Abstract
Selected Issues
Reform Priorities for Achieving Sustainable Growth
A. Introduction
1. Since the mid-2000s, growth in Paraguay has been much faster than before. However, the growth acceleration has been largely extensive—driven by increased production inputs, rather than productivity gain.
2. This paper conducts an international comparison of Paraguay versus a group of middle-income countries that were once at a similar development stage and subsequently achieved sustained growth and convergence (“high-convergence club”). Compared to the high-converging peers, Paraguay lags behind in its industrialization progress, as indicated by lower product complexity of the export sector, slower development of the manufacturing sector, and disproportional reliance on agricultural commodities.
3. Sustainable growth going forward needs to rely on higher productivity increase, which in turn requires further transition from an agricultural- to industrial- economy and a more diversified export structure.
4. To accomplish this transition, structural reforms to facilitate productivity growth and greater dynamism in the private sector would be helpful. The paper identifies several priorities for structural reforms in Paraguay, based on estimated impact on growth of structural indicators, as well as survey results from private firms. The top reform areas identified are quality of transport infrastructure, quality of human capital, rule of law, and customs and trade regulations.
B. Characteristics of High-Converging Countries: An International Comparison
5. This section aims to answer the question “what does Paraguay need to achieve sustainable growth and convergence?”. It compares Paraguay across different economic dimensions with countries that have been at a similar development level but have achieved (or have failed to achieve) significant convergence in the subsequent period.
Data and Variables
6. Countries that have been at a similar convergence stage as Paraguay are selected as the peer group. These include countries with a GDP per capita at 5 to 10 percent of the US level at any point in time since 1950.
7. Two sub-groups of the peer group are then selected as comparison sets: high-converging peers and low-converging peers. The high-converging peers are defined as the country-year combinations where the speed of the country’s GDP convergence to the US level in the subsequent 10-year period is higher than 75 percent of the countries in the peer group. In addition, being included in the high-converging country group requires that there hasn’t been any “relapse” in convergence, i.e., the country’s convergence level at the end of the data sample (2017) is still higher than in the beginning of its high-converging period. In contrast, the low-converging peers consist of countries in the peer group that have had a 10-year convergence rate slower than 75 percent of the countries in the peer group.
Examples of High Converging Peer Countries
(GDP per capita as percent of US level, not adjusted for PPP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Examples of High Converging Peer Countries
(GDP per capita as percent of US level, not adjusted for PPP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Examples of High Converging Peer Countries
(GDP per capita as percent of US level, not adjusted for PPP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.8. The high-converging peer group largely consists of emerging Asian and European countries that have experienced rapid growth in the recent economic history. In contrast, the low-converging peer group is more geographically diverse. (See the appendix for a complete list of countries belonging to the two groups and their starting years.)
9. Paraguay is compared with the two sub groups across four dimensions: 1) macroeconomic fundamentals, 2) institutional quality, 3) business environment, and 4) industrial and export structure. The time periods for the data observations chosen for the high-converging (low-converging) peer groups are the years in which a country’s superior (inferior) convergence performance started. And for Paraguay, the 2017 data (or data for the latest available year) is used.1
Results
Paraguay compares favorably in macroeconomic management
10. Macro stability in Paraguay fares better than that of both comparison groups, manifested as lower inflation,2 more stable price level, and lower government debt. This is consistent with the fact that macroeconomic management in Paraguay has been strong over the past 15 years, which offered a boon to growth.
11. It’s interesting to note the diverging trend in macro stability between the two peer groups. Although the macro stability indicators for the high-converging country group do not seem to be significantly better than those of the low-convergence group over during the starting years of their high-convergence episodes, the macro indicators improved for the high-converging group over the subsequent periods, while deteriorating for the second group. For example, the government debt to GDP ratio drops by an average of 2.4 percentage points over a 10-year high-converging period, while overall fiscal balance deteriorates by 2.3 percentage points over 10 years for the low-converging group.
Inflation
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Inflation
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Inflation
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Inflation Volatility
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Inflation Volatility
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Inflation Volatility
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Fiscal Balance
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Fiscal Balance
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Fiscal Balance
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Government Debt
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Government Debt
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Government Debt
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Investment and FDI are lower compared to the high-converging countries
12. At around 20 percent of GDP, the investment rate in Paraguay is low compared to the prevailing levels in both high-converging and low-converging peers, while the capital output ratio is close to the average level of high-converging peers. The combination of high capital-output ratio and low investment level suggests that the marginal return on investment may be low due to subdued productivity growth, consistent with the data on the historical trajectory of TFP in Paraguay. The lower investment rate is also in line with lower levels of domestic saving rate and foreign direct investment (FDI), both lagging behind the high-converging peers.
13. The high capital-output ratio of Paraguay is likely related to the relatively high capital intensity of the agriculture sector, which has reduced its labor input need in the recent decades thanks to increasing mechanization of the industry. To increase investment despite the already high capital stock, diversification into the more productive industrial sector is much needed (also see the next sub-section).
14. The subdued level of FDI in Paraguay is particularly striking. Since FDI is not only a component of investment, but also an important source of international production knowledge transfer, technology upgrade (and associated productivity increase), as well as a driver of export activities, measures to encourage foreign investments are likely to provide important leverage for growth going forward.
The industrialization level is low with export disproportionally concentrated in commodities
15. Compared to the high-converging countries, the Paraguayan economy is much less industrialized. The sectoral structure is close to that of the low-converging peers, with a high share of the primary sector. The sectoral structure is reflected in the composition of Paraguay’s exports as well, with agricultural commodities dominating the export basket and a low share of industrial exports. Although there has been a surge in growth of the labor-intensive, manufacturing export sector associated with FDIs (see Box 1), it started from a very low base and dwarfs in scale compared to the more traditional export sector.
16. Significant sectoral structural change has taken place in the economy in recent years, with the labor share of agriculture going down significantly due to increased labor substitution. However, employment has mostly transitioned into the non-tradeable service sector, while the share of employment in the industrial sector actually declined since 2007. (The share of non-agriculture, non-energy export in total exports is only around 7 percent.) The non-tradeable sector is generally less productive than the tradeable, industrial sector, with lower productivity growth and technological progress. To boost the overall labor productivity of the economy, a higher level of industrialization and expansion of the more productive tradeable sector need to occur. Since low TFP growth likely contributes to low investment growth, development of the emerging export industries with higher TFP will also help increase investment level.
Saving and Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Saving and Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Saving and Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Capital Stock to GDP Ratio
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Capital Stock to GDP Ratio
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Capital Stock to GDP Ratio
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Foreign Direct Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Foreign Direct Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Foreign Direct Investment
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Share of Export in GDP
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Share of Export in GDP
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Share of Export in GDP
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Industrial Structure
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculationIndustrial Structure
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculationIndustrial Structure
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculation
Export Composition
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculationExport Composition
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculationExport Composition
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculation
Share of Employment by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Share of Employment by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Share of Employment by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.
Share of Real Output by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Share of Real Output by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Share of Real Output by Sector
(In percent)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.
Labor Productivity
(Real output/ Worker)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Labor Productivity
(Real output/ Worker)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Labor Productivity
(Real output/ Worker)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.
Contribution to Real Output Growth
(In percentage point, 2002–2017)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Contribution to Real Output Growth
(In percentage point, 2002–2017)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Contribution to Real Output Growth
(In percentage point, 2002–2017)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.17. Paraguay significantly lags behind its high-converging peers in the economic complexity of its exports, due to the lack of export diversification and the fact that agricultural exports generally contain a lower level of technological value-added. Going forward, development of new industries and export categories, including by leveraging Paraguay’s unique comparative advantages (see Box 2), are likely to be crucial for sustainable growth and convergence.
Economic Complexity Index
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Economic Complexity Index
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Economic Complexity Index
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Maquila Industry in Paraguay
The maquila industry—FDI-driven, primarily labor-intensive manufacturing production operations with an export focus—started in Paraguay around 2007 and has experienced significant growth over the past 10 years. Investments in the industry have mainly come from Brazilian companies seeking to lower production cost in labor and electricity, as well as to take advantage of the lower tax rate in Paraguay. The exports from the industry are primarily labor-intensive goods, ranging from auto parts, to textiles and apparels, to plastics and leather.
Maquila Industry Profile
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Maquila Industry Profile
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.Maquila Industry Profile
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: BCP and IMF staff calculations.The average annual growth of the sector’s exports has been over 20 percent, which is not much lower than the pace of the export growth from similar industries in some of the countries and regions in the world that have historically specialized in labor-intensive manufacturing exports as a crucial part of their growth strategy, such as China and Taiwan province of China.
However, the development of the industry started from a very low base. It currently employs around 15,000 workers, less than 0.5 percent of total employment of the country, and exports around only 4 percent of the total exports. Still, if the current growth trend of the industry can continue, it has the potential to become a nonnegligible driver of growth and exports in the not-so-distant future. In the hypothetical scenarios where the industry’s exports continue to grow at a pace of 15 percent (25 percent) annual, the industry would eventually contribute to around 10 percent (30 percent) of the total exports of Paraguay by 2030.
The risks for such a scenario not materializing are substantial. Currently over 80 percent of investments in the Maquila industry come from Brazil. With the growth outlook of the Brazilian economy being less favorable, it is unclear whether Maquila-related FDIs from the country will continue to grow.
Labor-Intensive Exports
(In percent of total exports)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Labor-Intensive Exports
(In percent of total exports)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Labor-Intensive Exports
(In percent of total exports)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.
Annual Growth of Labor-intensive Exports
(In percent, expansion period)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Annual Growth of Labor-intensive Exports
(In percent, expansion period)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.Annual Growth of Labor-intensive Exports
(In percent, expansion period)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory of Economic Complexity and IMF staff calculations.A main selling point of Paraguay is its closeness to some of the biggest South American consumer markets, e.g., Brazil, Argentina, while the labor cost is lower than in those countries. However, the lack of connectivity and weak transport infrastructure make it harder to leverage Paraguay’s comparative advantage. To widen Paraguay’s international appeal as an investment destination, efforts are needed to improve the country’s infrastructure and institutions, as well as to encourage the development of new industries that leverage the country’s comparative advantage in electricity and labor costs.
GDP per Capita vs Labor Cost
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: ILO and IMF staff calculations.GDP per Capita vs Labor Cost
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: ILO and IMF staff calculations.GDP per Capita vs Labor Cost
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: ILO and IMF staff calculations.Electricity Cost and Industrial Structure: Paraguay vs Iceland
To achieve sustainable growth, a country’s industrial structure should align with its comparative advantages. As a country endowed with abundant hydro power resources, low-cost electricity is a natural comparative advantage that Paraguay should make use of.
Paraguay’s electricity export has been on the rise for the past 25 years, since the completion of the Itaipu project. Incomes from energy export has become a major source of government revenue and international reserves. In that sense Paraguay has indeed benefited from its electricity resources.
However, the economic structure has not evolved around this comparative advantage. There is hardly a presence of electricity intensive industries in the country, which currently only consumes 20 percent of its allotted share of power generated from Itaipu. The construction of new hydroelectric facilities has not changed Paraguay’s export basket in a tangible way, except for the rise in electricity export itself.
Similar to Paraguay, Iceland is a country endowed with abundant renewable electricity resources, mostly from hydro power and geothermal energy. But in contrast to Paraguay, the industrial landscape in Iceland has been significantly impacted by its power generation capacity, which allows the country to diversify from its traditional export profile that concentrated in fishery and related products.
Over the past twenty years, along with the establishment of several large-scale renewable energy projects, Iceland’s export structure has drastically evolved, from a dominance of fish and agricultural products, to a portfolio of electricity-intensive outputs, ranging from Aluminum, to information and communications technology (ICT) services, to transport and tourism. Iceland’s domestic electricity consumption is over eight times higher than the EU average, with the majority of the consumption from the industrial sector, especially the aluminum industry.
Electricity Consumption vs GDP per Capita
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Electricity Consumption vs GDP per Capita
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.Electricity Consumption vs GDP per Capita
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WEO and IMF staff calculations.
Export Structure of Iceland
(In percent of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.Export Structure of Iceland
(In percent of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.Export Structure of Iceland
(In percent of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.
Export Structure of Paraguay
(In % of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.Export Structure of Paraguay
(In % of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.Export Structure of Paraguay
(In % of total export)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: The Observatory for Economic Complexity and IMF staff calculations.Institutional quality is low, as well as the quality of human capital and infrastructure
18. Compared to both peer groups, Paraguay scores lower the quality of legal system, public observance of law, and corruption, using data from the International Country Risk Guide. In terms of other aspects of the business environment, Paraguay’s tax level on the corporate sector are at par with the high-converging peers (and much lower than in the low-convergence group), while the qualities of transport infrastructure and education are lower. These results are consistent with the firm-level survey data on main obstacles of doing business in Paraguay (see Section V), which indicate that the qualities of transportation infrastructure, labor force and public institutions are among the biggest deterring factors to business operation.
Institutional Quality
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Country Risk Guide and IMF staff calculations.Institutional Quality
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Country Risk Guide and IMF staff calculations.Institutional Quality
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Country Risk Guide and IMF staff calculations.
Corporate Tax Rate
(In percent of commercial profit)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Corporate Tax Rate
(In percent of commercial profit)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Corporate Tax Rate
(In percent of commercial profit)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.
Interest Rate Spread
(In percent, lending rate minus deposit rate)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Interest Rate Spread
(In percent, lending rate minus deposit rate)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Interest Rate Spread
(In percent, lending rate minus deposit rate)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.
Paved Roads
(In percent of total roads)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Paved Roads
(In percent of total roads)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Paved Roads
(In percent of total roads)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.
Quality of Education System
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Quality of Education System
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Quality of Education System
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Entrepreneurial activities are largely concentrated in the informal sector
19. Another important pattern that emerged among the cross-country comparisons is regarding the formation and mode of operation of businesses. Data shows that compared to the high-converging peers, Paraguay’s private sector contains a high percentage of informality, while the level of registered entrepreneurial activity is substantially lower compared to both peer groups. The prevalence of informality is not only detrimental to government revenue, but also to productivity growth, as past studies have shown that businesses in the informal sector tend to be smaller, less productive and grow slower.
20. This section compares Paraguay with other countries that have been at a similar convergence stage, across a wide range of economic factors that potentially impact long-term growth. The comparison set consists of two sub-groups: countries that have achieved fast convergence with advanced economies (high-converging peers), and countries that have failed to do so (low-converging peers). Data shows that the high-converging peers share some common characteristics. Compared to countries at a similar development stage what have subsequently failed to converge, the high-converging countries tend to have higher investment and FDI rates, export more, have a more industrialized sectoral structure with a higher level of product sophistication. These countries also tend to have higher-quality institutions, infrastructure and education systems, as well as a more entrepreneurial business sector.
Informal Employment
(In percent of total employment)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Informal Employment
(In percent of total employment)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Informal Employment
(In percent of total employment)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.
New Business Density
(New businesses/ 1000 adult population)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.New Business Density
(New businesses/ 1000 adult population)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.New Business Density
(New businesses/ 1000 adult population)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.21. Paraguay lags behind the high-converging peers regarding most of the parameters examined in this section, except for the macroeconomic stability factors. To join the high- converging country club, structural reforms on many levels are needed. The next section will conduct an empirical analysis to identify the priorities for structural reform in Paraguay.
C. Structural Reform Priorities for Sustainable Growth
22. The last session identified various structural characteristics of countries that potentially impact long-term growth, where significant gaps exist between Paraguay and the high converging countries. This section will look at these structural factors in more details, quantify their impact on growth, and identify priority areas for structural reforms.
Methodology and Data
23. The methodology of the analysis draws inspiration from Biljanovska & Sandri (2018). Specifically, the priority level of a reform area is evaluated on two dimensions: 1) the potential impact of the reform on convergence, and 2) the potential support of the reform among private sector agents. The latter ensures that the reform effort will be met with less resistance socially and politically, increasing the ease of implementation and the likelihood of reform success.
24. Reforms falling into the upper right quadrant should receive the highest priority, as the chart below illustrates. Because these reforms are likely to gain more public support, while yielding the highest long-term growth benefit. The upper left quadrant contains the second-highest priority reforms, which are likely to be popular with the private sector and also have some level of growth benefit. The lower left quadrant are reforms that are beneficial to growth and have received some private sector support, but to a lesser extent on both scales compared to other reforms.
25. One thing worth emphasizing is that in the results below, lower priority, i.e. placed in the lower left quadrant, does not mean the reform is dispensable, as all reform areas appearing in the four quadrants are strongly associated with growth performance empirically, and the lack of which have received at least some complaints from the private sector due to their effect on doing business in Paraguay. The goal of the analysis below is to shed some light on the sequencing and relative order of reforms that are ultimately all very important.
26. To gauge the potential growth/convergence impact of improvements in various structural areas, a cross-country growth regression is implemented, on the structural and institutional factors referenced in the previous section. The regression model takes the following format:
where the dependent variable is the annual growth of the gap in real GDP between Country i and the United States, i.e., git = Δyi,t, with yit = ln(realGDPit) – ln(USGDPt).
27. The dependent variable is a function of the lagged value in real GDP gap and the structural indicator under investigation. The standard economic convergence theory predicts a negative b1 as poorer countries are expected to converge with the richer ones, i.e. grow faster. b2 is the parameter of interest, as it measures the additional growth-acceleration effect from the improvement in the underlining structural indicator (SI), apart from what the classical convergence theory would suggest.
28. OLS and fixed-effect panel methods are applied to the model, on 1) a sample of countries at all development stages (“all countries” sample) and 2) a sample of countries at a similar convergence stage as Paraguay (“peer group” sample). The table below presents the OLS regression results, for the SI variables that have a positive and significant
29. The potential growth impact of a structural reform factor on Paraguay is then calculated as the estimated increment in convergence speed if Paraguay’s score in the factor catches up to the average level of the high-converging peers (HCP), i.e.
| Variables | Burdens of customs procedure | Quality of education | Government effectiveness | Law and order | New business density | Quality of transport infrastructure |
|---|---|---|---|---|---|---|
| Lagged relative GDP per capita | -0.0156*** | -0.0163*** | -0.0149*** | -0.00972*** | -0.305*** | -0.0162*** |
| (0.00298) | (0.00288) | (0.00317) | (0.00210) | (0.0312) | (0.00411) | |
| Structural factor | 0.0379* (0.0225) | 0.0449** (0.0204) | 0.0103* (0.00529) | 0.0362*** (0.00823) | 0.00284* (0.00168) | 0.0607*** (0.0219) |
| Constant | -0.0828** (0.0372) | -0.0902*** (0.0322) | -0.0156* (0.00855) | -0.0593*** (0.0142) | -0.798*** (0.0850) | -0.126*** (0.0373) |
| Observations | 1,385 | 1,385 | 3,275 | 4,120 | 256 | 993 |
| R-squared | 0.026 | 0.029 | 0.017 | 0.009 | 0.334 | 0.021 |
| Variables | Burdens of customs procedure | Quality of education | Government effectiveness | Law and order | New business density | Quality of transport infrastructure |
|---|---|---|---|---|---|---|
| Lagged relative GDP per capita | -0.0156*** | -0.0163*** | -0.0149*** | -0.00972*** | -0.305*** | -0.0162*** |
| (0.00298) | (0.00288) | (0.00317) | (0.00210) | (0.0312) | (0.00411) | |
| Structural factor | 0.0379* (0.0225) | 0.0449** (0.0204) | 0.0103* (0.00529) | 0.0362*** (0.00823) | 0.00284* (0.00168) | 0.0607*** (0.0219) |
| Constant | -0.0828** (0.0372) | -0.0902*** (0.0322) | -0.0156* (0.00855) | -0.0593*** (0.0142) | -0.798*** (0.0850) | -0.126*** (0.0373) |
| Observations | 1,385 | 1,385 | 3,275 | 4,120 | 256 | 993 |
| R-squared | 0.026 | 0.029 | 0.017 | 0.009 | 0.334 | 0.021 |
30. To gauge the potential support from the private sector agents for different reform areas, the data from the World Bank Enterprise Survey’s firm-level survey for Paraguay conducted in 2017 is used. In the survey, firms were asked to answer the question: “Is factor X a major constraint’ on business activities?” The percentage of firms that answered yes to this question for the structural factor X is used as a proxy for the extent of potential private sector support that reforms related to factor X may have, i.e. the potential “popularity” of reforms on X.
31. Given the importance of a strong export orientation in the growth process, we divided the survey sample by export status. The percentage-of-firms data reported in the priority ranking are the higher one between the exporting-firm sample and whole sample.
Major Obstacles to Private Firms
(In percent of firms)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: World Bank Enterprise SurveysMajor Obstacles to Private Firms
(In percent of firms)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: World Bank Enterprise SurveysMajor Obstacles to Private Firms
(In percent of firms)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: World Bank Enterprise Surveys32. Note that in fact, this survey question can be seen as measuring two things: the extent to which the business sector resonates with reforms on a particular structural factor, as well as the possible impact the reforms related to the factor may have on private business growth. Albeit as a proxy for the latter, it is more indirect and subjective. Therefore, we take the survey data as solely an estimate for the potential popularity of a structural reform, and use the cross-country regression estimates to measure the growth impact of improvement in a certain factor.
33. Since the structural factors covered in the Enterprise Survey data does not exactly match the factors used in the regressions, some assumptions need to be made on how the two sets of variables correspond to each other. The table below presents the matching assumptions used in the analysis.
| Customs and trade regulation | Burdens of customs procedure (GCP) | Customs and trade regulation |
| Quality of education | Quality of education (GCR) | Inadequately educated workforce |
| Government effectiveness | Government effectiveness (WGI) | Average of tax administration & labor regulations |
| Rule of law | Law and order (CRG) | Average of corruption, courts system, crime & theft |
| Business facilitation | New business density (WB) | Business licensing and permits |
| Transportation infrastructure | Quality of transport infrastructure (GCR) | Transportation |
| Customs and trade regulation | Burdens of customs procedure (GCP) | Customs and trade regulation |
| Quality of education | Quality of education (GCR) | Inadequately educated workforce |
| Government effectiveness | Government effectiveness (WGI) | Average of tax administration & labor regulations |
| Rule of law | Law and order (CRG) | Average of corruption, courts system, crime & theft |
| Business facilitation | New business density (WB) | Business licensing and permits |
| Transportation infrastructure | Quality of transport infrastructure (GCR) | Transportation |
34. The matching is not perfect. For example, the “government effectiveness” variable in the growth regression captures the quality of public services and the quality of policy formulation and implementation. It goes much beyond the effectiveness of tax administration and labor regulations. Still, complaints in the latter can be indicative of the general public’s opinion of the government’s effectiveness level, given the positive correlation in the quality of different government services. Similarly, “business facilitation” to encourage entrepreneurial dynamism in the private sector goes beyond efficientizing the processing of business licenses and permits. In particular, the survey data come from established private sector firms, which very likely under-captures the obstacles in this area faced by new firms.
Result and Comment
35. The chart below presents the results for structural reform priority quadrants, combining the estimates from the cross-country growth regressions and the firm-level survey data. As discussed earlier, the highest priority area is the upper right quadrant, where reforms may deliver a significant impact on growth, while having the highest level of public support. Structural factors in this quadrant are, in order of priority, transportation infrastructure, quality of education, and rule of law.
Potential Impact of Structural Reforms
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: National authorities and IMF staff estimates.Potential Impact of Structural Reforms
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: National authorities and IMF staff estimates.Potential Impact of Structural Reforms
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: National authorities and IMF staff estimates.36. The estimated high impact on growth from the top three priority areas is driven by their large coefficient in the cross-country convergence regression, as well as the fact that the empirical gap in these areas between Paraguay and the high-converging peers is substantial. These estimates are supported by private sector opinions, as the three areas are also identified by the largest percentage of firms as major business constraints.
Connectivity
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Connectivity
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Connectivity
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.
Skills
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Skills
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Skills
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.37. However, high impact on growth and high support from the public do not mean these will be easy reforms. Improving transport infrastructure requires substantial financial resources, which is a binding constraint on improvement efforts given the limited room to spend in the government budget. To make improvements in this area thus requires consistent implementation of strategic focus over an extended period of time on the part of the government.
Govenment Expenditure on Education
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Govenment Expenditure on Education
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.Govenment Expenditure on Education
(In percent of GDP)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: WDI and IMF staff calculations.38. The quality of education, on the other hand, does not seem to show a consistent relationship with resources spent on education. In fact, the government spending on education in Paraguay, around 5 percent of GDP, is higher than the averages in both its high-converging and low-converging peers. Given already high investment level and unsatisfactory result, the government should carefully look into where the missing links are to device an effective reform plan.
39. Improving rule of law, including reforming the courts system, reducing corruption and strengthening public security, requires less financial commitment. Still, reform efforts in this area can take a long time to take effect as the status quo tends to have deep roots in the cultural and political environment of a country. Nonetheless, given that reforms in this area are not as costly as far as government finance is concerned, and the potential growth benefit, improving rule of law is an ideal area to prioritize in the short to medium term.
40. In the second-priority quadrant (upper left) sits customs and trade regulations. Close to 30 percent of private sector firms that are exporters identify this area as a major constraint. And it is also estimated to have a significantly positive--albeit relatively small in magnitude—impact on convergence speed. Similar to rule of law, this is a reform area that does not cost substantial financial resources, while being easier to implement as the roadmap and action items tend to be concrete with benchmarked international best practices to borrow from. Therefore, improvements in this area is another ideal candidate to prioritize in the short term.
41. In the third-priority quadrant (lower right) is business facilitation. The factor is shown to have a very high impact on convergence speed, mostly driven by the large gap in new (formal) business density between Paraguay and the high-converging peers. The private sector support for reforms in this area is proxied by the percentage of businesses identifying business licensing and permits as a major constraint. This is, obviously, a very imperfect approximation to the actual demand for increased entrepreneurial activities in the private sector. In addition, beyond general advices for improving business environment, it is unclear according to existing data why the level of new business creation is so low in Paraguay and what exactly the government can do to help increase it. This is a topic beyond the scope of the current paper, but it deserves further investigation.
Reliability of Police Services
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Reliability of Police Services
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Reliability of Police Services
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.
Judicial Independence
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Judicial Independence
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Judicial Independence
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.
Anti-Corruption
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Anti-Corruption
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Anti-Corruption
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.
Efficiency of Legal Framework in Settling Disputes
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Efficiency of Legal Framework in Settling Disputes
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.Efficiency of Legal Framework in Settling Disputes
(In units)
Citation: IMF Staff Country Reports 2019, 112; 10.5089/9781498312318.002.A002
Sources: Global Competitiveness Report and IMF staff calculations.42. In sum, the empirical study in this section presents a list of priorities for improvements in structural factors. The priorities are determined according to the candidate factors’ estimated impact on the speed of convergence for Paraguay, as well as the potential popularities of various reforms among private businesses. The result identifies transport infrastructure, quality of education, rule of law, and customs and trade regulations as the top priority reform areas. Among these areas, improving rule of law and the quality of customs are the ideal targets for short-to-medium term reform efforts, as they do not impose substantial burden on the government budget and can have relatively concrete implementation plans. Improving transport infrastructure and quality of education is of paramount importance to sustainable convergence, though they do require more financial investments. Therefore, persistent implementation of gradual but focused strategic plans over extended periods are required to move the needle in these areas.
D. Conclusion
43. Over the past 15 years Paraguay has experienced relatively stable and rapid growth. Especially in the past 5 years or so, its economic performance has been increasingly decoupled from the rest of the region, and stands out as one of the fastest growing economies in South America. The elevated growth performance is largely the combined result of benign external environment, including a commodity price boom during the 2000s, and improved macroeconomic management and stability.
44. As a small open economy with limited domestic market size, external demand is the main driver of growth in Paraguay. And since exports consist of mostly agricultural commodities (non-agricultural, non-energy exports are only 3 percent of GDP), the movement of agriculture export prices is crucial for the economic performance.
45. After the boom in the 2000s, agricultural commodity prices have come down, and may well come down further going forward. The decline in commodity prices will not only negatively affect the export sector, but also the non-tradable sector through the income effect from exchange rate depreciation.
46. Development of new industries and faster growth of the non-agriculture, non-energy tradeable sector are therefore crucial for growth and convergence over the long run. Sustainable growth also calls for increases in TFP growth and higher private-sector driven investments. In this regard, development of the more productive, tradable sector will also help increase investments.
47. The paper compares Paraguay to the group of middle-income countries that have achieved sustainable convergence over the past few decades, across various economic and institutional factors. Paraguay fairs well in macro stability indicators, such as inflation, government balance and debt level. However, it lags behind the high-converging peers in almost all structural measures of the economy, including rates of investment and FDI, degree of industrialization, and level of economic complexity. Paraguay also trails behind its high-converging peers in many structural factors such as the quality of institutions, infrastructure, and education.
48. The paper then conducts an empirical analysis to identify the priority structural reform areas, combining the estimation of potential impact on convergence speed from various reforms, with the likely receptivity level of the private sector to the said reforms. The result indicates that the reform areas Paraguay should prioritize for are transport infrastructure, quality of education, rule of law, and customs and trade regulations.
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The Global Competitiveness Report 2017–2018. (2017).
Annex I. High and Low Converging Country Groups
High Converging Countries:
High Converging Countries:
| Country | Country Code | Starting Year |
|---|---|---|
| Chile | 228 | 1988 |
| Costa Rica | 238 | 1989 |
| Sri Lanka | 524 | 2009 |
| Korea | 542 | 1976 |
| Malaysia | 548 | 1972 |
| Maldives | 556 | 1992 |
| Thailand | 578 | 2006 |
| Botswana | 616 | 2002 |
| Mauritius | 684 | 1987 |
| Albania | 914 | 2005 |
| Georgia | 915 | 2007 |
| Bulgaria | 918 | 1995 |
| China | 924 | 2007 |
| Turkmenistan | 925 | 2003 |
| Montenegro | 943 | 2001 |
| Lithuania | 946 | 1996 |
| Macedonia, FYR | 962 | 1999 |
| Romania | 968 | 1995 |
| Estonia | 939 | 1994 |
| Latvia | 941 | 1994 |
High Converging Countries:
| Country | Country Code | Starting Year |
|---|---|---|
| Chile | 228 | 1988 |
| Costa Rica | 238 | 1989 |
| Sri Lanka | 524 | 2009 |
| Korea | 542 | 1976 |
| Malaysia | 548 | 1972 |
| Maldives | 556 | 1992 |
| Thailand | 578 | 2006 |
| Botswana | 616 | 2002 |
| Mauritius | 684 | 1987 |
| Albania | 914 | 2005 |
| Georgia | 915 | 2007 |
| Bulgaria | 918 | 1995 |
| China | 924 | 2007 |
| Turkmenistan | 925 | 2003 |
| Montenegro | 943 | 2001 |
| Lithuania | 946 | 1996 |
| Macedonia, FYR | 962 | 1999 |
| Romania | 968 | 1995 |
| Estonia | 939 | 1994 |
| Latvia | 941 | 1994 |
Low Converging Countries:
Low Converging Countries:
| Country | Country Code | Starting Year |
|---|---|---|
| Venezuela | 299 | 1994 |
| Jamaica | 343 | 1983 |
| Iran | 429 | 2012 |
| Yemen | 474 | 1994 |
| Timor-Leste | 537 | 2007 |
| Algeria | 612 | 1989 |
| Cameroon | 622 | 1981 |
| Congo, Rep of | 634 | 1974 |
| Benin | 638 | 1974 |
| Gambia, The | 648 | 1983 |
| Morocco | 686 | 1992 |
| Mozambique | 688 | 1976 |
| Nigeria | 694 | 2013 |
| Senegal | 722 | 1980 |
| Swaziland | 734 | 1980 |
| Tunisia | 744 | 1982 |
Low Converging Countries:
| Country | Country Code | Starting Year |
|---|---|---|
| Venezuela | 299 | 1994 |
| Jamaica | 343 | 1983 |
| Iran | 429 | 2012 |
| Yemen | 474 | 1994 |
| Timor-Leste | 537 | 2007 |
| Algeria | 612 | 1989 |
| Cameroon | 622 | 1981 |
| Congo, Rep of | 634 | 1974 |
| Benin | 638 | 1974 |
| Gambia, The | 648 | 1983 |
| Morocco | 686 | 1992 |
| Mozambique | 688 | 1976 |
| Nigeria | 694 | 2013 |
| Senegal | 722 | 1980 |
| Swaziland | 734 | 1980 |
| Tunisia | 744 | 1982 |
Annex II. Data Sources for Structural Indicators
| Corruption | Country Risk Guide |
| Government Stability | Country Risk Guide |
| Law and Order | Country Risk Guide |
| Government Effectiveness | World Governance Indicators |
| Regulatory Quality | World Governance Indicators |
| Rule of Law | World Governance Indicators |
| Informal Employment (% of total employment) | World Development Indicators |
| Interest Rate Spread | World Development Indicators |
| New Business Density | World Development Indicators |
| Paved Roads (as % of total roads) | World Development Indicators |
| Private Sector Credit (% of GDP) | World Development Indicators |
| Total Tax Rate (% of commercial profits) | World Development Indicators |
| Share of Primary Sector in GDP | World Development Indicators |
| Share of Manufacturing Sector in GDP | World Development Indicators |
| Share of Service Sector in GDP | World Development Indicators |
| Burden of Customs Procedures | Global Competitiveness Report |
| Quality of Education | Global Competitiveness Report |
| Quality of the Education System | Global Competitiveness Report |
| Quantity of Education | Global Competitiveness Report |
| Transportation Infrastructure | Global Competitiveness Report |
| Starting a Business | Doing Business Report |
| Years of Schooling | Barro and Lee Educational Attainment for Total Population, 1870–2010 |
| Economic Complexity Index | The Observatory of Economic Complexity |
| Share of Commodity in Total Export | The Observatory of Economic Complexity |
| Share of Manufacturing in Total Export | The Observatory of Economic Complexity |
| Corruption | Country Risk Guide |
| Government Stability | Country Risk Guide |
| Law and Order | Country Risk Guide |
| Government Effectiveness | World Governance Indicators |
| Regulatory Quality | World Governance Indicators |
| Rule of Law | World Governance Indicators |
| Informal Employment (% of total employment) | World Development Indicators |
| Interest Rate Spread | World Development Indicators |
| New Business Density | World Development Indicators |
| Paved Roads (as % of total roads) | World Development Indicators |
| Private Sector Credit (% of GDP) | World Development Indicators |
| Total Tax Rate (% of commercial profits) | World Development Indicators |
| Share of Primary Sector in GDP | World Development Indicators |
| Share of Manufacturing Sector in GDP | World Development Indicators |
| Share of Service Sector in GDP | World Development Indicators |
| Burden of Customs Procedures | Global Competitiveness Report |
| Quality of Education | Global Competitiveness Report |
| Quality of the Education System | Global Competitiveness Report |
| Quantity of Education | Global Competitiveness Report |
| Transportation Infrastructure | Global Competitiveness Report |
| Starting a Business | Doing Business Report |
| Years of Schooling | Barro and Lee Educational Attainment for Total Population, 1870–2010 |
| Economic Complexity Index | The Observatory of Economic Complexity |
| Share of Commodity in Total Export | The Observatory of Economic Complexity |
| Share of Manufacturing in Total Export | The Observatory of Economic Complexity |
Annex III. Structural Reform Regression Results
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0156*** (0.00298) | -0.318*** (0.0431) | -0.296*** (0.0221) | -0.587*** (0.0634) |
| Customs burden | 0.0379* (0.0225) | 0.0563 (0.0495) | 0.0612 (0.0439) | 0.0869 (0.0961) |
| Constant | -0.0828** (0.0372) | -0.910*** (0.150) | -0.695*** (0.0791) | -1.658*** (0.214) |
| Observations | 1,385 | 236 | 1,385 | 236 |
| R-squared | 0.026 | 0.335 | 0.154 | 0.474 |
| Number of country_code | 152 | 39 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0156*** (0.00298) | -0.318*** (0.0431) | -0.296*** (0.0221) | -0.587*** (0.0634) |
| Customs burden | 0.0379* (0.0225) | 0.0563 (0.0495) | 0.0612 (0.0439) | 0.0869 (0.0961) |
| Constant | -0.0828** (0.0372) | -0.910*** (0.150) | -0.695*** (0.0791) | -1.658*** (0.214) |
| Observations | 1,385 | 236 | 1,385 | 236 |
| R-squared | 0.026 | 0.335 | 0.154 | 0.474 |
| Number of country_code | 152 | 39 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0163*** (0.00288) | -0.323*** (0.0430) | -0.299*** (0.0206) | -0.567*** (0.0640) |
| Education quality | 0.0449** (0.0204) | -0.0372 (0.0394) | 0.0955* (0.0517) | -0.0899 (0.147) |
| Constant | -0.0902*** (0.0322) | -0.806*** (0.135) | -0.740*** (0.0736) | -1.385*** (0.215) |
| Observations | 1,385 | 236 | 1,385 | 236 |
| R-squared | 0.029 | 0.334 | 0.156 | 0.473 |
| Number of country code | 152 | 39 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0163*** (0.00288) | -0.323*** (0.0430) | -0.299*** (0.0206) | -0.567*** (0.0640) |
| Education quality | 0.0449** (0.0204) | -0.0372 (0.0394) | 0.0955* (0.0517) | -0.0899 (0.147) |
| Constant | -0.0902*** (0.0322) | -0.806*** (0.135) | -0.740*** (0.0736) | -1.385*** (0.215) |
| Observations | 1,385 | 236 | 1,385 | 236 |
| R-squared | 0.029 | 0.334 | 0.156 | 0.473 |
| Number of country code | 152 | 39 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0149*** (0.00317) | -0.300*** (0.0268) | -0.113*** (0.00940) | -0.446*** (0.0359) |
| Government effectiveness | 0.0103* (0.00529) | 0.00239 (0.0115) | 0.00933 (0.0130) | -0.00470 (0.0253) |
| Constant | -0.0156* (0.00855) | -0.776*** (0.0706) | -0.255*** (0.0229) | -1.168*** (0.0979) |
| Observations | 3,275 | 575 | 3,275 | 575 |
| R-squared | 0.017 | 0.312 | 0.088 | 0.461 |
| Number of country_code | 185 | 67 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0149*** (0.00317) | -0.300*** (0.0268) | -0.113*** (0.00940) | -0.446*** (0.0359) |
| Government effectiveness | 0.0103* (0.00529) | 0.00239 (0.0115) | 0.00933 (0.0130) | -0.00470 (0.0253) |
| Constant | -0.0156* (0.00855) | -0.776*** (0.0706) | -0.255*** (0.0229) | -1.168*** (0.0979) |
| Observations | 3,275 | 575 | 3,275 | 575 |
| R-squared | 0.017 | 0.312 | 0.088 | 0.461 |
| Number of country_code | 185 | 67 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.00972*** (0.00210) | -0.424*** (0.0316) | -0.122*** (0.0111) | -0.534*** (0.0366) |
| Law and order | 0.0362*** (0.00823) | 0.00849 (0.0124) | 0.0219* (0.0119) | 0.00455 (0.0220) |
| Constant | -0.0593*** (0.0142) | -1.128*** (0.0854) | -0.297*** (0.0274) | -1.419*** (0.107) |
| Observations | 4,120 | 622 | 4,120 | 622 |
| R-squared | 0.009 | 0.427 | 0.078 | 0.508 |
| Number of country code | 137 | 62 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.00972*** (0.00210) | -0.424*** (0.0316) | -0.122*** (0.0111) | -0.534*** (0.0366) |
| Law and order | 0.0362*** (0.00823) | 0.00849 (0.0124) | 0.0219* (0.0119) | 0.00455 (0.0220) |
| Constant | -0.0593*** (0.0142) | -1.128*** (0.0854) | -0.297*** (0.0274) | -1.419*** (0.107) |
| Observations | 4,120 | 622 | 4,120 | 622 |
| R-squared | 0.009 | 0.427 | 0.078 | 0.508 |
| Number of country code | 137 | 62 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0162*** (0.00263) | -0.305*** (0.0312) | -0.328*** (0.0206) | -0.629*** (0.0454) |
| New business density | 0.000381 (0.000595) | 0.00284* (0.00168) | -0.000690 (0.00278) | 0.000635 (0.00205) |
| Constant | -0.0184*** (0.00679) | -0.798*** (0.0850) | -0.629*** (0.0411) | -1.646*** (0.121) |
| Observations | 1,314 | 256 | 1,314 | 256 |
| R-squared | 0.033 | 0.334 | 0.225 | 0.583 |
| Number of country code | 137 | 38 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0162*** (0.00263) | -0.305*** (0.0312) | -0.328*** (0.0206) | -0.629*** (0.0454) |
| New business density | 0.000381 (0.000595) | 0.00284* (0.00168) | -0.000690 (0.00278) | 0.000635 (0.00205) |
| Constant | -0.0184*** (0.00679) | -0.798*** (0.0850) | -0.629*** (0.0411) | -1.646*** (0.121) |
| Observations | 1,314 | 256 | 1,314 | 256 |
| R-squared | 0.033 | 0.334 | 0.225 | 0.583 |
| Number of country code | 137 | 38 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0162*** (0.00411) | -0.312*** (0.0580) | -0.241*** (0.0197) | -0.531*** (0.0824) |
| Infrastructure quality | 0.0607*** (0.0219) | -0.0247 (0.0498) | 0.102 (0.0748) | 0.137 (0.164) |
| Constant | -0.126*** (0.0373) | -0.810*** (0.175) | -0.646*** (0.110) | -1.572*** (0.233) |
| Observations | 993 | 168 | 993 | 168 |
| R-squared | 0.021 | 0.342 | 0.098 | 0.386 |
| Number of country code | 151 | 34 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | OLS all countries | OLS peer group | Fixed Effect all countries | Fixed Effect peer group |
| Lagged relative GDP | -0.0162*** (0.00411) | -0.312*** (0.0580) | -0.241*** (0.0197) | -0.531*** (0.0824) |
| Infrastructure quality | 0.0607*** (0.0219) | -0.0247 (0.0498) | 0.102 (0.0748) | 0.137 (0.164) |
| Constant | -0.126*** (0.0373) | -0.810*** (0.175) | -0.646*** (0.110) | -1.572*** (0.233) |
| Observations | 993 | 168 | 993 | 168 |
| R-squared | 0.021 | 0.342 | 0.098 | 0.386 |
| Number of country code | 151 | 34 |
In the case where data for a country / target year combination is not available, data of the closest subsequent period for that country is used.
In the calculation of average inflation and inflation volatility for the two peer groups, outliners (higher than 75 percentile of the observations) were excluded. This is because several Eastern European countries had unusually high inflation during their transition period in the 1990s.
Results for the structural indicator variables with insignificant or negative coefficient estimates are omitted here. Those include interest rate spread, informal sector employment, regulatory quality, and average years of schooling.
