rafmacalaba/datause-encoder-data huggingface dataset:
plausible set of results has not been found in previous studies. However, with tariffs, the significance of human capital endowments disappears.

These results suggest that it might be fruitful to break down labor into three categories: non-educated, i.e. those who have never been to school or have not completed primary school (NO-ED); primary-educated or labor with a basic education (BS-ED); and those that have an education level beyond highschool (SK-ED). Such a breakdown is suggested by the discussion in section 2, and was carried out recently by Bensidoun et al. (2005) in a slightly different context.<sup>16</sup> The index of human capital endowment (average years of schooling) is now replaced by these three different categories of skill levels. We take the NO-ED variable from the Barro and Lee (2000) data set which is available on a five-year basis that corresponds to the 5-year averages used for all our variables.

As shown in Table 4, a convenient way to include these three levels of education is in ratio form: (SK-ED)/(BS-ED) and (SK+BS)/(NO-ED)<sup>17</sup> . We expect that during a trade liberalization, countries with a relatively (to the sample average) strong endowment in (SK-ED)/(BS-ED) to experience an increase in inequality, while, after having controlled for skill endowments, we would expect that countries relatively well-endowed in (SK+BS)/(NO-ED) would experience a decrease in inequality during a trade liberalization.

> 16 Bensidoun et al. argue that the Heckscher-Ohlin-Vanek (HOV) model is too restrictive, relying on factor-price-equalization (FPE) and hence identical production techniques in equilibrium. Using a more general approach that relaxes the FPE assumption (but still relies on other restrictive assumptions like homothetic preferences and unchanged production techniques following trade liberalization), they show that factor price changes are correlated with the factor content of trade, leading them to test their model using constructed estimates of the labor-capital content of net exports instead of factor endowments on a similar D-S inequality data set for 53 countries. However, their results are not strictly comparable with ours (different sample with no SSA countries, the use of PPP per capita GDP for NOED in most specifications, and a different definition of variables). 17 Thanks to Adrian Wood for this suggestion.

16
**Figure 10**

# **EM Assets' Betas during the 2000s**

This figure presents betas from country-by-country estimations of dlog(EM Asset) vs dlog(Global Asset) during 2000-2004 and 2005-2010. EM assets are MSCI country indexes, exchange rates and EM sovereign credit spreads. Global assets are the S&P, the Broad Dollar Index and the US HY corporate spread. Source: Bloomberg, MSCI, GEM World Bank, US Treasury.

<!-- Start of picture text --> Turkey<br><!-- End of picture text --> <!-- Start of picture text --> Philippines Thailand<br><!-- End of picture text -->
Bruvoll, Annegrete and Bodil Merethe Larsen, 2004. “Greenhouse Gas Emissions in Norway: Do Carbon Taxes Work?” _Energy Policy_ 32 No. 4 (March): 493-505.

- Bruvoll, Annegrete and Bodil Merethe Larsen, 2004. “Greenhouse Gas Emissions in Norway: Do Carbon Taxes Work?” _Energy Policy_ 32 No. 4 (March): 493-505.

- Dahlby, Bev, 2008. _The Marginal Cost of Public Funds – Theory and Applications._ Cambridge, MA: Massachusetts Institute of Technology.

- Daugbjerg, Carsten and Anders Branth Pedersen, 2004. “New Policy Ideas and Old Policy Networks: Implementing Green Taxation in Scancdinavia.” _Journal of Public Policy_ 24 No. 2 (August): 219-49.

- Dervis, Kemal, Jaime de Melo, and Sherman Robinson,1982. _General Equilibrium Models for Development Policy._ Cambridge: Cambridge University Press.

- Devarajan, Shantayanan and Delfin S. Go, 1998. “The Simplest Dynamic GeneralEquilibrium Model of an Open Economy.” _Journal of Policy Modeling_ 20(6):677714.

- Devarajan, Shantayanan, Karen Thierfelder, and Sethaput Suthiwart-Narueput, 2002. “The Marginal of Public Funds in Developing Countries.” In A. Fossati and W. Wiegard, eds. _Policy Evaluations with Computable General Equilibrium Models._ London: Routledge, pp. 39-55.

- Diamond, Peter A. and James A. Mirrlees, 1971. “Optimal Taxation and Public Production I & II.” _American Economic Review_ 61, 8-27 and 261-78.

- Eskeland, Gunnar S. and Shantayanan Devarajan, 1996. _Taxing Bads by Taxing Goods._ Directions in Development Series, The World Bank, Washington, D.C.

- Essama-Nssah, B. Delfin S. Go, Marna Kearney, Vijdan Korman, Sherman Robinson, and Karen Thierfelder, 2007. “Economy-wide and Distributional Impact of an Oil Price Shock on the South African Economy.” In Delfin S. Go and John Page (eds.) _Africa at a Turning Point? Growth, Aid, and External Shock._ TheWorld Bank, pp.485-530.

- Go, Delfin S., Marna Kearney, Sherman Robinson, and Karen Thierfelder, 2005.  “An Analysis of South Africa’s Value Added Tax.” Policy Research Working Paper 3671, Washington, DC.: The World Bank.

- Go, Delfin S., Marna Kearney, Vijdan Korman Sherman Robinson, and Karen Thierfelder, 2009.  “Wage Subsidy and Labor Market Flexibility in South Africa.” Economic Research Southern Africa Working Paper 114, University of Cape Town, South Africa.

29
**Table 4 Determinants of likelihood to receive remittances and their amount**

|VARIABLES|(1)<br>rflag|(2)<br>rflag|(3)<br>rflag|(4)<br>lremit|(5)<br>lremit|(6)<br>lremit|(7)<br>lremit|
|---|---|---|---|---|---|---|---|
|male_head|-0.787***|-0.764***|-0.811***|-2.451***|-2.389***|-2.532***|-2.068***|
|age_head|-0.0466**|-0.0459**|-0.0432**|-0.184***|-0.179***|-0.176***|-0.207***|
|age_head<sup>2</sup>|0.000646***|<br>0.000634***|<br>0.000629***|<br>0.00227***|<br>0.00221***|<br>0.00224***|<br>0.00229***|
|prim_head|0.169|0.192|0.176|0.442|0.518|0.455|0.363|
|seci_head|0.0555|0.0535|0.0744|0.239|0.227|0.297|0.0556|
|secc_head|-0.328*|-0.342*|-0.315|-0.455|-0.477|-0.432|-0.465|
|terc_head|-0.696**|-0.699**|-0.656**|-1.375*|-1.381*|-1.290*|-1.618**|
|married_head|-0.135|-0.150|-0.130|0.0462|0.0132|0.0612|-0.0338|
|hhsize|-0.0663***|-0.0688***|<br>-0.0678***|-0.227***|-0.229***|-0.230***|-0.236***|
|dep_child|0.305|0.365*|0.356|0.350|0.473|0.491|-0.171|
|ownhouse|-2.225***|-2.236***|-2.209***|-5.415***|-5.387***|-5.407***|-5.477***|
|ownfarm|0.524***|0.522***|0.539***|0.749**|0.742**|0.801**|0.439|
|selfemp|-0.0129|-0.0165|0.000282|-0.280|-0.280|-0.241|-0.421|
|urb|-0.301**|-0.293*|-0.321**|-0.522|-0.516|-0.578*|-0.272|
|lland|-0.00205|-0.00143|-0.00150|0.0110|0.0128|0.0124|0.00202|
|Western|0.736***|0.770***|0.787***|1.565***|1.659***|1.715***|0.933|
|Central|0.499*|0.472*|0.588**|1.039*|0.957|1.295**|0.0358|
|Volta|0.690***|0.682***|0.739***|1.673***|1.663***|1.815***|1.055|
|Eastern|0.113|0.143|0.176|0.450|0.554|0.612|-0.297|
|Ashanti|0.355*|0.307*|0.421**|0.802*|0.697|0.983**|0.0442|
|Brong_Ahafo|0.551**|0.577**|0.582**|1.144**|1.201**|1.237**|0.704|
|Northern|1.219***|1.262***|1.233***|2.723***|2.789***|2.775***|2.438***|
|Upper_East|0.547*|0.576**|0.511*|1.099*|1.111*|1.022|1.388*|
|Upper_West|1.543***|1.661***|1.555***|3.188***|3.453***|3.236***|2.338***|
|mstatus|0.356***|||0.983***|||5.035*|
|migrurb||0.769***|||1.883***|||
|migrrur|||0.289|||0.800||
|ρ|||||||-0.4428|
|Observations|3700|3700|3700|3700|3700|3700|3700|
|*** p<0.01, ** p<|0.05, * p<0.1.|||||||

_Note_ : Observations are weighted by sample weights and standard errors are clustered at the PSU level. Stratification at the region level is also taken into account in the estimates. Specifications (1)-(3) were estimated with a logit model. For specification (7), only the second stage estimates are shown; migration rate at the district level is the identifying variable in the first stage, and the table reports untransformed value of ρ although hypothesis testing was done on its inverse hyperbolic tangent (α tanh ρ).
||(0.032)|(0.010)|(0.032)|
|---|---|---|---|
|Aged 11-14|0.829***|0.856***|-0.028|
||(0.031)|(0.011)|(0.030)|
|Aged 15-17|0.569***|0.743***|-0.174***|
||(0.039)|(0.016)|(0.037)|

Note: Y1 is the observed schooling rate of children whose parents have disabilities. Y0 is the counterfactual schooling rate, that is their enrollment if their parent had not been disabled. ATT=Y1–Y0 is the effect of parental disability on children’s schooling rate.

* significant at 10%; ** significant at 5%; *** significant at 1%

Figures in parentheses are standard errors. Standard errors are corrected for sampling weights and estimated using bootstrap (non-parametric) with 500 replications.

Source: Estimation from the 2006 VHLSS.

Table A.5 in the Appendix presents the ordered probit regressions of academic records. We tried both ordered logit with and without instruments for parental disability. Estimates of the parental variables are very small and not statistically significant in all the regressions. This means that once attending school, children with disabled parents do not have worse educational performance than children with non-disabled parents given that the estimation procedure controls for other observed characteristics.

Finally, the regressions of school enrollment and academic records show some interesting findings on other factors associated with education. Girls have higher school enrollment as well as better educational performance than boys even after other observed variables are controlled. Gender equality in children‟s education is very important, since it can contribute to gender equality in wage and other social economic activities when the children become adults. Other control variables have expected signs. Older children tend to drop out of school. Urban children, Kinh children, and children whose parents have higher education and income are much more likely to attend school and have better educational records once attending school than rural and ethnic minority children, and children in lower education and income households.

15
<!-- Start of picture text --> . nExchange — 1996<br>— 2000<br>— 210<br>°<br>02 o4 06 o8 10<br><!-- End of picture text --> <!-- Start of picture text --> Fullness<br>— 1996<br>— 2000<br>20 — 2010<br>15<br>10<br>05<br>00<br>-02 00 02 04 os os 10 12<br><!-- End of picture text --> <!-- Start of picture text --> 5 Perimeter — 1996<br>— 2000<br>— 2010<br>3<br>1<br>°<br>00 02 04 06 08 10<br><!-- End of picture text --> <!-- Start of picture text --> Circuity_avg<br>* — 1996<br>— 2000<br>— 210<br>~»<br>18<br>10<br>5<br>o<br>10 14 12 13 14<br><!-- End of picture text -->
**Table 2:  Decomposition of Theil Inequality Index (Inter-Group Inequality), 1994-2001**

||**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|
|---|---|---|---|---|---|---|---|---|
|**Theil index**|**0.100**|**0.095**|**0.097**|**0.107**|**0.110**|**0.114**|**0.120**|**0.120**|
|**Decomposition **|||||||||
|Within groups|0.092|0.087|0.089|0.097|0.097|0.100|0.104|0.106|
|**Regional**<br>Between groups|0.008|0.008|0.008|0.010|0.013|0.014|0.016|0.014|
|Share of betweengroups|7.9%|8.3%|8.1%|9.4%|11.5%|12.5%|13.0%|11.6%|
|Within groups|0.087|0.083|0.084|0.092|0.093|0.095|0.097|0.097|
|**Education**<br>Between groups|0.013|0.012|0.130|0.015|0.017|0.020|0.023|0.023|
|Share of betweengroups|13.0%|12.9%|13.4%|13.8%|15.9%|16.5%|19.5%|19.1%|
|Within groups|0.098|0.093|0.095|0.105|0.107|0.111|0.118|0.118|
|**Age**<br>Between groups|0.002|0.002|0.002|0.002|0.003|0.003|0.002|0.002|
|Share of betweengroups|1.7%|2.2%|2.2%|2.0%|2.5%|2.4%|1.5%|1.7%|

_Source_ : Authors’ calculations based on HBS.

Generally speaking, when inequality increases (if other things are kept constant), poverty normally increases as well, thus dampening the beneficial effect of growth on poverty. Fortunately, changes in the shape of the distribution of consumption are much slower than the change in the mean. Therefore, the positive growth impact should outweigh the negative effect of a widening of the distribution itself.

# **Decomposing Trends in Poverty**

Having described the trends in growth and in inequality, it is important to analyze the effects of these trends on poverty.

**Table 3:  Decomposition of Changes in Poverty, 1995-2002 (percentage points)**

|**Poverty change **|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|
|Total|-0.4%|-1.5%|-0.3%|-1.6%|1.3%|0.5%|0.8%|1.0%|
|Due to growth|0.4%|-2.1%|-1.1%|-2.0%|0.5%|0.4%|0.6%|0.6%|
|Due to inequality|-0.9%|0.3%|0.8%|0.5%|0.8%|0.0%|0.2%|0.5%|

_Source:_ Staff calculations based on GUS’s data.

The results of a decomposition of changes in poverty for Poland into their growth and inequality components are presented in Table 3 and Figure 5<sup>7</sup> It is evident that economic growth was a main driving force in poverty reduction. Two periods are readily distinguishable. During Poland’s high growth years, 1994-98, the economic growth effect (5 percent) far outweighed the negative effect of the increase in inequality (-1 percent), resulting in a cumulative decline in poverty of about 4 percentage points. During the subsequent period, 1998-2002, and because of a decline in mean consumption, lack of growth and increased inequality jointly resulted in a cumulative increase in poverty of about 3 percentage points for that period.

> 7 For a description of the methodology used, see Bourguignon (2002).

7
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2455 The Effects on Growth of Commodity<br>Price Uncertainty and Shocks|Jan Dehn|September 2000|P. Varangis<br>33852|
|WPS2456 Geography and Development|J. Vernon Henderson<br>Zmarak Shalizi<br>Anthony J. Venables|September 2000|R. Yazigi<br>37176|
|WPS2457 Urban and Regional Dynamics in<br>Poland|Uwe Deichmann<br>Vernon Henderson|September 2000|R. Yazigi<br>37176|
|WPS2458 Choosing Rural Road Investments<br>to Help Reduce Poverty|Dominique van de Walle|October 2000|H. Sladovich<br>37698|
|WPS2459 Short-Lived Shocks with Long-Lived<br>Impacts? Household Income<br>Dynamics in a Transition Economy|Michael Lokshin<br>Martin Ravallion|COctober<br>2000|P. Sader<br>33902|
Table 7: Random Forest model Results in comparison with LASSO-selected Poisson Regression Results

||Ran|dom Fore|st|L|ASSO||
|---|---|---|---|---|---|---|
|Imagery Type|Out-of-<br>bag _R_<sup>2</sup>|RMSE|MAE|Out-of-<br>sample _R_<sup>2</sup>|RMSE|MAE|
|All sectors|||||||
|A. National sample|||||||
|1. Low resolution, public|0.767|1282|477|0.702|1463|563|
|2. All Public|0.783|1237|390|0.750|1323|459|
|B. Subsample of 55 sub-districts|||||||
|1. Low resolution, public|0.677|2088|948|0.677|1948|928|
|2. All Public|0.707|1984|784|0.710|1927|818|
|3. All Public + Propriety texture|0.719|1942|779|0.755|1845|808|
|4. All|0.792|1673|684|0.830|1506|698|
|Rural and Estate|||||||
|A. National sample|||||||
|1. Low resolution, public|0.776|696|329|0.726|788|375|
|2. All Public|0.832|601|246|0.808|653|289|
|B. Subsample of 55 sub-districts|||||||
|1. Low resolution, public|0.807|968|510|0.754|990|550|
|2. All Public|0.847|855|383|0.838|848|424|
|3. All Public + Propriety texture|0.849|850|380|0.882|788|415|
|4. All|0.872|781|352|0.869|747|355|
|Urban|||||||
|A. National sample|||||||
|1. Low resolution, public|0.598|3966|2285|0.540|4105|2344|
|2. All Public|0.612|3890|2079|0.569|3999|2343|
|B. Subsample of 55 sub-districts|||||||
|1. Low resolution, public|0.424|3680|2084|0.511|3277|1917|
|2. All Public|0.461|3559|1888|0.525|3357|1909|
|3. All Public + Propriety texture|0.491|3457|1850|0.668|2897|1676|
|4. All|0.626|2984|1620|0.804|2226|1446|

Note: Random Forest models are implemented using the randomForest() package in R with 500 trees, and using the census village population density as the dependent variable. The following independent variables were used in all models: Different types of satellite imagery sources as described in panel B in Table 1, district fixed effects, a binary indicator for urban villages, and log village-area. “Low-resolution public” indicators refer to the low-resolution and public indicators as defined in Table 1. “All public” indicators refers to all the public imagery based indicators, including the high-resolution imagery based indicators described in Table 1. “Proprietary texture” indicators include very high-resolution texture features based on Digital Globe from Engstrom et al. (2017) as defined in Table 1. “All” imagery refers to all public and proprietary texture indicators, and object identifiers, such as cars, roofs etc., as defined as in Table 1. The out-of-sample _R_<sup>2</sup> , Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for the LASSO based Poisson regression are from tables 8-10.

25
in changing management practices. Table 2 Column 2 reflects this and shows a statistically significant 25 percentage point treatment effect on management practices in 2011. We note that the change for the control firms is still an increase relative to the rest of the industry around Mumbai (more than 100 non-project plants), which did not change their management practices on average between 2008 and 2011.

Finally, since these are multi-plant firms and the consulting firm worked at the plant level, the treatment and control firms also had plants that were not part of the intervention, which we label “non-experimental plants.” For example, if a treatment Firm has three plants A, B and C and the diagnostic and implementation intervention was performed on plant A this would be a “Treatment Experimental plant” while plants B and C would be “Treatment Non-Experimental plants”. Likewise if a control firm had plants D, E and F and the diagnostic intervention was only performed on plant D, then D would be an “Control Experimental plant” while E and F would be “Control Non-Experimental plants”. Appendix Table A2 reports the breakdown of the plant count into these four groups.

Although the consulting firm did not provide consulting services to the non-experimental plants, it was still able to collect bi-monthly management data and some basic plant data for these other plants. The non-experimental plants in the treatment firms saw a substantial increase in the adoption of management practices. In these 5 plants the adoption rates increased by 17.5 percentage points by August 2010, without any drop back in the second year. This increase occurred because the executives of the treatment firms copied the new practices from their experimental plants over to their other (non-experimental) plants. Interestingly, this increase in adoption rates is similar to the control firms’ 12 percentage point increase, suggesting that the copying of best practices across plants within firms can be as least as effective at improving management practices as short (1-month) bursts of external consulting.

# **_II.C. The Initial Experimental Results – Firm Performance_**

Treatment firms experienced a significant increase in output of 9.4% relative to the control firms, which came about both by decreasing quality defects (so that less output was scrapped); and by undertaking routine maintenance of the looms, collecting and monitoring breakdown data, and keeping the factory clean, which reduced machine downtime. Total factor productivity (TFP) increased by 16.6% due to both the increase in output and a reduction in 7
38 of electricity and loses the shaded triangle in figure 3. In this case the change in consumer surplus will be:

# ACS = (QO-QB)(P -PB)/2)

# iACS/E = (QO-QB)(P**PB)/2E

Where p** is the price the consumer would be willing to pay to consume the lifeline block of energy.<sup>2 5</sup> Clearly, if he consumes below the cutoff QB both before and after lifeline pricing is implemented, his loss will be zero.

To estimate the impact of lifeline pricing on different income groups we examined the case of a single block of electricity and used demand information from the household budget survey with a range of reasonable price elasticities. The June, 1993 price of electricity was ZI 864 kwh. We assumed that the lifeline rate for the first block remains unchanged at ZL 864 kwh, and an 80 percent price increase is implemented for any additional consumption (table 11). We estimated the loss in consumer surplus for a bock of 100 kwh per household and 50 kilowatt hours per household, per month.

_Empirical results: lifeline pricing_ Although this is only illustrative exercise, it shows that some targeting can be achieved by offering a small block of cheaper electricity to consumers. There still are several problems however. For one thing, lifeline blocks can not be offered on a per-capita basis, which weakens their targeting power. Poor families tend to be larger than the non poor--the average household size of households with income below the poverty line is 4.27 persons, while those above the poverty line have an average size of 3 people. And, lifeline pricing is unlikely to be the most cost effective way of helping the poor consume energy: there is still leakage to the non-poor.

If raising prices to efficient levels for all consumers is not politically feasible at present, the evidence suggests that it may be socially _better to use lifeline pricing and a large price increase than_

> 25/ Note that, p** - **PB** = **PB(QB** QO)/QOc-
As explained in the previous section, the SDCyC considered that the duration of contracts between oil companies and gasoline stations in Argentina could be a barrier to entry to the downstream stage of the gasoline market. This section describes the evidence that the SDCyC used to prove this hypothesis.

The SDCyC also identified other factors that do not support a competitive gasoline market, being the lack of a well developed wholesale gasoline market the most important. However, the SDCyC did not make any explicit policy recommendation to help the development of a wholesale gasoline market. The reason for the lack of policy recommendations for this stage of the market is that the SDCyC considered that the best policy -force RepsolYPF to sell one of its biggest refineries- was impossible to implement because it would constitute a violation of property rights that would require the Argentine government to provide RepsolYPF a significant amount of compensation funds' **19** How has contract duration evolved since the deregulation of the Argentine gasoline market? To answer this question, the SDCyC analyzed a survey conducted by the Secretariat of Energy<sup>20</sup> during the first months of 2000. This survey covered more than 25% of the gasoline stations in the country and provided information about many characteristics of the contracts signed between oil companies and gasoline stations' owners.

Figure 6 shows the average duration of contracts that oil companies signed with gasoline stations after the gasoline market was deregulated. The contracts surveyed correspond to the DODO arrangement, that accounts for approximately 90% of branded gasoline stations. The largest firms, YPF, Esso and Shell have contracts with very similar average duration. Instead, EG3's portfolio of contracts has a shorter duration.

> **19** As explained in the section "Privatization model and its impact on competition", the Argentine government had the opportunity to sell one of YPF's biggest refineries during its privatization, but chose not to do it and pursue other objectives (for instance, maximize fiscal resources) **20** Resolution 25/2000.

20
<!-- Start of picture text --> o)<br>2000<br>a ar1s88<br>Cc<br>©o16—<br>= E2008<br>=me1S83 a“.tes = = deme<br>=<br>ce aa - iss =O M2<br>_— T1382 583<br>' 1988 wriss4<br>ise<br>1980 1985 1990 1995 2000 2005 2010 2015<br>7 ear<br><!-- End of picture text -->