rafmacalaba/datause-encoder-data huggingface dataset:
due to variations in electricity generation mix over time. Emission coefficients (i.e., carbon contents) for other fuels are assumed to be constant over time. The second and third terms represent the fuel mix (FM) or fuel switching and the modal mix (MM) or modal shift effects, respectively. The fourth term represents the transportation energy intensity (EI) effect. And finally, the fifth and sixth terms represent the economic activity or per capita GDP (PC) effect and population (POP) effect, respectively.

We have carried out the decomposition analysis on an annual basis over the twenty five year period between 1980 and 2005.

# **3.2 Data**

The study required a large set of data on energy consumption and emissions by fuel and by mode for the study period of 25 years. We explored every potential source of data, such as national energy statistics, UN energy statistics, and US Department of Energy databases. While national statistical agencies collect data at the level of detail needed in a few countries (e.g., Korea), they do not provide such data in most of the countries. Moreover, mixing data from different sources with different conventions and assumptions used for collection and aggregation would cause an artificial change in the trends. Therefore, we uses transport sector energy consumption data by fuel type and mode from the International Energy Agency (IEA, 2007b, 2007c). No source other than the IEA provides data at the required details and time series needed for the study.

Fuels included are biofuel (i.e., ethanol), natural gas, liquefied petroleum gases (LPG), motor gasoline, aviation fuels (i.e., aviation gasoline, kerosene and jet fuel), 27
transport costs, we obtain three main results: first, the marginal effect of transport costs on trade flows differs across the sub-samples; in particular, in the full sample the coefficient is lower compared to the one using actual MTCs but the value of trade is much more sensitive to transport costs in the positive trade flow sample. Second, the impact of conventional gravity variables such as geography and trade policy is substantially altered once transport costs are properly controlled for. Third, the two-stage approach allows us to quantify correctly the overall impact on trade flows of key determinants such as distance and investment restrictions. We discuss each result in turn.

**Table 4: Estimation results -- The gravity equation**

||<br>Act|<br> <br>ual Transport Co|<br>st|<br> <br>Estim|<br>ated Transport|<br>Cost|
|---|---|---|---|---|---|---|
||(1)<br>Full|(2)<br> <br>Full<br>|(3)<br>Positive|(4)<br> <br>Full<br>|(5)<br>Full|(6)<br>Positive|
|Per-unit MTC (actual)|-0.8495***|-0.8473***|-0.8444***||||
|Per-unit MTC (predicted)||||-0.5613**|-0.5118*|-1.2721***|
|Distance|-2.8519***|-2.2853***|-2.2134***|-1.4673**|-1.0001|0.8561|
|Contiguity|-5.9695***|-4.9197***|-4.8052***|-3.3675***|-2.6467**|-0.4290|
|PTA|0.3631***|1.0717***|1.0779***|0.2702**<br>|0.9138***|0.6125**|
|Avg applied tariff|-0.0041|-0.0042<br>|-0.0041|-0.0027<br>|-0.0027|-0.0014|
|STRI 2nd quartile||-0.9942*<br>|-0.9203**||-0.5536|-0.5086**|
|STRI 3rd quartile||-1.0699<br>|-0.8105*||-0.5202|-2.0817***|
|STRI 4th quartile||-0.6411<br>|-0.3203||-0.0473|-1.8456***|
|Cargo Reservations||-0.0917<br>|-0.1238||-0.1681|-0.3256|
|Liner Conference||0.5265***|0.5490***||0.5266***|0.5261|
|Observations|9966|9966<br>|4786|8774<br>|8774|4280|
|Log L|-2.850e+11|-2.846e+11|-2.844e+11|-2.856e+11|-2.853e+11|-2.773e+11|
|R2|0.6016|0.6003<br>|0.5991|0.6101<br>|0.6091|0.6079|

Source: Authors’ calculation. Notes: significance levels: * 10% level, ** 5 % level and *** 1 % level. The dependant variable is the log of seaborne imports, expressed in dollars. The variables _MTC_ and _Distance_ are in logarithms. All models estimated by Poisson Pseudo Maximum Likelihood (PPML) with robust standard errors clustered at the country-pair level. Full sets of origin, destination and commodity fixed-effects included in all specifications but not reported.

First, in the full sample including zero trade observations, the effect of transport costs on the value of imports is lower when predicted values of MTCs are employed (columns 4 and 5) as the effect of reverse causality is purged or at least substantially reduced. In the PPML specifications, coefficients on continuous variables can be interpreted as elasticities, i.e. a 1 percent increase in maritime transport costs would reduce imports by 0.51 percent (col.5). This cost effect strengthens substantially once zero trade observations are excluded. Hence, while maritime transport costs do play a role in affecting the extensive margin, they are quantitatively even more important as drivers of the intensive margin (column 6).

20
# **<u>Figure 3</u>**

<!-- Start of picture text --> Average student-teacher ratios (grades 1-6) in treatment and control schools<br>(2002-3 and 2011-12)<br>50<br>42.6 43.5<br>45 40<br>40 33.1 35.6 36.5 36.2 37 37 35.7 33.7 34.8 35.7<br>3530 29.8 28.5 31.2 31.3 27.5<br>24.5<br>22.9<br>25<br>20<br>15<br>10<br>5<br>0<br>Treatment Control Treatment Control Treatment Control Treatment Control Treatment Control<br> Nord Nord Est Centre Grand Anse Nord Ouest<br>2003 2012<br><!-- End of picture text --> Source: World Bank staff estimates based on 2002-3 and 2011-12 Haiti school census.

# **4. CONCLUSIONS**

This paper has sought to evaluate an important program for increasing primary school access in the context of low state capacity, both to inform future policy development in Haiti and to share lessons with those countries that still have large numbers of children outside the education system. We conclude that a school’s participation in the TWP results in having more students enrolled, more staff, and higher student-teacher ratios (still under 40 students per teacher). The program also reduces grade repetition and the share of students who are overage. While the increase in students at treatment schools does not directly equate to a reduction in the number of children out of school, it does demonstrate strong demand from families for the program. This demand has also been evidenced by the results of focus groups conducted by program verification firms with parents of students attending treatment schools. These parents report that the TWP has significantly reduced the financial burden of educating their children and their concerns about how to keep their children in school (FPN _(Fondation de Parrainage National)_ 2013 and FONHEP _(Fondation Haitienne de l’Enseignement Prive)_ 2013).

The program therefore seems to be achieving its objective of reducing the financial barriers to primary education faced by families in Haiti and may be supporting higher enrollment rates. Despite the program’s estimated effectiveness, many operational aspects of the program have 15
16 was to increase prices, followed by a gradual decline in these increases due to increasing competition.

Table 4

|Inflation equation f|or domestic servi|ces (a)||
|---|---|---|---|
||(1)|(2)|(3)|
|constant|1.077|1.709|1.417|
||_(1.43)_|_(2.37)_|_'1.95)_|
|Ac|0.257|0.259|0.134|
||_(4.38)_|_(4.74)_|_(1.52)_|
|Ap*|0.197|0.164|_0.055_|
||_(2.34)_|_(2.03)_|_(0.48)_|
|y (b)|4.229||3.776|
||_(2.75)_||_(2.62)_|
|x|-0.998|-0.176|-0.875|
||_(-3.25)_|_(-2.40)_|_(-3.00)_|
|AGDP||-0.022||
|||(-0.14)||
|1p3C-(l1-P)p*I1-|-0.087|-0.144|-0.118|
||_(-1.31)_|_(-2.28)_|_(-1.84)_|
|_with_||||
|f3(c)|0.518|0.612|0.566|
||_(3.38)_|_(12.92)_|_(6.75)_|
||||0.004|
||||(1.74)|
|Dummy|0.600|0.488|0.648|
||(5.76)|(5.20)|(6.41)|
|AdjR<sup>2</sup>|0.816|0.886|0.886|
|DW|2.49|1.80|2.16|

Notes: t-statistics in parenthesis. (a) Nominal wages have been proxied by the wages of the banking sector, and the dunmny variable accounts for the second hyperinflationary episode in Argentina in the last quarter of 1989; (b) Instrumented by lagged industrial GDP (up to four quarters), and the variations in the lagged real exchange rate and the lagged nominal wage;(c)the coefficients on c-l and P*_1 are constrained to sum to one.
18

# 6.  Expenditures, Remittances and Poverty

Table 6 reports observed, predicted and counterfactual expenditures for the three groups of households: those receiving no remittances, those receiving internal remittances (from Ghana) and those receiving international remittances (from African and other countries). On the basis of these expenditure levels, the table also reports levels of poverty based on a poverty line of 3,066,582 cedis/person/year which is equal to the poverty line used by the Ghana Statistical Service in 1998/1999, updated by inflation.<sup>17</sup> Three different poverty measures appear in Table 6. The first measure -- the poverty headcount -- shows the percent of the population living beneath the poverty line. However, this headcount index ignores the “depth of poverty,” that is, the amount by which the average expenditure of the poor fall short of the poverty line. The table therefore reports a second measure, the poverty gap. This index measures in percentage terms how far the average expenditures of the poor fall short of the national poverty line. The third poverty measure -- the squared poverty gap – shows the “severity of poverty.” The squared poverty gap index possesses useful analytical properties, because it is sensitive to changes in distribution among the poor. In other words, while a transfer of expenditures from a poor person to a poorer person will not change the headcount index or the poverty gap index, it will decrease the squared poverty gap index.<sup>18</sup> Columns (1) and (2) of Table 6 show that households with no remittances have mean per capita expenditures that situate them in the middle of the expenditure distribution of Ghana. For this reason, households with no remittances have less observed poverty on average than households receiving internal remittances (from Ghana), but more observed
Mymensingh, Rajshahi, Rangpur, and Sylhet. Each division is subsequently divided into 64 districts, or _zilas._ Each district is further subdivided into smaller geographic areas with clear rural and urban designations. In addition, urban areas in the main divisions of Chittagong, Dhaka, Khulna and Rajshahi are classified into City Corporations (CCs), or Metropolitan Areas and Other Urban areas. Table 2 shows the distribution of the population by division and across urban, rural, and CCs areas.

**Table 2 Distribution of population by division (%)**

|**Division**|**Number of**<br>**districts**|**Rural**|**Urban**|**City**<br>**Corporation**|**Total**<br>**(%)**|
|---|---|---|---|---|---|
|Barisal|6|5.3|1.1|-|6.3|
|Chittagong|11|14.3|1.6|3.2|19.1|
|Dhaka|13|13.6|1.7|10.6|25.9|
|Mymensingh|4|6.1|0.8|-|6.8|
|Khulna|10|9.2|1.4|1.3|11.9|
|Rajshahi|8|10.3|1.6|0.6|12.5|
|Rangpur|8|9.8|1.5|-|11.3|
|Sylhet|4|5.2|1.0|-|6.2|
|Total|64|73.7|10.6|15.7|100.0|

Source: Household Income and Expenditure Survey (2010) The sampling design for the HIES 2016/17 includes two different levels of stratification:

- (i) _<u>Division-Rural/Urban/CC</u>_ <u>: The eight administrative divisions in Bangladesh are initially</u> used to construct 16 sampling strata: one urban and one rural for each division. In addition, each urban sampling stratum in the four main divisions of Chittagong, Dhaka, Khulna and Rajshahi is further divided into two sampling strata: City Corporation and Other Urban. This brings the total number of sampling strata to 20: eight in rural divisions, eight in urban divisions, and four main CCs.

- (ii) _<u>District- Rural/Urban/CC:</u>_ As explained in detail below, PSUs in the HIES 2016/17 are allocated at the district level. Therefore, the sample is implicitly sub-stratified at the district level. Since there are a total of 64 districts in Bangladesh, the sample design includes a total of 132 sub-strata: 64 urban, 64 rural, and four main CCs.

10
# Non-Technical Abstract

Several studies of civil war have concluded that economic inequality between individuals does not increase the risk of internal armed conflict. This is perhaps not so surprising. Even though an individual may feel frustrated if he is poor compared to other individuals in society, he will not start a rebellion on his own. Civil wars are organized group conflicts, not a matter of individuals randomly committing violence against each other. Hence, we should not neglect the group aspect of human well-being and conflict. Systematic inequalities that coincide with ethnic, religious or geographical cleavages in a country are often referred to as horizontal inequalities (or inter-group inequalities). Case studies of particular countries as well as some statistical studies have found that such inequalities between identity groups tend to be associated with a higher risk of internal conflict. However, the emergence of violent group mobilization in a country with sharp horizontal inequalities may depend on the characteristics of the political regime. For example, in an autocracy, grievances that stem from group inequalities are likely to be large and frequent but state repression may prevent them from being openly expressed. This paper investigates the relationship between horizontal inequalities, political environment, and civil war in developing countries. Based on national survey data from 55 countries I calculate welfare inequalities between ethnic, religious, and regional groups for each country using indicators such as household assets and educational levels. All the inequality measures, but particularly regional inequality, are positively associated with higher risks of conflict outbreak. Furthermore, it seems that the conflict potential of regional inequality is stronger for pure democratic and intermediate regimes than for pure autocratic regimes. Institutional arrangements also seem to matter. In fact it seems that the conflict potential of horizontal inequalities increases with more inclusive electoral systems. Finally, the presence of both regional inequalities and political exclusion of minority groups seems to make countries particularly at risk of conflict. The main policyimplication of these findings is that the combination of _politically_ and _economically_ inclusive government is required to secure peace in developing countries.

World Bank Policy Research Working Paper 4193, April 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at <u>http://econ.worldbank.org/programs/conflict</u> ). An objective of the series is to get the findings 2
**Table 9: 2007 as non-reform year—By model design—Fixed effects—regression on firm profitability as dependent variable**

||No Dummies|Year Dum.|+Reg. Dum.|Full Model|
|---|---|---|---|---|
|**Treatment (ITT)**|-0.0146*|0.00581|0.00593|0.00619|
||(-1.92)|(0.55)|(0.56)|(0.59)|
|Reform Period|-0.00770**|-0.0331***|-0.0332***|-0.0328***|
||(-2.00)|(-3.48)|(-3.49)|(-3.44)|
|Constant|0.0463***|0.0534***|0.0639|-0.0184|
||(20.66)|(14.61)|(0.87)|(-0.23)|
|Year Dum.|No|Yes|Yes|Yes|
|Region Dum.|No|No|Yes|Yes|
|Sector Dum.|No|No|No|Yes|
|Observations|1 363 022|1 363 022|1 363 022|1 363 022|

**t-statistics shown in parentheses, treatment variable bolded *p<0.1          **p<0.05          ***p<0.01**

**Table 10: 2007 as non-reform year—By firm size—Random effects—regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|0.00702|0.00845|-0.195|-0.0481|
||(0.69)|(0.87)|(-0.53)|(-0.52)|
|Pre-reform<br>youth share|0.0398***|0.0546***|0.250|-0.0599|
||(5.63)|(7.74)|(0.86)|(-1.02)|
|Reform Period|-0.0326***|-0.0327***|0.00661|0.000525|
||(-3.41)|(-3.32)|(0.18)|(0.02)|
|Pre-reform<br>Education level|-0.0570***|-0.0539***|-0.670*|0.0497|
||(-6.15)|(-5.99)|(-1.77)|(1.58)|
|Pre-reform firm<br>size|-0.0000192**|-0.00398***|0.000213|0.00000337|
||(-2.20)|(-7.57)|(0.58)|(1.03)|
|Constant|0.199***|0.207***|1.078|-0.0808|
||(9.89)|(10.41)|(1.63)|(-0.60)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 363 022|1 321 871|34 695|7 160|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

23
Table 7. Pioneer firms and technology diffusion

||Dep variable: Normalized share of technology-related job adverts|
|---|---|
||(1)<br>(2)|
|Pioneer|3.131***<br>4.630***<br>(0.226)<br>(0.398)|
|Pioneer_×_years since emergence|-0.202***<br>(0.036)|
|Technology FE|Yes<br>Yes|
|Year FE|Yes<br>Yes|
|Firms FE|Yes<br>Yes|
|Adjusted R-squared|0.040<br>0.041|
|Observations|10,072,709<br>10,072,709|

Notes: Standard errors clustered at the firm level in parentheses. The normalized shared is calculated by Normalized share _i,τ,t_ =<sup>_share jobs exposedi,τ,t_</sup> . The normalized share is capped at the _share jobs exposedτ,t_ 99<sup>_th_</sup> percentile of non-zero observations. The specification excludes observations before the year of emergence of a technology.

# **4.2.2 Faster diffusion for firms with supply chain linkages**

We now present the results relating to the role of various types of pre-existing linkages to pioneer firms in shaping the spread of new technologies. The results in columns (2) to (4) of Table 8 show that firms that were buyers, suppliers or innovation partners of pioneers before the technologies emerged subsequently observe greater hiring related to these technologies after their emergence. There is less evidence of a decline in the importance of these linkages over time, as reflected by the weak significance of the negative coefficients of the linkage dummies interacted with years since emergence. The results in column (5) examine the extent to which there is also greater diffusion among firms that were affiliates of pioneer firms. In this case the coefficient of interest is also positive but not statistically significant.

Since the same entity can simultaneously have different types of pre-existing linkages with pioneer firms, in column (6) we include simultaneously all these linkage variables. The results reveal that the diffusion of new technologies occurs mainly through buyers and innovation partners of pioneer firms. The coefficients associated with these variables remain positive and statistically significant. In contrast, the coefficient on suppliers of pioneers is now close to zero and no longer statistically significant. Overall, these results suggest that, when accounting for a wide range of pre-existing firm-to-firm linkages, the diffusion of new technologies through global value chains is mainly observed among buyers and innovation partners of pioneer firms.

24
|**Table**<br>|**A5: Business Environment and Enterpri**<br>|**se Per**<br>|**form**<br>|**ance**<br>|**Sur**<br>|**vey (**<br>|**BPS)**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>|World Bank and European Bank for Reconstr<br>|uction<br>|and De<br>|velo<br>|pmen<br>|<br>|||||
|**Description**|Multilateral development banks headquartere<br>Kingdom|d in W|ashing|ton,|Unite|d Stat|es an|d Lo|ndon,|United|
|**Website**|http://www.worldbank.org/eca/governance||||||||||
|**Data Source**<br>|Business Environment and Enterprise Perform<br>|ance S|urvey.||||||||
|**Type**|Survey<br>||||||||||
|**Respondents**|Firms||||||||||
|**Frequency**|Every three years since 1999||||||||||
|**Coverage**|Transition economies in Eastern Europe and|Former|Soviet|Unio|n||||||
|**Public Access**|Full access to firm-level data through website|noted|above||||||||
|**Description**|This survey, part of the Investment Climate|Survey|proje|ct of|the|World|Ban|k, col|lects|a wide|
||range of data on firms' financial perform<br>investment climate.|ance a|nd the|ir pe|rcept|ions|of th|e reg|ulato|ry and|
|||2008|2007|2006|2005|2004|2003|2002|2000|1998<br>1996|
|**Voice and Accountability**<br>NA|||||||||||
|**Political Stability and Absence**<br>|**of Violence**||||||||||
|NA||..|..|..|..|..|..|..|..|..<br>..|
|**Government Effectiveness**|||||||||||
|How problematic are telecommu|nications for the growth of your business|X|X|X|X|X|X|X|X||
|How problematic is electricity for|the growth of your business.|X|X|X|X|X|X|X|X||
|How problematic is transportatio|n for the growth of your business.|X|X|X|X|X|X|X|X||
|**Regulatory Quality**|||||||||||
|How problematic are labor regula<br>|tions for the growth of your business.<br>|X|X|X|X|X|X|X|X||
|How problematic are tax regulati<br>|ons for the growth of your business.<br>|X|X|X|X|X|X|X|X||
|How problematic are cust oms an|d trade regulations for the growth of your business.|X|X|X|X|X|X|X|X||
|**Rule of Law**|||||||||||
|How often is following characteri|stic associated with the court system: Fair and honest|X|X|X|X|X|X|X|X||
|How often is following characteri|stic associated with the court system: enforceable|X|X|X|X|X|X|X|X||
|How often is following characteri|stic associated with the court system: Quickness|X|X|X|X|X|X|X|X||
|How problematic is crime for the|growth of your business.|X|X|X|X|X|X|X|X||
|How problematic is judiciary for t|he growth of your business.|X|X|X|X|X|X|X|X||
|**Control of Corruption**|||||||||||
|How common is for firms to have|to pay irregular additional payments to get things done|X|X|X|X|X|X|X|X|..<br>..|
|Percentage of total annual sales|do firms pay in unofficial payments to public officials|X|X|X|X|X|X|X|X|..<br>..|
|How often do firms make extra p|ayments in connection with taxes, cust oms, and judiciary?|X|X|X|X|X|X|X|X||
|How problematic is corruption fo|r the growth of your business.|X|X|X|X|X|X|X|X||
|**Country Coverage**||**30**|**27**|**27**|**27**|**27**|**27**|**27**|**18**|**..**<br>**..**|
|**Year of Publication**||**2008**|**2005**|**2005**|**2005**|**2002**|**2002**|**2002**|**2000**|**..**<br>**..**|

44
even a report that a chicken pecked one SUM device off a stove. We see that total SUM failures range from 42 in Round 3 to 73 in Round 2. The usable observations are therefore reasonably uniform across monitoring periods and highly uniform across treatments, with the possible exception of treatment 1, which has fewer usable observations in round 2 ~~.~~ Overall, these results suggest that any SUM failures were idiosyncratic and not systematic by treatment, which is very important.

Table 6 Mirt Stove Fuelwood Savings in kilograms per Week by Treatment

||Obs.|Median|Mean|S. D.|Max.|
|---|---|---|---|---|---|
|Received 50Birr use payment, but no network (Treatment 1)|157|6.87|9.91|13.52|59.41|
|Received 50 Birr use payment, plus network (Treatment 2)|199|4.63|13.34|17.64|67.40|
|Paid 25 Birr, but received no network (Treatment 3)|194|6.11|12.13|17.93|65.93|
|Paid 25 Birr for the stove, plus network (Treatment 4)|176|0.22|2.93|8.81|58.94|
|Received stove for free and no network (Treatment 5)|181|9.37|16.94|19.43|67.86|
|Received stove for free, plus network (Treatment 6)|179|2.33|7.50|16.84|68.59|

We find that fuelwood savings differ by treatment, with households receiving treatments 2 and 5 having the highest estimated average fuelwood savings. As the distribution is right-side skewed, medians are below means and households receiving treatments 1, 3 and 5 (all without networking) have the highest estimated median fuelwood savings. These results are shown in Table 6. Relying on our randomized design, we now formally test whether households receiving different treatments have statistically different fuelwood savings. As our fuelwood savings data are skewed, we rely on nonparametric Mann-Whitney rank-sum tests. In Table 7 we also present tests of equalities in medians (to adjust for the skewed distributions). These non-parametric median equality tests are also more interpretable than the rank-sum tests.

Table 7 Rank-Sum Mann-Whitney Tests that Households Receiving Different Treatments have Fuelwood Savings that Come from Same Populations and have Equal Medians(p Values)

||Treatment1|Treatment 2|Treatment 3|Treatment 4|Treatment 5|
|---|---|---|---|---|---|
|Treatment 2|0.12<br>(0.83<sup>)</sup>|||||
|Treatment 3|0.04<sup>**</sup><br>(0.24<sup>)</sup>|**0.02**<sup>******</sup><br>**(0.00**<sup>*****)**</sup>||||
|Treatment 4|**0.00**<sup>*******</sup>|**0.00**<sup>*******</sup>|**0.00**<sup>*******</sup>|||

**23 |** P a g e