rafmacalaba/datause-encoder-data huggingface dataset:
reforms; depending upon their power base, they might even be successful in completely stopping the implementation of desirable reforms. In this context, economic policymaking appears as an endogenous outcome of the political process (Murshed, 2002). Reforms that are only partially implemented, or when implementation is accompanied by policies that negate the effects of the reforms, are unlikely to contribute to achieving sustainable economic growth or reducing poverty. Such reforms present a challenge that can be better approached by using PE analysis.

For World Bank related work, whether dealing with national level policies or sectoral programs or even at an individual project level, Fritz, Kaiser and Levy (2009) argue that that PE analysis is especially necessary if weak governance is an issue. In this context, PE analysis can either be a national level diagnostic that results in a risk assessment or a detailed description of political economy drivers and risks of capture at the sector level.

Fritz, Kaiser and Levy (2009) identify several situations where carrying out governance and political economy analysis (GPE) is relevant for a better understanding of country-level dynamics. First, such analysis can be useful during the preparation of a Country Assistance Strategy (CAS), Country Partnership Strategy (CPS), or an Interim Strategy Note (ISN), particularly when there is a sense that past country programs and policy dialogue have not produced expected development outcomes. Second, these studies help better understand the context when significant changes have taken place in the country, such as changes in government, or the emergence of a natural resource industry, etc. Third, when there are important risks regarding governance, such as risks of state capture, fragility or coup d’état, which can lead to a stop of donors’ programs in the country. Finally, PE studies are generally useful since they benefit the various teams working on a country by facilitating a quicker and better understanding of the country context.

Fritz, Kaiser and Levy (2009) also talk about when PE analysis might be relevant at the sector level. First, these studies can provide useful context while supporting sectoral reforms that are likely to be difficult to implement either because of their distributional impact, their institutional complexity, and/or the structure of incentives and constraints. Second, when policy or institutional changes in a sector have been on the agenda for a long time without ever materializing or when previous improvements in sector policies or outcomes have been reversed. Third, when governments lack or have limited ability to manage institutional change and various stakeholders’ interests are likely to leave the reforms unaddressed, blocked or mismanaged.

While sectoral analysis can draw upon country level studies, another approach that might be relevant and is gaining increasing traction is the _value chain approach_ . By looking at each component of 5
|**Table 3: Temporal and regional**<br>|**elements of la**<br>|**nd aband**<br>|**onment**||||
|---|---|---|---|---|---|---|
|**By reason for fallowing**|**Total ar**<br>|**ea**<br>||**Length of fallow**<br>|**period in yea**<br>|**rs**<br>|
||**1000 ha**|**%**|**<3 **|**3-5 **|**6-10**|**>11**|
|Plot too far from house|69.729|40.10|6.38|30.79|30.03|32.81|
|Soil of poor quality|34.926|20.09|9.74|24.37|18.62|47.27|
|Plot too small to bother with|0.590|0.34|7.62|80.35|0.00|12.03|
|Seeds, fertilizer, too expensive|32.704|18.81|11.33|<br>43.28|15.35|30.04|
|Machinery too expensive|35.935|20.67|24.52|<br>27.90|21.26|26.32|
|Total (1000 ha)|173.885||20.41|<br>54.63|40.10|58.74|
|Col. %|||11.73|<br>31.42|23.06|33.78|
|**By region**||||**Length of fallow**|**period in yea**|**rs**|
||**Fallow%**|**of tot**|**<3**|**3-5**|**6-10**|**>11**|
|Coastal|11.52|54.18|14.74|31.36|18.29|35.62|
|of which economic|42.9||56.8|44.7|37.2|38.6|
|Central|11.27|30.63|14.98|30.20|24.98|29.84|
|of which economic|50.0||86.8|56.7|40.3|33.0|
|Mountain|5.44|12.97|8.73|41.94|34.24|15.09|
|of which economic|24.0||53.8|21.5|10.5|44.5|

_Source:_ Own computation from 2005 Albania LSMS 23
world. Another ambitious threshold would be the OECD or US level of (real) income per capita in 1980 – assuming that even by today’s standards, these countries were already “rich” in 1980. In terms of table 2, these higher thresholds imply moving up to higher rows in the table. In terms of specific country examples, the following figures display that a number of MICs could still reach the HIC status in 2035 – roughly one generation from now—if they can sustain per capita economic growth rates on the order of 2.5 to 4.5 percent. The horizontal lines used for comparison are the 1980 GDP per capita of the United States and (high-income) OECD aggregate (constant 2005 PPP dollars). The values (from World Development Indicators) are 25,510 and 19,386 for the US and OECD respectively. The countries were chosen for illustrative purposes only; however, they do represent a wide range of MICs. Note that in the Figure 3c, with 4.5 percent growth, a number of the more advanced MICs would also breach the 2011 levels of US and OECD GDP per capita ($42,436 and $32,726, respectively, in constant 2005 PPP dollars).

**Figures 3(a)-(c):  HICs in a generation with sustained moderate growth?**

<!-- Start of picture text --> (a) With per capita Growth of 2.5 percent<br>30000<br>25000 USA-1980<br>20000 OECD -1980<br>15000<br>10000<br>2011<br>5000 2035 (proj)<br>0<br>(b) With per capita Growth of 3.5 percent<br>40000<br>35000<br>30000<br>25000 USA-1980<br>20000 OECD-1980<br>15000<br>10000<br>2011<br>5000<br>2035 (proj)<br>0<br><!-- End of picture text --> 10
# III. RESULTS

Table 1 shows the main results, covering the 1974–2009 period. All data are averaged over four years. The dependent variable is the average annual growth rate of real GDP per capita; aid is measured as net Official Development Assistance (ODA) as a percentage of GDP.<sup>22</sup> When we do not account for diminishing returns to aid by including aid squared, the coefficient of the interaction term is negative and significant at the five‐percent level (column 1). When we include aid squared, the interaction becomes significant at the one‐percent level (column 2).<sup>23</sup> According to column 2, for any increase in _ΔAid_ , the effectiveness of this change in aid disbursements decreases with the share of the period the recipient country has spent on the UNSC two periods before (i.e., when the aid has been committed). The causal effect of a one percentage point increase in aid as a percentage of GDP on yearly economic growth is 0.64 percentage points higher if the recipient has not served on the UNSC compared to if it has served two years (i.e., 1/2 of the four‐ year period). Compared to the average growth rate of about 1.34 percentage points in our sample, this is a substantial reduction of almost half the average growth rate. The results thus support our hypothesis that aid committed during times of short‐term political importance is indeed less effective.

> 22. The original source for GDP per capita growth is the World Bank’s World Development Indicators; ODA is total net ODA in current US$ from table 2 of the OECD’s Development Assistance Committee in percent of GDP in current US$, taken from the World Development Indicators (see the Technical Appendix to Clemens et al. 2012). Data for the 2006–2009 period are from Minasyan (2016) and World Bank (2016). 23. We also tested whether the effect differs when we only take important years of UNSC membership into account, as suggested in Kuziemko and Werker (2006). The results remain unchanged.

23
**Table 5:** **<u>Net Cumulative Greenhouse Gas Savings (Gt CO2e)</u>**

|**Biofuel **|**Scenario**|**2000-2020**|**2000-2030**|**2000-2050**|
|---|---|---|---|---|
|**First generation**|WEO-V1|-2.4 to -1.6|-0.8 to 0.7|2.8 to 6.2|
||WEO-V2|-2.6 to -1.8|-1.3 to -0.3|1.4 to 5.3|
||TAR-V1|-5.3 to -4.1|-3.5 to -0.6|5.3 to 12.4|
||TAR-V3|-3.6 to -2.5|-0.8 to 1.6|5.7 to 10.8|
|**Second generation**|WEO-V1|< 0.05|0.1 to 0.4|1.8 to 3.3|
||WEO-V2|0|0|0.4 to 0.8|
||TAR-V1|< 0.05|0.2 to 0.8|3.4 to 6.2|
||TAR-V3|-0.2 to 0.6|1.4 to 3.5|9.4 to 16.2|
|**Total**|WEO-V1|-2.4 to -1.6|-0.7 to 1.1|4.5 to 9.4|
||WEO-V2|-2.6 to -1.8|-1.3 to 0.3|1.8 to 6.1|
||TAR-V1|-5.3 to -4.0|-3.2 to 0.2|8.7 to 18.6|
||TAR-V3|-3.8 to -1.8|0.7 to 5|15.1 to 27|

Source: Fischer et al. (2009) Note: Biofuel deployment projected by the IEA as the reference scenario in the World Energy Outlook 2008 is reflected in the WEO scenarios, while more aggressive biofuel penetration reflecting the successful implementation of announced biofuels targets around the world is encapsulated in the TAR scenarios. The scenarios are further differentiated by variants representing the schedule of second generation biofuel deployment, featuring either expected deployment (V1), delayed deployment (V2) or accelerated deployment (V3). The range of estimates signifies optimistic and pessimistic assumptions regarding the possible GHG savings from biofuels and the carbon impacts of land use change.

Some studies evaluate the attractiveness of biofuels as a GHG mitigation option. A rough, indicative calculation by the OECD projects that lowering GHG through policy support to biofuels in the US, Canada, and Europe in 2013-2017 would cost taxpayers and consumers on average between USD 960 and 1,700 per ton of CO2-equivalent avoided (OECD, 2008). Righelato and Spracklen (2007) find that emission reductions through biofuels would be less attractive economically as compared to afforestation in pasture land. Similarly, Danielsen et al. (2009) suggest that reducing deforestation may be a more effective climate change mitigation strategy than the use of biofuels. Tax credits for ethanol production tend to encourage an overall increase in vehicle miles traveled and a delay in the adoption of more fuel-efficient cars, which can lead to greater GHG emissions, while binding mandates, by pushing fuel prices up, may produce some GHG reductions from reduced vehicle miles traveled and increases in fuel economy (Bento, 2009). Tollefson (2008) asserts that an improvement of one mile per gallon in average US vehicle fuel efficiency may decrease GHG emissions the same as all current United States ethanol production from maize. Doornbosch and Steenblik (2007) estimate that GHG mitigation costs of US ethanol based on corn and EU ethanol based on sugar beet and corn 29
<!-- Start of picture text --> Intervention Intervention during<br>pre-pandemic pandemic<br>Nov-19 Mar-20 Jul-20 Apr-21<br>Sep-19 Dec-19 Mar-RO Jun-20 Sep-20 Dec-20 Mar-21 Jun-21 Sep-21 Dec-21 Mar-22 Jun-22<br>ee<br>Oct-19 Feb-20 May-20 Oct-21 Dec-21 May-22<br>Baseline First round of Final round of<br>data collection follow-up follow-up<br>data collection data collection<br><!-- End of picture text --> <!-- Start of picture text --> o By ()2<br>ORC? 2°<br>o| (OF@) se”.<br>£ SI<br>[e)= | BAANS = >< O<br>E | “So 1S)"<br>wm eV) t Yoo GD<br>2 @ \ DES<br><i \— Er<br>_ Q) a)<br>Air<br>Gen, OV)<br>1 @ O 0<br>OO<br>[o)<br>0 5 10 15 20<br>A; (months)<br>© Targeted Households © Untargeted Households<br><!-- End of picture text -->
catch-up‖ patterns.<sup>17</sup> For example, Figure 2.7.3 shows the intra-industrial and inter-industrial dimension of ―Flying Geese catch-up‖ patterns. Along the value chain to high value added products, Korea moved from exports of clothing to those of textiles and to production of synthetic fibers (Lim, 2011). In the electronics industry, a comparative advantage recorded by the net trade index reveals industrial upgrading from simple goods to more sophisticated goods (Lin & Chang, 2009). While Korea had a dominant comparative advantage in the home appliance electronics industry which started with the _assembly of radios_ from imported components (World Bank, 1987), it started to _gain_ a comparative advantage in electronic parts (i.e. transistor and semiconductor) in the mid of the 1980s, and in information communication & industrial electronics in the 1990s (Figure 2.7.4).

**Figure 2.7.4 Trade Specialization Index of Korea’s electronics**

**Figure 2.7.3 Revealed Comparative Advantage of Korea’s industries**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>Clothing<br>8<br>6<br>Footwear<br>4<br>Textiles<br>2<br>Electronics<br>0<br>62 67 72 77 82 87 92 97 02 07<br>Tran sport<br><!-- End of picture text --> <!-- Start of picture text -->
1.00<br>0.80 Home ap pliance<br>0.60<br>0.40<br>Comm. & Industrial<br>0.20<br>Parts & Components<br>0.00<br>74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06<br>-0.20<br>-0.40<br>-0.60<br>Source: Korea Electronic Association<br>Note: TSI=(export-import) / (export + import) for each<br>industry<br><!-- End of picture text -->

Source: based on COMTRADE data, SITC Rev1, 2 digits, <u>extracted from WITS</u> Note: RCA=the share of an industry in the economy‘s <u>exports /its share in global exports</u> In terms of the inter-industrial dimension, Korea‘s maintained high revealed comparative advantage (RCA) in clothing exports until the end of the 1960s, followed by the footwear until the 1980s. In the 1990s, it rapidly developed a high RCA in electronics exports which was replaced by transport equipment exports more recently.

Korea‘s ̳Flying Geese catch-up‖ model also had an international dimension which involves the relocation of an industry from one country to another. For example, it gained a sharp increase in RCA in footwear in the mid of the 1960s partly as a result of manufacturing alliances and

> 17 For the debate on whether the HCI drive in the 1970s is ―comparative advantage following‖ or ―comparative advantage defying‖, see Lin & Chang (2009).

28
almost identical growth rates over the periods 1963-70 and 1960-70 and for 1960-70 the same is the case for surveys, SMI and SII. Thus, for the 1960s it does not matter much which wage growth indicator we use. For 1970-80, censuses, surveys, and SMI give very similar growth rates. It is for this period that the insurance data, SII, show much lower growth. The SII shows much lower growth also for 1960-80 than the SMI. For the years 1980-84, survey and SII agree closely with the SMI showing somewhat lower increase.

In Col.s 6 and 7, finally, averages are shown for the aggregated sector manufacturing and mining, computed simply by adding payments of wages and salaries for the two sectors and dividing by the sum of employees, Col.6, and by the sum of persons engaged in manufacturing and employees in mining, Col.7. Both averages and growth rates are rough]y the same for all years and periods.

The SPO, finally, has made estimates of nominal wages, averages for private sector, public sector and for Turkey. It is not clear from the OECD source whether government is 'included in public sector and Turkey. The SPO averages are in-between the SII and SMI averages and must be based upon these latter averages. The rates of increase for 1980-84 are somewhat higher for the SPO estimates than for both SII and SMI averages. Notice that the rate of increase is higher for the SII averages than for the SMI averages for 1980-84. For 1980-84 (86) we finally have a series

- 53 -
Do Improved Biomass Cookstoves Reduce Fuelwood Consumption and Carbon Emissions? Evidence from Rural Ethiopia Using a Randomized Treatment Trial with Electronic Monitoring<sup>**1**</sup> Abebe Beyene<sup>A</sup> Randy Bluffstone<sup>B</sup> Zenebe Gebreegzhiaber<sup>A, C</sup> Peter Martinsson<sup>D</sup> Alemu Mekonnen<sup>A, E</sup> (corresponding author) Ferdinand Vieider<sup>F</sup> Keywords: improved biomass cookstoves; controlled cooking test; RCT; REDD+; Ethiopia. JEL classification: C93, D12, O13, Q41, Q56

> 1 Authorship is shared and authors are listed in alphabetical order.  AEnvironmental Economics Policy Forum for Ethiopia (EEPFE)/ Ethiopian Development Research Institute (EDRI) Addis Ababa, Ethiopia;<sup>B</sup> Department of Economics Portland State University USA;<sup>C</sup> Department of Economics Mekelle;<sup>D</sup> University of Gothenburg, Sweden; EDepartment of Economics, Addis Ababa University (alemu_m2004@yahoo.com); FWZB Berlin Social Science Center, Risk & Development Unit.

**Acknowledgements:** The authors would like to thank the World Bank for funding the research through the Knowledge for Change Program and the Trust Fund for Environmentally and Socially Sustainable Development.  We also acknowledge the Berkeley Air Monitoring Group for supplying and advising on the use of the electronic stove use monitors used in this study.  Ermias Dessie provided expert data processing that allowed the data to be analyzed.
**Table 2.  Determinants of the probability of ever having been in a union, having a first union -** **<u>or first child before age 18 for full sample of ever married/united respondents</u>**

||**Ever in uni**|**on**|**Young first un**|**ion**<br>|**Young firs**|**t parenting**|
|---|---|---|---|---|---|---|
|**Outcomes**|**Males**|**Females**|**Males**|**Females**|**Males**|**Females**|
||**Coeff**|**Coeff**|**Coeff**|**Coeff**<br>|**Coeff**|**Coeff**|
|_(1) Schooling only (probit)_|||||||
|Schooling|-0.015|<br>-0.032|-0.044|-0.137|-0.051|-0.122|
|z|-0.74|<br>-1.49|**-2.01**|**-7.89**|-1.48|**-6.76**|
|Wald Chi-2|0.55|<br>2.22|4.05|62.26|2.2|45.73|
|p-value|0.46|0.14|0.04|0.00|0.14|0.00|
|<br>n|572|<br>697|572|697|567|704|
|_(2) Schooling only (IV_<br>_probit)_|||||||
|<br>Schooling|-0.110|<br>-0.196|-0.123|-0.217|0.061|-0.189|
|<br>z|-1.38|<br>**-2.59**|-1.41|**-2.77**|0.55|**-2.06**|
|Wald test of exogeneity|1.32|<br>3.26|0.75|0.89|0.98|0.48|
|<br>p-value|0.25|0.07|0.39|0.34|0.32|0.49|
|Wald Chi-2|1.91|<br>6.73|1.99|7.68|0.31|4.24|
|p-value|0.17|0.01|0.16|0.01|0.58|0.04|
|n|572|<br>697|572|697|567|704|
|_(3) Schooling and height (IV_|_probit)_||||||
|Schooling|-0.085|<br>-0.197|n.e.-1|-0.226|n.e.-1|-0.191|
|z|-1.21|<br>**-2.10**||**-3.12**||**-2.60**|
|Height|-0.068|<br>0.010||0.047||0.035|
|z|-1.13|<br>0.14||0.76||0.56|
|Wald test of exogeneity|2.64|<br>4.75||6.27||4.96|
|p-value|0.27|0.09||0.04||0.08|
|Wald Chi-2|2.42|<br>6.07||15.12||10.38|
|p-value|0.30|0.05||0.00||0.01|
|n|422|<br>538||538||535|
|_(4) Schooling and height (IV_|_) and family ba_|_ckground_||||-|
|Schooling|-0.118|n.e.-2|n.e.-1|-0.219|<br>n.e.-1|0.164|
|z|-1.53|||**-2.96**||**-2.15**|
|Height|-0.067|||0.045||0.034|
|<br>z|-1.05|||0.73||0.56|
|Wald test of exogeneity|2.93|||4.46||2.88|
|<br>p-value|0.23|||0.11||0.24|
|Wald Chi-2|5.97|||20.79||16.98|
|p-value|0.43|||0.00||0.01|
|<br>n|422|||538||535|

n.e.-1 Not estimated due to conformability error n.e.-2 Not estimated because inverse matrix has missing values z values in bold are significantly nonzero at 0.05 level 17
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Tanzania > Nonproductive investments > Macro results**

||||N<br>Reduction in<br>h bli|onproduc|tive invest<br>I|ment<br>Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|7,925.60|–0.07|0.02|0.04|0.05|0.02|
|EV|Equivalent variation||–0.07|0.02|0.04|0.05|–0.46|
|s|Wage|1.00|–0.20|0.07|0.10|0.13|0.05|
|yg|Government income|668.13|0.09|5.44|5.44|5.44|5.44|
|g|Total government expenditure|516.07|–3.40|3.52|3.52|3.52|3.52|
|ye|Firm income|2,293.57|0.26|–0.08|–0.13|–0.17|–0.06|
|It|Total private investment|1,228.04|0.50|–0.13|–0.52|–1.07|–0.41|
|GDP|GDP|7,582.43|0.00|0.00|0.00|0.00|0.00|
|e|Nominal exchange rate|1.00|0.29|0.02|–1.00|–2.58|0.06|

# **Tanzania > Nonproductive investments > Sectoral results**

||||Reduction in<br>|No|nproductive<br>|||
|---|---|---|---|---|---|---|---|
||||other public||Import|||
|Variable|Sector|Base|<br>expenditure|VAT|<br>duties|Foreign aid|Income tax|
||Crop agriculture|1,964.70|0.08|–0.19|–0.14|–0.15|–0.35|
||Export agriculture|1,311.08|0.12|–0.28|–0.38|–0.48|–0.31|
|Va|Mining and gas|141.43|0.03|–0.05|–0.04|–0.01|–0.04|
|Value added or|Industries|1,242.16|0.08|–0.53|–0.25|0.06|–0.23|
|output|Construction|780.66|0.86|0.53|0.17|–0.20|0.62|
||Private services|1,672.10|0.02|–0.05|–0.09|–0.11|–0.11|
||Public services|470.30|–2.36|2.28|2.32|2.51|2.29|
||Crop agriculture|1.02|–0.15|0.20|0.14|0.08|–0.07|
||Export agriculture|1.02|–0.14|0.24|0.47|0.22|–0.10|
||Mining and gas|1.06|0.24|0.28|0.23|–1.62|–0.10|
|pq<br>Market prices|Industries|1.10|0.05|0.74|0.54|–0.80|–0.05|
||Construction|1.00|0.87|0.95|0.60|–0.05|0.73|
||Private services|1.00|–0.03|0.19|0.10|–0.09|–0.05|
||Public services|1.00|–0.07|0.21|0.18|0.00|0.07|
||Crop agriculture|1.00|–0.09|–0.20|–0.09|–0.07|–0.42|
||Export agriculture|1.00|–0.01|–0.37|–0.49|–0.62|–0.44|
|r<br>|Mining and gas|1.00|0.21|–0.73|–0.51|–0.03|–0.52|
|Rental rate of<br>capital|Industries|1.00|–0.05|–0.95|–0.37|0.26|–0.40|
||Construction|1.00|2.15|1.51|0.58|–0.40|1.73|
||Private services|1.00|–0.15|–0.08|–0.14|–0.18|–0.28|

40
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