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Using firm-level micro data for evidence based policy making

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Using firm-level micro data for evidence based policy making

  1. 1. Using Firm-level micro data for evidence-based policy making OECD Productivity Network Juan Rebolledo October, 2016 1
  2. 2. Research based on ENAPROCE’s policy-relevant data. Understanding the Productivity gaps across firms, sectors, and economic regions in Mexico. Mexico’s National Micro, Small and Medium Enterprise Productivity and Competitiveness Survey, the first effort to measure “hard-to-observe” skills at the Firm-level. INDEX 2
  3. 3. Productivity and low economic growth in Mexico Total Factor Productivity decline in Mexico (1990-2014)*** (***) Source: INEGI. 90 92 94 96 98 100 102 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average Growth in GDP and Productivity (Annual growth rate, 1980-2014) (*) Source: Penn World Table 8.1 (1980-2011) and The Conference Board (2011-2014). (**) Source: Penn World Table 8.1 (1980-1990) and INEGI (1990-2015), based on KLEMS methodology. 6.4% 4.0% 4.6% 2.5% 1.2% 0.6% -0.2% -0.8% Korea* Ireland* Chile* Mexico** GDP Productivity Total Factor Productivity (TFP) has declined since 1990. In 2014 it was 7.8% lower than in 1990, an annual fall of 0.4%, on average. 3
  4. 4. National AGS BC BCS CHI CH COA COL DF DURGUA GUE HID JAL MEX MIC MOR NAY NL OAX PUE QRO QR SLP SIN SON TAB TAM TLAX VER YUCZAC 70 170 270 370 470 570 670 0 5 10 15 20 25 30 35Labourproductivity(GDP/Employedpopulation) % of population living in extreme poverty Labour productivity and poverty Productivity gaps and poverty by state In 2014, 11/32 states were above the national average. The 5 most productive states were 3x more productive than the 5 least productive states. National Average Source: INEGI and CONAPO. 4
  5. 5. 0.5 times the national average 0.6 times the national average 0.7-0.8 times the national average 0.9 times the national average Above the national average In Mexico, the Northern border is 2.5x more productive than the South and has 2x more formal employment. Labour productivity by state Source: INEGI. 57.20% 40.43% 80.07% National Average Northern Border South Informality (% of working population) 2x The inefficient allocation of factors of production results in wide productivity gaps In Mexico, employment in manufactures along the northern border (20.4%) is more than twice that of the south (9.4%). 5
  6. 6. Firm-endogenous factors may also explain low productivity Managerial practices Innovation Production chain integration Training MSME productivity Financing However, recent literature suggests that other non-given firm-level characteristics, such as: managerial practices, training, use of ICTs, financial access, better capital or innovation also influence firm-level productivity2. 1For example: La Porta and Shleifer (2008), Gennaioli (2013) and Hurst and Pugsley (2011). 2 Bloom et al (2010). (*) Informality could be considered either endogenous or exogenous, or both. Most of the empirical evidence that explains the low productivity of firms across countries, especially among MSMEs, has studied exogenous factors that influence firm productivity, such as the quality of infrastructure, informality*, government regulations, trade policies, and levels of human capital1. 6
  7. 7. The productivity of Firms in developing countries appears to be extremely low and disperse Management Practices Poor management practices have a substantial negative impact on productivity in developing countries. Latin American countries have significantly lower average management scores. In India, training on basic management practices led to a 50% reduction in quality defects in 3 months1. Delegation of Decision Making The lack of delegation of decision making negatively impacts the productivity of firms in developing countries. In some, a sign- off from owners is required for every purchase, while managers in Japan and the US can make investments of up to $50,000 directly1. Human Capital Firms in cities with small growth rates in their college graduate populations miss out on potential human capital externalities and have lower productivity rates after controlling for firms’ actual levels of human capital2. 1Bloom et al. (2010) 2Moretti (2004) 3Suresh De Mel, McKenzie, and Woodruff (2007a); Banerjee and Duflo (2008) 4De Fuentes et al. (2004) *KIBS refers to Knowledge-intensive based services. ENAPROCE is a recent effort by the Mexican government to provide firm-level micro data precisely on these factors that may explain productivity. Relevant evidence points to the following factors: Financial Constraints Micro and small enterprises in developing countries have difficulty accessing formal financial resources, despite their marginal returns being higher than average interest rates. An experiment in Sri Lanka found a causal relationship between firms’ access to finance and growth in their profits or sales3. Innovation An IDB discussion paper finds that only 13.7% of Mexican firms surveyed reported having innovated. This was lower for firms in the services (6.6%) and KIBS (10.5%) sectors*. It also found intensive investment in innovation has a positive effect on labour productivity and is unrelated to firm size4. 7
  8. 8. 96% Response Rate Stratification by sector, region, and firm size 25,456 firms in sample ENAPROCE characteristics — 2015 baseline for panel data The information provided by ENAPROCE enables researchers to study the determining factors behind the lagging productivity of MSMEs in the country. managerial practices financing government support innovation training Main themes use of ICTs Source: ENAPROCE 2015. production chain integration business atmosphere Productivity measurement: Törnqvist changing-weight index A Törnqvist formula expresses the change in multifactor productivity as the difference between the rate of change in output and the weighted average of the rates of change in the shares and prices of inputs. human capital 8
  9. 9. ENAPROCE representativeness Source: ENAPROCE and World Bank. • Large sectors (industry, retail and services) • Size • Strategic sectors* **Regions are defined as follows: North: Baja California, Baja California Sur, Sinaloa, Sonora, Coahuila, Chihuahua, Durango, Nuevo León and Tamaulipas West: Aguascalientes, Colima, Guanajuato, Jalisco, Michoacán, Nayarit, San Luis Potosí and Zacatecas Center: Distrito Federal, Hidalgo, México, Morelos, Querétaro and Tlaxcala; South: Campeche, Chiapas, Guerrero, Oaxaca, Puebla, Quintana Roo, Tabasco, Veracruz and Yucatán State Level National Level Regional Level** • SMEs • Strategic sectors* • Micro enterprises • Strategic sectors* Observation unit: Firm Some firms have one or more establishments under the same business name. To account for location, the parent company or the establishment with the biggest number of employees is considered. *Strategic sectors: • Electrical appliances • Communication and electronics equipment • Ground transportation equipment • Medical equipment • Textile industry • Plastics and rubber • Wood products • Construction products • Business support services • Tourism services • Pharmaceutical products • Chemical industry • R&D services 9
  10. 10. Firm heterogeneity by size 305% 200% 274% Retail Manufactures Services Total Factor Productivity heterogeneity by sector (Gap between percentile 10 and percentile 90) Total Factor Productivity Kernel density by firm size Micro Small Medium Large Source: ENAPROCE and World Bank. 10
  11. 11. Preliminary data analysis - Relatives as owners of businesses - Relatives as firm decision-makers Family business practices - Target-based planning - Incentives and salaries - Employee monitoring Managerial practices - Share of uneducated workers - Share of workers with various levels of educational attainment Higher education Innovation - Patent registration - Use of ICTs - Presence of innovation spending - Innovation spending per worker - Presence of certifications - Presence of financing - Sources of financing - Costs of financing Financing 11
  12. 12. Firm characteristics prevalent in Mexico are associated with higher productivity Family business These practices are negatively correlated with higher firm productivity. Their presence reduces firm productivity by almost Dependent variable: ln(TFP) (1) (2) (3) (4) (5) Firm characteristics Share of workers with HE 0.0004 (0.0000)*** 0.0004 (0.0000)*** 0.0005 (0.0001)*** .0004 (.0000)*** .0004 (.0000)*** Family practices -0.15 (0.0230)*** -0.19 (0.0473)*** -.15 (.0230)*** -.14 (.0235)*** Survey indices Managerial practices z-score 0.11 (0.0166)*** Financing z-score .04 (.0081)*** Innovation z-score .09 (.0089)*** Note: all regressions control for K, L, and the state in which the firm is established. 15% The various indices are constructed using factor analysis. Firm weights were utilized, and in all cases, only the first factor was considered. The factor analysis strongly suggests that the indices are accurate representations of the underlying variables. Small and Medium Firms 12
  13. 13. Family business Their presence reduces firm total factor productivity by almost The presence of family business practices is associated with lower productivity levels Low human capital 33% lower share of HE workers Indebtedness and credit application TFP -10% = 15% Indebted Family business practices reduce scores by 0.09 SD Managerial practices -10% Loandenied 13
  14. 14. 0 0.2 0.4 0.6 0.8 1 2 3 4 5 6 7 8 9 10 Productivity Managerial practices decile Productivity by managerial practices score decile Relationship between managerial practices, innovation and productivity Firms that score higher on the managerial practices index tend to be more productive and spend more on innovation per worker. Source: ENAPROCE and World Bank.Source: World Management Survey, Management Matters, Manufacturing Report (2014). Mexico 0% 5% 10% 15% 20% 1 2 3 4 5 6 7 8 9 10 Shareoffirmsthatexport Managerial practices decile Share of firms that export by managerial practices score decile 14
  15. 15. Better Managerial Practices lead to improvements in productivity, and higher investment in innovation and training Bigger firms, better managers Managerial practices improve as firm size increases: Large - Medium = ∆ 0.4 SD Medium - Small = ∆ 0.2 SD Large - Small = ∆ 0.6 SD Skills formation Firms with higher MP scores have more training. Firms with training have a score 0.4 SD higher Managerial practices A 1 SD standard deviation rise in the MP score is associated with a productivity increase of 10% = Youngerfirms havelower scores 0.2 0.6 0.6 0.6 0.5 0.5 0.6 0.6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Score years 15
  16. 16. Firms with higher innovation scores train their workers more and experience improvements in productivity Investmentin innovationincreases dramaticallyafteryear1 = Innovation A higher score in the innovation index is also positively correlated with increases in productivity. A 1 SD rise is associated with a productivity increase of 9% Skills formation Firms with higher innovation scores have more training. Firms with training have a score 0.5 SD higher Biggerfirms scorehigher Larger firms invest 2x more on innovation than other firms 0.3 1.1 1.4 1 0.9 1.9 4.2 0 1 2 3 4 5 1 3 4 5 6 7-10 >10 Innovationexpenditures (mpp) Years 16
  17. 17. Financial capacity has mixed results on the mechanisms that lead to higher productivity Financing A 1 SD increase in the financing index score results in a productivity rise of 4% FDI Financing score is 0.4 SD higher if the firm has FDI Supplierof foreignfirms Supplierof exporters Financing score is 0.4 SD lower if the firm is a supplier to firms that are exporters Financing score is 0.4 SD lower if the firm is a supplier to foreign firms = 17
  18. 18. What do we learn from these preliminary results? 1. Family Business Align incentives for firms to hire professional managers instead of family members to improve corporate governance. 2. Managerial practices In order to meet the best international managerial practices, design policies aimed to support firms when they find themselves at their weakest: around the time they are established or when they take out loans. 3. Financing Given the analysis’s mixed results on financial practices and being a part of the global market, more research should be done in order to clarify the mechanisms that underlie MSME financing. According to the scores analyzed, in terms of productivity gains, policies that target family business and managerial practices offer the highest benefits. 18
  19. 19. Research agenda: moving forward Exporters and FDI Look at the characteristics specific to foreign firms, firms that export or firms that have shares of foreign capital. Leading and lagging firms: Analyze leading and lagging firms’ productivity and salaries by sector. Look at leading and lagging firms’ convergence rate across sectors and regions. Stock of Knowledge and RoR Observe differing patterns in terms of technology adoption and innovation practices. Firm Survival Identify the factors behind firm survival and the changes in financing, management practices and decision-making over time. 1. 2. 3. 4. 19
  20. 20. OECD Productivity Network Juan Rebolledo October, 2016 20
  21. 21. 21
  22. 22. Relationship between managerial practices, innovation and productivity Firms that score higher on the managerial practices index are more likely to export and have FDI. Source: ENAPROCE and World Bank.Source: World Management Survey, Management Matters, Manufacturing Report (2014).. 0% 2% 4% 6% 8% 10% 12% 14% 1 2 3 4 5 6 7 8 9 10 ShareoffirmswithFDI Managerial practices decile Share of firms with FDI by managerial practices score decile 0 1000 2000 3000 4000 1 2 3 4 5 6 7 8 9 10 Innovatoinspendingper worker Managerial practices decile Innovation spending per worker by managerial practices score decile 22

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