rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> CES<br><!-- End of picture text --> <!-- Start of picture text --> Labor-Capital-Energy<br>CES<br><!-- End of picture text --> <!-- Start of picture text --> Non-energy intermediate inputs<br>Leontief<br><!-- End of picture text --> <!-- Start of picture text --> NE 1<br><!-- End of picture text --> <!-- Start of picture text --> Labor<br><!-- End of picture text --> <!-- Start of picture text --> Capital-Energy<br>CES<br><!-- End of picture text --> <!-- Start of picture text --> NE 2<br><!-- End of picture text --> <!-- Start of picture text --> NE 11<br><!-- End of picture text --> <!-- Start of picture text --> NE 12<br><!-- End of picture text --> <!-- Start of picture text --> Capital<br><!-- End of picture text --> <!-- Start of picture text --> Energy<br>CES<br><!-- End of picture text --> <!-- Start of picture text --> Electric Power<br><!-- End of picture text --> <!-- Start of picture text --> Fossil Fuels<br><!-- End of picture text --> <!-- Start of picture text --> CES<br><!-- End of picture text --> <!-- Start of picture text --> COAL<br><!-- End of picture text --> <!-- Start of picture text --> Refined Petroleum & Gas<br>CES<br><!-- End of picture text --> <!-- Start of picture text --> Gas<br><!-- End of picture text --> <!-- Start of picture text --> Refined Petroleum<br><!-- End of picture text -->

**Figure 1.  Structure of the production function in the CGE model**

# **2.1.1 Gross Output**

At the top of the nesting structure in Figure 1, the aggregation of value-added and intermediate input is expressed as: where _QAi_ , _r_ is the total production of sector i in region r, _QVAi_ , _r_ and _QINTAi_ , _r_ are the input of _A_ value-added and intermediate input in sector i in region r respectively,  _i r_ , and α _i_<sup>_A_</sup> , _r_<sup>are the</sup> _A_ share parameter and efficiency parameter;  _i_ , _r_ is the substitution elasticity parameter between _A A A_ value-added and intermediate input, and  i, _r_  1 (1 _i_ , _r_ ) ,  _i_ , _r_ is the substitution elasticity between value-added and intermediate input.

Optimal factor input under total production: 7
# **Table 3. Cox Regression: Time Until the First Policy Response by Category in EMDEs (days since January 30**<sup>**th**</sup> **, 2020)**

|VARIABLES|(1)<br>Banking|(2)<br>Sector|(3)<br>Financial M<br>NB|(4)<br>arkets and<br>FIs|(5)<br>Liquidity a|(6)<br>nd Funding|(7)<br>Payment|(8)<br>s Systems|
|---|---|---|---|---|---|---|---|---|
|Log GDP per capita (USD)|1.762***|1.495***|1.381***|1.334**|1.502***|1.353**|0.820**|0.936|
||(0.199)|(0.203)|(0.164)|(0.192)|(0.190)|(0.195)|(0.082)|(0.109)|
|Log population size|1.120*|1.099|1.256***|1.306***|1.384***|1.424***|1.134**|1.217***|
||(0.073)|(0.081)|(0.080)|(0.095)|(0.103)|(0.116)|(0.067)|(0.073)|
|Days until 100<sup>th</sup>COVID-19 case|0.996*|0.996|1.000|1.002|1.002|1.004|0.997**|0.998|
||(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.002)|
|Government debt (% GDP)||0.997||0.997||0.992**||0.998|
|||(0.003)||(0.003)||(0.003)||(0.003)|
|Private debt (% GDP)||1.015***||1.005||1.009**||0.993**|
|||(0.004)||(0.004)||(0.004)||(0.003)|
|Current account (% of GDP)||0.993||0.997||0.996||0.988|
|||(0.010)||(0.008)||(0.011)||(0.008)|
|Fiscal balance (% of GDP)||1.001||1.005||0.994||0.947**|
|||(0.030)||(0.019)||(0.024)||(0.020)|
|Observations (countries)|113|105|113|105|113|105|113|105|
|Pseudo R<sup>2</sup>|0.035|0.054|0.016|0.019|0.025|0.038|0.017|0.027|

Note: The sample consists of EMDEs countries only (i.e., upper-middle, lower-middle, and low-income countries). The dependent variable is the time until 1<sup>st</sup> response for each policy category. Hazard ratios and robust standard errors are reported. For each specification, we test the proportional-hazards assumption separately for each covariate. We cannot reject the null hypothesis at the 10% level for all variables except the log of population size in column 3 (p-value 0.01), 4 (p-value 0.02), 5 (p-value 0.03), and 7 (p-value 0.07); and the log GDP per capita in column 3 (p-value = 0.04), and 5 (p-value = 0.06). *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively.

In ancillary regressions reported in Appendix C, Table C1, we explore the role of additional banking sector characteristics at the start of the pandemic. By including these variables, the sample size decreases substantially, so the results should be interpreted with care. First, the baseline results presented in Table 3 hold after including these additional explanatory variables. Second, none of these additional variables are consistently and significantly associated with the measures in all policy categories. However, regulatory Tier 1 capital is positively associated with a faster response in the _Banking Sector_ and _Liquidity and Funding_ categories. Private bank credit (as % of GDP) is also associated with a faster response in the _Banking Sector_ category, while countries which have adopted the capital requirements of the Basel III framework are slower in adopting _Financial Markets and NBFIs_ measures.

23
official start of the academic year 1992-1993 was delayed by two months. When the schools were opened in November, many concerned parents kept their children at home. In Dushanbe, the government sent students of professional technical institutions for an extended winter holiday from November 13, 1992 to February 1<sup>st</sup> , 1993, motivated by the low attendance of students and teachers due to the unstable situation in the capital.<sup>6</sup> Apart from closures, many schools suffered extensive physical damage. Approximately 20 percent of schools in Tajikistan were destroyed beyond repair during the conflict and many teachers fled war affected areas (IMF 1998).

The perceived and sometimes actual danger to children was high in conflict areas. For example, parents from Gharm raion located in the RRS region worried that older girls would be harassed or abused by soldiers at checkpoints on their way to school. In Western Khatlon children of Gharmi and Pamiri origins reported fears of physical violence and of being beaten by other children as the main reason for skipping school (Falkingham 2000).<sup>7</sup> In Dushanbe alone, two separate incidents of attempted hostage taking were registered in schools and colleges in October of 1992.<sup>8</sup> Conflict may have led to a change in the gender roles in an unexpected fashion. The southern regions of Tajikistan that were more affected by the conflict were also becoming more Islamic with women losing their rights and privileges acquired during the Soviet times.<sup>9</sup> However, in some areas during unstable times, older women were more likely to travel to market as they had better chances to pass through security checkpoints without serious trouble as compared to men who were afraid to leave their villages (Gomart, 2003). In many conflict-affected areas women had to take care of their households by either entering formal employment or engaging in various income generating activities because men

> 6 Narodnaya Gazeta, Nov. 13, 1992 and Jan. 23. 1993.

> 7 Pamiri and Gharmi ethnic groups or clans were strongly associated with supporting opposition forces. During the war, adults whose passports indicated that they were born in Pamir or Garm regions were killed or taken away by Narodnii Front or government associated militias and disappeared. Human Rights Watch (1994) reports that in late December 1992 Narodnii Front militias killed 300 people and took away hundreds of people in Dushanbe (unfortunately the data used in this paper do not allow for identification of various ethnic groups and clans in Tajikistan).

> 8 Narodnaya Gazeta, Oct. 15, 1992 and Oct. 16, 1992.

> 9 The age at first marriage has decreased in Tajikistan to 14 to 16 years old. Many religious parents believe that girls who reached puberty should not interact with non-related males. Such parents may prevent their daughters from attending secondary school. In some rural areas, women cannot travel long-distances without male chaperone. (Salimova 2008).

9
||Redu|ced fo|rm|Struct|ural form|
|---|---|---|---|---|---|
|Independent<br>variable|Marginal<br>effect||Standard<br>error|Marginal<br>effect|Standard<br>error|
|D2002*|0.016|*|(0.010)|||
|D2003*|0.043|***|(0.010)|||
|D2004*|0.042|***|(0.010)|||
|D2005*|0.054|***|(0.011)|||
|D2006*|0.023|**|(0.009)|||
|D2007*|0.008||(0.009)|||
|D2008*|-0.019|**|(0.008)|||
||Number of obs<br>|=|40,019|Number of obs<br>|=<br>40,019|
||LR:<br>2<br>χ (43)|=|3,103.83|LR:<br>2<br>χ(36)|=<br>3,329.76|
||Prob ><br>2<br>χ|=|0|Prob ><br>2<br>χ|=<br>0|

_*** p<0.01, ** p<0.05, * p<0.10._ The primary focus for the occupational choice model is the coefficient on the earnings differential variable ( _lnWnt - lnWt_ ), holding constant the other explanatory variables relating to various background and personal characteristics. A negative and significant coefficient on earnings differential variable would indicate that a collegeeducated worker is less likely to choose teaching as profession if other occupations pay better. Our estimate result shows the “right” sign and a high significance level. Several other clear effects are notable in predicting whether in teaching profession or not. Being a woman or being married is significantly correlated with being in teaching force. It also appears that the predominant job for a college-educated worker in rural area is teaching. The likelihood of being a teacher is 11 percentage points higher for a college graduate in a rural area than that in an urban area.

Based on the empirical results, double the salary of teachers with college education would result in an increase in the probability of college graduates choice of entering teaching force. The marginal effect would be: 24
the Uruguay Round when the three productivity linkages incorporated in the model are included. Inclusion of these possible dynamic effects further increases GDP growth in the developing ASEAN economies. The biggest relative gainers are Indonesia, Philippines, and Thailand, where GDP growth exceeds 3 percent, and exports expand by over $15 billion. Incremental Indonesian exports occur primarily in the apparel and light manufacturing sectors (which includes products such as footwear).

There are divergent perspectives within Asia on how the trading system should evolve following implementation of the Uruguay Round commitments. Some argue for an ASEAN focus, others expand their sights to include the broader APEC grouping, while another group stresses the need to follow up the successful conclusion of the Uruguay Round with similar initiatives to negotiate further multilateral liberalization in world trade. To illuminate the tradeoffs (if any) among these different perspectives, we have run a series of experiments with the APEC-CGE model that quantify the contribution of each to growth and structural change in the APEC economies.

**Table 8: GDP and Export Growth from Future Liberalization Alternatives**

||Increm|ental Effect of:||Total|
|---|---|---|---|---|
|URA-II|ASEAN FTA|APEC FTA|Free Trade|effect|
|_Change in GDP from Base (Percent)_|||||
|Indonesia<br>0.40|0.03|0.66|0.25|1.34|
|Thailand<br>0.59|0.18|0.82|0.37|1.96|
|Philippines<br>0.67|0.14|1.17|0.32|2.30|
|Singapore & Malaysia<br>0.23|0.04|0.29|0.09|0.65|
|China<br>0.12|-0.01|0.15|0.09|0.35|
|Korea & Taiwan<br>0.85|0.00|2.00|0.41|3.26|
|Japan<br>0.86|0.00|0.77|0.39|2.02|
|United States<br>0.05|0.00|0.01|0.02|0.08|
|European Union<br>0.13|0.00|-0.02|0.09|0.20|
|_Change in Exports from Base (Billion $)_|||||
|Indonesia<br>0.74|0.14|1.22|0.40|2.50|
|Thailand<br>1.51|0.59|2.20|0.99|5.29|
|Philippines<br>1.07|0.29|2.01|0.80|4.17|
|Singapore & Malaysia<br>0.34|0.06|1.16|0.05|1.61|
|China<br>2.11|-0.01|3.62|1.29|7.01|
|Korea & Taiwan<br>3.08|-0.01|6.49|1.52|11.08|
|Japan<br>30.60|-0.02|12.27|5.25|48.10|
|United States<br>9.83|0.00|4.25|1.32|15.40|
|European Union<br>13.39|0.02|0.01|12.50|25.92|
|Total<br>62.68|1.05|33.23|24.13|121.09|

Table 8 reports the impact on real GDP of various regional or multilateral trading initiatives. The numbers reflect the _incremental_ impact on GDP of each experiment: in other words, the effect of adding one new feature to those included in the previous experiment. The starting point is the implemented Uruguay Round in the model _without_ trade-productivity externalities: in other words, the results reported in columns 34 of Table 7. The first experiment considers the impact of another multilateral reformn along the lines of the Uruguay Round: tariffs and export subsidies are cut by 36 percent in all countries, non22
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members is by far the largest component of FAFH. The inclusion of all three components simultaneously gives us the actual official poverty status.<sup>18</sup> Once the poor population is identified under each scenario, we proceed to compute the corresponding poverty indices: poverty rate, poverty gap, and severity of poverty. We decompose the overall effect into expenditure and poverty line effects following the specification presented in section 2.2. Standard errors are estimated taking into account the sample design and population weights.

Table 1 presents the full set of results. The left panel presents extreme poverty estimates and the right panel moderate poverty estimates. For each case, the upper panel presents poverty rate estimates, the middle panel poverty gap estimates, and the lower panel severity of poverty estimates. Finally, column (1) of each panel corresponds to our baseline specification; in columns (2) to (4) a component of FAFH is included one at a time while leaving the other FAFH components out; and column (5) summarizes the overall effect of FAFH, and therefore corresponds to the official poverty statistics.

# **Expenditure effect**

The expenditure effect provides the estimated change in poverty statistics that result from taking into account the different components of FAFH in total expenditures, while leaving the poverty line unchanged. This effect coincides with the total effect in cases where different data sources are used to select the poverty line and compute total expenditures and only the data to compute the latter is improved to include FAFH. Alternatively, it applies to situations where an external poverty rate is used, such as the WB PPP poverty lines.

Focusing on the poverty rate, the results suggest that if information on FAFH was not included, extreme poverty in Peru in 2010 would have been 6.46 percent and moderate poverty 36.55 percent (table 1). Once all components of FAFH are included, the expenditure effect results in a reduction in extreme poverty by 3.3 percentage points and by 10.5 percentage points for moderate poverty. Both changes represent very sizable changes of an order of 30 to 50 percent relative to the baseline specification.

> 18 Note that Peru’s Statistics Institute already uses these FAFH components in its official poverty estimates since 2004.

18
# **Table 3: Share of 'Traditional' Products in Total Exports to EU, 1989-93 (ECU millfion and percentage)**

||**8**Digit Products|> =3 million|ECU in 1989|8 Digit Products|< 3 milHon E|CU in 1989|
|---|---|---|---|---|---|---|
||1989|1993|Gr. Rate|1989|1993|Gr. Rate|
|Algeria|4821<br>98.35%|4052<br>89.50%|4.25%|81<br>1.65%|476<br>10.50%|55.60%|
|Egypt|2259|1809|-5.41%|179|406|22.76%|
||92.67%|81.68%||7.33%|18.32%||
|Israel|2268|1804|-5.56%|767|1550|19.21%|
||74.72%|53.79%||25.28%|46.21%||
|Jordan|44|62|9.06%|46|206|45.69%|
||49.14%|23.28%||50.86%|76.72%||
|Kuwait|2661|1713|-10.42%|56|84|10.67%|
||97.94%|95.33%||2.06%|4.67%||
|Lebanon|37|12|-23.96%|63|54|-3.84%|
||36.76%|18.52%||63.24%|81.48%||
|Morocco|2202|2008|-2.28%|466|1187|26.32%|
||82.53%|62.85%||17.47%|37.15%||
|S. Arabia|6246|8204|7.05%|136|943|62.41%|
||97.88%|89.69%||2.12%|10.31%||
|**Syria**|735|1559|20.68%|38|93|24.91%|
||95.08%|94.39%||4.92%|5.61%||
|**Tunisia**|1617|1473|-2.30%|360|1013|29.56%|
||81.80%|59.24%||18.20%|40.76%||
|U A E.|1397|391|-27.25%|117|438|39.13%|
||92.27%|47.16%||7.73%|52.84%||
|MENA|25083|24795|-0.29%|1513|4742|33.06%|
||94.31%|83.95%||5.69%|16.05%||
|CEEC|9827|11706|4.47%|2136|8325|40.5%|
||82.14%|58.44%||17.86%|41.56%||

***** Percentages are the share of a country's total exports in a given year. Source: Calculated from the COMEXT database.

18
where Ys,ijt and Yc,ijt are vectors with elements {Ys,ijt , Yc,ijt}. The subscripts _s_ and _c_ refer respectively to sovereign and corporate, and “ _ijt_ ” refers to bond _i_ issued in country _j_ at time _t_ , where: Xjt denotes systematic (macroeconomic) factors Vt denotes global risk factors Zi denotes bond-specific features W denotes firm-specific characteristics Our formal econometric analysis of correlation risk between emerging market private corporate borrowers and their home-sovereigns is based on a large sample of 4,441 bond issuance denominated in U.S. dollars or euros offered by the government, public corporations, and private corporations from 59 emerging economies between 1995 and 2009 (see Annex I for the list of countries). Our sample represents a wide cross section of issues by country, industry, and bond attributes, the latter of which includes maturity, amount, coupon, rating, and applicable law and jurisdiction. The summary statistics for the universe of all bonds are reported in Table 1. Thus, total capital raised amounted to $1.4 trillion, with the bulk (80 percent) of new capital raised coming from dollar denominated bonds, and the rest from euro denominated bonds. We refer to sovereign bonds as bonds issued by governments, government agencies and public corporations whose payments are guaranteed explicitly by governments. Such bonds account for 60 percent of total issuance volume, but only 40 percent of deal numbers, reflecting their much larger deal size.

**Table 1. Summary statistics of emerging-market sovereign and corporate bond issuances: 1995–2009**

||**Number of**<br>**issuances**|**Total volume**<br>**raised**<br>($ millions)|_In USD_|_In EURO_|**Average**<br>**amount**<br>($ millions)|**Average**<br>**spread**<br>Basis points|**Average**<br>**maturity**<br>Year|**Average**<br>**rating**|
|---|---|---|---|---|---|---|---|---|
|**Sovereign issuers**|1,711|866.6|649.2|217.4|506.5|283.5|9.1|BBB-|
|**Government**|949|577.9|410.2|167.7|608.9|339.9|9.8|BB+|
|**Public corporate**|762|288.7|239|49.7|378.9|213.3|7.2|BBB+|
|**Private corporate**|2,730|596.7|537|59.6|218.6|310.7|6.5|BBB-|
|**Total **|**4,441**|**1,463.3**|**1,186.3**|**277**|**329.5**|**300.2 **|**7.3**|**BBB-**|

Several other distinct differences between emerging market sovereign and private corporate bonds, as highlighted in Table 1, deserve attention. Sovereign bonds tend to be larger in size, carry lower at –issue spreads, and issued in longer maturity than private corporate bonds. Such differences in bond attributes reflect several distinct characteristic differences between private corporate and sovereign borrowers in emerging market economies. First, corporate entities face higher information barriers and greater market constraints than sovereigns that derive advantages from membership in multilateral financial institutions and from the state-centric 15
Appendix 2: Test of balancing of covariates between non-farm wage employment participants and non-participants households, 20102013, rural Malawi

||Wage employment|Non participants|Wage employm|ent participants|t-test difference|
|---|---|---|---|---|---|
|Variables|Mean|SD|Mean|SD|pvalue|
|**Treatment variables**||||||
|Non-farm wage employment (0/1)|0.00|0.00|100.00|0.00||
|Non-farm self-employment (0/1)|21.53|41.11|21.89|41.37|0.668|
|**Outcome variables**||||||
|HHPCE|53,903.41|40,631.58|79,420.64|67,908.37|0.000|
|Log of HHPCE|10.70|0.62|11.04|0.67|0.000|
|Poverty incidence|40.03|49.00|21.29|40.96|0.000|
|Poverty gap||||||
|Poverty severity||||||
|Food insecurity (0/1)|34.01|47.38|24.52|43.05|0.001|
|Food consumption adequacy||||||
|1|43.51|49.58|29.90|45.81|0.000|
|2|50.45|50.00|60.29|48.96|0.002|
|3|6.05|23.84|9.81|29.76|0.003|
|Seeds purchase decision (0/1)|39.52|48.89|50.07|50.03|0.000|
|Fertilizer purchase decision (0/1)|36.71|48.21|51.79|50.00|0.000|
|Inputs purchase decision (0/1)|58.35|49.30|70.92|45.45|0.000|
|Seed purchase per acre (1000MKW)|0.46|1.56|0.91|2.26|0.000|
|Fertilizer purchase per acre (1000MKW)|3.10|8.51|6.57|15.06|0.001|
|Inputs purchase per acre (1000MKW)|3.57|8.92|7.31|14.60|0.000|
|Land cultivated (acres)|4.63|55.98|3.20|18.04|0.194|
|**Covariates**||||||
|Age of the household head|44.53|17.00|41.30|13.22|0.000|
|<br>Male headed-household|74.25|43.73|86.84|33.82|0.000|
|Highest level of formal education acquired by<br>household head||||||
|None|82.54|37.97|47.01|49.94|0.000|
|PSLC|9.71|29.61|11.84|32.33|0.012|

38