rafmacalaba/datause-encoder-data huggingface dataset:
12 favorable results in Mexico and in Venezuela, and moderately favorable ones in the other countries. In Mexico the debt deal has been associated with a sharp decline in domestic interest rates<sup>6</sup> , and both in Mexico and Venezuela the stock markets has risen, and a renewed access to the world financial markets has occi.red.7 For the other countries, the impact has been less pronounced. In case of Costa Rica and the Philippines, policy slippages and the impact of adverse internal and extemal shocks (Gulf crisis, earthquake, coup, lower export prices, etc.) initialy diluted or dissipated the possible positive effects of the agreements. In the case of Uruguay, the agreement is too recent to be able to fully evaluate it. In all cases, the policy framework has proven to be the most important factor.

# **_The_** _Net_ **_Beneogt of DDSR_**

To the extent that the operation entails efficiency gains, the debtor country could benefit from it. Note that the country's benefit is a welfare concept, and therefore the benefit is only the additional value of the investment return in excess of that of foregone current consumption. These benefits come at a cost to the country. In order to obtain the net benefit derived by the debtor country, the financial cost of the operation needs to be subtracted. This financial cost comprises the direct cost of debt reduction (e.g., the resources paid to banks in a buy-back operation) and the change in the expected present value of debt service payments on outstanding 1he domic interest rates fell by about 20 percent when the Brady agreement was announced, and by a further 10 percent towards the end of 1990 (see van Wijnbergen (1991a and 1991b)).

'Domestic stock markets increased by 25% percent in Mexico and 116% percent in Venezuela. Foreign capital held abroad by domestic residents has flown back to both Mexico and Venezuela in considerable amounts. Private and public sector entities in these two countries have been able to access voluntarily the private capital markets in significant amounts (see further the World Debt Tables 1991/1992 and Fernandez-Arias (1992)). lbe secondazy market prices for both Mexico and Venezuela (stripped of any enhancement values) have also increased considerably between the date of the agreement and mid-1991, by about 20 percentage points, reflecting further confidence.
results are qualitatively similar when we consider two year lagged values of the EBRD index.

**_Table 2_** _<u>:</u>_ **_<u>Effects of Services Liberalization on Manufacturing Productivity</u>_**

||Dependent V<br>Man|ariable is Labor<br>ufacturing Sub-S|First<br>Productivity in<br>ectors|
|---|---|---|---|
||OLS|Fixed Effects|<br>Differenced|
||(1)|(2)|(3)|
|Linkage Finance + Electricity, Water, Roads,||||
|Telecom to One Year Lagged EBRD|2.107|2.107|0.196|
||[0.368]***|[0.202]***|[0.618]|
|Year Dummies|Yes|Yes|Yes|
|Country Dummies|Yes|||
|NACE 2-Digit Dummies|Yes|||
|Year Dummies*NACE 2-Digit Dummies|Yes|||
|N. Observations|820|820|695|

Notes: Robust standard errors in parenthesis. *** indicates significance at the 1% confidence level.

Our findings support the idea that services liberalization in ECA countries is beneficial for the productivity of the manufacturing sector. Our findings across 9 ECA countries mirror those in Arnold et al. (2006a) for a single country, despite the fact that we use a simpler productivity measure and we do not control for other determinants of manufacturing productivity such as FDI or competition. However, we should note that for the 9 ECA countries considered the beneficial effect of liberalization occurs only when reforms in **_both_** essential backbone services - finance and infrastructure - occur. In fact, unreported results on the effects of liberalization in each of the sub-sectors separately suggest that liberalization in a single sub-sector is not conducive to higher productivity in downstream manufacturing.

# **6. Conclusion**

This paper examines recent trends in the structure and performance of the services sector in ECA countries between 1997 and 2004. Services represent on average 46% of total value added and 31% of total employment in the ECA region during this period. The major services sub-sectors in terms of value added and employment are wholesale trade, retail trade, inland transport, telecommunications, and real estate activities.

We find a clear divide in terms of labor productivity in services separating EU-5 countries from South Eastern European countries and Ukraine. While we also find a large 38
information on slightly less than 10,000 households in about 1000 communities each.<sup>9</sup> More importantly, about 370 communities were visited in both periods, allowing us to have a moderately sized panel of communities that can be used to make inferences on changes in the variables of interest. We first focus on the dependent variables, civil strife and measures of households' investment behavior, before turning to the right hand side variables.

Information on whether households were affected by _civil strife,_ theft, and violent attacks, was collected in the UNHS for 1999 and for 1992 retrospectively. In line with the literature which uses different definitions to distinguish civil strife from violence directed at specific persons (Singer and Small 1994), enumerators were instructed to differentiate between civil strife and other forms of violence using two characteristics. On the one hand, civil strife is primarily addressed at groups rather than individuals. Furthermore, the main motive for civil strife is normally not individual economic gain even though economic factors at the group level may play a role.'° The information thus collected was used to construct two variables measuring the incidence of civil strife, theft, and physical attacks. The first is a simple 0/1 dummy indicating whether a community is affected by civil strife, theft, or attacks implying that the incidents in question is reported by at least one household in the sample without making reference to how widespread it was. The second measure uses the share of households in any community who reported to have been affected by civil strife, attacks, or theft." To measure households' _investment_ behavior we rely on two indicators. The first one is the increase in enterprise assets, defined to include agricultural assets, structures, and transport equipment but excluding household durables and other consumer goods. This is based on questions on current endowments and the

> ' For purposes here we take enumeration areas as a "community". This is certainly justified in the large majority of cases (especially in rural areas) where enumeration areas correspond to natural villages.

> " The phrasing of the three questions was as follows: (i) "Has your production of crops/cattle or livestock rearing/trading activities been harmed by civil strife over the last 12 monthsT'; (ii) "How many incidents of theft of property has the household suffered in the last 12 months?"; and "How many incidents of physical attacks on members of the household have there been over the last 12 months?'. In the interviewer training, it was highlighted that civil strife refers to violence that is not directed at any specific person but rather at a group. All of the questions were asked

> retrospectively for 1992 as well. Based on this information we classify a community as affected by civil strife, theft, or attacks, if it is reported by at least one household.

> " Unfortunately, the survey does not contain information on the magnitude of the damage inflicted on individual households, something that would have allowed to measure the severity of violence in a given location.

13
**Table 4.2: Probability of migrating from Southeast to**<sup>**Northeast**</sup>

|Probitestimates|||||Numb<br>LRch<br>Prob>|erofobs<br>i2(10) =<br> chi2=|=33369<br>1038.63<br>0.0000|
|---|---|---|---|---|---|---|---|
|Log likelihood=|-3042.2307||||Pseud|oR2<br>=|0.1458|
||dF/dx|Std.Err.|z|P>z|x-bar|[95perc|entC.I.]|
|Age|-0.0006|0.0000|-12.86|0.00|37.07|-0.0007|-0.0005|
|female*|-0.0062|0.0010|-6.23|0.00|0.38|-0.0081|-0.0043|
|Famsize|-0.0007|0.0003|-2.15|0.03|3.75|-0.0013|-0.0001|
|priml*|-0.0095|0.0012|-7.04|0.00|0.27|-0.0118|-0.0071|
|prim2*|-0.0085|0.0010|-6.95|0.00|0.15|-0.0104|-0.0067|
|secu*|-0.0229|0.0018|-14.86|0.00|0.42|-0.0264|-0.0194|
|uni*|-0.0133|0.0009|-11.98|0.00|0.13|-0.0150|-0.0115|
|MinasGerais*|-0.0281|0.0013|-20.21|0.00|0.37|-0.0306|-0.0255|
|EspiritoSanto*|-0.0101|0.0008|-7.15|0.00|0.05|-0.0117|-0.0086|
|Rio*|-0.0106|0.0009|-11.49|0.00|0.24|-0.0123|-0.0089|

obs. P 0.0223 pred. P 0.0106 (at x-bar) (*) dF/dx is for discrete change of dummy variable from 0 to 1, z and P>Izl<sup>arethetestof theunderlying</sup> coefficient being 0

**Table 4.3: Probability of migrating from Northeast to Southeast**

|Probitestimates<br>Loglikelihood=-2|407.8167|||||Numberofo<br>LRchi2(15)<br>Prob>chi2<br>PseudoR2|bs =28153<br>=294.27<br>=0.0000<br>=0.0576|
|---|---|---|---|---|---|---|---|
||dF/dx|Std.Err.|z|P>z|x-bar|[95percentC.I|.<br>]|
|Age|-0.0007|0.0001|-11.04|0.00|37.23|-0.0009|-0.0006|
|<br>female*|-0.0013|0.0014|-0.93|0.35|0.38|-0.0039|0.0014|
|Famsize|-0.0012|0.0004|-3.16|0.00|4.10|-0.0019|-0.0004|
|priml*|0.0092|0.0025|4.15|0.00|0.27|0.0043|0.0141|
|prim2*|0.0018|0.0023|0.84|0.40|0.14|-0.0026|0.0063|
|secu*|0.0014|0.0019|0.75|0.45|0.34|-0.0024|0.0052|
|Uni*|0.0026|0.0034|0.80|0.42|0.07|-0.0041|0.0093|
|Maranhao*|-0.0064|0.0020|-2.57|0.01|0.06|-0.0102|-0.0025|
|Piaui*|0.0010|0.0032|0.32|0.75|0.04|-0.0052|0.0072|
|Ceara*|-0.0101|0.0013|-6.02|0.00|0.20|-0.0127|-0.0075|
|RioGrandeN.*|-0.0076|0.0019|-2.88|0.00|0.05|-0.0115|-0.0038|
|Paraiba*|0.0121|0.0039|3.88|0.00|0.05|0.0044|0.0198|
|Pernambuco*|-0.0078|0.0014|-4.86|0.00|0.21|-0.0105|-0.0051|
|Alagoas*|0.0039|0.0035|1.22|0.22|0.04|-0.0030|0.0107|
|<br>Sergipe*|-0.0070|0.0021|-2.49|0.01|0.04|-0.0111|-0.0028|
|<br>obs.P.0181508<br>pred.P.0137763|(atx-bar)|||||||

(*) dF/dx is for discrete change of dummy variable from 0 to 1,z and<sup>P>Izlarethetestof theunderlying</sup> coefficient being 0.

23
return compared to both documented return migrants and non-migrants. This suggests that undocumented migrants do not gain from their overseas migration experience in terms of skill acquisition the way documented migrants do. Our results show that the longer undocumented migrants stay abroad, the more their human capital is depleted, while the opposite is true for documented migrants. Indeed, we find evidence that illegal migrants experience a waste of human capital overseas, as they have lower earnings and end up in lower-ranked occupations while abroad, which might explain their lower earnings upon return.

Our results are the first to show the negative impact associated with illegality on the migrants, even after they return to their country of origin. These findings have important policy implications, as they suggest that there is a need to raise awareness among potential migrants about the negative consequences of illegal migration experiences. Furthermore, countries of origin should maximize the benefits of migration by supporting legal migration and curbing illegal migration.

# **References**

- Abrego, L. J. (2011). Legal consciousness of undocumented Latinos: Fear and stigma as barriers to claims-making for first-and 1.5-generation immigrants. _Law & Society Review_ , _45_ (2), 337–370.

- Ambrosini, J. W., Mayr, K., Peri, G., & Radu, D. (2015). The selection of migrants and returnees in Romania: Evidence and long-run implications. _Economics of Transition_ , _23_ (4), 753–793.

- Assaad, R., & Krafft, C. (2013). The Egypt labor market panel survey: introducing the 2012 round. _IZA Journal of Labor & Development_ , _2_ (1), 8.

- Awad, I., & Aziz, N. A. (2017). Egyptian irregular migration in the GCC countries. In _Skillful survivals: Irregular migration to the Gulf._ Cambridge: Gulf Research Centre Cambridge.

- Azose, J. J., & Raftery, A. E. (2019). Estimation of emigration, return migration, and transit migration between all pairs of countries. _Proceedings of the National Academy of Sciences_ , _116_ (1), 116–122.

- Bayer, R. (2006). Diplomatic exchange data set, v2006. 1. Retrieved from `http:// correlatesofwar.org`

- Bean, F. D., Lowell, B. L., & Taylor, L. J. (1988). Undocumented Mexican immigrants and the earnings of other workers in the United States. _Demography_ , _25_ (1), 35– 52.

- Borjas, G. J., Freeman, R. B., & Lang, K. (1991). Undocumented Mexican-born workers in the United States: how many, how permanent? In _Immigration, trade, and the labor market_ (pp. 77–100). University of Chicago Press.

29
third restriction eliminates the need to model fathers’ labor supply, further reducing dimensionality with limited loss of empirical generality because most fathers with small children work full-time. Our estimation sample includes households in which, on average, parents are more educated than in the full dataset and earn higher wages. Appendix C provides further comparison between our estimation sample and the full dataset.

## **3.2 Descriptive Statistics**

Our estimation sample consists of 600 two-parent households and 150 single-mother households that meet the criteria described above (sample sizes are rounded to the nearest 50 to comply with ECLS-B confidentiality rules.) In Table 1 we define the main variables used in estimation and report descriptive statistics. About 27 percent of two-parent households and 41 percent of single-mother households use a child care center as the main form of care and pay on average $4.50 and $3.88 per hour of full-time care, respectively (all monetary values are expressed in dollars of 2003). The average quality of child care centers, as measured by the ITERS score, is on average satisfactory, as values of 4 and 5 denote satisfactory and good quality, respectively. This average is in line with previous work for the US population (e.g., Blau and Mocan (2002)) and is the same as the mean value of ITERs for two-year olds in the full ECLS sample. About 5 percent of households pay non-relatives for home-based care and 20 percent use relatives as the main form of care. Single mothers rely more on relative care than two-parent households. The average child cognitive score is approximately equal to 129 and 124 in two-parent and single-mother households, respectively.

We observe the usual gender differentials in parental labor market outcomes, as women participate substantially less than men (only 50 percent of them work) and earn lower wages on average. The average gender wage gap in two-parent households is 15 percent, smaller than in the full dataset, and the correlation between father’s and mother’s wage is 0.54. In about half of two-parent households both parents have completed college, whereas only 12 percent of single mothers have completed it. In both cases, however, parents have higher educational attainment than in the full dataset.

# **4 Econometric Issues**

## **4.1 Empirical Specification**

### **4.1.1 Household Heterogeneity**

Recall that household types vary in wage offers **_w_** ( _j_ ), preferences **_α_** ( _j_ ), initial child development _q_ 0( _j_ ), and availability of relative care _d_<sup>_R_</sup> ( _j_ ). When computing the equilibrium for estimation or counterfactuals, we treat each household in the sample as a different type, in which _q_ 0 is measured as the child’s cognitive score at age one and serves as the initial condition in the value-added specification of child development production in equation (6).

As mentioned in Section 2, in addition to two-parent households we model one-parent households (henceforth, single-mother households or single mothers). We treat them as a 13
- Markusen, James R. (1990), "Derationalizing Tariffs with Specialized Intermediate Inputs and Differentiated Final Goods," _Journal of International Economics_ , 28:375-384.

- Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs," _American Economic Review_ , 79:85-95.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,‖ _Canadian Journal of Economics_ , 38(3): 758-777..

- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,‖ World Bank Policy and Research Working Paper Number 2413.

- Markusen, James R. and Anthony J. Venables (2000),  "The General Theory of Endowment, Intra-Industry, and Multinational Trade‖, _Journal of International Economics_ , 52, 209234.

- Markusen, James R, and Anthony Venables (1998), "Multinational Firms and the New Trade Theory," _Journal of International Economics_ , Vol. 46, 183-204.

Marshall, J.N. (1988), _Services and Uneven Development_ , London: Oxford University Press.

- Matoo, Aaditya, Randeep Rathindran and Arvind Subramanian (2006), ―Measuring Services Trade Liberalization and its Impact on Economic Growth: An Illustration,‖ _Journal of Economic Integration_ ,‖ Vol. 21(1), 64-98.

- Matusz, Steven J, and David G. Tarr (2000), ―Adjusting to Trade Policy Reform,‖ in A. Krueger (ed.), _Economic Policy Reform_ , Chicago: University of Chicago Press.

- McGuire, Greg and Michael Schuele (2000), ―Restrictiveness of International Trade in Banking Services,‖ in Findlay, Christopher and Tony Warren (eds), _Impediments to Trade in Services: Measurement and Policy Implications_ , (London: Routledge).

- McKee, D.L. (1988), _Growth, Development, and the Service Economy in the Third World_ , New York: Praeger Publishers.

- Moriarity, C. Scheuren, F., 2003. A Note on Rubin‘s Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations, _Journal of Business and Economic Statistics,_ 21, 65-73.

- Piggott, John, and Whalley (1985), John, _UK Tax Policy and Applied General Equilibrium Analysis_ , New York: Cambridge University Press.

89
<!-- Start of picture text --> Changing growth rates Changing inequality<br>NN<br>22<br>wow=> _oSS wgw=o oN— _—— -——_—<br>g TM“ g ~<br>fe)a~~—fe)a~~<br>©>. © ~~<br>—— -~L_ _<br>7 vT<br>2015 2018 2021 2024 2027 2030 2015 2018 2021 2024 2027 2030<br>——— Growth rates from 2018-2013 =——————_—“No change in Gini<br>—-—-= Growth rates from 2018-2023 +/- 1 pct. pt. —e_-= +/- 1% annual Gini change<br>Growth rates from 2018-2023 +/- 2 pct. pt. +/- 2% annual Gini change<br><!-- End of picture text --> <!-- Start of picture text --> (a) Poverty rate (b) Millions of poor (c) Share of global poor<br>uw2 S5 =—S<br>= ? _8S =e7 Oo<br>83<br>iaaSea =Ea<br>&2 == ae<br>= S o<br>om = fia]<br>ao<br>aews i=]Somo ©<br>2015 2020 2025 2030 2015 2020 2025 2030 2015 2020 2025 2030<br>-2% annual change in Gini -1% annual change in Gini =— Nochange =— 1% annualchangein Gini — 2% annual change in Gini<br><!-- End of picture text -->
reported by the household in the 15 days prior to the survey—differ significantly from the non-annualized distributive results of method 1,(see Figures 20a and 20b). Method 2 calculates a lower share of benefits accruing to the richest quintile and, more importantly, produces a mixed picture as far as net unitary benefits are concerned. Net unitary benefits increase with welfare levels for the first three quintiles but decrease thereafter, and the change is especially pronounced from the fourth to the fifth quintiles, (see Figures 15a and 15b).

Two factors contribute to these results. First, method 2 turns sporadic visits during the 15 days prior to the survey into frequent visits throughout the year. First, mistakes from annualizing sporadic visits may be larger for health posts than they are for hospitals, since hospital visits are typically associated with more severe conditions. Second, those visiting hospitals may have previously visited clinics or health posts, but these visits may not be reported as the individual is asked only to identify the facility from which services were received. As poor households use clinics and health posts more frequently, and some clinic and health-post visits by richer households are not reported in the survey, these two effects make the distribution less regressive than in method 1.

Figures 15a and 15b: Gross and Unitary Benefits of Public Healthcare, Method 2 <!-- Start of picture text --> Gross (a)<br>6.0 5.4<br>5.0<br>5.0 4.6<br>4.2<br>4.0 3.6<br>3.0<br>2.0<br>1.0<br>3.4 4.2 4.8 3.8 1.8<br>-<br>(1.0)<br>(2.0)<br>(3.0)<br>Q1 Q2 Q3 Q4 Q5<br>(Poorest 20%) (Richest 20%)<br>Private (negative) Public Net<br>KMZ billions<br><!-- End of picture text --> <!-- Start of picture text --> Unitary (b)<br>30 27<br>25 23 23<br>19 20<br>20<br>15<br>10<br>5<br>18 18 20 18 11<br>-<br>(5)<br>(10)<br>(15)<br>(20)<br>Q1 Q2 Q3 Q4 Q5<br>(Poorest 20%) (Richest 20%)<br>Private (negative) Public Net<br>KMZ thousands<br><!-- End of picture text --> Figures 16a and 16b disaggregate the analysis between health posts/clinics and hospitals. The BIA results are driven by the mixed pattern of hospital-related net benefits. Again, the richest quintile captures more benefits than the poorest quintile—both in total and per-patient terms—while the opposite is the case for 32
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Electricity generation capacity additions that are either planned or are under construction between 2018 and 2035 are presented in Table 2. Of the total planned capacity additions, more than half will be natural gas based. Renewable sources of electricity (solar, wind and hydro) will share more than 10% of the total planned or committed capacity in the region. Note that the addition of new power plants also replaces retiring capacities.

**<u>Table 2. Planned/Under Construction Capacity by 2035 (MW)</u>**

|**Economy**|**GT**|**CC**|**ST**|**DG**|**Hydro**|**Nuclear**|**Coal**|**Wind**|**PV**|**CSP**|**IGCC**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Algeria|10,800|-|-|-|-|-|-|70|200|-|<br>-|**11,070**|
|Bahrain|4,125|-|-|-|-|-|-|1|105|-|<br>-|<br>**4,231**|
|Egypt, Arab<br>Rep.|2,430|7,940|10,550|-|32|2,400|1,950|160|179|-|<br>-|**25,641**|
|Iraq|15,000|2,500|13,000|-|-|-|-|-|2,500|-|<br>-|**33,000**|
|Jordan|3,000|-|900|-|-|2,000|-|600|1,100|-|<br>-|<br>**7,600**|
|Kuwait|9,350|-|-|-|-|-|-|-|60|50|<br>-|<br>**9,460**|
|Lebanon|570|-|-|-|126|-|-|-|51|-|<br>-|<br>**747**|
|Libya|250|2,910|2,800|-|-|-|-|27|15|-|<br>-|<br>**6,002**|
|Morocco|2,400|-|38|-|1,375|-|3,026|3,011|3,339|650|<br>-|**13,839**|
|Oman|1,245|-|-|78|-|-|-|50|200|-|<br>-|<br>**1,573**|
|West Bank<br>and Gaza|650|-|-|-|-|-|-|-|-|-|<br>-|<br>**650**|
|Qatar|3,549|-|-|-|-|-|-|-|200|-|<br>-|<br>**3,749**|
|Saudi Arabia|12,913|30|4,490|-|-|3,200|-|400|300|-|<br>1,995|**23,328**|
|Sudan|-|-|-|37|360|-|600|-|-|-|<br>-|<br>**997**|
|Syrian Arab<br>Republic|100|-|-|-|-|-|-|100|10|-|<br>-|<br>**210**|
|Tunisia|450|-|-|-|-|-|-|-|10|-|<br>-|<br>**460**|
|United Arab<br>Emirates|2,299|-|1,440|-|-|2,800|-|-|-|-|<br>-|<br>**6,539**|
|Yemen,Rep.|-|-|<br>-|<br>-|<br>-|<br>-|-|<br>-|<br>-|<br>-|<br>-|<br>**-**|
|Total|**69,131**|**13,380**|**33,218**|**115**|**1,893**|**10,400**|**5,576**|**4,419**|**8,269**|**700**|**1,995**|**149,096**|

Note: CC = Combined Cycle; GT = Gas Turbine; ST = Steam Turbine; DG = Diesel Generator; Hydro = Hydroelectric; PV = Solar Photovoltaic; CSP = Concentrated Solar Power; IGCC = Integrated gasification combined cycle; UAE = United Arab Emirates Source: World Bank (2020).

# **4.2. Peak and Energy Demand Projections**

We used various sources, but mostly government sources, for the projection of peak load and energy demand. The sources include energy ministries and national electricity utilities and the Arab Forum for Environment and Development (AFED). Table 3 presents the peak load and electricity demand projections. The percentages in the table represent the annual average growth rates for given time period specified in each column. It is possible that demand growth would 11