rafmacalaba/datause-encoder-data huggingface dataset:
_Table 5: Regression of incidence of disease and mortality on gas flaring controlling for cluster characteristics: cluster level_

|VARIABLES|(1)<br>Cough rate|(2)<br>Respiratory<br>Issues rate|(3)<br>Fever rate|(4)<br>Diarrhoea<br>rate|(5)<br>Stunting<br>rate|(6)<br>Wasting<br>rate|(7)<br>Underweight<br>rate|(8)<br>Infant<br>Mortality<br>rate|(9)<br>Child<br>Mortality<br>rate|(10)<br>Under-5<br>Mortality<br>rate|
|---|---|---|---|---|---|---|---|---|---|---|
|Flaring exposure|0.0930**|0.0840**|0.0940**|-0.0198|0.0798**|0.0806*|0.1021**|0.0102|-0.0091|0.0044|
||[0.0424]|[0.0425]|[0.0423]|[0.0424]|[0.0404]|[0.0430]|[0.0415]|[0.0423]|[0.0426]|[0.0422]|
|Wealth quintile|-0.0650|-0.0741|-0.2181***|-0.1710**|-0.2816***|-0.1103|-0.2702***|-0.1371**|-0.1447**|<br>-0.1810***|
||[0.0698]|[0.0699]|[0.0696]|[0.0698]|[0.0671]|[0.0714]|[0.0690]|[0.0697]|[0.0702]|[0.0695]|
|Poor roof rate|-0.0186|-0.0218|0.0073|0.0208|-0.0189|-0.0058|0.0464|-0.0645|0.0190|-0.0506|
||[0.0518]|[0.0519]|[0.0517]|[0.0518]|[0.0494]|[0.0525]|[0.0508]|[0.0517]|[0.0521]|[0.0516]|
|Poor wall rate|-0.0438|-0.0550|-0.1358**|-0.1430**|0.0083|-0.0873|-0.0894|-0.0324|-0.0570|-0.0514|
||[0.0609]|[0.0610]|[0.0607]|[0.0609]|[0.0580]|[0.0617]|[0.0596]|[0.0608]|[0.0612]|[0.0606]|
|Persons per room|0.1034**|0.1022**|0.0418|0.0107|0.0549|0.0075|0.0382|-0.0035|-0.0055|-0.0050|
||[0.0414]|[0.0415]|[0.0413]|[0.0414]|[0.0398]|[0.0423]|[0.0409]|[0.0413]|[0.0416]|[0.0412]|
|Reads newspaper rate|0.0616|0.0514|0.0681|-0.0355|-0.1137**|-0.0060|-0.0655|0.0584|-0.0018|0.0511|
||[0.0477]|[0.0477]|[0.0475]|[0.0477]|[0.0459]|[0.0488]|[0.0471]|[0.0476]|[0.0479]|[0.0474]|
|Rural|0.0235|-0.0111|0.0385|0.0074|-0.0342|0.0046|-0.0689|0.2275**|-0.0217|0.1959**|
||[0.0953]|[0.0955]|[0.0950]|[0.0953]|[0.0909]|[0.0966]|[0.0933]|[0.0951]|[0.0958]|[0.0949]|
|Year=2018|0.0003|-0.0001|0.0005|0.0001|-0.0003|-0.0005|-0.0015|0.0028|-0.0003|0.0024|
||[0.0832]|[0.0833]|[0.0829]|[0.0832]|[0.0791]|[0.0841]|[0.0812]|[0.0830]|[0.0835]|[0.0827]|
|Constant|-0.0143|0.0068|-0.0235|-0.0045|0.0208|-0.0028|0.0420|-0.1386|0.0132|-0.1194|
||[0.0873]|[0.0874]|[0.0870]|[0.0873]|[0.0829]|[0.0881]|[0.0851]|[0.0871]|[0.0877]|[0.0868]|
|Observations|603|603|603|603|595|595|595|603|603|603|
|R-squared|0.0229|0.0205|0.0293|0.0230|0.1248|0.0104|0.0764|0.0278|0.0143|0.0327|
|Standard errors in brack|ets*** p<0.01, *|* p<0.05, * p<0.1|||||||||

14
of one-party-ruled countries, where political purges and turnovers are important concerns for politicians. _De facto_ one-party rule has been quite common in the world. Indeed, at the country level over the past four decades, about 45 percent of country-years (among a sample of 179 countries) can be characterized as having _de facto_ one-party rule. 18 In future work, we will continue to examine the connections between factional competition, local accountability, political survival, and local development in other provinces in China, and around other parts of the world.

# **References**

- [1] Acemoglu, Daron, Simon Johnson and James A. Robinson (2001). “The Colonial Origins of Comparative Development: An Empirical Investigation.” _American Economic Review,_ 91 (5), 1369-1401.

- [2] Acemoglu, Daron, Simon Johnson and James A. Robinson (2001). “Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution.” _Quarterly Journal of Economics_ , 117 (4), 1231-1294.

- [3] Acemoglu, Daron and James Robinson, James (2001). “A Theory of Political Transitions.” _American Economic Review_ , 91, 938–963.

- [4] Acemoglu, Daron and James Robinson (2006). _Economic Origins of Dictatorship and Democracy_ . Cambridge, MA: Cambridge University Press.

- [5] Acemoglu, Daron and James R. Robinson (2012). _Why Nations Fail: the Origins of Power, Prosperity and Poverty_ . New York: Crown Publishing.

- [6] Acemoglu, Daron, Tristan Reed, and James A. Robinson (2014). “Chiefs: Economic Development and Elite Control of Civil Society in Sierra Leone.” Journal of Political Economy, 122 (2), 319-368.

- [7] Altonji, Joseph, Todd Elder and Christopher R. Taber (2005). “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ , 113 (1), 151184.

- [8] Ansolabehere, Steven and James M. Snyder. (2006). “Party Control of State Government and the Distribution of Public Expenditure.” _Scandinavian Journal of Economics_ , 108 (4), 547-69.

- [9] Banerjee, Abhijit and Lakshmi Iyer (2005). “History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India.” _American Economic Review_ , 95(4): 1190-1213.

- 18This is based on calculation using the Database of Political Institutions 2015 (Cruz, Keefer, and Scartascini, 2016).

53
Table 7 – Bilateral trade in prepared poultry meat (Market share pre- and post-HPAI)

|Importers||Brazil|China|Thailand|USA|Others|
|---|---|---|---|---|---|---|
|European|pre|19%|0%|63%|0%|17%|
|Union|**_post_**|**_28%_**|**_0%_**|**_56%_**|**_0%_**|**_16%_**|
|Jaan|pre|0%|64%|33%|2%|1%|
|p|**_post_**|**_1%_**|**_56%_**|**_42%_**|**_0%_**|**_1%_**|
|Canada|pre|0%|0%|2%|98%|0%|
||**_post_**|**_0%_**|**_0%_**|**_3%_**|**_96%_**|**_0%_**|
|USA|pre|0%|1%|0%|0%|99%|
||**_post_**|**_0%_**|**_22%_**|**_0%_**|**_0%_**|**_78%_**|
|Others|pre|4%|9%|12%|23%|53%|
||**_post_**|**_6%_**|**_8%_**|**_8%_**|**_14%_**|**_64%_**|

Source: COMTRADE Database Note: Excludes intra-EU trade. Three year averages are reported for 2001-2003 and 2004-2006 in bold italic.

Bilateral market shares indicate the key role that Thailand plays in exporting both to the Japanese and European Union markets. Thailand’s position in these markets has not been affected by HPAI outbreaks. Thailand’s slight loss of market share in the European Union is compensated by large gains the Japanese market. Similar to Thailand, China’s position in the Japanese market has not substantially changed. Brazil has not been able so far to penetrate the Japanese market for processed poultry and its exports are almost exclusively relegated to the European Union.<sup>20</sup> In any case, as the trade of prepared poultry has boomed in the last few years, modest losses in market shares still correspond to large gains in trade flows. In this regard Thailand and China have fared

> 20 China has also increased its presence in the US markets to reach about 20 percent in 2006. However, US imports of unprepared poultry are relatively small. China’s exports to the USA totaled only 30 million USD in 2006.

20
This estimate is roughly half the 1277 kilogram estimate of Dresen et al. (2014), which used survey methods to calculate the fuelwood and carbon savings of the Mirt stove. As discussed by Thomas et al. (forthcoming), a lot of this discrepancy is likely due to overstating improved stove use. They find in Rwanda that respondents overstated their actual use in surveys by approximately 40%. We therefore believe that our estimates are more realistic.

Table 3 Mean Mirt Stove Fuelwood Savings in kilograms per Week by Regional State and Round

||Overall|Round 1<br>(June-August<br>2013)|Round 2<br>(August<br>-<br>October 2013)|Round 3<br>(March-May<br>2014)|Round 4<br>(May<br>–<br>July<br>2014)|
|---|---|---|---|---|---|
|Amhara|23.99|15.02|27.21|26.15|26.17|
|Oromiya|9.53|3.68|10.54|11.62|11.98|
|SNNP|3.32|1.65|3.63|4.07|3.97|

Table 3 examines fuel savings by round and region. As was found in Gebreeziabher et al. (2015) and Beyene et al. (2015), significant differences exist across the regional states. In all rounds Amhara has significantly larger fuelwood savings than the other two regional states. This is because many fewer Mirt stoves in households in Amhara use <u>more fuelwood than the traditional three-</u> stone tripods. Whereas across the four rounds the smallest measured fuelwood savings was -0.71 kilograms (i.e. increased fuelwood use) per week in Amhara, in Oromiya and SNNP the smallest savings are -25.00 kilograms per week. As a result, while in Amhara at the 25<sup>th</sup> percentile the Mirt stove is estimated to save 10.00 kg per week, in Oromiya the value is 0.89 kilograms and in SNNP -1.91 kilograms per week. At the upper levels of the distributions, fuelwood savings are comparable across the three regional states. At this point we are not able to fully explain these interregional differences.

# _4.2 Effects of Randomized Behavioral Treatments on Fuelwood Savings_

We now turn our attention to why the Mirt stove reduces fuelwood consumption, with a main emphasis on the six treatments equally implemented when the 360 stoves were randomly distributed to households. As discussed in the previous section, treatments were applied at the site because we do not have continuous stove temperature monitoring (i.e. logging only every 10 minutes), there must be a downward bias in our cooking duration and therefore fuelwood savings estimates.

**21 |** P a g e
**Table 13:** _Heterogeneous Effects on Vaccines from reaching Decision Point_

|Child/Mother Characteristic:|(1)<br>Girl Infant|(2)<br>Poor|(3)<br>No Formal<br>Schooling|(4)<br>Rural<br>Resident|
|---|---|---|---|---|
|Dependent Variable: BCG Vacci|ne||||
|Decision Point|0.0238|0.0162|0.0108|-0.0133|
||(0.0155)|(0.0146)|(0.0153)|(0.0170)|
|Decision Point_×_Characteristic|0.0033|0.0333***|0.0310***|0.0563***|
||(0.0020)|(0.0089)|(0.0076)|(0.0130)|
|F stat (p value)|(0.0843)|(0.0088)|(0.0166)|(0.0115)|
|Mean (Characteristic=1)|0.8397|0.7732|0.7340|0.8057|
|Mean (Characteristic=0)|0.8419|0.8662|0.9126|0.9123|
|Number of Observations|867,342|839,803|855,169|867,342|
|Number of Mother Groups|19,484|15,614|19,253|19,484|
|Number of Countries|54|54|54|54|
|Dependent Variable: DPT1 Vacc|ine||||
|Decision Point|-0.0238|-0.0291|-0.0569|-0.0491*|
||(0.0316)|(0.0318)|(0.0367)|(0.0282)|
|Decision Point_×_Characteristic|0.0041*|0.0258***|0.0741***|0.0396**|
||(0.0022)|(0.0086)|(0.0201)|(0.0156)|
|F stat (p value)|(0.5341)|(0.9201)|(0.4983)|(0.7833)|
|Mean (Characteristic=1)|0.8220|0.7578|0.7111|0.7883|
|Mean (Characteristic=0)|0.8244|0.8479|0.8962|0.8943|
|Number of Observations|863,512|836,152|851,365|863,512|
|Number of Mother Groups|19,461|15,603|19,230|19,461|
|Number of Countries|54|54|54|54|
|Dependent Variable: Measles Va|ccine||||
|Decision Point|-0.0065|-0.0146|-0.0185|-0.0431|
||(0.0336)|(0.0344)|(0.0341)|(0.0329)|
|Decision Point_×_Characteristic|0.0042|0.0344***|0.0297***|0.0560**|
||(0.0026)|(0.0099)|(0.0097)|(0.0111)|
|F stat (p value)|(0.9440)|(0.5500)|(0.7447)|(0.7123)|
|Mean (Characteristic=1)|0.7522|0.6770|0.6281|0.7190|
|Mean (Characteristic=0)|0.7547|0.7825|0.8347|0.8236|
|<br>Number of Observations|865,299|837,862|853,141|865,299|
|Number of Mother Groups|19,473|15,605|19,242|19,473|
|<br>Number of Countries|54|54|54|54|

37
large part by greater flows of FDI from industrialized, high-income countries to developing nations.

A key difference between the Petri (1997) and the Dee and Hanslow (2001) approach is that Petri modelled customers as first choosing among firms (suppliers) on the basis of their ownership, and then among the locations that the firms were based in. This treatment assumed that in any given sector, Japanese-owned firms were closer substitutes for each other than for Australian firms in the relevant sector, irrespective of location. By contrast, Dee and Hanslow (2001) assumed that customers choose between phyisical location first (where the services are produced) and then select between suppliers in a given geographic location on the basis of ownership (nationality). As noted by Dee (2003), from an Australian perspective, a US multinational located in Australia is assumed to be a closer substitute for an Australian-owned firm than it is for a US firm located in the United States.

Others have followed the example of the Dee and Hanslow/FTAP effort, including the Central Planning Bureau in the Hague, which extended its WorldScan model to include FDI in services (Lejour, Rojas-Romagosa and Verweij, 2008), and Brown and Stern (2001) and Brown, Deardorff and Stern (2001), who extended the Michigan model to include FDI along Petri lines. A major limiting factor here is the lack of detailed bilateral FDI data by sector. In addition, an issue rarely discussed is the use of FDI data as a proxy for actual (unobserved) sales of services by foreign affiliates when representing the operations of multinational enterprises.

While the inclusion of FDI further increases the data challenges confronting modelers (Stone et al., 1999; Hanslow et al., 1999), extending CGE models to include FDI is critical as it is a major channel for the international exchange of services. As discussed subsequently, the extension of CGE models to include FDI has been shown to be very important from a welfare (real income) and growth (productivity) perspective. The results of the simulations of liberalization that emerge from the various CGE models all suggest that there are significant potential welfare gains from further liberalization, with both developed and developing countries having an export interest in services liberalization, as well as potentially benefitting from greater import competition (Francois, van Meijl, and van Tongeren, 2005).More recent work that is anchored on the Markusen (1997) knowledge-capital model of FDI suggests that a major potential source of gain from liberalization comes from reducing barriers to entry into services and the increase in the variety of services that is associated with FDI inflows and subsequent ”downstream” productivity impacts.<sup>26</sup>

> 26The knowledge-capital model postulates that firms can separate the knowledge- (skill-) intensive dimensions of their activities (so-called headquarter services) from production of output, which may be dispersed across different locations as a function of differences in factor endowments and minimization of trade and transactions costs.

23
In the latter three rounds 25% of households use the stove an average of at least three times per week and except for in Round 4, 90% of users average positive usage. Select users cook with the Mirt stove an average of 2 to 3 times per day, suggesting they use it for purposes in addition to cooking _injera_ . This finding is also in accord with a satisfaction survey we conducted that is discussed in Gebreegziabher et al. (2015) in which 84% of users report using the stove for purposes in addition to cooking _injera_ , such as baking bread, making coffee and cooking stews.

Table 4 Mean Cooking Events per Week by Regional State and Round

||Overall|Round 1<br>(June-August|Round 2<br>(August<br>-|Round 3<br>(March-May|Round 4<br>(May<br>–<br>July|
|---|---|---|---|---|---|
|||2013)|October 2013)|2014)|2014)|
|Amhara|2.640|2.381|2.500|2.901|2.731|
|Oromiya|2.500|1.976|2.140|3.000|2.83|
|SNNP|1.672|1.350|1.691|2.170|1.480|

Table 4 presents the mean number of cooking events per week by region and round. Consistent with the previous table we find roughly increasing average usage across rounds regardless of region, with a dip between rounds 3 and 4, perhaps due to seasonality. Amhara and Oromiya Regional State have higher average usage than SNNP, with Oromiya having the highest average number of cooking events in rounds 3 and 4. SNNP is lowest, because the cultural practice is not to eat _injera_ at every meal. As discussed above, the Mirt stove, while being capable of cooking other foods, is primarily designed to cook _injera_ .

# _5.2 Effect of Treatments on Stove Use Frequency Based on Randomized Distribution_

In the above discussion we have shown that households use the Mirt stove on average consistent with its expected purpose to cook _injera_ . We now turn our attention to a preliminary analysis of why the stove is used, with a main emphasis on the six treatments equally applied when the 360 stoves were randomly distributed to households. As discussed in the previous section, treatments were applied at the site level so that within villages the distribution would be viewed as fair and equitable. For example, everyone who lived in a village paid or received the same amount of money for using the stoves. These treatments were assigned completely randomly, but it remains important to check that the intended randomization was successful.

**19 |** P a g e
26 devaluation not occurred. The third approach applies econometric methods to time-series to determine the impact of the exchange-rate changes on various performance variables. This approach has a number of limitations since the specification of the output equation estimated is essentially ad-hoc. Finally, the fourth, somewhat less direct approach, uses simulation models or reduced-form equations to analyze the effect of the exchange rate on output. This approach has a major advantage-it gives more information on the short run dynamics of the effects of a devaluation. However, the results rely often on "guess estimates" of values of elasticities and parameters. **_15/_** Table 5 is a matrix giving an overview of empirical studies done on the effects of devaluation. In a seminal paper, Cooper (1971) employs the **"before and after"** approach to examine 24 devaluations between 1959 and 1966, involving 19 developing countries (none of which belongs to SSA). The study assessed the extent of the response (one year before and one year after) of various elements of aggregate demand, balance of payments and inflation. Cooper's results show that some elements of aggregate demand demonstrate contractionary tendencies after devaluation. Results show, however, that the trade balance and the balance of payments did improve in most cases. While Cooper attempted to control for changes in the international and domestic environment when measuring responses to devaluation, his focus on one-year changes was limiting. Typically, a devaluation cannot be expected to have its principal effect in the year after its implementation.

Kamin (1988) is one of the first studies based on the "control group" methodology. This study follows the evolution of different variables for seven years (the year of the devaluation, three years before and three years after). Kamin's research exploits data for 50 to 90 devaluations out of a sample of 107 (including 22 devaluation episodes in twelve SSA countries) that took place between 1953 and 1983. Kamin's results show that devaluation has a positive or at least no effect on output, but a sharp and significant decline in output growth is registered by the devaluing countries, in the year preceding the devaluation. This lower rate

**_15/_** Empirical studics **based** or **_the_** "before and aiter" appmach include Dinz Alejandro (1965), Cooper (1971), **Krueger** (1978). Regrssion analyses **are** applied in Khan (1974), Goldstein (1974), Miles (1976), Edwards (1986), Edwards (1989) **and** Faini (1992). **Results** presented in **simulations** models **are presented** in **Gylfasn and** Shnmid **(1983)** ,Gylfason and **Risagcr** (1984), Solimano **(1986),** Brnson (1986), Gylfason (1991), and Agenor (1991).
**Table 7. The role of education and skills for economic upgrading through GVCs**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|
|---|---|---|---|---|
|**Backward links**|||||
|_FVAX_|0.0411|0.0235|-0.0313|-0.0118|
||(0.0346)|(0.0298)|(0.0506)|(0.0542)|
|_FVADP_|0.252***|0.273***|0.2510***|0.2523***|
||(0.0268)|(0.0265)|(0.0267)|(0.0268)|
|_FVAX*Schooling_|0.0033<br>(0.0039)||||
|_FVAX*Secondary degree_||0.0006<br>(0.0004)|||
|_FVAX*Educ. Quality_|||0.0223**<br>(0.0110)||
|_FVAX*Training_||||0.0185<br>(0.0121)|
|_Constant_|6.135***|5.603***|5.9181***|5.8125***|
||(0.189)|(0.182)|(0.1819)|(0.1847)|
|Observations|7,276|6,819|7,276|7,276|
|R-squared|0.852|0.850|0.853|0.852|
|**Forward links**|||||
|_DVAR_|0.0888**|0.106***|0.0436|0.0777|
||(0.0437)|(0.0387)|(0.0630)|(0.0804)|
|_FVADP_|0.232***|0.247***|0.2313***|0.2329***|
||(0.0258)|(0.0256)|(0.0257)|(0.0257)|
|_DVAR*Schooling_|0.0152***<br>(0.0053)||||
|_DVAR*Secondary degree_||0.0020***<br>(0.0006)|||
|_DVAR*Educ. Quality_|||0.0387***<br>(0.0139)||
|_DVAR* Training_||||0.0319*<br>(0.0180)|
|_Constant_|4.974***|4.828***|4.9067***|5.0618***|
||(0.177)|(0.190)|(0.1821)|(0.1825)|
|Observations|7,284|6,822|7,284|7,284|
|R-squared|0.870|0.871|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Labor Standards_**

For our last three policy measures, we focus exclusively on the condition of inputs and output factors. This means we look at indicators that differentiate a country’s products based on specific standards in production. As a result, we expect on the one hand that the absorptive capacity channel is likely to become less prominent since our indicators do not necessarily look at the quality of input factors, as was done in the previous section. On the other hand, we expect these policy areas to matter much more for forward links through signaling effects.

Table 9 on the role of labor standards confirms this hypothesis. Such standards do not seem to affect economic upgrading through GVC integration as a buyer. This might in fact be an interesting result in itself. It shows that, despite their often cost-increasing nature, there is no evidence on an upgrading-preventing effect either. Therefore, a race to the bottom in terms of labor standards is not justified from a GVC perspective.

27