rafmacalaba/datause-encoder-data huggingface dataset:
608 more costly than delaying sectors with low rates of learning. The same argument applies to sectors 609 with high degrees of inter-sectoral knowledge spillovers, such as energy (Richels and Blanford, 2008). 610 Finally, having a credible carbon price in place, even if temporarily deflated, may spur additional pri611 vate sector investment in abatement technologies (Brunner et al., 2012) which could bring down costs. 612 This would reinforce our overall conclusion that preserving the optimal timing of abatement investment 613 is of a higher priority than preserving the optimal allocation of emissions across sectors.

# 614 **VI.3 Generalized insights**

615 While we focused our discussion on the issue of combating climate change, a generalization of our 616 model of carbon emissions abatement investment, given by either (II.1) or (II.3), to general abatement 617 investment is natural. In essence, our model takes atmospheric CO2, a previously nonrival good between 618 sectors of the economy, and makes it rivalrous between sectors via a quota on net accumulated use; 619 the quota is represented by _B_ . The remaining parameters can be generalized as follows: _a_ ̄ _i_ represents 620 the exogenous use-rate of the good in each sector; _xi_ ( _t_ ) represents the investment in a generalized 621 abatement technology, with convex investment costs _ci_ ( _xi_ ( _t_ )); _ai_ ( _t_ ) represents a generalized abatement 622 capital stock; the economy-wide use of the good is accumulated via _ψ_ ( _t_ ); and the shadow value of a 623 marginal unit of abatement is given by _μ_ . Given this “mapping”, our insights are made generalizable to 624 any nonrivalrous good that is made rivalrous via an economy-wide quota, as are the cost implications 625 of delay-based policies in transitioning away from the use of this good. In particular, our findings via 626 Theorem IV.5 still hold; that is, implementing some policy in the near-term is a more cost-effective 627 approach to complying with PECs than delaying implementation of the theoretically optimal response 628 in the future, regardless of the nature of the good itself, insofar as it can be reasonably modeled by our 629 framework.

# 630 **VI.4 The timing of damages**

631 While the policies we discuss in our setup are always compliant with the implied temperature target, 632 the rate at which the temperature target is reached varies across policies. This owes to the fact that 633 abatement schedules are different across policies, and cumulative emissions are constrained only to not 634 exceed the ETS cap. While this approach has the advantage of getting around thorny issues of defining 635 a damage function for climate change (that is highly uncertain (Intergovernmental Panel on Climate 636 Change, 2022a) with existing parameterizations having been widely criticized (Stern, 2013; Pindyck, 31
find that 2,616 (40.5%) firms change their managers. In the regression we use the original criteria.

- _Becoming an exporter_ : this dummy variable equals 1 if a firm becomes an exporter between the two waves of the survey, it equals 0 otherwise.

-

- _Change in the number of competitors_ : this dummy variable equals 1 if the number of a firm’s competitors increases between the two waves of the survey, it equals 0 otherwise.

- _Firm size_ : small (0-20 full-time employees), medium (21-100 employees) and large (more than 100 employees).

- _Rule of law_ : percentile rank of the country. We calculate the ranking from the entire population of countries provided by the Governance Indicators. The rank is 100 for the highest ranked countries, and 1 for the lowest ranked. The detailed ranking of countries for Rule of Law and other institutional variables are shown at Table A2 in the Appendix.

- _Regulatory quality_ : percentile rank of the country. We calculate the ranking from the entire population of countries provided by the Governance Indicators. The rank is 100 for the highest ranked countries, and 1 for the lowest ranked.

- _Property right_ : percentile rank of the country. We calculate the ranking from the entire population of countries provided by the Heritage Foundation. The rank is 100 for the highest ranked countries, and 1 for the lowest ranked.

- _Patent protection_ : percentile rank of the country (by our own calculation from the entire population of countries). The rank is 100 for the highest ranked countries, and 1 for the lowest ranked. The data is a proxy for how well a patent is protected in a country. The data are from Park (2008). It is <mark>the sum of five separate scores for: coverage (inventions that are patentable); membership in international treaties; duration of protection; enforcement mechanisms; and restrictions (Park, 2008).</mark>

- _Sector fixed effects_ : 2-digit ISIC revision 3. This is to capture industry specific characteristics that may affect the return to innovation. For example, one might argue that a new product in electronics is likely to have better sales than a new line of shoes.

-

- _Country*time fixed effect_ : to capture a country’s macroeconomic effect.

# **3.2 Discussion about potential estimation biases**

It is difficult to isolate and capture exogenous sources of innovation. There are several issues when it comes to measuring the impact of innovation. First is a concern that inherently good firms in general 9
The third adjustment is related to the "Balance Sheet adjustment".22 The idea of this adjustment is that the macroeconomic impacts of the "skeletons" (old debts that were eventually repaid) occurred in the Fast. For example, the public debt issue for Banco do - - Brasil's recapitalization whose accumulated losses were threatening its solvency recognized losses derived from bad credit expansions in the past. Indeed, the debt issue was not related to deficits _during_ the recapitalization period, but to _old_ deficits, that had never been recognized until then. Thus, it i.s necessary to subtract the Balance Sheet adjustment's variation from the total net debt to obtain the debt variation concept comparable to the public deficit, namely, the "Net Fiscal Debt without Privatization".

Therefore, the following accounting identity should hold: for the fiscal statistics published by the Brazilian Central Bank.

Increase in Net Fiscal Debt without Plrivatization = Nominal Deficit.

Making the adjustments, we obtain: some state-owned enterprises held public debt as part of their assets. This debt, apparently was part of the gross debt, but, since it belonged to a slate owned enterprise, was not part of the net debt. After the privatization, it also became part of the net debt. Therefore the study of the relation between privatization and the public debt is still a work in progress.

> 22 Footnote no I of Table XXI of _Nota pcra Imprensa_ (Monetary) of _Banco Central do Brasil_ defines the "Balance Sheet Adjustment"as the following:

"(basis: Dec/95) Computes the bond issues relative to the Banco do Brasil's recapitalization, the reduction of the investment on the monetary reserve fuiid due to the court ruling involving the liquidation of the banks Comind and Auxiliar, securitization of debts, the use of "privatization money" in the PND, renegotiation of the Itaipu and Eletronorte debts with the SFN, the inclusion of constitution funds, besides the foreign debt difference, due to balance conversions, end of period exchange rates and the flows by the monthly average rate." 19
Figure 2: Total debt as percentage of GDP, 2000–14 <!-- Start of picture text --> 160.0<br>140.0<br>120.0<br>100.0<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text -->

# Source: Data from Ministry of Finance and Economic Planning, Ghana.

Such large-scale borrowing—made possible by the large expected oil revenues—had the effect of crowding out the private sector, which was increasingly unable to borrow to finance its growth. Risk-free treasury bill rates of 10.6 per cent in 2007—when the oil discovery was made—rose to around 25 per cent ahead of the 2008 elections, before dipping and returning yet again to around 25 per cent by 2014. Bank lending rates, at approximately 30 per cent in 2014, were also on the rise due to increasing public debt. Figure 3 shows that the 91-day treasury bill rate, which had steadily declined prior to the oil discovery, moved thereafter into a more volatile upward trend.

14
# **Are Trade Preferences a Panacea?**

# **The Export Impact of the African Growth and Opportunity Act***

Ana M. Fernandes<sup>**a**</sup> Alejandro Forero<sup>**b**</sup> Hibret Maemir<sup>**c**</sup> Aaditya Mattoo<sup>**d**</sup> Originally published in the Policy Research Working Paper Series on _Februay 2019._ This version is updated on _March 2021._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

**JEL Classification Codes** : F13, F14, F68, O2, O55.

**Keywords** : tariff preferences, Africa, AGOA, GSP, exports, Multi-Fiber Arrangement.

> a Ana Margarida Fernandes. The World Bank. Email: afernandes@worldbank.org.

> b Alejandro Forero. The World Bank. Email: aforero@worldbank.org.

> c Hibret Maemir. The World Bank. Email: hmaemir@worldbank.org.

> d Aaditya Mattoo. The World Bank. Email: amattoo@worldbank.org.

<mark>* This paper is a modified version of the World Bank Working Paper 8753 with the title “Are Trade Preferences a Panacea? The African Growth and Opportunity Act and African Exports”. This research was sponsored by the Africa Chief Economist Office at the World Bank and their support is gratefully acknowledged. The authors would like to thank Natalie Hanson, Souleymane Coulibaly, Woubet Kassa, Emmanuel Lartey, and Albert Zeufack for helpful discussions and insightful comments. We are grateful to Senidu Fanuel for arranging meetings with garment exporters in Ethiopia. Research for this paper has also in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development and the Strategic Research Program n Economic Development. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or its member countries.</mark>
Policy Research Working Paper 8937

# **Abstract**

This paper exploits information from two different datasets to provide a novel and multi-dimensional picture of the engagement of all sub-Saharan African countries in global value chains (GVCs). It documents in detail the nature of the underlying data and the way it is used to construct several indicators of GVC engagement. As a companion to the paper, the data files are made available to interested researchers. While it is impossible to summarize the broad range of experiences that we document, two patterns stand out. First, the level of GVC engagement of most countries in sub-Saharan Africa is rather low, especially for their manufacturing sectors. Second, while there is increased GVC engagement over time in some countries, this pattern is by no means universal. The average engagement for the region over the time period studied (1995–2018) is not even positive on average across countries for several indicators.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jo.vanbiesebroeck@kuleuven.be.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
in the pre-period of 2006, denoted as _t_ = 0: We use the SNAP QC sample of participants in 2006 to conduct the simulation. The state-level benefits per population _Bst_ are obtained from the FNS data.

Given that we use log points instead of levels in our empirical specification to translate into elasticities, the percentage changes in simulated benefits per population can be approximated as, where ∆<sup> ̄</sup> _s_ is the average difference operator.

The IV is similar in spirit to that of Currie and Gruber (1996) and used in a series of related approaches. The instrument maps to a generalized Bartik-style shift-share framework recently laid out by Goldsmith-Pinkham et al. (2020) and Borusyak et al. (2018); however, our strategy depends on an additional source of variation compared to traditional shift-share instruments. Our initial measure of local shock exposure is pre-recession SNAP household characteristics while the shocks are policy changes both at the national and local level, and we also allow for local shock exposures to be updated over time by plausibly exogenous local policy changes, namely, changes in the SUA.

How are changes to the SUA determined? As documented in detail in Appendix Section C.1, the intent of the government was to have the SUA reflect state-specific energy quantities consumed and prices by fuel type, and states were given the flexibility to use their own formulas to determine how to reflect these costs. Most states ended up using two main methodologies to set the SUA: (1) 43% update the SUA annually from a base using lagged changes in fuel CPI, and (2) 57% recalculate “averages” based on recent utility data. According to state surveys on this topic, these state-specific methodologies were determined independently of each other decades prior to 2006.<sup>17</sup> Anticipating the discussion, we show our results do not change when estimated separately for either set of states.

How are changes in the SUA plausibly exogenous? States had their hands tied in the updating of these numbers. Therefore, changes in synthetic benefits, especially after

> 17Only two states, New York and Vermont, changed their formulas in 2011 in all the years of our analysis from 2006-2015 (Holleyman et al. 2017).

10
an ideal case because the country has, since its independence on June 1, 1960, undergone several tumultuous periods, from “independence to anarchy” (Turner 2009, 346). Data from the Polity IV data set indicate that MEPVs occurred in 36 of the years between 1960 and 2012. Figure 1 shows the various MEPVs registered in the country between 1960 and 2012. Over these years, there were three distinct periods of MEPVs: 1960–67, 1977–84, and 1992–2012; each episode can be assigned a value of intensity on a scale from 1 (lowest) to 10 (highest). The durations of the MEPVs also varied, ranging from 7 years in both 1960–67 and 1977–84 to 20 years in 1992–2012.<sup>8</sup>

**Figure 1:** Magnitudes of (Societal and Interstate) MEPVs, **Figure 2:** Circumstances and Developmental Stages the Democratic Republic of Congo, 1960–2012

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1960 64 68 72 76 80 84 88 92 96 2000 04 08 2012<br><!-- End of picture text --> <!-- Start of picture text --> 2012<br><!-- End of picture text --> <!-- Start of picture text --> 0 year 18 years 25 years<br>Circumstances in childhood Circumstances in early Adulthood<br>up to age of consent: e.g.: adulthood: e.g.:<br>social backgrounds adverse shocks<br><!-- End of picture text --> _Source:_ Polity IV data, MEPV version, May 2016. _Note:_ Scale: 0 = no episode; 1 (lowest) to 10 (highest).

Analyzing these MEPVs in the light of the impressionable years hypothesis suggests that individuals in the 18–25 age-group may experience different frequencies and degrees of MEPV in the Democratic Republic of Congo. For instance, some individuals may have experienced MEPVs during one to seven years, and, in extreme cases, some may never have experienced MEPVs, while others may have experienced MEPVs over their entire early adulthood, that is, for up to eight years.

To put this into perspective in terms of circumstances relevant to discussions on the inequality of opportunity, figure 2 displays developmental stages among individuals ages 0–25, along with probable circumstances associated with each stage. The first segment portrays the stage from childhood to the age of consent (up to age 18). The literature on inequality of opportunity mostly emphasizes circumstances related to genetic and social backgrounds during this stage. The second segment, the focus of this paper, is early adulthood (ages 18–25). During this phase, factors related to later brute luck are of interest. An example is the exposure of individuals to adverse shocks, such as violent conflicts. The third phase, adulthood, is beyond the scope of this analysis.

> 8 The cutoff year in the sample is 2012 because the macrodata can be linked with the household survey that was conducted in 2012. Nonetheless, this should not be taken to mean the country has not registered any MEPVs since then. For more on MEPVs in recent years, see “INSCR Data Page,” Center for Systemic Peace, Vienna, VA, http://www.systemicpeace.org/inscrdata.html.

7
41 Table 1: Summary Statistics by Type of Crime

|Variable|Mean|Minimum|Maximum|
|---|---|---|---|
|Residential burglary|963|0|17,834|
|VehicleTheft|319|0|16,553|
|Seriousassault|1,002|0|41,848|
|Rape|185|0|8,696|
|Murder|89|0|3,804|
|Robberywithaggravatedcircumstances|155|0|7,781|

Crime variables are reported per 100,000 inhabitants for the year 1996. The figures are for 1,066 police station jurisdictions. The means reported here are simple means, i.e. not weighted by population size.

Table 2: Crime and Inequality

|Property|Crlmes|ViolentCri|mes|
|---|---|---|---|
|ResidentialBurglary|VehicleTheft|AggravatedAssault|Rape|
|I**n**eqalit<br>iolic<br>Staion1.17<br>InequalityinPoliceSbtaton<br>(009)**|0.74<br>_(0.15)**_|0.57<br>(0.08)**|0.53<br>(0.08)**|
|0.86<br>Population<br>(0.02)*|0.95<br>(0.04)**|0.79<br>(0.02)**|0.86<br>(0.02)**|
|ProvinceDummies||||
|-0.88<br>EasternCape<br>(0.10)**|-1.02<br>(0.16)**|-0.18<br>(0.09)*|-0.21<br>(0.08)*|
|-1.21<br>NorthernCape<br>(0.13)**|-1.94<br>(0.23)**|-0.03<br>(0.11)|-0.25<br>(0.11)*|
|-0.93<br>FreeState<br>(0.12)**|-1.03<br>(0.20)**|-0.50<br>(0.10)**|-0.26<br>(0.10)**|
|-0.51<br>Kwazulu-Natal<br>(0.10)*|0.50<br>(0.16)*|-0.72<br>(0.09)**|-0.16<br>(0.08)*|
|-1.03<br>Northwest Province<br>(0.13)**|-0.94<br>(0.21)**|-0.30<br>(0.11)**|-0.05<br>(0.10)|
|Gauteng<br>0.69|1.76|0.03|0.45|
|<br>(0.11)**|(0.18)**|(0.10)|(0.09)**|
|-0.39<br> <br>**|-0.11<br>|-0.28<br>**|-0.07<br>|
|Mpumalanga<br>(0.12)|(0.20)|(0.10)|(0.10)|
|-0.99<br>NorthernProvince<br>(0.13)**|-1.07<br>(0.22)**|-0.02<br>(0.11)|-0.25<br>(0.11)*|
|-2.06<br>Consbant<br>(0.23)*|-4.83<br>(0.40)*|-2.03<br>(0.18)**|-4.57<br>(0.19)**|
|PseudoR2<br>.075|.081|.076|.123|
|Observations<br>1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; ** significant at 1%
average domestic price level of these products, changes in their average Rupee reference price, and changes in their average world US dollar price. Especially when expressed in constant prices, we believe this provides a useful perspective on the main forces that have determined agricultural incentives over the period, including agricultural trade policies, trends and fluctuations in world agricultural prices, and changes in the Rupee exchange rate.

## _Statistics_

In order to summarize various characteristics of the price series, we have calculated a number of standard statistics, including the coefficient of variation and the average annual percentage rate of change (LS growth) estimated from a log-linear regression of the price series over the 29 years from 1965 to 1994. As an indicator of the variability of prices during the period, in addition to the coefficient of variation, we also show a variant of the Z statistic used in a large multi-country study of agricultural pricing policy (Schiff and Valdes, 1992).As pointed out out in this study, producers may be more concerned with year to year changes in prices than with deviations from the mean of prices over a long period, and to capture this the authors calculate the square root of the average squared difference between the current price and the previous year's price.1<sup>2</sup> To make this concept comparable as between different price series and for symmetry with the coefficient of variation, we have defined the Z statistic as this number divided by the mean. That is:

**_n n_** _Z= [,(p, _ p, _ 1)2 / (n - 1)]"2 I [Pn / n]_ i=2 _1_

where P is the price at time t.

It would also be possible to summarize price variability with respect to trend values rather than the period mean, but for the constant price series of most of the commodities included in the study, the resulting summary statistics would not greatly differ from the statistics actually calculated.

# **Effective Protection**

The effective protection of a commodity is an estimate of the extent to which the margin between its selling price and the cost of its internationally tradeable inputs has been widened or narrowed by the combined effect of the protection of the commodity and the protection (which could be negative i.e. a subsidy) of the tradeable inputs. In this study it is measured as the effective protection coefficient ( EPC), which is defined as the ratio of the value added at domestic prices (i.e. the observed value added) of the production activity, to the estimated value added at reference prices. That is:

# EPC = VAD/VAR

= (pD - Sum (aj PD)) _I_ (pR.- Sum (a PR.))

- = (pD - PDSum(aj)) / (pR - Sum(aj POb/ NPCq)

where, EPC = Effective protection coefficient VAD = Value added at domestic prices 12 Schiff and Valdes (1992), p56. The prices for which the Z statistic is calculated in this study are relative prices with respec **t** o the same numeraire, so that the Z statisitics for different price series are comparable.

xxii