rafmacalaba/datause-encoder-data huggingface dataset:
|VARIABLES|(1)|(2)|(3)<br>Heig|(4)<br>ht-for-age-z-s|(5)<br>cores|(6)|(7)|
|---|---|---|---|---|---|---|---|
|completed [5-7] years of sch.|0.147*|0.147*|0.143*|0.141|0.135|0.139|0.132|
||(0.087)|(0.087)|(0.087)|(0.086)|(0.087)|(0.086)|(0.087)|
|completed [8-10] years of sch.|0.054|0.059|0.054|0.044|0.047|0.042|0.045|
||(0.104)|(0.105)|(0.104)|(0.105)|(0.104)|(0.105)|(0.105)|
|completed 11 + years of sch.|0.148|0.148|0.146|0.144|0.156|0.139|0.139|
||(0.100)|(0.100)|(0.100)|(0.100)|(0.100)|(0.099)|(0.100)|
|Self-employed in agriculture|0.103*|0.116*|0.118*|0.121**|0.125**|0.120**|0.121**|
||(0.061)|(0.061)|(0.061)|(0.061)|(0.061)|(0.061)|(0.061)|
|Ethnicity||||||||
|Brahman/Chhetri|-0.187|-0.173|-0.172|-0.172|-0.168|-0.169|-0.171|
||(0.141)|(0.141)|(0.141)|(0.142)|(0.141)|(0.141)|(0.140)|
|Middle class|-0.493***|-0.455***|-0.448***|-0.455***|-0.452***|-0.445***|-0.462***|
||(0.165)|(0.166)|(0.166)|(0.166)|(0.166)|(0.166)|(0.165)|
|Janajati|-0.201|-0.188|-0.190|-0.155|-0.156|-0.170|-0.171|
||(0.145)|(0.145)|(0.145)|(0.147)|(0.146)|(0.145)|(0.145)|
|Dalit|-0.374**|-0.368**|-0.365**|-0.337**|-0.343**|-0.357**|-0.359**|
||(0.155)|(0.155)|(0.155)|(0.157)|(0.156)|(0.155)|(0.155)|
|Others|-0.297|-0.256|-0.257|-0.260|-0.271|-0.239|-0.241|
||(0.189)|(0.189)|(0.189)|(0.190)|(0.189)|(0.189)|(0.189)|
|Female-headed HH|0.093|0.085|0.087|0.102|0.099|0.091|0.081|
||(0.070)|(0.071)|(0.070)|(0.070)|(0.070)|(0.070)|(0.071)|
|_Mother's Education_||||||||
|Literate, or < 5 years of sch.|0.103|0.106|0.097|0.093|0.091|0.105|0.106|
||(0.083)|(0.083)|(0.083)|(0.082)|(0.083)|(0.082)|(0.082)|
|completed [5-7] years of sch.|0.163*|0.164*|0.154*|0.145*|0.143*|0.158*|0.155*|
||(0.086)|(0.086)|(0.087)|(0.086)|(0.086)|(0.086)|(0.086)|
|completed [8-10] years of sch.|0.162|0.167|0.158|0.145|0.150|0.168|0.160|
||(0.106)|(0.107)|(0.107)|(0.106)|(0.107)|(0.106)|(0.106)|
|completed 11 + years of sch.|0.364***|0.372***|0.364***|0.346***|0.356***|0.344***|0.352***|
||(0.113)|(0.114)|(0.114)|(0.113)|(0.114)|(0.114)|(0.114)|

62
Gidey K, Gidey MT, Hailu BY, Gebreamlak ZB, Niriayo YL (2023). Clinical and economic burden of healthcare-associated infections: A prospective cohort study. PLoS One. 18(2): e0282141.

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24
- 28 -

# **Table 4.** **<u>Nested logit model</u>**

||Model (1)||Model(2)|||
|---|---|---|---|---|---|
||||Export cro|ps|Domestic crops|
|Choice-specific variables:||||||
|Transport cost|-0.010|***|-0.026|***|-0.040<br>**|
||(0.002)||(0.004)||(0.020)|
|Crop suitability|-0.099|***|0.172||-0.011|
||(0.021)||(0.284)||(0.007)|
|Crop price|-0.245|***|0.048||-0.079|
||(0.095)||(0.088)||(0.052)|
|Access to fertilizer|0.441|**|0.465|*|0.042|
||(0.206)||(0.273)||(0.034)|
|Access to irrigation|0.178|***|||0.022|
||(0.032)||||(0.014)|
|Access to improved seed|1.705|***|3.776|***|0.145|
||(0.325)||(0.453)||(0.096)|
|Type-specific variables:<sup>1</sup>||||||
|Area of plot|0.070|**|0.068|**||
||(0.035)||(0.028)|||
|Steep slope|0.020||0.037|||
||(0.189)||(0.203)|||
|Land title|-0.029||0.036|||
||(0.308)||(0.327)|||
|Electricity access|-0.804||-0.713|||
||(0.624)||(0.658)|||
|Mobile access|0.066||0.053|||
||(0.180)||(0.189)|||
|Distance from road|0.000||0.024|||
||(0.028)||(0.030)|||
|Male household head|-0.014||-0.029|||
||(0.226)||(0.232)|||
|HH head age|-0.013|**|-0.020|***||
||(0.006)||(0.007)|||
|HH head's year of schooling|-0.067|**|-0.103|***||
||(0.034)||(0.038)|||
|Household size|0.043|*|0.018|||
||(0.024)||(0.026)|||
|HH wage income|-0.056||-0.124|||
||(0.042)||(0.086)|||
|Obs.|17325||17325|||
|No. of cases|2475||2475|||
|Wald chi2|898.19||385.81|||
|Dissimilarity parameters:||||||
|Export crops|0.625||1.120|||
||(0.138)||(0.242)|||
|Domestic crops|0.851||0.077|||
||(0.162)||(0.051)|||

1/ The export crop specific coefficients are shown, while domestic crops are used as a baseline.
# DBVELOPING COUNTRY **EXPERIENCE** IN TRADE RE0ORM

# Introduction

After a period of brisk growth during 1965-81, gross domestic product (GDP) and export growth rates in developing countries decelerated significantly, while current account deficits and debt indicators worsened sharply in the lat. 19709 and 19809. A major contributing factor was external shocks -- the oil price hikes, interest rate increases and their effects on the debt problem, and terms of trade shocks that continued through most of the decade (for more details, see World Bank 1988). Domestic policy weaknesses prevented the majority of countries from adjusting quickly to these external shocks. To address increasing debt burdens, especially in the face of slaggish world growth, many developing countries focused their attentior. on export expansion. In the majority of these countries, disincentives to the production of tradables relative to nontradables needed to be reduced. In particular, the antiexport bias needed to be lessened by exchange rate depreciation, lowering export disincentives, reducing import protection, or a combination of these. In addition, reform has also included actions in such related areas as infrastructure, marketing and technology that are undertaken to promote a more internationally competitive tr-ade sector.

During the 1980s, many developing countries have received financial and policy support from the World Bank and the International Monetary Fund for trade policy reform. This paper evaluates these policy reforms using cross-country data and country studies. The analysis considers reform proposals in the forty countries that received trade adjustment loans and the extent of implementation in twenty-four countries
Table A.12: Parameter Estimates: Pulp, Paper, Paper Products, Printing and Publishing

||VC|M|P|-R|
|---|---|---|---|---|
||coef.|s.e.|coef.|s.e.|
|Labor Share Equation: Wage|0.06<sup>_∗∗_</sup>|(0.02)|0.05<sup>_∗∗∗_</sup>|(0.02)|
|Labor Share Equation: Capital|0.02|(0.02)|0.01|(0.02)|
|Labor Share Equation: Energy Price|0.02|(0.02)|0.02|(0.01)|
|Labor Share Equation: Materials Price|-0.03|(0.02)|-0.03|(0.02)|
|Labor Share Equation: Output|-0.03<sup>_∗_</sup>|(0.02)|-0.03<sup>_∗_</sup>|(0.01)|
|Labor Share Equation: Time Trend|-0.0001|(0.0007)|-0.001|(0.0008)|
|Labor Share Equation: Constant|0.5<sup>_∗∗∗_</sup>|(0.01)|0.5<sup>_∗∗∗_</sup>|(0.008)|
|Energy Share Equation: Wage|0.02<sup>_∗_</sup>|(0.008)|0.007|(0.006)|
|Energy Share Equation: Capital|0.02<sup>_∗∗∗_</sup>|(0.007)|0.01<sup>_∗∗_</sup>|(0.007)|
|Energy Share Equation: Energy Price|0.01<sup>_∗∗_</sup>|(0.006)|0.01<sup>_∗∗∗_</sup>|(0.005)|
|Energy Share Equation: Materials Price|-0.01|(0.008)|-0.005|(0.006)|
|Energy Share Equation: Output|-0.02<sup>_∗∗∗_</sup>|(0.005)|-0.01<sup>_∗∗∗_</sup>|(0.005)|
|Energy Share Equation: Time Trend|-0.00003|(0.0003)|-0.0003|(0.0003)|
|Energy Share Equation: Constant|0.03<sup>_∗∗∗_</sup>|(0.004)|0.03<sup>_∗∗∗_</sup>|(0.003)|
|Euler Equation for Capital: Adjustment Term|1.5<sup>_∗∗∗_</sup>|(0.1)|1.6<sup>_∗∗∗_</sup>|(0.1)|
|Euler Equation for Capital: Wage|0.9<sup>_∗∗∗_</sup>|(0.3)|0.6<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Capital|0.4<sup>_∗_</sup>|(0.2)|0.3|(0.2)|
|Euler Equation for Capital: Energy Price|0.2|(0.2)|0.3<sup>_∗_</sup>|(0.2)|
|Euler Equation for Capital: Materials Price|-0.02|(0.3)|-0.05|(0.2)|
|Euler Equation for Capital: Output|-1.2<sup>_∗∗∗_</sup>|(0.2)|-1.0<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Time Trend|-0.02<sup>_∗_</sup>|(0.009)|-0.04<sup>_∗∗∗_</sup>|(0.01)|
|Euler Equation for Capital: Constant|-1.3<sup>_∗∗∗_</sup>|(0.1)|-1.2<sup>_∗∗∗_</sup>|(0.1)|
|Number of observations|26|3|2|63|
|Labor Share Equation: _R_<sup>2</sup>|0.|88|0.|88|
|Energy Share Equation: _R_<sup>2</sup>|0.|85|0.|84|
|Euler Equation for Capital: _R_<sup>2</sup>|0.|87|0.|87|
|LR Test: _φl_ =_φe_ =_φm_ =_ζ_ = 0, _χ_2 (pval)||16.58|(0.00)||

Notes. VCM: Vintage Capital Model. P-R: Pindyck and Rotemberg (1983) Model. coef.: estimated coefficient. s.e.: standard error.<sup>_∗∗∗_</sup> p _<_ 0.01,<sup>_∗∗_</sup> p _<_ 0.05,<sup>_∗_</sup> p _<_ 0.1 47
regions in these countries likely also increased their refugee populations at the same time, but by less than the 50,000 cutoff; we are exploring this and other hypotheses. We use the absolute increase of 10,000 refugees as the main event definition to balance the sample size, in terms of number of region-events and countries, and the potential magnitude of the effects.

Table 3: Varying the Definition of an Event

|VARIABLES|(1)<br>Attitudes<br>Event Regions|(2)<br>Attitudes<br>All Regions|(3)<br>Attitudes<br>Event Regions|(4)<br>Attitudes<br>All Regions|(5)<br>Attitudes<br>Event Regions|(6)<br>Attitudes<br>All Regions|
|---|---|---|---|---|---|---|
|Post-event: _≥_5,000 increase|0.015|-0.033*|||||
||(0.022)|(0.018)|||||
|Post-event: _≥_50,000 increase|||0.080**|0.029|||
||||(0.039)|(0.044)|||
|Post-event: _≥_100% increase|||||0.051*|-0.017|
||||||(0.030)|(0.025)|
|IHS refugee population|-0.002|0.002|-0.002|0.006|-0.011**|-0.002|
||(0.004)|(0.004)|(0.008)|(0.008)|(0.005)|(0.005)|
|IHS region population|0.090|0.063|-0.830**|-0.630**|-0.028|0.096|
||(0.175)|(0.117)|(0.375)|(0.246)|(0.203)|(0.139)|
|Observations|90,496|268,390|28,540|87,366|60,654|195,809|
|R-squared|0.102|0.133|0.099|0.163|0.102|0.147|
|Event FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|Yes|No|Yes|Yes|
|Country*Year FE|No|Yes|No|Yes|No|No|
|Never treated Regions|No|Yes|No|Yes|No|No|
|Dep Var Mean|0.611|0.611|0.597|0.596|0.612|0.618|
|Events|150|150|37|37|108|108|
|Years|12|12|12|12|12|12|
|Regions|132|697|34|232|104|529|
|Countries|41|41|18|18|35|35|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the attitudes towards immigrants question, equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals. Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1, 3, and 5) and countryyears (columns 2 and 6) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 5,000 refugees (row 1), 10,000 refugees (row 2) or 50,000 refugees (row 3) in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

24
Table 3: Vulnerable Population Estimates (million)

|Inundation<br>Depth|(a)<br>At Present|(b)<br>2050 Without<br>Climate Change|Percent<br>Change<br>between (a)<br>and (b)|(c)<br>2050 with<br>Climate<br>Change|Percent<br>Change<br>between (b)<br>and (c)|
|---|---|---|---|---|---|
|More than 1 m|16.83|28.27|+ 68%|35.33|+25%|
|More than 3 m|8.06|13.54|+68%|22.64|+67%|

Table 4: Parameters used for computation of Agricultural Loss

||Area exposed to storm<br>surge inundation in 2050<br>without Climate Change|Area exposed to storm surge<br>inundation in 2050 with<br>climate change|Yield (ton/<br>hectare) in<br>2005|
|---|---|---|---|
||(hectares)|(hectares)||
|_Aman_|1,092,645|1,305,028|1.99|
|_Aus_|526,040|618,897|1.69|
|_Boro_|272,768|388,828|3.44|
smaller banks and that this will affect economic conditions, they will also reduce lending, exacerbating the situation further (Acharya et al, 2012). The financial crisis has highlighted the fact that smaller banks can increase risk premia in financial markets and have real effects on the economy.

**Figure 13.a: Total assets of domestic banking groups and foreign‐controlled subsidiaries and branches as a share of GDP (%)**

<!-- Start of picture text --> 1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>AT BE CY DE EE ES FI FR GR IE IT LT LU LV MT NL PT SI SK EA<br>domestic banking groups foreign subsidiaries and branches<br>2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014 2008 2013 2014<br><!-- End of picture text --> Note: Figures for Luxembourg are not fully shown in the chart. End of year values for 2008 and 2013, and June value for 2014. Source: European Central Bank and Eurostat.

**Figure 13.b: Gross NPLs, 2008‐2015** (% of total loans)

**Table 8: Gross Nonperforming Exposure (NPE)** (asset‐weighted average; % of total assets, 2014)

|50|||Total|Corporat|e Retail|<br>%<br>of GDP|
|---|---|---|---|---|---|---|
|40||Ireland|32.2|50.2|21.7|40.9|
|30|2015|Greece|25.3|23.2|26.9|25.4|
|20|Peak|Spain|12.2|18.8|6.8|9.1|
|10|2008|Portugal|7.9|11.1|5.7|7.3|
|0<br>Cyprus<br>Greece<br>Ireland<br>Italy<br>Slovenia<br>Portugal<br>Malta<br>Spain<br>Lithuania<br>vak Rep.<br>Latvia<br>France<br>Belgium<br>Austria<br>herlands<br>Germany<br>Estonia<br>Finland<br>embourg||Belgium<br>France|3.4<br>3.2|5.1<br>2.9|2.4<br>3.4|2.3<br>2.7|
|Slo<br>Net<br><br>Lux||Finland|1.7|1.8|1.6|0.9|

2015 values are used for all countries, except for France (2013) and Finland (2012), Italy (2014), Germany (2014) and Luxembourg (2014). Source: Aiyar et al (2015).

Source: IMF, Global Financial Stability Report and Financial Soundness Indicators.

_<u>Credit access, firm size and real effects.</u>_ The decline of the banking sector and associated credit crunch tends to have a disproportionate effect on smaller and younger firms. SMEs, employ around 2/3 of the euro area workforce and generate around 60% of value added on average (ECB, July 2014).<sup>68</sup> In Germany and Ireland, they account for half of value‐added and in Italy, Spain and Portugal, more than 65%. The ECB has found that short term lending rates (spreads) for loans to SMEs<sup>69</sup> increased relative to other loans after the onset of crisis.<sup>70</sup> Younger and smaller firms in Portugal, for example, which also tend to be the more entrepreneurial ones were unable to substitute credit and faced a credit constraint, unlike

> 68 SME contribution varies from sector to sector, ranging from 24% in energy to more than 80% in construction and real estate.

> 69 They use the category of small loans (up to I million euros to approximate for loans to SMEs).

> 70 They also find that as firms become larger, banks become less important.

30
The first policy scenario presented in Table 6 models a recent 28 cent per liter reduction in the gasoline tax (assuming it occurred in the 2010/11 marketing year); ethanol prices decline by 1 cent as a result. The comparative statics results presented in Appendix 1 show that a decrease in the gasoline tax has identical effects – in both sign and magnitude –as a reduction in the gasoline price. This makes our exposition easier because the gasoline price in Brazil is believed to be below its world market counterpart by approximately the same amount as the recent reduction in the gasoline tax. Thus, the results for the first scenario are not only informative of the magnitudes of the market effects of a gasoline tax shock, but also of the effects of exogenously pegging the gasoline price in Brazil below the world prices.

The instantaneous effect of a lower gasoline tax is a reduction in the price of fuel from _PF0_ to _PF1_ in Figure 9. This results in an increase in the consumption of fuel by the distance _a_ , 25 percent of which is anhydrous ethanol when the mandate is 25 percent (i.e., with E25). With this decline in the fuel price, the parity gap between fuel and E100 prices declines to _PF1_ – _PE100_ and so some E100 consumers switch to fuel consumption. As a result, the hydrous demand curve shifts in by the same amount (distance _e_ ) as the fuel demand curve shifts out (distance _b_ ); the distances are measured in gasoline energy-equivalent liters.

The reduced demand for hydrous ethanol results in a new demand curve _D'H_ , with a decline in the price of hydrous ethanol to _P'E100_ , thus partially offsetting the decline in hydrous ethanol consumption by distance _d_ to yield a net reduction in hydrous ethanol consumption of distance _e_ – _d_ . If the fuel price stayed at _PF1_ , resulting fuel consumption would correspond to _C'F_ . But because the hydrous and anhydrous ethanol prices are linked on the supply side, the anhydrous ethanol falls, resulting in a further reduction in the fuel price, denoted by _PF2_ . Of the additional fuel consumption associated with this price decrease, 25 percent is anhydrous ethanol. In total, a reduction in the gasoline tax brought about an increase in fuel consumption of _a_ + _b_ + _c_ , and hence a higher need for anhydrous ethanol of 0.25 x ( _a_ + _b_ + _c_ ). On the other hand, the net decrease in the use of hydrous ethanol is _e_ – _d_ . Therefore, if 0.25 x ( _a_ + _b_ + _c_ ) < _e_ – _d_ , then the total use of ethanol declines, resulting in a decrease in both hydrous and anhydrous ethanol prices.

The effect of the government arbitrarily reducing the gasoline price below world prices follows the same set of arguments as the reduction in the gasoline tax while an increase in the tax 29
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31
_Table 1: The aggregate impact of the oil shock_

||Percent change<br>with respect to<br>no shock|
|---|---|
|Consumption per capita|‐6.9|
|GDP per capita|‐1.1|
|_Labor demand_||
|Unskilled (Agriculture)|6.9|
|Unskilled (No‐Agriculture)|‐0.6|
|_Wages_||
|Unskilled (Agriculture)|‐3.9|
|Unskilled (No‐ Agriculture)|‐10.6|
|Skilled|‐11.5|
|Skill Premium|‐1.5|
|Urban Premium (for unskilled)|‐7.0|
|_Consumer Price Index_||
|Food|‐7.8|
|Othergoods|‐12.7|

Source: GTAP and ROSSTAT data, and CGE simulation results Following the reduction of demand, prices of non‐tradables decrease making tradables relatively more expensive. This real exchange rate depreciation induces shifts in economic activity and employment that are quite different across sectors. Table _2_ shows that, in terms of output contraction, besides the oil sector, the hardest hit sectors are non‐tradables, namely construction, transport and communication, other business services and public services. While import competing industries, and export oriented sectors, benefitting from the real depreciation, experience output expansions. The important caveat of the model operating as if the economy adjusts to its new long term equilibrium without frictions needs to be reiterated here. The gains of the expanding sectors are genuine opportunities, but they may not necessarily be realized quickly in the real economy. The analytical framework used here to model the oil shock is signaling what could happen if the markets were fully flexible and resources were allowed to respond to the new price signals; whereas, in reality, it may not be possible to reallocate resources as markets are just imperfectly flexible. For example, closure of firms in urban shrinking sectors such as in construction may be costly, or banks and other financial intermediaries may not easily start lending to new firms in expanding sectors. Nevertheless, if the relative prices change triggered by the oil shock is, as it is believed, not a temporary fluctuation but a long‐term shift, not adjusting to these sectorial reallocations – by, for example, imposing price controls or other restrictions – will be futile and counterproductive.

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