# copy the linklink copied!Chapter 3. The dashboard of indicators to monitor reforms

This chapter proposes a scorecard that includes a series of indicators for monitoring progress on the implementation of the reforms proposed in the areas of productive diversification, transport connectivity and the formalisation of jobs. After presenting the methodology behind the scorecard, the chapter presents the objectives for each indicator that Peru should aim to achieve by 2025 and 2030.

Peru can track its progress towards inclusive growth and greater well-being for all by monitoring the implementation of public policies through an informed range of indicators. Peru pegged the success of its development agenda to the achievement of the Sustainable Development Goals (SDG) by 2030.

This chapter aims to provide a tool that should enhance the capacity of the government to monitor various development indicators. These indicators are closely related to the expected results of the reforms proposed by the OECD in the previous chapters, and are validated by the government of Peru. The set of proposed indicators provides an overview of progress towards the goals of diversifying the productive structure, improving transport connectivity, and formalising jobs and economic activities while mitigating the pervasive impact of informality.

By setting targets and clearly tracking their achievements, the scorecard contributes to the transparency of the government’s action. The extent to which citizens and stakeholders can monitor the evolution of the development agenda is essential for a balanced social contract. More accountability, moreover, improves public administration’s capacity and encourages the engagement of citizens. For this to happen, the scorecard must rely on recent, high-quality and sufficiently disaggregated data.

## copy the linklink copied!The scorecard proposes indicators for achieving the Sustainable Development Goals by 2030

For the three action plans identified in the previous chapter and MDCR volumes (economic diversification and productivity; transport connectivity; formal jobs and economic activities), the scorecard presents a number of primary and secondary indicators, as well as the goals in each of them. Primary indicators aim to be a summary measure of overall progress in each one of the three priority areas. Secondary indicators monitor accurately the expected results of each proposed reform.

For all these indicators, the scorecard presents the following values:

• The level reached by Peru at the launch of the Strategic National Development Plan (SNDP) “Perú hacia el 2021” (i.e. 2016, or latest available year).

• The level attained in the three years before the launch of the SNDP and in 2017, when available.

• The objectives to be attained by 2030, as well as the intermediate objectives (in 2025).

The scorecard combines data from international sources with local data and surveys. Estimations of the targets are based on data released by: the World Development Indicators by the World Bank; the International Labour Organisation; the institute of statistics (UIS) of the United Nations Educational, Scientific and Cultural Organisation (UNESCO); and the World Integrated Trade Solution by the World Bank.

Targets to be achieved in 2030 are calculated as follows:

First, benchmarking Peru’s performance to countries with a similar income per capita: According to the latest classification by the World Bank, Peru currently belongs to the group of upper middle-income economies. By 2030, Peru is expected to be right at the threshold between upper middle-income and high-income economies. For this reason, targets were calculated using both categories as a reference country group.

Although not all upper middle-income and high-income countries are necessarily a model of development for Peru, on average they are likely to share similar socio-economic characteristics.

Second, for some connectivity indicators, targets were set using OECD calculations based on OECD/ITF (2015). These targets reflect the potential values to be reached under a predominantly public transport-oriented transport policy (i.e. high public transport, low road provision, high fuel costs).

The benchmarking methodology is based on a three-step procedure:

1. 1. First, the 2030 target for gross domestic product (GDP) per capita in Peru is computed.1 The figure is based on the 2017 level of GDP per capita (USD 12 237) and on the objective of constant annual GDP growth rate (6%), as specified in the SNDP. Under this scenario, Peru is expected to attain a per capita GDP of USD 24 623 by 2030.

2. 2. Each indicator is regressed on the GDP per capita of each country in the sample. High-income or upper middle-income countries with an outlying GDP per capita are excluded from the analysis.2 Calculations are based on the value of the indicator in 2017, or latest available year.

Technically, in each regression equation, the dependent variable is the indicator of interest for country i in 2017 (or latest available year); the independent variable is the GDP per capita of country i in 2017 (or latest available year) (Equation 1).

(1)

1. 3. For each indicator and the respective estimated coefficients, the 2030 targets for Peru are generated. In particular, the estimated intercept (that captures the mean characteristics of the relevant benchmarking group of countries) is added to the product of the estimated coefficient of GDP per capita – as computed in step 2 - and of the long-term target for GDP per capita - as computed in step 1 (Equation 2).

The intermediate objectives (2025) are calculated by linearly interpolating the last available figure for Peru and the target of 2030 (independent of the method of calculation used). Linear interpolation is a method of estimating the value of an indicator between two points in time. For instance, based on the value of a certain indicator in 2017 and its target in 2030, the intermediate target in 2025 is calculated according to the following equation:

$\frac{{indicator}_{2025}-{indicator}_{2017}}{2025-2017}=\frac{{indicator}_{2030}-{indicator}_{2017}}{2030-2017}$

The targets presented in the scorecard should be interpreted with caution. For example, sampling errors specific to each of the sources may bias the estimations. Moreover, the way Equation 1 and Equation 2 model the relationship between the indicators and the GDP per capita may fail to capture non-linearity and therefore introduce other specification errors. Finally, the estimations do not take into account potential future shocks and global trends that may accelerate or slow down the evolution of certain indicators, further biasing the above estimations. Yet, the scorecard delineates a trend that Peru should follow in order to achieve long-term sustainable and inclusive growth. For this reason, the target values should be interpreted while taking into account the past values, rather than focusing on year-to-year changes.

Peru has developed a quality and independent statistics system, yet progress could be made in collecting better data on the productive structure of the country and sub-national level data. The Instituto Nacional de Estadística e Informática (INEI) is committed to producing and disseminating the official statistical information that the country needs, with the adequate quality, timeliness and coverage, with the purpose of contributing to the design, monitoring and evaluation of public policies. Given Peru’s current challenges and the need for further data to assess and overcome these challenges, it is key to enhance co-operation with the private sector in order to better understand the diverse dynamics across sectors. For example, transport costs could be analysed in a more in-depth way if this were to become more of a priority. To create such evidence, Peru has to promote a logistics observatory in charge of gathering and processing data on key items related to connectivity.

Efforts could also be made to ensure user-friendly data access and dissemination. This is a key aspect so that statistics are presented in a clear and understandable way, released in a suitable and convenient manner, including in machine-readable form (“open data”), can be found easily, and are available and accessible to all with supporting metadata and guidance. The relevant authorities must respond to major misinterpretations of data by users.

Table 3.1. General indicators of economic performance and statistical capacity

2010

2015

Objective 2025

Objective 2030

Source

GDP per capita (constant LCU)

13 008

15 377

25 944

32 166

WDI

GDP per capita, PPP (constant 2011 international dollar)

9 957

11 770

19 859

24 623

WDI

Overall level of statistical capacity (scale 0-100) *

81.11

93.33

95.47

98.89

WDI

Note: The Statistical Capacity Indicator is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. The overall Statistical Capacity score is calculated as a simple average of all three area scores on a scale of 0-100. *This target was set by taking the highest peak of the own series.

Table 3.2. Achieving higher economic diversification and productivity

Primary indicators

2010

2015

Objective 2025

Objective 2030

Source

Economic Complexity Index

-0.63

-0.98

-0.01

0.37

OECD

Secondary indicators

2010

2015

Objective 2025

Objective 2030

Source

Research and development expenditure (% of GDP)

0.12

0.70

0.99

WDI

Time spent dealing with the requirements of government regulations (% of senior management time)

14.10

10.85

10.26

WDI

Subnational public expenditure (% of GDP)

9.43

9.80

OECD

Time required to start a business (days)

27.50

26.50

22.03

19.24

WDI

Note: ECI measures the knowledge intensity of an economy by considering the knowledge intensity of the products it exports.

Ease of doing business ranks economies from 1 to 190, with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation. The index averages the country's percentile rankings on 10 topics covered in the World Bank's Doing Business. The ranking on each topic is the simple average of the percentile rankings of its component indicators.

Table 3.3. Improving transport connectivity

Primary indicators

2010

2015

Objective 2025

Objective 2030

Source

Logistics performance index: Overall (1='low' to 5=high)

2.80

2.89

2.99

3.05

WDI

Logistics performance index: Quality of trade and transport-related infrastructure (1='low' to 5=high)

2.66

2.62

2.84

2.96

WDI

Secondary indicators

2010

2015

Objective 2025

Objective 2030

Source

Logistics performance index: Ability to track and trace consignments (1='low' to 5=high)

2.89

2.94

3.00

3.04

WDI

Logistics performance index: Efficiency of customs clearance process (1='low' to 5=high)

2.50

2.76

2.82

2.86

WDI

Logistics performance index: Ease of arranging competitively-priced shipments (1='low' to 5=high)

2.75

2.91

2.97

3.01

WDI

Logistics performance index: Competence and quality of logistics services (1='low' to 5=high)

2.61

2.87

2.94

2.99

WDI

Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1='low' to 5=high)

3.38

3.23

3.35

3.42

WDI

Burden of customs procedure, WEF (1='extremely' inefficient to 7='extremely' efficient)

4.47

3.80

4.18

4.29

WDI

Quality of port infrastructure, WEF (1='extremely' underdeveloped to 7='well' developed and efficient by international standards)

3.30

3.60

4.11

4.37

WDI

Note: The overall score of the Logistics Performance Index reflects perceptions of a country's logistics, based on efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively-priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance.

The 2030 target reflects the potential value to be reached under a predominantly public transport-oriented transport policy (i.e. high public transport, low road provision, high fuel costs series). Values for 2010 and 2015 were taken from the baseline series.

Source: OECD/ITF calculations based on OECD/ITF (2015), “Logistics strategy and performance measurement: Mexico’s national observatory for transport and logistics”, Case-Specific Policy Analysis, OECD/International Transport Forum, Paris, available at www.itf-oecd.org/logistics-strategy-and-performance-measurement-mexico%E2%80%99s-national-observatory-transport-and-logistics.

Table 3.4. Creating more formal jobs and economic activities

Primary indicators

2010

2015

Objective 2025

Objective 2030

Source

Informal employment (% of total non-agricultural employment)

69.93

60.11

48.67

42.20

WDI

Secondary indicators

2010

2015

Objective 2025

Objective 2030

Source

Coverage of unemployment benefits and ALMP (% of population)

3.51

4.68

WDI

Inspectors per 10 000 employed persons

0.30

0.30

0.62

0.78

ILO

Social health protection coverage as a percentage of total population (%)

64.40

85.16

92.08

ILO

Time required to start a business (days)

27.50

26.50

22.03

19.24

WDI

Time spent dealing with the requirements of government regulations (% of senior management time)

14.10

10.85

10.26

WDI

Lower secondary completion rate, total (% of relevant age group)

88.59

85.84

88.64

90.03

WDI

Government expenditure per student, secondary (% of GDP per capita)

10.77

14.34

17.52

19.32

WDI

Proportion of 15-24 year-olds enrolled in vocational secondary education, both sexes (%)

0.45

5.67

8.53

UIS

## References

ILO (2018), ILO Statistics (database), www.ilo.org/global/statistics-and-databases/lang--en/index.htm (accessed July 2019).

OEC (2019), Economic Complexity Index Atlas (database), https://oec.world/en/rankings/country/eci/ (accessed July 2019).

OECD (2018), Global Revenue Statistics (database), www.oecd.org/tax/tax-policy/globalrevenuestatistics-database.htm (accessed July 2019).

OECD/ITF (2015), ITF Transport Outlook 2015, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789282107782-en.

UNESCO (2019), UIS.Statistics (database), http://data.uis.unesco.org/ (accessed July 2019).

World Bank (2019), World Development Indicators (database), http://data.worldbank.org (accessed July 2019).

## Notes

← 1. We consider GDP per capita in power purchasing parity and 2011 international USD.

← 2. Outliers are defined as those high-income (or upper high-income) countries with a GDP per capita that is at least 1.5 higher – or lower – than the interquantile range of the GDP per capita distribution – that is, the difference between the 75th and 25th percentiles.