rafmacalaba/datause-encoder-data huggingface dataset:
the selected dimensions and indicators for the region. The Arab MPI was based on three dimensions: education, health, and living conditions.

Table 4. Dimensions and indicators of deprivation for household poverty in Arab multidimensional <u>poverty index</u>

|Dimensions<br>|Deprivations|
|---|---|
|educa<br>tion|Years of schooling<br>School attendance|
|ealth|Child mortality<br>Nutrition|
|h|FGM/early pregnancy|
|s|Electricity|
|ion|Sanitation|
|dit|Water|
|con|Floor/roof|
|ng|Cooking fuel|
|Livi|Overcrowding<br>Assets|

Source: Authors’ compilations based on UN (2017).

Constructing the MPI using MICS or DHS could potentially provide an important alternative to monetary poverty in MENA countries where collection of HBSs is not possible, or where existing surveys are old. Figure 10 shows the most recent years for HBS, MICS, and DHS in MENA countries. Algeria, Iraq, the West Bank and Gaza and Tunisia could benefit from more recent MICS data compared to their outdated HBSs. However, the situation in the most data-deprived countries, such as the Syrian Arab Republic or Libya, cannot be improved given that there are no recent DHS or MICS surveys. In addition, the MPI, depending on deprivations selected, may not be very sensitive to economic changes in the country. For example, number of years at school may not change quickly during the economic crisis.

It is also important to remember that tracking multidimensional poverty over time requires comparable DHSs or MICSs. Comparability also requires identical definition of deprivations in the different years, including definitions of indicators, cut-offs, and weights. Finally, countries will have to conduct DHSs and MICS regularly and frequently, which is not always the case. For example, MICSs were conducted only every six to seven years in the MENA region.

Besides availability of microdata, several important technical issues should be addressed when constructing MPIs. The first relates to applicable population—the group of people for whom an indicator is relevant. Very often, MPIs define deprivations only for a group of people. For example, MPIs measure school enrollment typically for children at the age of compulsory schooling, and measure employment for particular adults. As a result, there are inapplicable households. A decision should be made on how to treat these cases. One widely used option is to assume that these households are not deprived, a conservative approach that may underestimate poverty. The World Bank (2019) takes a different approach by shifting the weight for the missing indicator to other indicators within the dimension so that each dimensional weight remains unchanged.

16
- Kelly, D. L., C. Kolstad, G.T. Mitchell. 2005. Adjustment costs from climate change. _Journal of Environmental Economics and Management,_ 50 (3):468-95.

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- Kurukulasuriya, P., R. Mendelsohn, R. Hassan, J. Benhin, M. Diop, H. M. Eid, K.Y. Fosu, G. Gbetibouo, S. Jain, , A. Mahamadou, S. El-Marsafawy, S. Ouda, M. Ouedraogo, I. Sène, N. Seo, D. Maddison and A. Dinar, 2006. Will African Agriculture Survive Climate Change? _World Bank Economic Review,_ 20: 367-388.

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- Mendelsohn, R., W. Nordhaus, and D. Shaw,1999. The Impact of Climate Variation on U.S. Agriculture. p. 55-74. In Mendelsohn, R., and J. Neumann. (eds). _The Economic Impact of Climate Change on the Economy of the United States_ . Cambridge University Press, Cambridge, United Kingdom.

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19
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# **A Materials and Methods**

## **A.1 Classification of production states**

Infrared detection rules out venting and site inactivity, as the observed radiance indicates night fires that are almost surely natural gas flares given the location of the observed signal. The flaring site is thus in state of production with flaring when infrared signals are detected. However, depending on the temperature and the surface area of the gas flare, detection on one or more sensors will occur. Figure A.6 shows the distribution of radiance for M10 detections in our study sample. No infrared signal was detected with a radiance below 0.01 W.sr<sup>-1</sup> .m<sup>-2</sup> .nm<sup>-1</sup> .

While lower-intensity flares fail to trigger an M10 detection, they always do so on the wideband DNB. Thus, when no infrared signal is detected, we rely on DNB to assess which state a given flaring site is in, since low-intensity flaring could well go undetected by infrared sensors. However, given the sensitivity of DNB to lunar illuminance, we first proceed by suppressing the radiance effects stemming from moonlight reflection on the Earth or on clouds at night. Such procedure yields the Spike Median Index (SMI), which allows detecting the presence of surface lighting and ranking the brightness of sources over extended periods of time [11].

Figure A.7 shows the distribution of SMI measurement over four sites which are relatively isolated: three small towns in Syria and a regional airport in Lebanon. The only source of nighttime lighting in these four sites is electric.

As indicated on the histogram, no signal above an SMI value of .6 was detected over the period March 2012 to October 2015, at the exception of a few outliers. We thus consider that SMI levels observed on flaring sites above .6 are associated with low-intensity flaring, so that the site is in state of production with flaring ( _S_ 1). Conversely, when SMI indicates that no significant night-time light is detected above the lunar cycle for a period of 7 consecutive days, we rule out venting and determine that the site is inactive. Alternative cutoffs for SMI and 23
equal. To avoid the endogeneity of the import share variable, we use instead a variable that is the import share of current partners in the first period of the sample, as explained in Section III. Thus, when a new partner receives a preference, we add its pre-agreement import share in the sector to the RTA import share. That variable therefore varies over time only as a result of new entrants. However, because most of the variation is across industries, we use industry (instead of country-industry) and country-year fixed effects when we include it. We interact this variable with a dummy for the presence of a preference margin that is likely to be utilized (i.e., a preference margin above 2.5).

The results are reported in Table IV. The first column shows the impact of preferential import shares, alone and interacted with the CU dummy, when meaningful preference margins are present. Column (2) adds the lagged changes in the preferential tariff variables. In column (3) we estimate equation (5) fully, including the dummy that defines whether a meaningful margin is present in the previous period and the initial import share by themselves (and their CU interactions). In all specifications, the coefficient on preferential import shares interacted with _MRG_ is negative and statistically significant, indicating that higher preferential import shares are associated with reductions of MFN tariffs when meaningful preference margins are present. This effect is neutralized in customs unions. On the other hand, the preferential import share, regardless of whether it is interacted with the CU dummy, is never statistically significant by itself, showing that the effect of import share on changes in MFN tariffs is only present when there is a preference margin. Thus, goods in which preferential imports are more important tend to be liberalized faster vis-à-vis outsiders, but only in FTAs and only if a meaningful margin of preference is present.

The coefficients on _MRG_ and _MRGCU_ are also highly significant, showing that tariffs on 29
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31
- Increasing the job content and poverty alleviation of growth, i.e. moving toward “inclusive growth.”

These co-benefits and the environmental benefits can be captured in many ways. In a Green Growth framework, getting the price right is important, and should be the first step, but is likely to be insufficient or inapplicable. Indeed, it is often politically difficult to change prices to more appropriate values, and many market failures explain why prices cannot be the silver-bullet of environmental policies. Because of these failures, it is necessary to complement price-based policies with other policies. Additional policy instruments such as norms and regulation, public production and direct investment, information creation and dissemination, education and moral suasion, or industrial policies and the setting of long-term targets can also be useful. Since green growth policies pursue a variety of goals, they are best served by a combination of instruments.

Specific work is required to analyze the various policies that can meet these goals, and to assess the actual extent of synergies that can be achieved. This paper is about a framework to assess green growth opportunity. Only policy- and context-specific studies can provide quantified estimates of the synergies and trade-offs that are implied by a green growth strategy.

# **References**

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31
**Table 5. Negative-Binomial Estimation Results:**

|**Determina**|**nts of Export**<br>|**Discovery Cou**<br>|**nts during 199**<br>|**4-2003 by Co**<br>|**untry**<br>|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Ln(GDP per<br>capita)|8.635***<br>(5.70)|8.319***<br>(5.21)|8.321***<br>(3.14)|15.452**<br>(2.79)|7.183<br>(0.78)|
|Ln(GDP per<br>capita)^2|-0.519***<br>(5.51)|-0.500***<br>(4.98)|-0.507***<br>(3.18)|-0.904***<br>(2.85)|-0.441<br>(1.13)|
|Ln(Historical<br>discoveries)|0.342**<br>(2.09)|0.341**<br>(2.08)|0.268**<br>(2.49)|0.258**<br>(2.42)|0.229*<br>(1.87)|
|Factor<br>endowments|0.100<br>(1.10)|0.102<br>(1.11)|0.097<br>(1.44)|0.098<br>(1.32)|0.024<br>(0.29)|
||0.189<br>(0.72)|0.266<br>(0.94)|0.282<br>(1.36)|0.350<br>(1.55)|-5.611*<br>(1.93)|
|_barriers_<br><br>|0.356<br>(0.77)|0.405<br>(0.98)|0.437<br>(1.43)|0.522*<br>(1.63)|1.865*<br>(1.93)|
|Barriers|-0.083<br>(0.42)|-0.044<br>(0.18)|-0.022<br>(0.09)|5.349<br>(1.38)|0.793<br>(0.10)|
|)<br>ln(_GDPpc_<br><br>||||-0.497<br>(1.17)|0.035<br>(0.03)|
|Leamer-industry<br>Dummies|Yes|Yes|Yes|Yes|Yes|
|Barriers index<br>excludes costs of<br>entry|No|Yes|Yes|Yes|Days to<br>clear<br>customs<br>for exports<br>only (log)|
|Regional<br>Dummies|No|No|Yes|Yes|Yes|
|Regional<br>Dummies times<br>Barriers|No|No|No|Yes|Yes|

Notes: Absolute value of robust z-statistics reported within parentheses. Significant at 1%***, 5%**, 10%*. The dependent variable is the count of export discoveries by country during 1994-2002. They correspond to estimations with clustered errors around countries. Factor endowments are proxied by the average of net exports of each Leamer industry during 1989-1993. π is the profitability of exports for each Leamer industry proxied by export growth of each industry (without the contribution of export discoveries) minus non export GDP per capita growth. The barriers index under columns 2-4 excludes the data on costs of starting a business. The excluded industry is mining and petroleum and the excluded Region is South Asia in the specification with industry and regional dummies. In models 4 and 5, the only other significant interaction is the one between Africa and the barriers index, which is negative. See text and appendix tables for further details.

28
I'lie regression results for interprovincial differences in per capita current, capital and total expenditures presented in Table 14 generally confimi thcsc observations. In all the equations. the regression coefficient of per capita GDP is positive and significant for both 1992 **aiid** 1993. Similarly, population density shows significant diseconomies of scale though the rate of increasc in expenditures was lower as the rate of population density increases (log-linear equation). These regressions do suggest, however, that there is some attempt to spend more in provinces with lower agricultural productivity and higher illiteracy rate. But per capita expenditures **were** not significantly related to other need variables such as infant mortality and road length per sq. knm. of area.

Unfortunately, data on provincial expenditures at the required level of disaggregation are available only for education and health. Similar correlation and regression analysis was carried out for per capita education and health expenditures with various -capacity' and need' variables. The results (not presented in detail here) are broadly similar to those already discussed, although some interesting observations emerged. For example, although the budgeted expenditures on education and health in 1993 in different provinces were not si-nificantly correlated with per capita GDP, actual expenditures in 1992 were significantly hiiher in provinces having higher per capita GDP.

Expenditure on education was higher in provinces with a higher illiteracy rate, but there was no significant correlation with population density. Interestingly, education expenditure was negatively and significantly correlated with enrolment ratios. It is not clear how to interpret this result. If the low enrolment ratio is due to inadequate facilities for education, presumably higher expenditures in low enrolment provinces would increase enrolment. But this scenario seems unlikely in Viet Nam, where facilities (as evidenced by the ratio of teachers to school going population) are fairly evenly spread across the country. In this instance, the negative correlation may perhaps indicate that more is spent on education in provinces where the effectiveness of such expenditure may be lower.

36