rafmacalaba/datause-encoder-data huggingface dataset:
|**el**|IV<br>_Two girls_|(6)|0.073<br>(0.060)<br>119.3<br>4.514<br>(3.632)<br>85.07<br>0.068<br>(0.063)<br>117.6<br>0.026<br>(0.037)<br>87.25<br>Yes<br>Yes<br>Yes<br>_red, Age at First birth_<br>cludes women aged 20<br>ith 2 or more children<br>** Indicates statistical<br>ge estimation.|
|---|---|---|---|
|**Educ. Lev**<br>_schooling)_|IV<br>_Same-sex_|(5)|-0.023<br>(0.080)<br>77.94<br>3.398<br>(4.660)<br>62.51<br>0.034<br>(0.077)<br>90.03<br>0.008<br>(0.044)<br>72.56<br>Yes<br>Yes<br>Yes<br>_ge, Age Squa_<br>of mothers in<br>ied women w<br>nce at 10%. <br> the frst-sta|
|**e Primary **<br>_8 years of _|OLS<br>—|(4)|-0.059***<br>(0.011)<br>-1.755**<br>(0.728)<br>-0.017<br>(0.013)<br>0.018**<br>(0.007)<br>Yes<br>Yes<br>Yes<br>ovariates: _A_<br>The sample <br>s of the marr<br>ical signifca<br>orresponds to|
|**Abov**<br>_(>_||Obs.|3504<br>2284<br>3040<br>2482<br>llowing c<br>_oy 2nd_. <br> husband<br>es statist<br>uments c|
|||Mean|0.286<br>19.09<br>0.482<br>0.062<br>clude the fo<br>_y 1st_ and _B_<br>includes the<br>s. * Indicat<br>luded instr|
||IV<br>_Two girls_|(3)|_Mothers_<br>0.033<br>(0.021)<br>161.8<br>4.092*<br>(2.185)<br>95.82<br> _Fathers_<br>0.060<br>(0.049)<br>104.2<br>0.052*<br>(0.028)<br>85.45<br>Yes<br>Yes<br>Yes<br>he models in<br>cators for _Bo_<br>le of fathers <br>t fxed efect<br>tic of the exc|
|**c. Level**<br>_chooling)_|IV<br>_Same-sex_|(2)|_Panel A: _<br>0.014<br>(0.028)<br>90.22<br>5.523*<br>(3.012)<br>58.72<br>_Panel B: _<br>0.030<br>(0.077)<br>48.10<br>0.055<br>(0.040)<br>44.69<br>Yes<br>Yes<br>Yes<br>theses. All t<br> include indi<br>d. The samp<br>y and distric<br> The F-Statis|
|**imary Edu**<br>_8 years of s_|OLS<br>—|(1)|-0.019***<br>(0.004)<br>0.140<br>(0.461)<br>-0.024**<br>(0.010)<br>-0.000<br>(0.006)<br>Yes<br>Yes<br>Yes<br> are in paren<br> and (3) also<br>n 18 years ol<br>d with surve<br>ance at 1%.|
|**Pr**<br>_(≤_||Obs.|3900<br><br>2578<br>2420<br>2048<br>PSU level <br>lumns (2)<br>unger tha<br>s estimate<br>al signifc|
|||Mean|0.054<br>14.56<br>0.283<br>0.054<br>d at the <br>dels in co<br>hildren yo<br> model i<br>s statistic|
||t for Fertility:|t variable|**_of-farm_**<br>_F-stat FS_<br>**_r week_**<br>_F-stat FS_<br>**_of-farm_**<br>_F-stat FS_<br>**_ccupation_**<br>_F-stat FS_<br>xed-Efects<br>xed-Efects<br> errors clustere<br>_ucation_. IV Mo<br>with 2 or more c<br> years old. Each<br>%. *** Indicate|
||Method<br>Instrumen|Dependen|**_Worked _**<br>**_Hours pe_**<br>**_Worked _**<br>**_Second o_**<br>Survey Fi<br>District Fi<br>Controls<br>_Notes:_ Standard<br>and _Years of Ed_<br>to 49 years old <br>younger than 18<br>signifcance at 5|

43
mitigated with respect to the previous scenarios. For _Lombardia_ and _Puglia_ the impact on production has a reversed sign. In general, five regions show negative effects on production. Among these regions, _Lombardia_ and _Trentino Alto Adige_ would experience a positive effect in terms of welfare. _Piemonte_ and _Umbria_ , two regions with boosted production, would be slightly worse off than the others in the same category.

Based on the needs of governments and policy-makers, this model can represent a starting point to evaluate fiscal and economic shocks in Italy, with time specification and regional/environmental data. In future research steps, the authors will work on improving the quality of data used as input by regionalizing environmental data through statistical or heuristic procedures. In addition, future versions of the model will be directed in testing closures other than the classic Walrasian one as sensitivity analysis for the Italian regions study case. Finally, the inclusion of information on the regional tax gap in the Italian regional SAM and in the IRENCGE-DF model would make a great contribution to a better representation of the Italian economic system and more complete policy analyses.

# **References**

- Anderson, K. and Martin, W. (2005). Agricultural trade reform and the doha development agenda. _World Economy_ , 28(9):1301–1327.

- Armington, P. S. (1969). A theory of demand for products distinguished by place of production. _Staff Papers_ , 16(1):159–178.

- Bigano, A., Bosello, F., Roson, R., and Tol, R. S. (2008). Economy-wide impacts of climate change: a joint analysis for sea level rise and tourism. _Mitigation and Adaptation Strategies for Global Change_ , 13(8):765–791.

- Bouët, A., Bureau, J.-C., Decreux, Y., and Jean, S. (2005). Multilateral agricultural trade liberalisation: The contrasting fortunes of developing countries in the doha round. _World Economy_ , 28(9):1329–1354.

- Brandsma, A., Kancs, d., Monfort, P., and Rillaers, A. (2015). Rhomolo: A dynamic spatial general equilibrium model for assessing the impact of cohesion policy. _Papers in Regional Science_ , 94:S197–S221.

- Britz, W. (2020). Comparing three penalty functions-cross entropy, quadratic and linear loss–in sam splitting applications.

- Canning, P. N. and Tsigas, M. E. (2000). Regionalism, federalism, and taxation: A food and farm perspective. Technical report.

- Cherubini, L. and Los, B. (2016). Regional employment patterns in a globalizing world: A tale of four italies. _Global value chains: new evidence and implications_ , pages 213–235.

- Cicowiez, M. and Lofgren, H. (2017). Building macro sams from cross-country databases: method and matrices for 133 countries. _World Bank Policy Research Working Paper_ , (8273).

- Cirillo, C., Imperioli, L., Manzo, M., et al. (2021). The value added tax simulation model: Vatsim-df (ii). Technical report.

26
3

# 1. **Introduction**

In the fall of 1982 the Mexican government announced that Mexico was no longer able to fulfill its external commitments. A run out of developing country debt ensued. It was the first major default of a sovereign country after the crisis of the 1930s, and the event was unexpected, despite the high real interest rate and falling commodity prices that obtained at the time. Most of the debt was held only by several hundred commercial banks.

One of the main inefficiencies associated with the negotiation process that followed was that the "rules of the game" were not clear or predictable. Different strategies were proposed in the 1980s, ranging from the "involuntary lending" of the early 1980s to the Brady agreements of the past few years. One important milepost in the debt crisis was the introduction of a secondary market in 1985. Since most claims were held by a relatively small group of banks, the market was initially thin. The process of securitization of the developing countries debt, though, accelerated in the last few years.

The Brady agreements are based on the exchange, on a voluntary basis, of commercial debt for a menu of options including bonds or new money - the bond option being the most popular one. Brady agreements have involved debt reductions and have usually been implemented when the debtor country was already undergoing economic reforms. Debt reductions and economic reforms have relaxed the credit constraints that were binding for most of the debtors in the 1980s, thereby providing the new market instruments with a relevant degree of liquidity.

As a consequence, the _Brady bond_ market developed very quickly in the past few years. The Emerging Market Trading Association was founded in 1990 (a successor to the earlier "LDC Debt Traders Association"), and by now most bonds are traded continuously all over the world. Trading volumes in 1994 reached US$2.8 trillion (from US$100 billion in 1990), the total amount outstanding of the US dollar-denominated Brady market in 1994 had an approximate face value of US$138 billion, and future and option market are now traded for all major Bradies. l This impressive development of the secondary market largely resulted from the new approach introduced by the Brady plan. Brady agreements have aimed at and actually led to a securitization of the external debt, to a relaxation of credit constraints and to a restored access to the international financial market. This approach has been consistent with the view that international financial market can be a fair and relatively certain environment to satisfy the financial needs of middle-income countries, probably a better environment than the lasting "bargaining process" that prevailed in the 1980s.

i The reader interested in knowing more (daily prices , futures and options, analysis and ratings, news and forum) can now browse the pages of the BradyNet (http://www.2020tech.com/bradynet).
**Figure 9:  Change in Manufacturing Exports ‘Half-way to the Global Average’ Scenario: Gains from Domestic and Partners’ Reform**

<!-- Start of picture text --> Domestic Reform Partners' Reform<br>16 16<br>14 14<br>12 12<br>10 Ports 10<br>Customs<br>8 8<br>Regulations<br>6 Services 6<br>4 4<br>2 2<br>0 0<br>Source: Authors’ calculation. Source: Authors’ calculation.<br>Figure 10: Trade Gains from Domestic and Partners’ Reform: Country Examples<br>Domestic Reform Partners' Reform<br>18 18<br>16 16<br>14 14<br>Ports Ports<br>12 12<br>10 Customs 10 Customs<br>8 Regulations 8 Regulations<br>6 6<br>Services Services<br>4 4<br>2 2<br>0 0<br>% change % Change<br>OECD East Asia ECA LAC MENA South Asia Sub-Sah. Africa OECD East Asia ECA LAC MENA South Asia Sub-Sah. Africa<br>% change % change<br>Guatemala Nigeria Indonesia Guatemala Nigeria Indonesia<br><!-- End of picture text --> <!-- Start of picture text --> Partners' Reform<br>16<br>14<br>12<br>10 Ports<br>Customs<br>8<br>Regulations<br>6 Services<br>4<br>2<br>0<br>% Change<br>OECD East Asia ECA LAC MENA South Asia Sub-Sah. Africa<br><!-- End of picture text -->

**Figure 10: Trade Gains from Domestic and Partners’ Reform:  Country Examples**

Source: Authors’ calculation.

Source: Authors’ calculation.

40
protect them against the hazard of exposure to international financial volatility. On the international front, since capital account liberalization implies welfare gains globally, there exists a justification and rationale for international action-of policy coordination, prudential financial regulation and supervision, and lender-of-last-resort activity to provide liquidity and emergency financial assistance.

# **II. Rapid Expansion in International Capital Markets**

By any measure, the growth in international financial markets in the 1990s has been stupendous. The volume of international lending in new medium- and long-term bonds and bank loans reached an estimated value of $1.2 trillion in 1997, compared with $0.5 trillion in 1988 (BIS, 1998). World trade in goods and services, though having grown significantly since the early 1970s, is now dwarfed by international financial transactions, at more than five times the value of world trade (Figure 5.1). Some other statistics: Cross-border transactions in bonds and equities in OECD countries, less than 10 percent of GDP in 1980, reached more than 100 percent of GDP in 1995. Average daily turnover in foreign exchange markets reached $1.6 trillion (up from $0.20 trillion in 1986), compared to the $4.7 trillion a year in trade in goods and services. The ratio of total global market capitalization of stock markets relative to world GDP rose from 23:1 in 1986 to 68:1 in 1996, while derivative markets expanded from $7.9 trillion in 1991 to $40.9 trillion in 1997 (Table _5.1)._

**Figure 5.1: Financial markets take off: Global financial market size and world trade**

**Percent of world GDP**

<!-- Start of picture text --> 100<br>1 Finance<br>80 X X Trade<br>60-<br>40 -1<br>40<br>1980 82 84 86 88 90 92 94 96<br>Financial markets include stocks and international bonds and loans outstanding<br>Trade figures are averages of imports and exports.<br>Sources: IFC, BIS, and IMF.<br><!-- End of picture text --> _5_
TFP growth, and in our projections of sector productivity terms in eqn. (A3) we initially set all the  _j_ 's to the same value, 0.018. These are then adjusted to match actual GDP growth rates in the initial years for which we have actual data.

The value share parameters of the production functions (  _Kj_ ,  _Lj_ , etc.) are set to the values in the 2010 IO table in the first year of the simulation. For future periods we change most of these parameters so that they gradually resemble the shares found in the US input output table for 1997. The exceptions to this are the coal inputs for all the sectors, this is set to converge to a value between current Chinese and US1997 shares.<sup>25</sup> The rate of reduction in energy use is set at a modest level relative to the rapid improvements in the recent Chinese history. We assume that the share of energy in industry output is reduced gradually to 60% of the 2005 levels in 40 years. This is conservative compared, for example, to the performance in the electric power industry during the 1990-99 period. In that time the thermal output grew 88% whereas coal input only rose 61%, a rate of improvement of some 1.5% per year.<sup>26</sup> _C_ The  _it_ parameters of the consumption function are set in a similar way. That is, for the first period they are equal to the shares in the 2010 Social Accounting Matrix, and for the future periods they gradually approach US 1997 shares except for coal. This implies a higher projected demand for private vehicles and gasoline than that assumed in most other models of China. The coefficients determining demand for _I G_ different types of investment goods (  _it_ ), and different types of government purchases (  _it_ ), are projected identically.

The import and export elasticities are set to the values in GTAP v4. The base share of exports and imports are taken from the SAM.

> 25 We have chosen to use U.S. patterns in our projections of these exogenous parameters because they seem to be a reasonable anchor.  While it is unlikely that China’s economy in 40 years time will mirror the U.S. economy of 1997, it is also unlikely to closely resemble any other economy.  Other projections, such as those by the World Bank (1994), use the input‐output tables of developed countries including the U.S.

> 26 China Energy Statistics Yearbook 1997‐1999, Tables 4‐5 and 4‐15.

‐82‐
## **Table 6**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|Utilization of<br>postnatal maternal<br>health care|NNS Form<br>3.1|**Postnatal care**<br>Postnatal breast<br>&<br>complementary<br>feeding<br>counseling|**Received postnatal check-up right after**<br>**birth by skilled practitioner (Doctor,**<br>**nurse or midwife)**<br>After birth, received counseling on breast<br>& complementary feeding|**Yes**<br>Yes|**Yes**<br>No|
|Health status|NNS Form<br>3.1|Acute diarrhea in<br>past 2 weeks|Child experienced acute diarrhea in prior<br>two weeks|Yes|No|
|||**Neonatal**<br>**screening**|**Child underwent neonatal screening**|**Yes**|**Yes**|
|Health screening|NNS Form<br>4.2|Postnatal growth<br>control visit|Child participated in operation Timbang<br>Plus or other growth monitoring<br>government program|Yes|No|
|Health<br>interventions|NNS Form<br>4.2|**Age**<br>**appropriate**<br>**vaccinations**|**Child vaccinated according to WHO**<br>**schedule. BCG, Oral Polio, DPT (or**<br>**pentavalent), Measles (allowance for**<br>**delayed vaccinations)**|**Yes**|**Yes**|
|||Dewormed|Child received deworming medication<br>from government program|Yes|No|
|Nutrition<br>supplementation|NNS Form<br>4.2|Vitamin A<br>supplementation|Child participated once or twice in<br>Vitamin A supplementation government<br>program|Yes|No|

Nationally, skilled postnatal care for the mother, neonatal screening and up to date vaccinations, cover 78, 64 and 67 percent of the target population. With the exception of vaccinations, differences in coverage associated with family income and maternal education are wide. For example, 86 percent of children in the richest quintile underwent neonatal screening shortly after birth, compared to 45 percent of children in the poorest quintile. About 40 percent of children under-2 nationally show evidence of having access to all three sub-indicators of postnatal care. While differences in coverage of adequate postnatal care associated with income and mother’s education are wide, it is not significant between urban and rural areas.

# **_5.6 Household environment: Water, sanitation and hygiene infrastructure_**

Whether a child comes into contact with infectious diseases or other environmental pathogens is partly determined by the conditions of the dwelling and of the surrounding community. A set of 6 indicators from the NNS were identified as measures of the sanitary and hygienic conditions of the household, of which three are considered necessary evidence of adequate WSH conditions for the child: the 36
deterioration as a result of higher food prices. With an increase in the headcount ratio of 6.34 percentage points, East Asia is, by far, the region experiencing the largest increase in poverty; this region by itself saw an increase of 51 million individuals in urban areas below the extreme poverty line. This massive increase in the number of poor is explained by the importance of food items in poor urban households and a large increase in food prices. Middle East and North Africa also experienced a relatively large increase in urban poverty due to a sharp increase in the relative prices of food in this region (12.54 percent).

**Table 2: Urban Poverty Effects of the Changes in Relative Food Prices (Jan. 2005 – Dec. 2007)**

|Region|Shock to<br>Food Prices<br>(%)|ˆamong the<br>Poor (%)|Ini<br>(circa<br>0<sup>_P_</sup>|tial<br>2005)<br>1<sup>_P_</sup>|0<sup>_P_</sup>|Change<br>1<sup>_P_</sup>|<br>Number of<br>Poor<br>(Million)|
|---|---|---|---|---|---|---|---|
|East Asia|13.81|67.46|13.28|2.69|6.34|1.86|51.08|
|Eastern Europe|-0.49|56.87|1.31|0.22|0.04|0.01|0.12|
|Latin America|1.64|40.36|3.73|1.39|0.12|0.02|0.51|
|Middle East|12.54|57.03|2.71|0.48|2.49|0.72|4.36|
|South Asia|4.84|61.86|32.27|8.07|1.89|0.66|8.16|
|Sub-Saharan Africa|<br>4.91|52.75|34.09|12.97|1.65|0.75|4.57|
|DevelopingWorld|4.10|58.76|15.17|4.29|2.86|0.89|68.80|

* Notes: (1) The regional changes in food prices are weighted averages of the cumulative increase in domestic food CPIs relative to non-food CPI observed between January 2005 and December 2007; (2) the poverty line is set at $1.25 (2005, PPP) per day; (3) the share of food consumption to total consumption among the poor is computed as described in De Hoyos and Lessem (2008); (4) to get the increase in number of poor the regional change in headcount was applied to all countries in the region; (5) East Asia does not include China and the Middle East includes only Jordan, Morocco and Yemen.

These results should be taken with caution as they represent an upper bound of the real poverty impact. In the medium-to long-run, urban households would change their consumption patterns towards less expensive food baskets; additionally, some of the general equilibrium effects of higher incomes in the agricultural sector will eventually benefit urban areas. These effects will be explored in more detail in section 5.

# **4.2.2 Poverty Effects in Rural Areas**

As we already mentioned, the adverse poverty effects of higher food prices documented in the previous section could be compensated by an increase in farmers’ income. Since the incidence of poverty among agricultural households --the beneficiaries of higher food prices-- is higher than among non-agricultural households (see Table 1), a net poverty reduction as a result of a rise in food prices is not an implausible outcome (Aksoy and Isik-Dikmelik, 2008).

The GIDD dataset classifies each household as “rural” and “urban” according to the official domestic classification. This classification of rural household agglomerates into a 10
# **References**

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- Ag ̈uero, Jorge M., Ursula<sup> ́</sup> Aldana, Erica Field, Veronica Frisancho, and Javier Romero (2020). “Is Community-Based Targeting Effective in Identifying Intimate Partner Violence?” _AEA Papers and Proceedings_ 110: 605–609.

- Ag ̈uero, Jorge M. and Veronicar Frisancho (2021). “Measuring Violence Against Women with Experimental Methods.” _Economic Development and Cultural Change_ .

- Chuang, Erica, Pascaline Dupas, Elise Huillery, and Juliette Seban (2021). “Sex, lies, and measurement: Consistency tests for indirect response survey methods.” _Journal of Development Economics_ 148: 102582.

- Cullen, Claire (2020). “Method Matters: Underreporting of Intimate Partner Violence in Nigeria and Rwanda.” World Bank Policy Research Working Paper 9274.

- Devries, Karen et al. (2013). “The Global Prevalence of Intimate Partner Violence Against Women.” _Science_ 340 (6140): 1527–1528.

- Ellsberg, Mary, Lori Heise, Rodolfo Pena, Sonia Agurto, and Anna Winkvist (2001). “Researching Domestic Violence against Women: Methodological and Ethical Considerations.” _Studies in Family Planning_ 32 (1): 1–16.

- Falb, Kathryn et al. (2016). “Implementation of Audio-Computer Assisted Self-Interview (ACASI) among adolescent girls in humanitarian settings: feasibility, acceptability, and lessons learned.” _Conflict and Health_ 10 (32).

- Fincher, Danielle, Kristin VanderEnde, Kia Colbert, Debra Houry, L. Shakiyla Smith, and Kathryn M. Yount (2015). “Effect of Face-to-Face Interview Versus Computer-Assisted Self-Interview on Disclosure of Intimate Partner Violence Among African American Women in WIC Clinics.” _Journal of Interpersonal Violence_ 30 (5): 818–838.

- Garcia-Moreno, Claudia, Christina Pallitto, Karen Devries, Heidi Stockl, Charlotte Watts, and Naeema Abrahams (2013). _Global and Regional Estimates of Violence Against Women: Prevalence and Health Effects of Intimate Partner Violence and Non-partner Sexual Violence_ . World Health Organization.

- H ̈oglinger, Marc and Ben Jann (2018). “More is not always better: An experimental individual-level validation of the randomized response technique and the crosswise model.” _PLOS ONE_ 13 (8).

17
- In the median home country of EMDE groups, the proportion of overseas EMDE group assets in bank subsidiaries relative to total home bank assets located in EMDE hosts has more than quadrupled to 2.1 percent in 2013, from 0.4 percent in 2000.

These medians mask substantial variation across countries, as shown by the 75th-25th percentile ranges in Figure 2.

**Figure 2: The growing importance of cross-border banking ties between EMDEs**

EMDE groups are growing in importance relative to bank assets in their EMDE host countries...

...but also relative to total bank assets in their home countries <!-- Start of picture text --> 35 8<br>30 7<br>6<br>25<br>5<br>20<br>4<br>15<br>3<br>10 2<br>5 1<br>0<br>0<br>Note: Median in blue, dotted lines are 75 th and 25 th percentiles Note: Median in blue, dotted lines are 75 th and 25 th percentiles<br>EMDE home countries have cross-border banking ties to South-Africa, Russia, and Colombia are home to the most<br>multiple EMDE hosts, particularly in SSA EMDE banks which are active in other EMDEs; Lebanon’s<br>banks are active in many host countries<br>16 25 No. banks, 2013 16<br>14 14<br>No. host countries, 2013 (rhs)<br>20<br>12 12<br>10 10<br>15<br>8 8<br>6 10 6<br>4<br>4<br>5<br>2<br>2<br>0<br>0 0<br>All EAP ECA LAC MENA SAR SSA<br>regions min median max<br>host-country's financial sector) home-country's financial sector)<br>Total Assets in host countries (% Total assets Total Assets in host countries (% Total assets<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>No. of home-country banks No. of host countries<br>No. host countries per home country<br>ZAF RUS COL CHN TUR NGA KEN LBN MLI IND MAR MYS BRA LBY ECU<br><!-- End of picture text --> South-Africa, Russia, and Colombia are home to the most EMDE banks which are active in other EMDEs; Lebanon’s banks are active in many host countries 12