metadata: authors: 0: author: Barco Rondan,Daniel Francisco
1: author: Celiku,Bledi
2: author: Chávez,Paulo
3: author: Galego Mendes,Arthur
4: author: Pennings,Steven Michael
5: author: Resk,Elena
count: Peru,World
docna: 0: docna: Long-Term Growth Prospects in Peru :
            Leveraging the Global Green Transition and the Reforms
            Needed to Become a High-Income Country
docty: Policy Research Working Paper
theme: Inclusive Growth,Mitigation,Job Creation,Gender,Human Development and Gender,Data Development and Capacity Building,Economic Policy,Green Growth,Economic Growth and Planning,Environment and Natural Resource Management,Private Sector Development,Public Sector Management,Climate change,Jobs,Adaptation,Data production, accessibility and use,Public Assets and Investment Management,Spatial Growth,Public Administration,Macroeconomic & Structural Policy Modelling,Structural Transformation and Economic Diversification
abstracts: cdata!: This paper uses the World Bank Long-Term
            Growth Model and extensions to study Peru&#x2019;s
            long-term growth prospects and its potential to attain
            high-income economy status. Under a business-as-usual
            baseline, Peru&#x2019;s potential GDP growth declines
            slowly from 2.1 to 1.7 percent over the next two decades,
            due mostly to demographic factors. In this baseline, it
            takes more than half a century to reach high income status.
            To accelerate growth, the paper considers moderate and
            ambitious reform scenarios for the non-resource sector
            through faster total factor productivity (TFP) growth, human
            capital growth, and higher investment rates. The ambitious
            reform path accelerates growth to an average of 4.3 percent
            in the simulation period (2024&#x2013;50), allowing Peru
            to reach high-income status by 2045. The paper also
            considers a Global Green Transition scenario, where Peru
            takes advantage of higher global demand for copper from
            clean technologies. In that scenario, higher copper prices,
            greater exploration, improved mining technology, and
            reinvested copper windfalls increase baseline growth to 3.1
            percent by 2035. If Peru were able to harness the global
            green transition and implement ambitious reforms in the
            non-resource sector, growth could accelerate to an average
            of 5 percent, and the country could reach high-income status
            by 2042.
display_title: Long-Term Growth Prospects in Peru :
            Leveraging the Global Green Transition and the Reforms
            Needed to Become a High-Income Country
listing_relative_url: /research/2024/09/34387128/long-term-growth-prospects-peru-leveraging-global-green-transition-reforms-needed-high-income-country
topicv3: Economic Growth m1326241 471,Climate Change m1326829 567
publishtoextweb_dt: 2024-09-06T00:00:00Z
dois: 10.1596/1813-9450-10900
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 31
author_keys: 0: author_key: 1371033
1: author_key: 1157598
2: author_key: NA
3: author_key: 1464295
4: author_key: 1324256
5: author_key: 2013289
model_extractions: Policy Research Working Paper 10900 Long-Term Growth Prospects in Peru Leveraging the Global Green Transition and the Reforms Needed to Become a High-Income Country _Daniel Barco Bledi Celiku Paulo Chávez Arthur Mendes Steven Pennings Elena Resk_ Development Research Group & Macroeconomics, Trade and Investment Global Practice September 2024
Policy Research Working Paper 10900

# **Abstract**

This paper uses the World Bank Long-Term Growth Model and extensions to study Peru’s long-term growth prospects and its potential to attain high-income economy status. Under a business-as-usual baseline, Peru’s potential GDP growth declines slowly from 2.1 to 1.7 percent over the next two decades, due mostly to demographic factors. In this baseline, it takes more than half a century to reach high income status. To accelerate growth, the paper considers moderate and ambitious reform scenarios for the non-resource sector through faster total factor productivity (TFP) growth, human capital growth, and higher investment rates. The ambitious reform path accelerates growth to an average of 4.3 percent in the simulation period (2024–50), allowing Peru to reach high-income status by 2045. The paper also considers a Global Green Transition scenario, where Peru takes advantage of higher global demand for copper from clean technologies. In that scenario, higher copper prices, greater exploration, improved mining technology, and reinvested copper windfalls increase baseline growth to 3.1 percent by 2035. If Peru were able to harness the global green transition and implement ambitious reforms in the non-resource sector, growth could accelerate to an average of 5 percent, and the country could reach high-income status by 2042.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. 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 dbarcorondan@worldbank. org, bceliku@worldbank.org, pchavezuni@gmail.com, agalegomendes@worldbank.org, spennings@worldbank.org, and eresk@worldbank.org.

_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
# **Long-Term Growth Prospects in Peru: Leveraging the Global Green Transition and the Reforms Needed to Become a High-Income Country**<sup>**1**</sup>

Daniel Barco Bledi Celiku Paulo Chávez Arthur Mendes Steven Pennings Elena Resk

**Keywords** : Economic growth, human capital, investment, total factor productivity, innovation, education, market efficiency, infrastructure, institutions, Peru, green transition

**JEL** : 054, H54, O13, 041

> 1 Date: September 2024. Corresponding author: Bledi Celiku (bceliku@worldbank.org). The views are those of the authors, and not necessarily those of the World Bank. The authors thank Guido Damonte for research assistance and Jorge Guzman for work on an early draft of this paper.
# **1. Introduction**

Peru experienced strong and sustained economic growth in the post-1990 period, which supported remarkable gains in social and economic development. Between 2002 and 2019, Peru grew at an annual 5.1 percent rate on average (Figure 1), well above the 2.7 percent average in Latin America. High growth rates enabled Peru to double its per capita income and reach upper-middle-income status in 2008. Between 2002 and 2013, GDP growth was exceptionally high, averaging 6.1 percent annually (5.2 percent in per capita terms), driven by good economic reforms and the commodity boom.

However, economic growth halved to 3 percent in the 2014-2019 period, partly driven by the end of the commodity supercycle and because of a lack of substantial reforms. The less favorable external environment during the 2014-2019 period brought to light the more profound structural challenges of Peru’s growth model, such as low productivity growth (World Bank, 2022). The pursuit of economic development in Peru in the coming decades requires the search for new growth drivers.

This paper studies Peru’s long-run economic growth prospects and what it would take for the country to attain high-income (HI) status. Accelerating economic growth is crucial. For example, if Peru continued at its current trend growth rates in the post-COVID period of 2.5%, it would take almost half a century to achieve HI status. In contrast, if Peru grew at a rate of 5 percent, the average rate during the 2002-2019 period (including the commodity boom and bust period), it would only take 14 years (Figure 2).

Figure 1. Historical GDP Growth Figure 2. Real GNI Per Capita<sup>2</sup> <!-- Start of picture text --> 10.0 32,000<br>Actual<br>5.0<br>16,000<br>0.0<br>8,000<br>-5.0<br>4,000<br>-10.0 Forecast<br>-15.0 2,000<br>HL GDP growth GDP PC growth GNI Per Capita Simulated GNI per capita */<br>Forecast HL GDP growth Forecast GDP pC growth High Income Threshold Baseline<br>Source: World Development Indicators and WB staff`s Source: World Development Indicators.<br>forecasts */ Simulated with a constant growth rate of 5% (2022-19<br>Note: The series are the 3-year centered moving average. average).<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> We use the World Bank Long-Term Growth Model suite (LTGM, Loayza and Pennings 2022) to evaluate ways to boost growth and achieve high-income status more quickly. The standard LTGM is a Solow-Swan 2 Methodology used for Figure 2: Calibrate the levels to the GNI per capita Atlas method (NY.GNP.PCAP.CD) for 2018, and then cast backwards using real GNI growth (NY.GNP.PCAP.KD.ZG). The horizontal line shown is the 2022-23 high income threshold.

2
style neoclassical growth model, though can be extended in a range of directions depending on the country and context. We use the LTGM Natural Resource Extension (LTGM-NR, Loayza et al. 2022) as our core model, as Peru is a resource-rich country. The LTGM-NR augments the standard neoclassical growth model with a natural resource sector and government fiscal policy that determines how resource revenues are spent. This modeling approach provides a more accurate analysis than the main LTGM for resource-rich economies, as it accounts for heterogeneity across the resource and non-resource sectors (Appendix A1). We also use the LTGM-TFP extension (Kim and Loayza 2019) and the LTGM-Human capital extension to model Total Factor Productivity (TFP) growth and human capital growth, respectively.<sup>3</sup> We first model Peru’s business-as-usual growth path, where the growth drivers – the investment-to-GDP ratios, TFP, human capital, and labor force participation rates – follow their historical or recent trends.<sup>4</sup> We find that potential GDP growth in Peru is likely to fall from around 2.1 percent in 2024 to around 1.4 percent in 2050, driven mostly by demographic factors, but with a secondary contribution from declining human capital growth (as the workforce is already better educated). As growth slows it takes even longer to reach HI status in the baseline—almost 65 years.

Next, we assess the extent to which domestic reforms to improve the investment rate, human capital and TFP growth can accelerate GDP growth. Specifically, we simulate moderate and ambitious shocks to the factors of production with targets based on the distribution of those growth fundamentals among HI countries. Ambitious reforms generate growth that averages 4.3 percent in the 2024-2050 period, compared to the 1.9 percent average in the business-as-usual baseline.<sup>5</sup> We also simulate the long-term growth under a global “green transition” scenario where the increased demand for copper, if the world achieves Net Zero Emissions (NZE), results in higher copper prices, higher copper investment, and greater exploration. The estimated GDP growth is roughly one percentage point higher compared to the baseline, which would help Peru considerably in achieving its growth targets. The combined scenario of the global green transition and ambitious domestic reforms accelerates Peru’s GDP growth significantly and the country reaches the target of HI status by 2042.

The rest of the paper is organized as follows. Section 2 presents past growth dynamics. Section 3 discusses the assumptions used in the baseline simulation, presents the baseline growth path to 2050 and the contribution of each growth driver to the slowing growth rate. Section 4 presents the combined impact on GDP growth due to reforms on each growth determinant. It shows Peru’s standing in comparison to structural and regional peers on five separate TFP determinants and quantifies the impact from converging to frontier countries on TFP growth. Section 5 presents the global green transition scenario, which analyses the effects of higher copper prices that would result from global NZE, a shock to the copper sector TFP, higher investment, and long-term production. It then combines the domestic reform and the global green transition scenario to quantify the combined impact of GDP growth. The conclusion provides a summary of the main findings and policy implications.

> 3 The LTGM-HC tracks the years of schooling and the quality of education and health across age cohorts to determine the human capital of the workforce. In the LTGM-TFP, the TFP growth rate is calculated as the compositive effect of TFP determinants: innovation, education, market efficiency, infrastructure, and institutions. See the Appendix for details.

> 4 Future demographic projections are taken from the United Nations (UN).

> 5 Ambitious reform is the mix of shocks to the investment rate, human capital and TFP which are detailed in section 4.

3
# **2. Historical evolution of GDP growth and its drivers in Peru**

During the 1990s, Peru experienced an average growth rate of 3.2%, closely tied to the implementation of economic reforms following the hyperinflation period of the 1980s. These reforms, primarily macroeconomic in nature, aimed at trade liberalization, privatization of non-profitable state-owned enterprises, fiscal discipline, financial sector reform, and the promotion of private investment. The combination of these measures resulted in a significant accumulation of capital and laid the foundation for growth in the new millennium.

Building on this momentum, from 2002 to 2013, Peru's growth rate doubled compared to the previous decade, with an average rate of 6.1%. This success was driven by good macroeconomic policies, political stability, and favorable external conditions. The country emerged as one of the world's leading copper producers, thanks also to various large-scale mining projects that were launched during this time. The commodities boom contributed to a remarkable 73% growth in terms of trade, bolstering private investment, which tripled by the end of 2013. The accumulation of physical capital played a significant role, contributing 2.7 percentage points to the period’s overall growth, marking the highest value in the past two decades (Figure 3).

Furthermore, increased fiscal resources resulting from improved tax collection facilitated the financing of public investment. Total investment as a percentage of GDP reached 24%, the highest since the early 1980s (Figure 4). Additionally, there was 2.1% growth in TFP (Figure 5) of which Castillo and Rojas (2014) attribute a quarter to improved terms of trade. The fiscal revenue boost also supported reforms in health care and education, while new social programs provided strengthened assistance to vulnerable groups.

From 2014 to 2019, Peru faced unfavorable external conditions and a lack of new economic reforms, leading to a significant drop in its growth rate, almost cutting it in half to 3%. Although the macroeconomic fundamentals were stable, the lack of visible policy initiatives aimed at fostering sustained growth became apparent.

Both capital and productivity contributions were lower during this period (Figure 3). World Bank (2022) attributes the slow productivity growth to a predominance of small-scale enterprises and the absence of incentives to promote their expansion. High informality in Peru contributes to the overall lower productivity. The limited progress made in addressing these challenges can be linked to the weak capacity of public institutions. Institutional instability has eroded the state's ability to implement necessary reforms, develop quality infrastructure throughout the country, and mitigate market rigidities.

4
**Figure 3.** Factor contributions to Short Run GDP Growth, period averages, percentage points (Excluding indirect effects of non-capital factors on capital deepening)

<!-- Start of picture text --> 8.0<br>6.0<br>2.1<br>4.0 0.6 0.1<br>0.3<br>1.3 0.9 0.2<br>2.1 1.0<br>2.0<br>2.7 2.6<br>1.6 2.0<br>0.0<br>-0.5 -0.7<br>-1.4<br>-2.0<br>1992-2001 2002-2013 2014-2019 2020-2022<br>Growth = 3.8 Growth = 6.1 Growth = 3.1 Growth = 1.7<br>Physical Capital Labor Human Capital TFP growth GDP Growth<br><!-- End of picture text --> Source: MTI Growth Accounting Tool, WB Staff calculations Amid the challenges, there were also some notable achievements. The 2002-2019 period saw positive developments in poverty reduction, a decline in inequality, and improvements in human capital (Figure 6). Peru has also made significant progress in improving access to education and quality of education in the last 20 years. The percentage of young adults (ages 25-34) with a secondary education increased from 43% in 2000 to 60% in 2017, and the percentage of young adults with a tertiary education increased from 12% to 20% over the same period. Human Capital's contribution to growth increased during 2014-2019 but deteriorated during the COVID-19 pandemic (see Figure 3).

Total population growth was close to 1.2% in the last decade after experiencing a downward trend since 1990 (Figure 7). Population growth has increased in the last 5 years due to Venezuelan immigration to Peru going from around 1.1% in 2014 to 1.9% by 2019. After the COVID crisis, the population growth decreased to 1% in 2022.

Between 2020 and 2021, during the COVID-19 crisis, accumulated GDP growth was less than 1 percent and the country is now recovering from this significant shock. Over this period the structural challenges were highlighted, and the potential growth was affected due the lower productivity and labor contributions.

5
Figure 4. Historical Investment-to-GDP (%) <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Figure 6. Historical Human Capital Growth (%) Figure 5. Historical TFP Growth (3yma) <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br><!-- End of picture text --> Figure 7. Historical Total Population Growth (%) <!-- Start of picture text --> 2.5 3.5<br>3<br>2<br>2.5<br>1.5<br>2<br>1<br>1.5<br>0.5 1<br>0 0.5<br>0<br>-0.5<br>-1<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Source: World Development Indicators, Penn World Table 10.01 6
# **Box 1. Chile’s Economic Growth Experience in Comparison to Peru**

This box aims to compare the Chilean economic growth experience from 1991 (when Chile was at approximately at the same level of development as Peru in 2021) onwards. As Chile transitioned to high-income status, the historical drivers of growth were diverse, but were led by higher private investment, followed by higher TFP growth rates, and higher Human Capital growth rates, a demographic dividend that lasted for 22 years. Headline GDP growth and GDP per capita growth averaged 5.1% and 3.9%, respectively, throughout that process. Box Figure B1b shows that it took Chile 21 years to become a high-income economy.

<!-- Start of picture text --> Figure B1-a. Chile’s Economic Growth Figure B1-b. Chile’s path to High Income Status<br>Performance<br><!-- End of picture text --> <!-- Start of picture text --> 25,000<br>15 Chile (years from 1991)<br>HL GDP Growth HI Threshold 2022<br>20,000<br>10 GDP PC Growth<br>Peru years from 2021 (April<br>2023 WB MPO Estimate)<br>15,000<br>5<br>10,000<br>0<br>1991 1996 2001 2006 2011 2016 2021<br>5,000<br>-5<br>0<br>-10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22<br>Source: World Development Index Source: WDI, WB projections.<br><!-- End of picture text --> Growth drivers during this time were 1) investment-to-GDP ratio averaged 23% (private investment averaged 20.5% and public investment 2.5%), 2) average saving was 22.3% and Current Account Balance-to GDP ratio was, on average, -0.8%, 3) human capital growth averaged 0.6%, 4) TFP growth averaged 0.5%, 5) the growth of the working age-to-total population ratio (WATP) was around 0.35%, indicating a demographic dividend throughout this period and 6) total population growth declined from 1.65% to 1.05%.

# **3. Growth drivers in the business-as-usual baseline**

In this section we calibrate the LTGM-NR model to simulate Peru’s business-as-usual baseline over the 2023-50 period. To create a baseline for the Peruvian economy in the long run, we calibrate the future paths of the growth drivers such as investment, human capital, the growth of TFP in the non-resource and natural resource sectors,<sup>6</sup> as well as other key parameters. These assumptions are summarized in Table 1. We calibrate the LTGM-NR as the foundation of the analysis and add the LTGM-TFP extension and the LTGM-HC extension to simulate non-resource sector TFP growth and human capital growth, respectively. We use population growth projections (by age cohort) from the UN for demographic trends until 2050.

6 The resource sector is composed of the copper mining sector, which is around 8 percent of the total GDP and is expected to grow in the short run by around 2 percent due to the entrance of the Quellaveco mine. In the long run, the depletion in reserves will shrink the sector to 6 percent of GDP and grow below 1 percent on average.

7
The remaining projections are determined by assuming that long-term trends in Peru remain constant and by performing steady state calculations. The labor share in the non-resource sector N = 0.49 is calibrated by adjusting the aggregate labor share proportionally in relation to non-resource GDP (see Loayza et al. 2022).<sup>7</sup> Using the calibration of Loayza ββ et al. (2022), the capital share in the mining sector ( )=0.84 is calculated with the natural resource rents shares ( ) from the Global Trade Analysis Project (GTAP), and averaged over 2004, 2007, 2011, and ii 2014. The tax rate in the resource sector, = 0.621 −γγ , is calibrated using data on government natural ii γγ resource revenues and resource GDP from the IMF’s World Commodity Exporters Dataset (IMF-WCE). For Peru, the = 0.62 is set to match the historical average in the last 20 years of resource revenues as a ττ<sup>NN</sup> share of resource GDP. Following the standard model of the LTGM-NR, we calibrate the fiscal rule like a Balanced Budget-Hartwick Rule, which implies a marginal propensity to invest of ττ<sup>NN</sup> . GDP for 2021 is taken from World Bank’s World Development Indicators (WB-WDI), in constant 2015 U.S. θθ= 1 GDP for 2021 is taken from World Bank’s World Development Indicators (WB-WDI), in constant 2015 U.S. dollars. In the absence of comprehensive information on GDP at the copper sector level, we proxy GDP in the copper sector by the exports of copper. For Peru, GDP in the copper industry is set to match the value of exports as a share of GDP in 2021. The export data is taken from the UN-Comtrade Database (UN-CT), which provides information on export value for all 11 commodities and all 56 countries, with a time series that usually starts in 2002.

As in the standard LTGM, the initial stock of capital-to-GDP ratio of 3.6 is calculated using the most recent observation from PWT 10.01. The initial capital stock is split across activities to equalize the initial marginal revenue product of capital across the copper and non-resource sectors.<sup>8</sup> In the resource sector, the reserves are calibrated using the initial stock of reserves in industry is taken from the U.S. Geological Survey Database (USGS) for mining industries, such as copper, gold, and iron. As a stock variable, initial ii reserves are set to match the most recent observation (usually 2017).

The model requires the paths of exogenous variables from 2021 until 2050, and we assume that the variables will follow recent trends in the long-term. Discoveries of natural resources are calibrated using data on annual production and reserves of the resource good , taken from the USGS. The time series of discoveries of good in period is computed as the change in reserves from period to plus production in period . In the baseline, the trajectory of discoveries of good ii from 2021 to 2050 is set to ii tt tt− 1 tt match the historical average over the past 15 years of 6.5 million metric tons (mt). tt ii The trajectory of copper prices is from Chile’s Advisory Committee projections for the Copper Reference Price 2023, which is a survey of 20 experts that publishes their expected prices of copper until 2032. We use the average of the 20 responses and convert it to real prices of 2015 in US$/mt. For the trajectory of prices between 2032 and 2050 we assume copper prices are constant at their 2032 value.

> 7 N P × G /G 0 where P is the share of labor compensation in GDP in period , taken from Penn World Table 0 10.01 (PWT10), and PP G is non-resource GDP in period PP . 8 Following Loayza et al. (2022), the initial capital stock in activity ββtt = ββtt PPtt PPtt ββtt must satisfy the following tt equations, tt PP tt 2021 2021 = 0 )G jj 2jj021 f NN+ 1 jj iijj )G 2021 + ∑ii )G 2021 (1 −γγ II where G II2021jj ≡ ~~�~~ pp̅2015jjpp̅tjj �GKK PP2021ii /YY denotes real GDI in activity (1 −ββ<sup>N</sup> PP ii , year NNii=1(1 −γγtt ; γγ0 = ββii<sup>N</sup> IIii . ff ii= 0, ... , NN

8
In the default model, we decompose total investment into private and public investment (these have identical effects in the LTGM-NR, but have different determinants). Private investment as a percentage of GDP is assumed to remain at approximately 17.7%, which is the historical average between 2004 and 2019 according to data from the Central Bank of Peru. Public investment is set at 4.7% using data from the IMFFAD, which represents the average of the last 10 years of available data.

Total factor productivity growth of the non-resource sector is generated using the LTGM-TFP extension (Kim and Loayza 2019) assuming that the determinants of TFP - innovation, education, market efficiency, infrastructure, and institutions – increase at their historical rate over the past decade.<sup>9</sup> This results in an almost-constant path of TFP growth over the simulation period of 0.5%-0.6%, which is similar to the historical average over 2004-19.

The human capital growth path for the baseline is generated using the LTGM-HC extension, assuming that the current quality and quantity schooling and health of today’s children persists throughout the simulation period. This generates human capital growth of 0.3% in 2022 and declines to about 0.1% in 2050. We assume the average expected years of schooling in the future is the same as that of today’s children, 12.7 years, taken from the World Bank Human Capital Index. Because today’s new workers are better educated than older workers moving to retirement, the average human capital of the workforce grows over time – despite no increase in the schooling of children. The rate of human capital growth slows over time due to rising average education quantity of the workforce, which reduces the educational gap between new workers and incumbents. Human capital also includes the quality of education, as well as health. The health of the population is measured by adult survival rates (ASRs), which is the probability that a 15-year-old will reach their 60th birthday, and stunting rates, defined as the fraction of 5 years old that are not stunted (see equation below).<sup>10</sup> In the baseline, we assume that schooling quality remains at its original level of 0.65, ASRs stay at 0.88 and the not stunted rate stays at 0.87. Data on the health and education variables are taken from the World Bank’s Human Capital Project. Due to a lack of historical data, we also assume that those rates apply to the whole working age population, and so schooling quality and health make no contribution to human capital growth in the baseline.

> 9 Specifically, we applied the average annual change in the overall determinant index over 2010-2019 to the period of 20212050.

> 10 hH = S hf × H Y ×Q −14 ) <u>� StS t</u> 2t <u>ASA t �</u> . The return to education is γγ t(N ttSSttS ttNNdd t−1)+γγ A(AASSAA t−1) assumed to be 12%. HH φφ(Y YYho lltt QQlliittyytt tt tt tt ooiioogg HHootth = HH × HH

9
Table 1. Assumptions for the Peru Business-as-Usual Baseline (2023-50)

||Value|Unit of measurement|Time Series|Source/Target|
|---|---|---|---|---|
|Baseline<br>|||||
|A. Main Parameters<br>|||||
|Depreciation rate (δ)<br><br>|4.2%|Percent/year|2009-19 average|PWT 10.01, Average depreciation rate of the capital stock<br>PWT 10.01, Share of labor compensation in GDP at current|
|Labor share in the non-res sector (βN)<br> <br>|0.49|[0, 1]<br>|2019|prices|
|Capital share in mining sector (1-γ)<br>|0.84|[0, 1]|2004-14 average|GTAP, 2004, 2011, 2007, 2014|
|Govt. share in the resource sector (τ)<br>|0.62|Share of copper GDP<br>Share of extra res.|Last 20 years, average|WEO, IMF-WEC, UN-Comtrade|
|Marginal propensity to invest (θ)|1.0|Revenue|Hartwick Rule (invest windfalls)|WEO/Match historical public investment to expenditure ratio|
|Long-run public investment|4.7%|% of GDP|Last 10 years, average|WEO, general government investment|
|B. Initial Values|||||
|Per capita GDP|6,446.9|Real, 2015 US$|2021|WDI, Real GDP per capita (NY.GDP.MKTP.KD)|
|Exports of copper|8.2%|% of initial PC GDP|2021|UN-Comtrade|
|Capital-to-GDP ratio|3.6|[0, inf]|2021|PWT 10.01|
||||Equalize the VPMK across sectors in||
|Non-resource sector|3.15|[0, inf]|2019<br>Equalize the VPMK across sectors in||
|Copper sector|0.46|[0, inf]|2019||
|Reserves of natural resources|||||
|Copper sector|81|Million of Metrics tons|2017|U.S. Geological Survey|
|C. Assumed paths of standard drivers of grow|th, 2021-50||||
|Total Investment|22.4%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Private, Investment|17.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Public, Investment|4.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|TFP, non-resource sector|0.6%|Annual growth rate|LTGM-TFP|Aggregate TFP growth from PWT 10.01|
|TFP, mining sector|-0.5%|Annual growth rate|TFP average growth in Chile in 2000-17|Productivity Commission Chile (2016)|
|Human Capital|0.2%|Annual growth rate|LTGM-HC|PWT 10.01, Human Capital index (hc)|
|Demographics:|||||
|Population|0.7%|Annual growth rate|2021-2050, average|ILO's estimates, total population|
|Working-age population|65.2%|% total population|2021-2050, average|ILO's estimates, population aged 15-64|
|Population, Male|49.2%|% total population<br>initial % of working age|2021-2050, average|ILO's estimates, population, male|
|Participation rate, Male|88.1%|pop.|2021|WDI, Labor force participation rate, male (SL.TLF.ACTI.MA.ZS)|
|Participation rate, Male|0.4%|Annual growth rate<br>initial % of working age|2021-2050, average|WDI, Labor force participation rate, male (SL.TLF.ACTI.MA.ZS)|
|Participation rate, Female|74.1%|pop.|2021|WDI, Labor force participation rate, female (SL.TLF.ACTI.FE.ZS)|
|Participation rate,Female|1.0%|Annualgrowth rate|2021-2050,average|WDI,Labor forceparticipation rate,female(SL.TLF.ACTI.FE.ZS)|

10
# **3.1. BAU baseline over 2023-2050.**

Under a baseline or business-as-usual growth path, Peru’s potential GDP growth would slow down from 2.1 percent in 2024 to around 1.4 percent by 2050 (Figure 8). The BAU baseline measures the potential growth rate of the economy and is not a forecast of actual growth. The recent forecast of GDP growth and GDP per capita growth (dotted lines in Figure 8) are slightly above the potential growth as it incorporates the short run expectations about investment and trade balance effects due to starting operations of Quellaveco mine, one of the top ten copper mines in terms of proven reserves. However, short run deviations will not have much effect on the long-run growth potential that is the focus of this paper. This section explains the drivers of current growth, and the reasons for the declining trend. Under this baseline simulation, Peru will achieve HI status in 64 years.

Figure 8. Peru’s BAU Baseline GDP and GDP PC Growth <!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>HL GDP growth LTGM GDP PC growth LTGM<br>HL GDP growth WB projections GDP PC growth WB projections<br>Source: LTGM simulations, World Bank Staff’s calculations<br>2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050<br><!-- End of picture text -->

# **What explains current trend growth rates?**

Investment in the non-resource sector and non-resource TFP growth are the largest drivers of current economic growth, followed by a declining contribution of labor. Current growth can be decomposed using log-linear approximation of the production function. Investment in the non-resource sector is the most important growth driver in different periods of the simulation, with a contribution around 0.9ppts, which is almost half of total growth (Figure 9). The next largest contribution is from TFP growth of non-resource sector, with a contribution of 0.5ppts, with labor force growth of 0.3ppts.

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Figure 9. Contributions of Factors to GDP Growth (short-run method) <!-- Start of picture text --> 2.1% 2.0%<br>2.00% 0.10%<br>0.08%<br>1.70% 1.6%<br>0.07% 1.4%<br>1.40% 0.96% 0.99% 0.06% Copper reserves<br>Copper investment<br>Copper TFP<br>1.10% 0.97% Non-res investment<br>0.92% Labor force growth<br>0.80% 0.29% 0.29% Human Capital growth<br>Non-res TFP<br>0.50% 0.14% 0.10% 0.07%0.05% 0.03% Total GDP growth<br>0.20% 0.56% 0.53% 0.49% 0.47%<br>-0.02%<br>-0.10%<br>2025 2035 2045 2050<br>Source: LTGM simulation, World Bank staff.<br><!-- End of picture text -->

# **Why is the baseline growth rate declining?**

To understand why baseline growth falls, we run counterfactual simulations where the growth rate of each growth driver is kept constant at current rates. Table 2 depicts the impact of each driver on the fall in GDP growth between 2023-2050 (figures are in Appendix A2). These are normalized contributions, which means that the individual contributions are scaled so that they add up to the total fall in growth. The normalization is necessary because over the long term, the model is non-linear.

Demographics are the most important factor and explain almost two-thirds of the 0.78 ppts fall in GDP growth over 2023-50. Population growth is expected to fall 0.3 ppts over 2023-2050. Declining population growth reduces the growth of the labor force, which directly reduces GDP growth. This also reduces the marginal product of capital (which is proportional to the capital-to-output ratio), which reduces the effect of investment on growth.<sup>11</sup> The falling working age-to-total population ratio (WATP ratio) reduces GDP and per capita GDP growth in the late 2030s. The growth rate of the WATP falls by 0.3 ppts by the end of the simulation period, resulting in a normalized contribution of 29% to the overall fall in GDP growth over 2023-50.

The combined fall in TFP growth in the resource and non-resource sectors and HC growth over 2023-50 account for around 0.3 ppts of the fall in the GDP growth over 2023-50. The baseline TFP growth rate in the non-resource sector is expected to decrease from 0.61 percent (the value in the counterfactual) to 0.53 percent over the next three decades (Figure A2-e, appendix), a 0.1 ppts decline. The TFP growth rate in the resource sector is assumed to be constant at -0.5 percent in the baseline scenario (in the counterfactual, resource TFP growth is kept constant). Human capital growth falls from 0.31 percent (the value in the counterfactual) to 0.09 percent in the baseline, a 0.2 ppts decline (Figure A2-f, appendix). While this decline in human capital growth is larger than that of TFP non-resource growth, GDP growth rates are also less sensitive to human capital, resulting in a similar contribution.

> 11 The declining population growth increases GDP per capita growth (in contrast, other growth drivers have the same effect on GDPPC and GDP growth). In the baseline, GDPPC growth decreases by 0.29 ppts, and declines 0.46ppts with constant population growth.

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The decrease in the growth rates of reserves reduce GDP and per capita GDP growth along the period 2023-2050. The growth rate of the reserves per capita falls by 2.5 ppts by the end of the simulation period, resulting in a normalized contribution of 0.04 ppts to the overall fall in GDP growth over 2023-50. This result suggests that GDP growth rates are less sensitive to reserves per capita.

**Table 2. Understanding the Drivers of Peru’s GDP Growth Slowdown**<sup>**12**</sup>

||Total Decreas<br>**He**<br>|e of GDP Growth(2023-2050)<br>**adline GDP Growth**<br>|
|---|---|---|
|**Baseline**|-0.78ppts|100.0%|
||**Factor Contribution**|**Normalized Share of Fall in Growth**|
|**Population Growth**|-0.27ppts|30.2%|
|**Working age-to-totalpopulation Growth**|-0.26ppts|29.4%|
|**HC Growth**|-0.15ppts|17.0%|
|**Non-Res. TFP Growth**|-0.12ppts|14.1%|
|**Copper TFP Growth**|-0.04ppts|4.8%|
|**Copper Reservesgrowth**|-0.04ppts|4.5%|

_Note:_ Investment is held constant throughout the simulation period, so we remove its contribution. The capital-to-output ratio is also approximately constant.

Decelerating economic growth is not a unique phenomenon to Peru. Kose and Ohnsorge (2024) finds that an internationally widespread decline in potential growth occurred in 2011-21 relative to 2000-10. This widespread decline reflected a multitude of factors. In terms of the production function framework, all the fundamental drivers of growth faded in 2011-21: TFP growth slowed, investment growth weakened, and labor force growth declined. The study also finds that recessions after strong shocks such as the Covid19 pandemic, were associated, on average, with a decline of about 1.4 percentage points in potential growth even after five years.

# **4. Reform scenarios for Peru to achieve High-Income status.**

In this section, we examine how different factors, namely investment, human capital, and TFP, affect economic growth in the non-natural resource sector. Our objective is to assess the feasibility of accelerating GDP growth to achieve HI status within the next two decades through domestic reforms. We analyze the individual impact of each growth factor and find that growing at around a 5 percent rate is not possible with a single shock to only one of them. Therefore, we simulate moderate and ambitious reform scenarios combining different shocks to all the aforementioned factors together. These scenarios allow us to examine the potential outcomes and determine the best approach for achieving the desired economic growth and HI designation. We first consider reform scenarios to TFP, as they are some of the most important (Section 4.1) and then reforms to boost human and physical capital (Section 4.2), with the resulting growth paths discussed in Section 4.3.

> 12 Because the model is nonlinear, the sum of the effects of changing growth factors at one-by-one is not equal to the total change in growth when the growth factors change together. This is especially true over the long term when capital in the nonresource and resource sectors change.

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|**Box 2. The Long Te**<br>This box describes<br>TFP growth. The d<br>physical infrastruc<br>constructed based<br>subcomponent ind<br>a). To measure th<br>growth rate is dec<br>Finally, the relatio<br>estimated as follo<br>T<br>TP gfgthY,t: an<br>IoIHxY,t−5: overall<br>ftfroH: TP oHlHo<br>θY: country effect.<br>δt: time effect.|**rm Growth Model - TFP Extension**<br>the LTGM-TFP extension (Kim and Loayza 2019) used to quantify the specific determinants to<br>eterminants of TFP are classified into five categories: innovation, education, market efficiency,<br>ture, and institutions (governance). For each category, a set of subcomponent indexes are<br>on the most relevant and available empirical indicators using the factor analysis. These<br>exes are combined into an overall TFP index using the principal component analysis (Table B2-<br>e relative contribution of the five main determinants of TFP growth, the variation of the TFP<br>omposed to that explained by each determinant across countries over the period 1985−2014.<br>nship between the overall determinant index and the change in the growth rate of TFP is<br>ws:<br>P gfgthY,t= β0+ β1ln (IoIHxY,t−5) + β3ln(ftfroH)Y,t−5+ θY+ δt+ εY,t <br>nualized TFP growth overt−5andt.<br>determinant index att−5.<br>(2011 = 1)|
|---|---|
|**Table B2-a. TFP de**<br>|**terminants and Related Indicators**|
|Innovation|R&D expenditure, public and private (percent of GDP)<br>Number of patents, residents, and nonresidents (per 100 people)<br>Number of scientificjournal articles(per 100people)|
|Education|Government expenditure on education (percent of GDP)<br>Proxy for primary education: secondary enrollment rate (gross percentage)<br>Proxy for secondary education: PISA score<br>Proxyfor tertiaryeducation: completed tertiaryeducation(percent ofpopulation 25+)|
|Market Efficiency|Goods market: Doing Business index.<br>Financial market: IMF Financial development index<br>Labor market: Minimum wage (percent of VA per worker), severance pay for redundancy dismissal<br>(weeks of salary),and share of women in wage employment in non-agricultural sectors|
|Infrastructure|Fixed telephone and mobile subscription (per capita)<br>Length of paved roads (kilometer per capita)<br>Electricity production (kilowatt per capita)<br>Access to improved water source (percent of population)<br>Access to improved sanitation facilities(percent ofpopulation)|
|Governance<br>Source: Kim and Loayza|Voice and accountability, corruption control, government effectiveness, political stability, regulatory<br>quality,and rule of law<br> (2019).|

# **4.1. Reforms to boost TFP growth in Peru**

Identifying ways to boost TFP growth is often challenging because TFP growth is a residual. While reforms to schooling and the investment climate (such as access to credit) are better understood, TFP is generally regarded as a residual component of growth representing economic efficiency and technological improvement. However, this loose and broad definition impedes a precise understanding of how to improve TFP growth.

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In this section, we investigate ways to achieve the moderate and ambitious reform TFP paths using the LTGM-TFP extension of Kim and Loayza (2019). Based on an extensive literature review, Kim and Loayza (2019) identify the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions (Box 2). Based on underlying proxies, that paper constructs indexes representing each of these main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. TFP growth has a positive and significant relationship with the proposed TFP determinant index, and this relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved.

The Innovation and Infrastructure determinants of TFP perform worse in Peru than in its peers. Within the five components of TFP growth in Kim and Loayza (2019), Peru underperforms in Innovation and Infrastructure compared to aspirational and structural peers, lagging also behind the LAC, UMIC and HIC Median (Table 3). These are the two areas where the country possesses a wider gap with the rest. The stance regarding Market Efficiency and Institutions is lower than the aspirational peers, although it surpasses some of the structural peers and the regional median. Peru fares relatively well compared to peers on Education.

**Table 3. Main determinants of TFP in Peru and peer countries**

|||Asp|irational P|eers|S|tructural Pe|ers||||
|---|---|---|---|---|---|---|---|---|---|---|
||Peru|Chile|Malaysia|Romania|Ecuador|Colombia|South Africa|LAC Median|UMIC Median|HIC Median|
|Overall determinant index*|43.8|56.4|60.8|55.2|38.8|44.6|50.4|40.4|44.9|78.9|
|Innovation|3.6|11.7|23.7|13.0|7.9|6.5|11.2|4.0|6.6|40.2|
|Education|53.2|62.5|55.5|66.4|52.1|48.8|51.2|41.9|51.1|71.5|
|Market Efficiency|67.0|71.3|82.3|71.7|48.1|68.3|79.3|55.5|63.2|86.9|
|Infrastructure|42.4|52.8|60.4|53.7|43.4|46.6|46.7|45.4|48.3|68.9|
|Institutions|56.8|76.1|67.0|63.7|50.0|55.2|60.0|53.4|53.6|82.0|
|*The composite index of the subc<br>Scale from 1 to 100 (best) as of 2|omponen<br>019 excep|t indexes f<br>t for group|or the five ca<br>medians|tegories of|the determi|nants|||||

Convergence to HI country values in the Innovation and Infrastructure sub-dimensions produces the largest boosts to growth, followed by Education and Institutions, and lastly market efficiency. To assess the sensitivity of TFP growth to each component, we simulate a shock to the five components one at a time, mimicking the trajectory of the 75<sup>th</sup> percentile index among HIC for each component (while the rest are held constant). We then compare the results to a scenario where all components remain constant. Among the five determinants, raising the Innovation and Infrastructure components to this target will lead to the fastest TFP growth, 0.29 and 0.28 percentage points higher on average annually between 20242050 (Figure 10). Education and Institutions have the second largest impact, at about 0.24 percentage points. Market Efficiency has a slightly lower impact on TFP growth.

Experiences from other countries, such as the fast-growing Asian Tigers and China, show that sustaining high TFP growth for a long period is possible but requires continuous reforms to remove constraints and distortions in the market. Given that Peru’s TFP fundamentals could be further improved, closing the gap with HI countries will be important to sustain high TFP growth. However, it will require substantial efforts to remove market imperfections to improve market competition, infrastructure, and human capital as well as to create an enabling environment for innovation.

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**Figure 10. TFP growth difference (percentage points)**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Innovation Infrastructure Education Institutions Efficiency<br><!-- End of picture text --> Note. The comparison is between a scenario of convergence to the 75<sup>th</sup> percentile index among HIC in each component and a baseline with constant levels

**Figure 11. TFP growth path**

<!-- Start of picture text -->
1.6%<br>1.3%<br>1.0%<br>0.7%<br>0.4%<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious<br><!-- End of picture text -->

Source: LTGM simulations, World Bank Staff’s calculations The moderate and ambitious reform scenarios assume the TFP determinant index converges to HI country levels, but with higher targets within that group for the ambitious scenario. In the moderate reform scenario, each subcomponent index for innovation, education, market efficiency, infrastructure, and institutions, increases linearly to the 75<sup>th</sup> percentile among HI countries by 2050, which results in TFP growth accelerating from 0.6% to 1.1% by 2035, and then maintaining that pace for the next decade (Figure 11).<sup>13</sup> The ambitious scenario is simulated similarly, but we assume that each subcomponent reaches the highest overall index among HI countries by 2050. While this target is extremely optimistic, it is mostly the rate of improvement that drives TFP growth, and many countries have had periods of rapid reform. The ambitious reform scenario results in TFP growth accelerating to 1.6% by 2035, though with some deceleration in the following decade. However, even with 1.6% TFP growth in the ambitious scenario, Peru is unable to reach HI status, with growth only reaching 3%. Instead, complementary reforms are needed to physical and human capital accumulation, which we discuss next.

> 13 Although the resulting path for TFP growth is moderate, the pace of reform would have to be fast.

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# **4.2. Physical and human capital**

The reform scenarios assume increases in private investment above the 17.7% of GDP in the baseline, based on reforms such as an improved business environment and greater access to credit. In the moderate reform scenario, we assume that private investment will increase to the 75<sup>th</sup> percentile of private investment among high income countries (20.4% of GDP over 2015-19) in the span of 10 years. While private and public investment are perfectly substitutable in the LTGM-NR, the investment rate is formed by combining an increase in private investment with an unchanged 4.7% of GDP rate for public investment (so total investment is 25.1% of GDP). The ambitious reform scenario is similarly calibrated but targets a private investment rate at the 90<sup>th</sup> percentage of HI countries, which results in private investment increasing to 25% of GDP by 2033 (Figure 12) (so total investment at 29.7% of GDP) **.** Increases in investment bring a large boost to growth. Figures 16 and 17 show that improvements in investment are projected to spur GDP growth by 0.5 and 1.1 percentage points on average in the moderate and ambitious reform scenario respectively in the entire period to 2050.

For human capital, we raise levels to match those seen in the 75<sup>th</sup> percentile of high-income countries (HIC). In the moderate scenario, we set targets based on median values from available data for HIC countries, resulting in Expected Years of Schooling at 13.6, Quality at 0.83, Adult Survival Rate at 0.94, and Fraction of Children Under 5 Not Stunted at 0.94.<sup>14</sup> In the ambitious scenario, we aim for even higher targets, aligning with the best values observed in HIC countries. Both moderate and ambitious reforms can enhance the Human Capital Index, but with a delay as illustrated in Figure 13. This delay arises because these reforms influence today's children, who take time to enter the workforce.

**Figure 12. Private Investment to GDP ratio path**

**Figure 13: Human Capital growth path**

<!-- Start of picture text --> 27%<br>25%<br>23%<br>21%<br>19%<br>17%<br>15%<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious<br><!-- End of picture text --> <!-- Start of picture text --> 2.0%<br>1.6%<br>1.2%<br>0.8%<br>0.4%<br>0.0%<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious<br> Source: LTGM simulations using LTGM-HC extension<br><!-- End of picture text --> Source: LTGM simulations using LTGM-HC extension 14 We assume the reforms are implemented immediately through the education and health systems. However, the reforms take some time to have their full effect in Figure 13 because it takes time for healthier and bettereducated children to become more productive workers. Older children who have completed part of their education already are only partially treated.

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# **4.3. Combined results from reform scenarios**

The effects of the reform scenarios on the GDP growth outlook are shown in Figure 14 and on the GNI per capita (relative to high income) in Figure 15. In the moderate reform scenario, headline GDP will accelerate to achieve GDP growth of 3-3.5 percent from 2030 onwards, with a growth boost of 1.6ppts by 2050. Instead, with an ambitious reform package, GDP growth accelerates to 4.6% by 2033, which is 2.6 ppts above the baseline, and HI status is achieved by 2045.<sup>15</sup>

**Figure 14: GDP growth path**

**Figure 15: Evolution of GDP per capita with the simulated scenarios***

<!-- Start of picture text --> 6.0% 32000<br>5.0%<br>16000<br>4.0%<br>8000<br>3.0%<br>4000<br>2.0%<br>1.0% 2000<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious Ambitious Moderate<br>Baseline HI Threshold<br>Source: LTGM simulations Source: LTGM simulations. *The series are in logarithms base 2<br>Figure 16. Moderate reform contributions to Figure 17. Ambitious reform contributions to<br>GDP growth GDP growth<br>4.5%<br>3.5%<br>1.3% 1.4%<br>0.6% 0.6% 1.4%<br>0.3% 0.7% 3.5% 0.7% 1.4%<br>2.5% 0.2% 0.5% 0.5% 0.8% 0.3% 0.7% 0.7%<br>0.4% 0.5% 0.4% 0.6%<br>0.5% 0.4% 2.5% 0.8% 0.9% 0.6%<br>0.9%<br>0.5%<br>0.8%<br>0.4%<br>1.5% 0.7%<br>2.1% 2.0% 1.8% 1.6% 1.4% 1.5% 2.1% 2.0% 1.8% 1.6% 1.4%<br>0.5% 0.5%<br>Baseline Non-res TFP<br>Baseline Non-res TFP<br>Human Capital Non-res private investment<br>Human Capital Non-res private investment<br>Source: Peru’s LTGM simulations, World Bank Staff Source: Peru’s LTGM simulations, World Bank Staff<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>2024 2027 2030 2033 2036 2039 2042 2045 2048 2024 2027 2030 2033 2036 2039 2042 2045 2048<br><!-- End of picture text --> 15 Policies can also aim to raise the employment share of the working-age population, in particular of discouraged workers or groups with historically low participation rates, such as women. The female employment-to-population ratio was 16ppt below that of men in 2018, and closing this gap will increase long run GDP by around 12% (Pennings 2022). However, those policies are not discussed in this paper.

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# **5. The global energy transition as a long-term growth factor**

The global energy transition implies a considerable increase in the demand for copper in the following decades, which represents a golden opportunity for the Peruvian economy. The increased mineral intensity from greener technologies, such as renewable energy and electric vehicles, in addition to the new infrastructure needed, will significantly raise the demand for minerals during the next two decades (World Bank, 2020). Recent projections forecast the world’s appetite for copper will be more than double the current global mine production (S&P Global, 2022). Achieving global climate ambitions will thus require a substantial increase in copper supply. Being the second largest copper producer in the world— representing around 10 percent of the global supply—and having the second largest mine capacity (IHS Markit, 2022), Peru’s production would need to double if it were to maintain its share of the global copper market. While this is a highly ambitious scenario, it illustrates that copper could be an important driver for Peru’s long-term growth. The mining sector currently contributes around 10 percent of GDP and 15 percent of total tax revenue. Optimizing the use of fiscal resources from mining is thus also important to potentiate a future copper windfall.

To leverage the energy transition, Peru needs to create the right conditions to attract the necessary investments, guarantee that mineral exploitation is environmentally sustainable, and promote profits that reduce territorial and social disparities. The efficient extraction and processing of copper will require large-scale capital investment and improved technological methods to ensure sustainability. This requires a “Climate-Smart Mining” approach to mineral exploration, extraction, and refining in Peru, which includes a clear strategy for maintaining the country’s position as a major global player in the supply of critical minerals while decarbonizing the mining value chain; improving processing technologies; reducing mining’s footprint on water, energy, and waste. It also requires ensuring human rights and enabling territorial development in mining regions, to create an appropriate political environment for future mining projects.

As above, we use the LTGM-NR to analyze global energy transition growth scenarios in Peru, though now we shock growth drivers in the resource sector. The resource sector dynamics differ from the nonresource sector in three ways. First, production depends on the industry-specific productivity, the stock of physical capital, and the country’s proven reserves of the required natural resource (but not on labor or human capital). Second, the stock of natural resources increases with an exogenous stream of discoveries but depletes via extraction. Third, all production generated in the resource sector is exported at international commodity prices, which fluctuate exogenously and have a direct impact on the income generated by the sector. The LTGM-NR includes a fiscal rule that determines whether extra government revenue from the taxes on copper production are saved abroad, spent on government consumption, or spent on public investment (which has the same effects on growth as private investment). The first two have little effect on growth, though the final “Hartwick” rule can generate substantial extra investment and growth when commodity prices are high.

# **5.1. Supplying the Global Green Transition Scenario**

We construct a Global Green Transition Scenario using the path for future copper prices estimated by Boer et al. (2021). The paper identifies metal-specific demand shocks, estimates supply elasticities, and determines the price impact of a global net zero emissions scenario on metal prices. According to these estimates, in a net-zero emissions scenario, copper prices would increase by more than 60 percent 19
compared to the baseline, peak around 2030,<sup>16</sup> and stay at these high levels for more than a decade. This scenario uses global copper consumption provided by IEA (2021). The impact of such a scenario on Peru’s GDP growth depends on the country’s fiscal policy because it determines public investment, which is a main channel through which an initial shock to commodity prices generates long-term effects on growth. The increase in copper prices raises government revenues, leading to an upsurge in government expenditure. As such, the model-implied long-term trajectory of GDP growth is different depending on whether the increase in revenue is only spent on government public investment (Hartwick’s rule), if windfalls are completely saved (Structural Surplus Rule), or if a share of windfalls are invested, and the rest consumed (Balanced Budget Rule)<sup>17</sup> (building on the baseline).

To achieve Net Zero Emissions (NZE) by 2050 will likely require major innovations in technology and policies that encourage long-term investments to increase supply. In this scenario, we simulate a doubling of long-term copper production in Peru by 2035, which is similar to the copper production estimates for Peru under a Net Zero Emissions scenario provided by S&P Global (2022). In the model this could be achieved with a combined shock: first, higher copper prices drive a higher investment rate as the larger demand generates a plan to increase investment; based on the historical evidence, we use a long-term elasticity of total private investment as a share of GDP to the percentage change in the copper price of 0.25. This scenario entails an increase in production driven by higher investments in the mining sector which would be unlocked from a clearer pro-development policy agenda, rule of law, and an improved political environment.

Second, there is a boost to TFP in the copper sector in the short run as the industry does not operate at full capacity and the global green transition boosts the utilization rate of other inputs; in particular, we assume that through innovation, adoption of new technology and more efficient equipment, the copper sector TFP annual growth rate increases to 1 percent in 2025 and then gradually returns to baseline by 2050.<sup>18</sup> The global energy transition can increase long-term growth in Peru and move the country closer to HI status. The results show that GDP growth accelerates significantly under this Supplying the Global Green Transition scenario (figure 18 below, the orange line is roughly one percentage point higher on average than the baseline in the period 2024-2050). Thus, this would help Peru considerably in achieving its growth target; the energy transition gives more opportunities and fiscal resources to Peru to do the domestic reforms needed to increase productivity, investment and human capital and accelerate economic growth.

# **5.2. Combination of the Supplying the Global Green Transition scenario and domestic reforms**

In this section, we combine the Supplying the Global Green Transition scenario from Section 5.1 and the domestic reforms to increase Human Capital and TFP growth shown in Section 4. The scenario which includes ambitious reforms yields a 5 percent GDP growth on average in the simulation period (Figure 18). Under this scenario, Peru would reach the HI threshold in 2042 (Figure 19).

> 16 Rises in demand are front-loaded and the initial price boom induces a supply reaction.

> 17 This scenario sets the share of investment in total expenditure to 25 percent, which was the observed average in 2000-2019. 18 The baseline for TFP growth of -0.5 percent in the copper sector is based on the evidence from Chile, where the long-run copper TFP has stagnated or even declined over the past decades ("Productivity in the Chilean Copper Mining Industry", National Productivity Commission, 2017).

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efficient extraction and processing of copper will require large-scale capital investment and improved technological methods to ensure sustainability. Hartwick rules that invest all extra public resource revenues from the commodity windfall generate the largest increase in GDP. Higher investments in the mining sector would be unlocked from a clearer pro-development policy agenda and an improved political environment.

Adding ambitious domestic reforms to increase Human Capital and TFP to the “Supplying the global green transition” Scenario yields 5 percent GDP growth on average in the forecast period. Under this scenario, Peru would reach the HIC threshold in 2042. The global energy transition gives more opportunities and fiscal resources to Peru to implement the domestic reforms needed to increase productivity, investment and human capital and accelerate economic growth.

Increasing long-term growth to a level that would put Peru on a path to reach HI status requires strong reform efforts. The country can benefit from a green growth dividend, but to fully leverage it, it will need to advance important structural reforms. The mining sector needs to create the necessary conditions, within a context of social legitimacy, to move forward with the development of an extensive mining project pipeline and increase the sector’s productivity. Promoting exploration, making the projects in the pipeline viable, and ensuring the normal operation of current activities require strengthening environmental sustainability and improving the socioeconomic impact of mining projects. Peru faces the challenge of improving the sector’s policies and management to guarantee its long-term contribution to the country’s sustainable development (World Bank 2021). In addition, given that mining is a capitalintensive sector, reforms in the non-resource sector are also important for inclusive growth. Reforms that improve human capital, increase investment and the productivity of the non-resource sector are also needed. Achieving this would require efforts from diverse stakeholders, as sustainable collaboration among the government, the private sector, and civil society will be necessary.

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