In Search of Larger Per Capita Incomes: How to Prioritize Across Productivity Determinants?

Transcription

1 Chief Economists Meeting, Lima October 8, 2015 In Search of Larger Per Capita Incomes: How to Prioritize Across Productivity Determinants? Alejandro Izquierdo Jimena Llopis Umberto Muratori José Juan Ruiz

2 Motivation Criticism of non-borrowing IDB Board members: Where is the strategy in country strategies? Demand driven process The Cardenas question: A finance minister has limited resources and a narrow window of reform Which are the sectors that pay off the most in terms of increasing per capita incomes, and can be quickly implemented at the lowest possible economic and political cost?

3 Review of Existing Frameworks to Determine Priorities Growth Diagnostics (Hausmann et al (2005): The problem of lagging investment. Is it inadequate returns to investment, inadequate private appropriability of returns, or inadequate access to finance? Roadmap to identifying hurdles to investment and growth. However, it does not provide a framework to quantify explicitly the value of the constraints, although it relies on a relevant set of variables to make an informed assessment.

4 Review of Existing Frameworks to Determine Priorities Another popular approach: gaps relative to some benchmark to determine priorities World Economic Forum s Global Competitiveness Report (2014): Rankings across different dimensions Using executive opinion surveys, it looks into institutions, policies, and factors that determine the level of productivity of a country which, in turn, sets the level of prosperity that can be reached by an economy World Bank s Doing Business Report (2014): compares countries using a series of measures of business regulations and their enforcement to facilitate firm start-up and growth View: without a dynamic private sector, no economy can provide a good, and sustainable, standard of living This methodology also provides rankings, as well as distances to the frontier or best observed performance

5 Review of Existing Frameworks to Determine Priorities Development Gaps approach (Borensztein et al (2010)): gaps are constructed relative to predicted values consistent with a country s income per capita With these gaps at hand, the largest ones are chosen to determine priorities Going for Growth (OECD (2005)): focuses on gaps in outcome and policy variables across different sectors affecting productivity and participation Priorities are determined when gaps in a particular sector both in outcomes and policies lie below the OECD average

6 Issues with Existing Frameworks Growth Diagnostics: While growth diagnostics provide an important framework to search for constraints, the analysis is carried out without comparable metrics across sectors The framework does not quantify explicitly the value of constraints, although it relies on a relevant set of variables to make an informed assessment. Gap approach: The size of a gap may or may not identify most binding constraints (institutional gaps in China, innovation gaps in Nicaragua) Why measure against own neighborhood? Could measure against higher income per capita groups Comparison problems (gap is measured with existing data in each country, so for one country only one indicator may be used to measure performance in one sector, while for another country five indicators are used instead

7 Issues with Existing Frameworks Going for Growth: This approach is reasonable for relatively homogeneous countries, but it may be difficult to identify priorities when there is heterogeneity: everything or nothing could be a priority Ranking approaches: comparison against the frontier may result in unattainable goals Every framework (including ours!) has caveats

8 Questions Raised by the Literature that we Keep and Additional Concerns Washington consensus critique Rodrik (2013): He embeds capabilities in a model in which an economy s long-run level of productivity is determined by its capabilities, and accumulation of such capabilities must be large enough in order to obtain measurable increases in productivity, given the multidimensional nature of these capabilities and the complementarity among them Hausmann et al (2005): binding constraints may affect each other. Relaxation of one constraint may not necessarily lead to higher growth as long as some other related constraint still binds Which are the most binding and how do constraints interact? Hausmann and Hidalgo (2011): Since many products require the combination of several capabilities, countries with few capabilities will have a lower probability of finding alternative uses for any additional capability than countries having a large pool of capabilities, leaving countries with low capabilities with little incentives to invest in the accumulation of additional capabilities.

9 Questions Raised by the Literature that we Keep and Additional Concerns All these studies argue for non-linearities and interactions (hence our choice of a Generalized Ordered Probit for estimation) Another key issue raised in DIA (Pagés, 2010): much of the gap in income per-capita levels between Latin American countries and the US is not due to a lack of factor accumulation, but rather, to a fall in productivity levels vis-àvis those of the US. Key question: how can governments help in increasing productivity by boosting sectors that influence productivity levels? Looking into productivity determinants

10 Relative Productivity Losses are the Main Cause of relative GDP per capita Losses in LAC Evolution of relative GDPpc, TFP, and factor accumulation vs U.S GDP per capita Factor accumualtion TFP Divergence vs U.S. between 1960 and 2013 grew 10% Factor accumulation was not enough to compensate for negative TFP Source: Fernández-Arias E Productivity and Factor Accumulation in Latin America and the Caribbean: A Database (2014 Update).

11 Our Questions 1 What did successful countries invest in that let them reach higher income per capita groups? 2 Which productivity determinants are the most relevant when it comes to increasing the likelihood of jumping to larger income per capita groups? 3 How do these determinants vary depending on the income per capita level of the country under anaysis? 4 How do these determinants interact to enhance the probability of reaching a larger income per capita group?

12 Optimal Number of Income per Capita Clusters (PPP Adjusted, Latin America and OECD, ) -49 countries along period (timeframe restricted by sectoral data)

13 Cluster List Cluster 1 Cluster 2 Cluster 3 Cluster 4 Bolivia Bolivia Argentina Australia Honduras Colombia Brazil Austria Nicaragua Costa Rica Chile Belgium Dominican Republic Colombia Canada Ecuador Costa Rica Denmark El Salvador Czech Republic Finland Guatemala Dominican Republic France Jamaica Estonia Germany Panama Greece ; Greece Paraguay Hungary Iceland Peru Israel Ireland Korea Israel Mexico Italy Panama Japan Peru 2012 Korea Poland Netherlands Portugal Norway Slovak Republic Slovenia Slovenia ; Spain Turkey Sweden Uruguay Switzerland Venezuela United Kingdom United States

14 Productivity Determinants (OECD Guidance) Sectors affecting productivity based on OECD work in Going for Growth exercise, supported by related literature Bouis et al (2011): general overview Bassanini and Scarpetta (2001): impact of human capital on output per capita growth Égert et al (2009): effects of infrastructure investment on growth Janz et al (2004): relationship between innovation, productivity, and growth Alcalá and Ciccone (2004): effect of trade on productivity Dahl et al (2010): effect of information and communication technology (ICT) on productivity growth Bloom et al (2003): effects of health on aggregate output Levine (1997): financial depth and growth

15 Dimensions Affecting Productivity and Income Education Labor Markets Financial sector Telecommunications Infrastructure Integration and Trade Innovation Health

16 Sectoral Performance Measurement Grassroots process in consultation with all IDB sectoral departments related to these dimensions Guiding concept: if you had to present to a finance minister sectoral performance and policies at a glance using key indicators, which ones would you pick? Original set of 83 indicators After data reality check and depuration for all 49 countries: 34 indicators spread across 8 sectors Surprisingly poor time series at the sectoral level

17 Dimensions Affecting Productivity and Income Education - Education expenditure (%GNI) - % population age 25+ with secondary or tertiary schooling - % population age 25+ with no education - Net enrollment rate, pre-school Financial sector - Credit to private sector (% of GDP) - Real lending interest rate - Market capitalization (% of GDP) - Property rights Index - Rule of law Infrastructure - Road density (km per 100 sq. km) - Road safety (fatalities per 100,000 inhabitants) - Energy use (kg of oil equivalent per capita) - Electric power consumption (KWh per capita) - Electric power transmission and distribution losses (% of output) Innovation - Exports of high and medium technology (% of total) - Scientific and technical journal articles (per total population) - Quality Management Certificates (per billion PPP$ GDP) Labor Markets - Formal employment (% of labor force) - Formal employment (% of labor force) - Workers with low education (% of labor force) - NEETS youth - Unemployment Telecommunications - Internet users (per 100 people) - Mobile lines (per 100 people) - Telephone lines (per 100 people) Integration and Trade - Foreign direct investment (% of GDP) - Trade openness (% GDP) - Hummels-Klenow extensive margin index: products - Hummels-Klenow extensive margin index: markets Health - Life expectancy - Public expenditure on health (% GDP) - Infant mortality rate under-1 (per 1000 live births) - Mortality rate under-5 (per 1000 births) - Maternal mortality rate (per births) - Immunization, measles (% of children ages months)

18 Sectoral Performance Measurement Each series was normalized using sample means and standard deviations Sectoral indices were constructed as simple averages of normalized indicators These sectoral indices were normalized once again (given correlation across indicators and averaging, sectoral indicators no longer have unit variance) Sectoral indices are comparable across countries and time An aggregate indicator summarizing all sectors was also constructed

19 Vector Error Correction Granger Causality: Unidirectional, from Indicators to GDP per Capita Dependent Variable: Aggregate Indicator Excluded Variable Chi-squared Differences Probability Log GDP p/c, PPP Adjusted Dependent Variable: Log GDP p/c, PPP Adjusted Excluded Variable Chi-squared Differences Probability Aggregate Indicator Observations Countries Note: The Null Hypothesis of the Granger Causality Test is that the excluded variable does not Grangecause the Dependent Variable.

20 Generalized Ordered Probit Approach Cluster 1 Cluster 2 Cluster 3 Capital Markets *** (0.728) (0.466) (0.624) Education 1.117** (0.533) (0.292) (0.466) Health 0.681** 0.681** 0.681** (0.321) (0.321) (0.321) Infrastructure *** (0.864) (0.361) (1.077) Innovation 3.428* (1.964) (1.015) (0.560) Integration & Trade ** (1.185) (0.560) (0.261) Labor *** (1.443) (0.386) (0.462) Telecommunications (0.294) (0.294) (0.294) VIX *** *** *** (0.009) (0.009) (0.009) Observations Countries Note: Coefficient standard deviations are estimated using clustered errors at the country level. ***Significant at the 1% level, **Significant at the 5%level, *Significant at the 10% level. - We lag all sectors (potential endogeneity issues) - We use clustered errors at the country level - We test for common coefficients, only health, telecom and the VIX index accept common coefficient restriction

21 Putting Results to Work

22 Priority Matrix We identify a priority matrix that affects the probability of jumping the most for each of the first 3 clusters of countries (where Latin American countries stand) CLUSTER 1 CLUSTER 2 CLUSTER 3 1 ST PRIORITY EDUCATION INTEGRATION INFRASTRUCTURE 2 ND PRIORITY HEALTH LABOR CAPITAL MARKETS 3 RD PRIORITY HEALTH HEALTH We call these Priorities for Productivity and Income (PPI)

23 Sectoral Indicators: The Case of Peru (compared against Outgoing Cluster and Incoming Cluster) Evolution of Peru s sectors: Comparison with means of Outgoing cluster and Incoming cluster Aggregate Indicator Diff. Outgoing Cluster= 0.19 Diff. Incoming Cluster= Capital Markets Diff. Outgoing Cluster= 0.08 Diff. Incoming Cluster= Education Diff. Outgoing Cluster= 0.40 Diff. Incoming Cluster= Health Diff. Outgoing Cluster= 0.25 Diff. Incoming Cluster= Infrastructure Diff. Outgoing Cluster= 0.23 Diff. Incoming Cluster= Innovation Diff. Outgoing Cluster= Diff. Incoming Cluster= Integration and Trade 1 Diff. Outgoing Cluster= Diff. Incoming Cluster= Labor Diff. Outgoing Cluster= 0.09 Diff. Incoming Cluster= Telecommunication Diff. Outgoing Cluster= Diff. Incoming Cluster= Peru Average Outgoing Cluster Average Incoming Cluster

24 Probability Sensitivity to Increases in Priority Areas: The Case of Peru How much would the probability of jumping to Cluster 4 change if the country were able to increase each of the 3 priorities in ½ a standard deviation? ½ a standard deviation is the average difference across groups and sectors, we assume that larger increases would be very costly to implement. A low probability (9%) of jumping to the next cluster is consistent with the fact that Peru has only recently (2012) entered cluster 3. Allocation in Std. Dev. Probability at 2012 values Increase in Infrastructure Incrase in Capital Markets Increase in Health Probability After Increase

25 How Much Needs to be Done to Raise Probability Substantially? The Case of Peru We also learn about the relevance of the interaction across sectors Peru: Increasing the Probability of Jumping to Cluster 4 to 75% Peru would need to increase: - Infrastructure by 1 stdv - Capital Markets by 0.51 stdv - Health by 0.5 stdv 1 to reach a 75% probability of jumping to Cluster 4

26 Sectoral Gaps: A Comparison of Peru Against its own Cluster and the Next Cluster Comparison by Sector: Peru's Own Cluster and Next Cluster (Means) 2012 Values Evaluation Peru's Normalized Value Mean Own Group Mean Next Group Gap with own group Gap with next group Innovation Health Telecommunication Capital Markets Labor Education Infrastructure Integration and Trade Aggregate Indicator In this case, only one priority coincides with gaps against own income per capita group - Although we think gaps should be measured against the next income per capita group

27 Sectoral Gaps: A Comparison of Peru Against its own Cluster and the Next Cluster Comparison by Sector: Peru's Own Cluster and Next Cluster (Means) 2012 Values Evaluation Peru's Normalized Value Mean Own Group Mean Next Group Gap with own group Gap with next group Innovation Capital Markets Infrastructure Health Telecommunication Labor Education Integration and Trade Aggregate Indicator In this case, 2 out of 3 priorities coincide with gaps against next income per capita group

28 Sectoral Gaps: A Comparison of Colombia Against its own Cluster and the Next Cluster 2012 Values Evaluation Colombia s Normalized Value Mean Own Group Mean Next Group Gap with Own Group Gap with Next Group Education Labor Health Integration and Trade Innovation Infrastructure Telecommunication Capital Markets Aggregate Indicator This time, only one of the three largest gaps coincide with PPIs

29 Available Tools and Uses Country templates with abovementioned analysis for each of 19 Latin American countries A database with more than 80 indicators, including the ones used in the analysis Standardized series for comparison and build of tailor made sectoral indicators This methodology will be used for the determination of priority sectors in country strategies at IDB

30 Chief Economists Meeting, Lima October 8, 2015 In Search of Larger Per Capita Incomes: How to Prioritize Across Productivity Determinants? Alejandro Izquierdo Jimena Llopis Umberto Muratori José Juan Ruiz