|0.000|0.000|
|Over-Identification Test||||
|Hansen J-Statistic|-|-|2.85|
|Chi-Square P-Value|-|-|0.415|
|Observations|3611|3599|3599|
|R-Squared|0.324|-|-|
|**Panel B: Returns to Years of Schooling -- L**|**inear inSchooling**|||
|Model #|1|2|3|
|Model|OLS|IV|IV|
|Dependent Variable|ln(wage)|ln(wage)|ln(wage)|
|Years of Schooling|0.083|0.077|0.075|
||(0.004)|(0.018)|(0.018)|
|Significance of Instruments||||
|Years of Schooling: F-Test|-|76.46|40.22|
|F-Probability|-|0.000|0.000|
|Over-Identification Test||||
|Hansen J-Statistic|-|0.19|3.26|
|Chi-Square P-Value|-|0.664|0.516|
|Observations|3610|3598|3598|
|R-Squared|0.333|-|-|

**Table 4 Continued on Next Page**

37
private school students from consideration here precisely because their distribution of observables (and unobservables) is so different in Venezuela. Public school students, however, form an excellent control group.

Second, the same questionnaire is administered to both treatment and control, and both groups are in a "common economic environment." These issues, of course, relate primarily to problems encountered with evaluation of job training programs. All of our data come from the same administration of the same test, with the same demographic questions asked of each student. Furthermore, unlike the American SAT, all graduating Venezuelan high school students take the PAA. Therefore, although the interpretation of the ATE is limited to those students who have not dropped out of school beforehand, there is no selection bias into the test itself.

Although the Fe y Alegría natural experiment described above forms the basis of our assumption of selection on observables, we cannot fully rule out bias due to unobservables. However, the Heckman, Ichimura, and Todd (1997) job training data show that this bias can actually be less important than lack of overlap and differing economic environments, problems from which we do not suffer.

The consistency of the matching estimator requires two assumptions, which together are called “strong ignorability of treatment” (Rosenbaum and Rubin, 1983): conditional mean independence and propensity scores strictly between zero and one.

• Assumption I: for i={0,1}

- Assumption II:

Assumption I is the crucial assumption underlying any application of matching estimators. In the application to school choice, the concern would be that unobservable attributes of the student or her family such as motivation, proactivity, or valuation of education would cause the same types of students who select into Fe y Alegría to also do better in the counterfactual. If these decisions were made in a statistically random way, or
# **IV. Conclusions**

Workers’ remittances, flows received from migrant workers residing abroad, have become the second largest source of external finance for developing countries in recent years. In addition to their increasing size, the stability of these flows despite financial crises and economic downturns make them a reliable source of funds for developing countries. While the development potential of remittance flows is increasingly being recognized by researchers and policymakers, the effect of remittances on financial development remains largely unexplored. Better understanding the impact of remittances on financial development is important given the extensive literature on the growth enhancing and poverty reducing effects of financial development.

This paper is a first effort to try to fill this gap in the literature. Using balance of payments data on remittance flows to 99 countries for the period 1975-2003, we investigate the impact of remittances on bank deposits, as well as on bank credit to the private sector. We find that remittances have a significant and positive impact on bank deposits and credit to GDP. This result is robust to using different estimation techniques and accounting for endogeneity biases arising from omitted factors, reverse causation, and measurement error.

> 16 See Appendix 4 for a discussion about coefficient biases due to measurement error.

19
# **What Kinds of Organizations Do Employers Use to Fill Job Vacancies and Provide Training?**

Of interest to policy makers is the extent to which employers are using VETA/VTA training institutions<sup>34</sup> as a source of high-skill talent and training. After all, employers pay a levy for such services. The survey asked them how often they benefited from these institutions to fill specific TVET vacancies or train their employees. Table 6 provides those numbers, for job vacancies and for in-service training.

**Table 6. VETA/ VTA as a Major Skill Source for TVET Vacancies** **<u>and  In-Service Training</u>**

|||Regiono<br>|f Firm Re|spondent|||
|---|---|---|---|---|---|---|
||||||||
|**Skill Sources**|Arusha|ar es<br>Salaam|Mbeya|Mwanza|Zanzibar|anzana|
|**_1. TVETJob Vacancies_**|||||||
|VETA/VTA institutions|4|24|1|5|12|46|
|All TVETsources|6|56|3|6|47|118|
|**_2.In-Service Training_**|||||||
|VETA/VTA institutions|1|4|1|1|0|7|
|Allexternaltraining sources|7|34|7|5|20|73|
|Source: TESS, 2015.<br>Note:Figures are counts of institu|tions accordi|ng to the type|ofskills sour|ces|||

As the data show, VETA/VTA institutions are a major source of pre-employment TVET skill vacancies in most regions, accounting for between 26 and 83 percent of skill sources in this category. In Dar es Salaam, firms listed VETA institutions 24 times out of 56 skills sources, Mbeya firms cited VETA as 1 of 3 skill sources, and Arusha firms cited VETA 4 out of 6.

The survey also asked employers whether they used VETA/VTA institutions as a source of training. The results presented in Table 6 show that few firms cite VETA/VTA as a source of inservice training, even in Dar es Salaam where VETA institutes are only cited 4 times out of 34 sources. In the autonomous region of Zanzibar, firms cite VTA institutions as a major source of TVET graduates in 12 out of 47 skill sources, but never as a provider of in-service training (0 out of 20 sources). In other words, despite the levy, employers receive little support in developing the skills of their workforce.

If firms are not going to VETA/VTA institutions for their training, what other organizations are providing training? Employers cited several options:

> 34 “VETA/VTA training institutions” refer to public training centers and institutions under VETA or VTA.

40
18 estimate the forward-looking economic cost of the service without having to rely on detailed cost studies that otherwise would be necessary. This alternative methodology provides a non-discretionary framework within which regulators and firms can discuss with a significant degree of objectivity, and which could provide an independent check on the accuracy of firms' cost studies. However, lunch is not free: these alternative approaches require much more time and effort in both data collection and preparation as well as the time and effort spent on model design.

In general, reliable and detailed information (as required by costs models) is a scarce good in developing countries. In this paper we have established the minimum information requirements that a regulator needs to implement a cost proxy model approach, and we have shown that this 'data constraint' need not be that binding. In particular, the HCPM can run with the following inputs:

- **Census data.** Each unit surveyed in the Census (e.g. Census block, or Census block group) should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the mrodel can be modified to work with another system -even with an Excel worksheet- when latitude and longitude coordinates are not available). There should be information on the number of households in each Census unit, and on the number of units in a Census unit group.

- **Location of wire centers.** Each wire center in the study area should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the model can be modified to work with another system -even with an Excel worksheet- when latitude and longitude coordinates are not available). There should be information on the number of lines provided by each of the wire centers. The telephone companies could provide this information.

- All other inputs are provided by the model (i.e. factor prices, technologies, etc.), based on information for the US, but can be freely varied by the regulator when this information becomes available in the country.

We have also shown that Census data is a good substitute for the individual customer locations, and that the level of aggregation of the Census information does not markedly
# **_Table 1. Modeling the Poverty and Distributional Impacts of Macroeconomic Shocks and Policies_**

|**Models/Approaches**|**Macroeconomi**<br>**Model**|**c**<br>**Linkage Variables**|**Specification of**<br>**Distribution**<br>|
|---|---|---|---|
||||**(Micro-Model)**|
|**A. Distributional Focus (No E**<br>**Lorenz Curve Approach**|**xplicit Macro-M**|**odel)**<br>||
|_1. POVCAL_|• Implicit|• Mean consumption/income of<br>the distribution|• Parameterized<br>Lorenz function|
|_2. SimSIP Poverty_|• Implicit|• Mean consumption/income of<br>the distribution|• Parameterized<br>Lorenz function|
|**Unit Record Approach**||||
|_1. PovStat_|• Implicit|• Per capita household<br>consumption<br>• Sector of employment of the<br>household head<br>• Growth rate of per capita output<br>in the sector of employment|• Unit record data|
|_2. The Envelope Model_<br>_(Microsimulation-1)_|• Implicit|• Changes in supply prices for<br>household production activities<br>• Changes in demand prices<br>• Changes in incomes|• Unit record data|
|_3. Household Income and_<br>_Occupational Choice Models_<br>_(Microsimulation-2)_|• Implicit|• Changes in commodity prices<br>• Changes in incomes (wages and<br>self-employment income)<br>• Changes in employment by type<br>f tin|• Unit record data|
|**B. Standard General Equilib**|**rium Analysis**|o occupao<br>||
|_1. CGE–Representative_<br>_Household_|• Static CGE|• Changes in factor and<br>commodity prices<br>• Changes in employment|• A few<br>representative<br>households|
|_2. CGE–Extended_<br>_Representative Household_<br>**C. Sequential Macro-Micro L**|• Static CGE<br>**inkages**|• Changes in factor and<br>commodity prices<br>• Changes in employment|• A few<br>representative<br>households<br>• A model of size<br>distribution|

5
Maskus, K., T. Otsuki and J. S. Wilson (2005), ‘The Cost of Compliance with Product Standards for Firms in Developing Countries: An Econometric Study’, Policy Research Working Paper No. 3590, The World Bank.

Maertens, M. and J. F. M. Swinnen (2009), ‘Trade, Standards and Poverty: Evidence from Senegal’, _World Development_ , 37(1), 161–178.

Mayneris, F. and S. Poncet (2015), ‘Chinese Firms’ Entry to Export Markets: The Role of Foreign Export Spillovers’, _World Bank Economic Review_ , 29(1), 150-179.

Melitz, M. (2003), ‘The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity’, _Econometrica_ , 71(6), 1695-1725.

Moulton, B. (1990), ‘An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units’, _Review of Economics and Statistics_ , 72(2), 334-38.

Otsuki, T., J. S. Wilson and M. Sewadeh (2001a), ‘Saving Two in a Billion: Quantifying the Trade Effect of European Food Safety Standards on African Exports’, _Food Policy_ , (26), 495–514.

Otsuki, T., J. S. Wilson and M. Sewadeh (2001b), ‘What Price Precaution? European Harmonization of Aflatoxin Regulations and African Groundnut Exports’, _European Review of Agriculutural Economics_ , 28 (2), 263-283.

Shepherd, B. and N. Wilson (2013), ‘Product Standards and Developing Country Agricultural Exports: The Case of the European Union’, _Food Policy_ , 42, 1–10.

Tran, N., N. Wilson and S. Anders (2011), ‘Standard Harmonization as Chasing Zero (Tolerance Limits): The Impact of Veterinary Drug Residue Standards on Crustacean Imports in the EU, Japan and North America’, _American Journal of Agricultural Economics_ , 94(2), 496–502.

Wilson, J. S., T. Otsuki and B. Majumdsar (2003), ‘Balancing Food Safety and Risk: Do Drug Residue Limits Affect International Trade in Beef?’, _Journal of International Trade and Economic Development_ , 12(4), 377–402.

Winchester, N., M. L. Rau, C. Goetz, B. Larue, T. Otsuki, K. Shutes, C. Wieck, H. L. Burnquist, M. J. Pinto de Souza and R. N. de Faria (2012), ‘The Impact of Regulatory Heterogeneity on Agri-Food Trade’, _The World Economy_ , 35(8), 973-993.

Xiong, B. and J. Beghin (2010), ‘Does European Aflatoxin Regulation Hurt Groundnut Exporters from Africa?’, _European Review of Agricultural Economics_ , 39(4), 589-609.

29
To our knowledge, our study is the first experimental evaluation of the impact of an ECD stimulation intervention on long-term economic outcomes and inequality in a developing country.<sup>10</sup> This study contributes to a small literature on labor market returns to ECD programs including Perry Preschool, the Chicago Parent Child program, Abecedarian and Head Start, all of which are located in the U.S.<sup>11</sup> We find that the Jamaica stimulation program had substantially larger effects on earnings than any of the U.S. programs.

# **2 The Jamaican Study**

## **2.1 The Intervention and Experimental Design**

In 1986-1987, the Jamaican Study enrolled 129 stunted children age 9-24 months that lived in poor disadvantaged neighborhoods of Kingston, Jamaica (Walker et al., 1990). Stunting was defined as having a standardized height for age _z_ -score less than -2. The children were stratified by age (above and below 16 months) and sex. Within each stratum, children were sequentially assigned to one of four groups using a randomly generated seed to begin the assignment. The four groups were (1) psychosocial stimulation (N=32), (2) nutritional supplementation (N=32), (3) both psychosocial stimulation and nutritional supplementation (N=32), and (4) a control group that received neither intervention (N=33). All children were given access to free health care regardless of the group to which they were assigned.

The stimulation intervention (comprising groups 1 and 3) consisted of two years of weekly one-hour play sessions at home with trained community health aides.<sup>12</sup> The curriculum for the cognitive stimulation was based on Piagetian concepts (Powell and Grantham-McGregor,

> 10 While ours is the first to study labor market returns to ECD psychosocial stimulation in a developing county, there are labor market follow-ups to nutritional interventions. See, for example, Hoddinott et al. (2008), Maluccio et al. (2009).

> 11See Heckman et al. (2010a), Heckman et al. (2010b), Reynolds et al. (2004), Reynolds et al. (2007), Reynolds et al. (2011), Campbell et al. (2002), Campbell et al. (2012), Campbell, Conti, Heckman, Moon, and Pinto (2012); Aughinbaugh (2001), and Garces et al. (2002).

> 12The aides received 8 weeks of training in nutrition and primary health care and another 8 weeks of training in child development, teaching techniques and toy making.

3