rafmacalaba/datause-encoder-data huggingface dataset:
lnyijt = β0 + β1 × lnFDIijt + γit + θjt + μij+εijt, (1) where the yijt refers to the labor productivity of country _i_ sector _j_ in year _t_ , FDI ijt the amount of FDI at the same level and the error term obeying the standard normal distribution. To control for the country-level factors such as the institutional quality, financial development, market size and policy environment etc., we include the country-year fixed effect γit. The sector-year fixed effect is θjt is included as a control to exclude the effects of price volatility and sectoral shocks. The comparative advantage of a country varies, and it determines the production cost of a sector in each country. Government subsidies and taxes for the same sector in different countries could differ. To take care of these issues, we control for the country-sector fixed effect μij.

To explore how GVC participation shapes the effects of FDI, we include an interaction term to equation (1) and the specification becomes: lnyijt = β0 + β1 × lnFDI ijt + β2 × lnFDI ijt × GVCijt + β3 × GVCijt + γit + θjt + μij+εijt, (2) where _GVCijt_ is the GVC participation of country _i_ sector _j_ in year _t_ .

# 5.2 FDI has higher spillovers for sectors with lower GVC participation

This section reports the empirical results to show the direct effect of FDI on a sector’s productivity and the role of GVC participation in shaping this effect. The main results consider average labor productivity at the 35-sector level according to the MRIOT (see appendix table).

## **Main results**

The main empirical results are reported in Table 3, where the dependent variable is log of sector labor productivity. In column 1, the coefficient of _ln FDI_ is positive yet statistically insignificant. This result is consistent with most of the previous literature, in which there is ambiguity on the direct effect of FDI. When we include the interaction term to investigate how the GVC participation shapes FDI effects (column 2), the coefficient of _ln FDI_ is positive and statistically significant, and the coefficient of the interaction term ( _ln FDI_ × _GVC_ ) is negative and statistically significant. The results suggest that the average FDI spillover effect is positive, _and_ a sector with lower GVC participation receives higher benefits from FDI than a sector with higher GVC participation. For a
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 15. High international call charges driven both by technology and market power**

|$|Call within<br>region|Call to the<br>United States|Internet<br>dial-up|Internet<br>ADSL|
|---|---|---|---|---|
|Without submarine cable|1.34|0.86|68|283|
|With submarine cable|0.57|0.48|47|111|
|Monopoly on international gateway|0.70|0.72|37|120|
|Competitive international gateway|0.48|0.23|37|98|

_Source:_ Minges and others 2009. _Note:_ ADSL = asymmetric digital subscriber line.

# Financing Burkina Faso’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Burkina Faso needs to expand its infrastructure assets in key areas (table 16). The targets outlined in table 16 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country targets that can be modified or delayed as needed to achieve financial balance.

**Table 16. Illustrative investment targets for infrastructure in Burkina Faso**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Install fiber-optic links to neighboring capitals and the<br>submarine cable.|Provide universal access to the GSM signal and<br>public broadband facilities.|
|Power|New generation capacity and interconnectors.|Increase electrification to 23 percent (100 percent<br>urban and 6 percent rural).|
|Transport|Achieve regional (national) connectivity with good-quality<br>two-lane (or one-lane) paved road.|Provide rural road access to 25 percent of the<br>highest-value agricultural land, and urban road<br>access within 500 meters.|
|WSS|n.a.|Achieve Millennium Development Goals, clear sector<br>rehabilitation backlog.|

_Source:_ Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. _Note:_ ICT = information and communication technology; WSS = water supply and sanitation; GSM = global system for mobile communications. n.a. = Not applicable.

Meeting these illustrative infrastructure targets for Burkina Faso would cost $613 million annually through 2015. Capital expenditure would account for 65 percent of this requirement. Requirements for transport, at $144 million per year, are calculated on the basis of achieving regional connectivity with a good-quality two-lane paved road, and provide rural road access to 25 percent of the rural population and urban road access within 500 meters. Improving power access will require an estimated $142 million per year to increase the electrification rate to 23 percent at the national level. The water and sanitation sector has the highest spending needs: almost $208 million will be needed each year to meet the MDGs, with capital expenditure accounting for three-quarters of the total. While less than the amounts needed for these sectors, requirements for ICT are also high in absolute terms, amounting to around $120 million per year (table 17).

35
We address at least two further challenges with the structure of the primary dataset for creating our independent variables. First, we find several discrepancies between the same policy's yes/no and years variables. Appendix Table A.2 describes the eight types of discrepancies in the dataset and our solution to address each type of discrepancy.

Second, there are occasional continuous variables in the dataset and variables that we cannot transform into panel data. These occasional continuous variables disrupt our efforts to address collinearity and reduce the dimensionality of the dataset, as discussed in the following section. Please refer to Appendix Table A.3 for more details.

The final dataset has 76 policies over a panel of 133 countries between 1980 and 2018, as most policies were not implemented before then (see Appendix Table A.4).

# **Reducing dimensionality**

We must reduce the highly dimensional policy instrument data appropriately for regression analysis and interpretation. ESMAP indicators include 76 renewable energy policies grouped into seven high-level energy policy packages, or “Headings” (Table 2). Each energy policy package contains a different number of policies and sometimes contains clusters (or groups) of policies. The dendrogram in Appendix B shows that similar policy instruments usually reside within the same Heading. We, therefore, use these Headings, or energy policy packages, as our independent variables.

Table 2: Headings in the RISE Renewable Energy Pillar

||**Heading / Energy policy package **|**Acronyms**|
|---|---|---|
|1|Legal framework for renewable energy|LF|
|2|Planningfor renewable energyexpansion|PE|
|3|Incentives and regulatorysupport for renewable energy|IR|
|4|Attributes of financial and regulatoryincentives|AI|
|5|Network connection and use|NC|
|6|Counterpartyrisk|CR|
|7|Carbonpricingand monitoring|CP|

_Source: RISE website_ , <u>https://rise.worldbank.org.</u> 12
services policies influence economic performance, and that the assessments of restrictiveness based on the simple STRI are quite similar to those based on more sophisticated methods.

# **3.1 The Services Trade Restrictions Index (STRI)**

We first develop a measure of the restrictiveness of a country‘s policy regime based on a broad view of the policy information alone. This measure is most convenient to depict overall patterns in policy, across countries and sectors. It builds on a relatively long tradition of restrictiveness indices, ranging from simple counts of policy barriers (Hoekman, 1996) to more complex weighted averages, where weights reflect prior (usually subjective) assessments of the relative restrictiveness of specific policy barriers; work currently being undertaken at the OECD uses an elaborate version of this method (OECD, 2009a).<sup>13</sup> We construct a single measure of overall openness for any subsector-mode combination, e.g. one for the cross-border supply of bank loans and another for accepting bank deposits by establishing commercial presence abroad. This measure of openness is also subjective but relatively simple and transparent. It avoids the pitfalls of the approaches that assign fixed weights to all types of restrictions (entry, operational, regulatory) and treat the restrictions as additive. For instance, if foreign suppliers are not allowed to enter in the first place, then that restriction is binding and other restrictions on operations and regulatory environment simply do not matter. Similarly, a foreign equity limit of 49 percent already precludes foreign corporate control and so adding to it a further (frequently encountered) requirement that the majority of board of directors be nationals would amount to double counting.

Essentially, we assess policy regimes in their entirety and assign them into five broad categories: completely open, i.e. no restrictions at all; completely closed, i.e. no entry allowed at all; virtually open but with minor restrictions; virtually closed but with very limited opportunities to enter and operate; and a final residual ―middle‖ category of regimes which allows entry and operations but imposes restrictions that are neither trivial nor 12
Table 6: Estimates of catch-up <u>growth in BMI-for-age outcomes (conditional model)</u>

|||Between||With|in|Both|
|---|---|---|---|---|---|---|
||_α_0|_β_0|?|_ραβ_|?|?|
|_Externally_|_standardiz_|_ed differenc_|_e_(BAZ):||||
|Ethiopia|-1.08|-0.13|N|-0.09|Y|N|
|India|-1.24|-0.02|N|0.23|N|N|
|Peru|0.59|-0.02|N|-0.10|Y|N|
|Vietnam|-051|-003|N|037|N|N|
|_Ratio to ref_<br>|.<br>_erence me_<br>|.<br>_dian_(BAR_×_<br>|100):<br>|.<br>|||
|Ethiopia|-8.98|-1.33|N|-0.05|Y|N|
|India|-9.67|-0.23|N|0.68|N|N|
|Peru|8.33|0.34|N|0.30|N|N|
|Vietnam|-3.45|-0.18|N|0.62|N|N|
|_Difference t_|_o referenc_|_e median_(B|AD):||||
|Ethiopia|-1.50|-0.25|N|0.00|N|N|
|India|-1.58|-0.05|N|0.71|N|N|
|Peru|1.37|0.08|N|0.38|N|N|
|Vietnam|-0.57|-0.04|N|0.66|N|N|
|_BMI-for-ag_|_e is not lo_|_w_(> 5th per|centile):||||
|Ethiopia|-25.80|-4.69|N|0.36|N|N|
|India|-30.05|-1.26|N|0.14|N|N|
|Peru|3.42|-0.08|N|-0.03|N|N|
|Vietnam|-9.36|-1.66|N|0.41|N|N|
|_BMI-for-ag_|_e is health_|_y_(5th – 85t|h percentil|e):|||
|Ethiopia|-17.91|-3.53|N|0.18|N|N|
|India|-19.04|-1.50|N|0.10|N|N|
|Peru|-12.29|0.45|Y|-0.22|Y|Y|
|Vietnam|-4.13|-2.01|N|0.32|N|N|

Note: columns _α_ 0 _, β_ 0 _, ραβ_ report parameter estimates based on the FEIS estimator; all specifications include a set of time-varying controls; columns denoted ‘?’ report the decision (N = no; Y = yes) of whether we can reject the (composite) null hypotheses regarding the presence of specific forms of catch-up growth. Source: own estimates.

23
# **Annexes:**

## **Table A.1. Summary of the literature on the impact of QE policies**

|**Long term bond yields (bps)**|**Fed1**|**Fed2**|**Fed3**|**BoE**|**ECB**|**Riksbank**|
|---|---|---|---|---|---|---|
|Bauer and Neely 2014|-30.7|-7.6|-7.2||||
|Khrisnamurty and Vissing Jorgensen 2011|-21.4|-1.8|||||
|Gagnon et al 2010|-11.4||||||
|Glick and Leduc 2012|-20.0|0.2||-9.8|||
|Altavilla et al 2015|||||-29.0||
|Fukugana et al 2015|||||||
|De Rezende et al 2015||||||-7.0|
|Georgiadis and Grab 2015|||||-7.0||
|**Short term bond yields (bps)**|||||||
|Bauer and Neely 2014|-10.7|-2.3|-2.2||||
|Khrisnamurty and Vissing Jorgensen 2011|-5.0|-0.2|||||
|Gagnon et al 2010|-4.3||||||
|Altavilla et al 2015|||||-30.0||
|De Rezende et al 2015||||||-7.0|
|**Long-term inflation expectations (bps)**|||||||
|Khrisnamurty and Vissing Jorgensen 2011|19.0|1.0|||||
|Gagnon et al 2010|-12.6||||||
|Georgiadis and Grab 2015|||||8.0||
|**Currency markets (percent)**|||||||
|Glick and Leduc 2012|-1.2|-0.6||-0.7|||
|Georgiadis and Grab 2015|||||-1.8||
|De Rezende et al 2015||||||-0.8|

Notes: This table summarizes the results of event studies analyzing domestic effects of QE policies. Fed1, Fed2, and Fed3 represent the three successive asset purchase programs undertaken by the US Federal Reserve. The event dates vary depending on the study (see the corresponding papers for detailed descriptions of events). Long term bond yield is the yield on the 10-year benchmark government debt securities. Short term bond yield is the yield on 1, 2, or 3-year government debt, depending on the study. Long term inflation expectations are 10-year inflation swap rates. Currency market reflects either nominal effective exchange rate, or bilateral exchange rate against USD or EUR, depending on the study. The reported numbers represent changes within one-day/two-day event windows or estimated coefficient of event dummy variables in regressions, depending on the study.

49
# **International Willingness to Pay for the Protection of the Amazon Rainforest**

# **1. Introduction**

The Amazon rainforest, the world’s largest tropical rainforest, is an important constituent of the global biosphere and a global public good that benefits the international community by supporting an array of ecosystem services (Daily 1997). It is home to possibly a quarter of the world's terrestrial species (Dirzo and Raven 2003), accounts for about 15% of global terrestrial photosynthesis (Field, Behrenfeld et al. 1998), provides a major carbon sink (Pan, Birdsey et al. 2011; Grossman 2016), and drives atmospheric circulation and precipitation across South America and beyond (Werth and Avissar 2002). However, the Amazon rainforest is also one of the most threatened environments worldwide, with deforestation (Achard, Eva et al. 2002; Hansen, Potapov et al. 2013) a particularly pressing problem (Soares-Filho, Nepstad et al. 2006; Malhi, Roberts et al. 2008), mostly in the form of agricultural expansion (Nepstad, Stickler et al. 2006; Barona, Ramankutty et al. 2010). Since 1970, about 15% of the Amazon rainforest, an area larger than France, has been lost, and deforestation is expected to continue despite efforts to control it (Malhi, Roberts et al. 2008; Soares-Filho, Moutinho et al. 2010). Much of the current international funding to prevent deforestation aims at reducing carbon emissions (UNDP 2008). Although a global carbon market might someday generate adequate funds to protect the Amazon rainforest, progress toward a global climate agreement required to create such a market remains slow (Angelsen 2012).

Here, we examine an additional rationale for international funding of Amazon protection: its value to the international community as part of global natural heritage. Using the classification of ecosystem services by the Millennium Ecosystem Assessment (Assessment 2005), this value corresponds to the value of cultural ecosystem services associated with the nonmaterial benefits supported by the continued existence of the Amazon rainforest. This benefit, as an economic value, 2
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administering the legislation is not simply a task of following a set of unambiguous rules" (2). Carmichael gives as an example the determination of which good sold in the home market is "like" the good sold for export. That determination involves judgment and discretion about which technical rules to apply and demands expertise in the application of the selected technical procedures.

Similar mixes of judgment and skill are required in many other parts of both pricing and injury determinations. Gary Banks (PRE Working Paper Series No. 551), reports several examples that Carmichael provided. To mention only one of them, the cherries-in-brine case, the outcome of the case was significantly influenced by the administrative decision to combine the four grades of cherries commercially recognized in the exporting country into only three data categories for the investigation. The outcome of the case was also significantly affected by the way the investigating agency chose to value the containers in which the cherries were shipped.

The Swedish stainless steel case (PRE Working Paper Series, forthcoming) illustrates how many adjustments might be made in an individual case. In its request for information on which to base its assessment of the exporter's antidumping liability, the U.S. Commerce Department requested computerized data on over 50,000 individual transactions and _thirty-eight fields of data_ for each transaction. Each of the thirty-eight dimensions was requested because it was to be the basis for an adjustment.

Given the number of adjustments that are made and the number of ways each of them might be made, the transparency of the adjustment process is like the tranapara=7 of a crystal chandelier rather than that of a windowpane. An expert will be able to predict how a ray of light will pass through any prism or plate in the chandelier. But it will be impossible to predict how each prism and plate will be turned at any moment, and hence how a ray of light will pass through the complexity of the whole.

- 34 -
environment, as captured by the interaction term of INIT ~~P~~ C and supervision. At the average supervision quality index (=2.8), a project started in post-conflict has a 39 percent higher chance of success than a project started in a peaceful country, without introducing the effect of time since peace onset.

Column (5) of Table 3 also includes information as to when during the post-conflict period the projects have a higher chance of success. The variable PC ~~D~~ URATION, its square, and their interactions with supervision suggest that the relationship between time since peace onset and the probability of success is non-linear and depends on the quality of supervision.

Taking supervision at its mean (=2.8), Figure 2 shows that the probability of success is higher in post-conflict environments than in peaceful environments during the first 4 to 5 years. The marginal impact of time since peace onset is negative, though, until the 12th year of peace (as suggested by the negative slope of the blue line until the 12th year). However, for very low values of supervision, projects in both peaceful and post-conflict situations have a very low probability of success. For very high values of supervision, the probability of success is very high, and there is almost no difference between post-conflict and peaceful countries, although again, the difference between the two red curves indicates a slightly higher probability of the project being successful in post-conflict environments.

The absorptive capacity in post-conflict seems to be conditional to the capacity to implement projects. Supervision is a good proxy for implementing capacity. It is implementation rather than design that matters for the success of projects in post-war environment. This conclusion is verified at the national level. Collier and Duponchel (2010) analyze the economic legacy of war on firms using data collected in Sierra Leone. They find that the main consequence of civil war on firms is a severe lack of skills. To increase the chances of success of projects this shortage needs to be filled. The effect of supervision decreases overtime following a U-shaped trend as illustrated by Figure 3. Arguably, national skills slowly recover a decent level as peace is maintained; hence, the additional benefits associated with the quality of supervision fade as time goes on and as peace lasts. According to Figure 3, by the 5th year since the peace-onset, the impact of supervision reaches the same level as in environments at peace. This could be explained by the supra-normal growth in the second phase of post-conflict (Collier and Hoeffler, 2004) that creates a more secure environment for projects. Thus supervision might be less important for success during this phase of boom in the post-conflict economy.

In column (6) we calculate the time since peace onset taking as a reference the middle date of the projects. In doing so, we give each year the same weight. Previously, while using the time since peace-onset at the starting date of the project, more weight is put on the beginning of the project.

14
# **6. Poverty and the sectoral structure of net domestic product**

It is also of interest to know how the relationship between poverty and the sectoral structure of national output has evolved over time. By decomposing the overall growth rate of net domestic product (NDP) into its components we can investigate how the composition of NDP growth has mattered to poverty reduction. The national accounts do not of course have an “urban-rural” breakdown, so this dimension is now latent.

_k_ We divide NDP per capita,<sup>_y_</sup> _t_<sup>, into</sup><sup>_k_sources as</sup> _yt_   _yit_ . Taking the differences over _i_  1 time, we can write the growth rate of NDP as the share-weighted sum of the growth rates across _k_ the _k_ sources, i.e.,  ln _yt_   _sit_  1<sup></sup> ln _yit_ where _sit_  _yit_ / _yt_ is the share of NDP due to the _i_ ’th _i_  1 source. (This uses the approximation that  _yt_ / _yt_  1   ln _yt_ .) Given that the NDP data are annual (unlike the NSS data used in the last section to study the urban-rural pattern of growth), we can now test for a one-year lag in the effect of growth. We allow for sectorally-differentiated impact of growth by estimating a regression equation of the following form: Here, _i_ =1,2,3 denotes the primary, secondary and tertiary sector respectively, and _X_ denotes controls (discussed further below). In the special case in which  0 _i_   0 and  1 _i_   1 for _i_ =1,2,3, the above equation collapses to a simple regression of the rate of poverty reduction on NDP growth and its lag (  ln<sup>_y_</sup> _t_ and  ln<sup>_y_</sup> _t_  1 ), with the controls. Thus testing the null hypotheses H0:  _ji_   _j_ ( _j_ =0,1) for all _i_ tells us whether the composition of growth matters. We also allow the  parameters to differ across the pre-1991 and post-1991 periods, thus allowing us to separately test for growth composition effects for the two periods.

Our estimates of equation (11) are reported in Tables 8 (for H at both lower and higher poverty lines) and 9 (for PG and SPG at the higher line). There was no sign of a significant lagged effect of NDP growth, so we dropped the corresponding lagged terms in (11). We give both the unrestricted model in Table 8 and the model with data-consistent restrictions imposed. Again we control for changes in whether the survey used a uniform or mixed recall period as 24