rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6145

# **Abstract**

The authors conduct a randomized experiment among women in urban Sri Lanka to measure the impact of the most commonly used business training course in developing countries, the Start-and-Improve Your Business program. They work with two representative groups of women: a random sample of women operating subsistence enterprises and a random sample of women who are out of the labor force but interested in starting a business. They track the impacts of two treatments— training only and training plus a cash grant—over two years with four follow-up surveys and find that the short and medium-term impacts differ. For women already in business, training alone leads to some changes in business practices but has no impact on business profits, sales or capital stock. In contrast, the combination of training and a grant leads to large and significant improvements in business profitability in the first eight months, but this impact dissipates in the second year. For women interested in starting enterprises, business training speeds up entry but leads to no increase in net business ownership by the final survey round. Both profitability and business practices of the new entrants are increased by training, suggesting training may be more effective for new owners than for existing businesses. The study also finds that the two treatments have selection effects, leading to entrants being less analytically skilled and poorer.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Senior middle schools are usually the responsibility of a higher-level government – municipalities and counties. Provinces are usually involved in education at the university level (where the central government is also involved). Lower-level governments that have resources can and do offer more – townships sometimes have their own senior middle schools and technical vocational schools, and large municipalities often have their own universities. At the same time, provinces directly finance some primary and middle schools that are attached (and subsidiary) to provincial-owned universities and organizations.

What emerged from this system of informal expenditure assignments was that responsibilities for basic education and public health were primarily at the lowest tiers, where the schools, clinics and hospitals are located. This is how counties, townships and city districts came to account for 60 percent of total public expenditures in education and more than 40 percent of those for health. Likewise, social welfare relief is the responsibility of the lowest tier, sometimes jointly with the next higher level. For example, the “five guaranteed households”, a rural welfare program for elderly with no family support, was the responsibility of the village community.

Rural local government expenditures are overwhelmingly concentrated in the provision of social services, administration, and economic services. In aggregate terms these three categories together account for 75 and 80 percent of expenditures at the county and township levels, respectively (Table 5). Recent reforms have moved some expenditure assignments from the township to the county level – most notably the payment of teachers’ salaries in 2002-2003. While these changes have affected the relative shares between the township and county levels, they do not represent a reduction in the responsibilities for rural governments (counties plus townships) as a whole. Instead, many new programs have been rolled out in rapid succession, thus adding to expenditure burdens.<sup>15</sup>

> 15 Examples include the New Cooperative Medical Scheme (NCMS), the recently announced “free rural education” program, the rural minimum living stipends, and others.  See World Bank 2008.

12
**Table 2 OECD regulatory questionnaire for postal services**

|Weights<br>by theme|Question<br>weights|Score|Question|
|---|---|---|---|
|1/2|||**_Entry regulation_**|
||1/3||**Do national, state or provincial laws or other regulations restrict**<br>**the number of competitors allowed to operate a business in at**<br>**least some markets in the sector: national post — basic letter**<br>**services?**|
|||6<br>|Yes, in all markets<br>|
|||3<br>|Yes, in some markets<br>|
|||0|No, free entry in all markets|
||||**Do national, state or provincial laws or other regulations restrict**<br>**the number of competitors allowed to operate a business in at**<br>**least some markets in the sector: national post — basic parcel**|
||1/3||**services?**|
|||6|Yes, in all markets|
|||3|Yes, in some markets|
|||0|No, free entry in all markets|
||||**Do national, state or provincial laws or other regulations restrict**<br>**the number of competitors allowed to operate a business in at**<br>|
||||**least some markets in the sector: courier activities other than**<br>|
||1/3||**national post?**|
|||6|Yes|
|||0|No|
|1/2|||**_Public ownership_**|
||||**What percentage of shares in the largest firm in the sector:**<br>|
||||**national post — basic letter services are owned by the**<br>|
||1/3||**government?**<br>|
|||6<br>|100%<br>|
|||3|Between 0 and 100%|
|||0|None|
||||**What percentage of shares in the largest firm in the sector:**<br>**national post — basic parcel services are owned by the**|
||1/3||**government?**|
|||6|100%|
|||3|Between 0 and 100%|
|||0|None|
||||**What is the extent of public ownership in the courier activities**|
||1/3||**(other than national post) sector?**|
|||6|Govt controls all dominant firms in the sector|
|||3|Govt controls at least 1 firm, but other firms operate as well|
|||0|No govt involvement in the sector|

Source: OECD (2005b).

31
Figure 2: Public debt in LICs (A) Change in creditor composition of public and publicly guaranteed external debt, 2007-16 <!-- Start of picture text --> Non-Paris<br>Club bilateral<br>Commercial<br>Bonds<br>Multilateral<br>Paris Club<br>bilateral<br>-6 -4 -2 0 2 4<br>Percent of GDP<br><!-- End of picture text -->

## (B) Share of non-concessional debt

<!-- Start of picture text --> Percent of government debt<br>80 Median<br>70<br>Interquartile range<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# (C) Interest payments

(D) Share of LICs in debt distress or at high risk of distress <!-- Start of picture text --> Percent of revenue<br>12 Median<br>10 Interquartile range<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> Percent of countries<br>40<br>High In debt distress<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. GDP-weighted average across 32 low-income countries. “Bilateral” includes public and publicly guaranteed (PPG) loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. “Multilateral” includes PPG loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. It excludes loans from funds administered by an international organization on behalf of a single donor government. “Bonds” include PPG bonds that are either publicly issued or privately placed. “Commercial” includes PPG debt from commercial bank loans from private banks and other private financial institutions, as well as export and supplier credits.

B.C. Dashed blue lines denote the interquartile range, while solid blue line is the median. Includes 30 low-income countries and excludes Somalia, South Sudan, and Syria due to data restrictions.

D. Figure shows the percent of low-income countries eligible to access the IMF’s concessional lending facilities that are either at high risk of, or in, debt distress. A country is considered to be in debt distress if it is experiencing difficulties in servicing its debt, as evidenced, for example, by the existence of arrears, ongoing or impending debt restructuring, or if there are indications that a future debt distress event is probable. The sample includes 30 low-income countries.

5
<!-- Start of picture text --> Afghanistan Cambodia Cuba Hong Kong<br>100 ee ~<br>~/\—-~4 ae<br>\_<br>50 . N NOt \.<br>~n. . a<br>se -- 4<br><2 >+ |TT<br>0 a ee<br>x Libya North Korea Saudi Arabia Somalia<br>100 =—— _—___arr rr<br>-<br>y ~~. \<br>N b-~-~—__we ~<br>~~. -sJO~~<br>50 Ne ~ ~ i<br>0o,---->>>>> -- 8 OO<br>2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020<br>—— $2.15 predictions —-— $6.85 predictions $24.35 predictions<br>— $2.15 from PIP — $6.85 from PIP $24.35 from PIP<br><!-- End of picture text -->
# **S1 Peruvian Mining Context**

Peru has one of the world’s leading mining sectors<sup>28</sup> . This sector has been growing consistently in the last few years with an annual real average growth of 6.5% in its GNP between 2002 and 2006<sup>29</sup> . The prices of minerals have increased dramatically since the beginning of 2002, reaching historic highs in 2006<sup>30</sup> . For example, the price of copper (the second most important metal in Peru) increased by 331% between those years (See **Figures S1.1** and **S1.2** )<sup>31</sup> . Hence the amount of royalties assigned to each region has increased dramatically due both to price increases and production upsurges. **Figure S1.3** illustrates the change in the value of mineral production in each region calculated using 2001 mineral production levels and changes in international prices between 2002 and 2006. Similarly, the value of royalties in different regions for both periods is shown in **Figures S1.4** and **S1.5** .

> 28 According to statistics from the Peruvian Ministry of Energy and Mines, in 2006 Peru ranked worldwide 1st in the production of silver, 3<sup>rd</sup> in the production of copper, tin and zinc, 4<sup>th</sup> in the production of lead, and 5<sup>th</sup> in the production of gold.

> 29 Statistics are from the Peruvian Ministry of Energy and Mines (MINEM).

30 Between 2002 and 2006 the price of the leading exported metals (presented here in order of importance) increased enormously: silver by 150%, copper by 331%, tin by 116%, zinc by 321%, lead by 185%, and gold by 95%. Source: London Metal Exchange and London Bullion Market Association. Oil and gas prices also soared by 156% and 118% respectively. Source: NGA, annual reports. Oil Patch Research Group.

> 31 These massive increases are not the product of a price drop in 2002 as can be seen in **Figure S1.1** , where prices are shown steadily increasing since 2000.

3
not measure the positive effects on children born to participant families after the study period (Temple and Reynolds 2006).

In Brazil, a benefit-cost ratio for preschool is 2 and the rate of return is 12-15 percent. Most cost-benefit ratios for industrial and agricultural projects are less than 2 (World Bank 2001). In Egypt, focusing only on costs and benefits due only to increased enrollment in basic education and decreased repetition, a simulation shows a benefit-cost ratio of 2.3:1. Researches have estimated benefit-cost ratios of 2.4-3.2 for Bolivia (Van der Gaag and Tan 1998) and 3.0 for the Philippines (Glewwe _et al_ . 2000; Table 2).

<u>Table 2: Benefits of various early intervention programs</u>

||Perry<br>(US)|Abecedarian<br>(US)|Child-<br>Parent<br>Centers<br>(US)|Brazil|Egypt|Bolivia|Philippines|
|---|---|---|---|---|---|---|---|
|Program cost|$15,386|$63,476|$7,738|$178|$83|||
|_Benefits_||||||||
|Child care|919|27,612|1,916|||||
|Compensation|79,743|37,531|32,099|||||
|K-12 schooling|8556|8836|5,634|||||
|College/adult ed|-1309|-8128|-644|||||
|Crime|173,959|0|15,329|||||
|Welfare|774|196|546|||||
|Compensation||||||||
|future gen.||5722|4,894|||||
|Abuse/neglect|||344|||||
|Maternal||||||||
|compensation||68,728||||||
|Health/smoking||17,781||||||
|Total benefits|$262,642|$158,278|$60,117|||||
|NPV|$247,256|$94,802|52,380|||||
|Benefit-costratio|9:1|2.5:1|7.8:1|2:1|2.3:1|3:1|3:1|

Sources: Heckman 2004, 2006; Barnett and Masse 2006; World Bank 2001, 2002; Temple and Reynolds 2006; Van der Gaag and Tan 1998; Glewwe _et al_ . 2001 Notes: US programs use 3% discount rate, 2004 $; other studies use figures from the year of study; Philippines 3% discount, benefits measured are wage increases; Bolivia, Brazil, Egypt benefits are labor market earnings; 7% discount rate for Bolivia Evidence of cost effectiveness available for CCT comes from a study by Coady and Parker (2002), in which they compare the cost-effectiveness of cash transfer with the school 9
Volume in Monitored Outlets: Cheese (01/21/92-08125192) <!-- Start of picture text --> 12<br>is 1 ------ ............................................................... , '<br>10<br>16 - ------------- .,...............................................-..----..; : -.....-.-1<br>-<br>; f12 -------- . ................. ......................<br>;..:::<br>14~~~~~~~~~~~~~~~~~~~~~~~.<br>10 . '''''''' ............. ............................................- 4............ . 6<br>2~~~~~~~~~~~~~~~~~ t '''''''-- i;*- - - ---------.............. 0*----<br>- ---- I,<br>4 t -- - ... .................................................. ...,,,,,,,,,,, .2<br>Date<br>Market Volume ...... State Volume |<br><!-- End of picture text -->

46
Arellano, Manuel and Olympia Bover (1995). “Another Look at the Instrumental Variable Estimation of Error‐Components Models”. Journal of Econometrics. 68(1):29‐51.

Barro, Robert (2000). “Inequality and Growth in a Panel of Countries”. Journal of Economic Growth. 5(1):5‐32.

Baum, Christopher, Mark Schaffer, and Stephen Stillman (2007). “Enhanced Routines for Instrumental Variables/GMM Estimation and Testing”. Stata Journal. 7(4):465‐506.

Bazzi, Samuel and Michael Clemens (2013). “Blunt Instruments: Avoiding Common Pitfalls in Identifying the Causes of Economic Growth”. American Economic Journal: Macroeconomics. 5(2):152‐186.

Blundell, Richard and Stephen Bond (2000). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”. Journal of Econometrics. 87(1):115‐143.

Bun, Maurice and Frank Windmeijer (2009). “The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models”. The Econometrics Journal. 13:95‐126.

Caselli, Francesco, Gerardo Esquivel and Fernando Lefort (1996). “Reopening the Convergence Debate: A New Look at Cross‐Country Growth Empirics”. Journal of Economic Growth. 1(3):363‐89.

Castelló‐Climent, Amparo (2010). “Inequality and Growth in Advanced Economies: An Empirical Investigation”. Journal of Economic Inequality. 2010(8):293‐321.

Castelló‐Climent, Amparo and Rafael Domenech (2002). “Human Capital Inequality and Economic Growth: Some New Evidence”. Economic Journal. 112:C187‐C200.

Chernozhukov, Viktor and Christian Hansen (2008). “The Reduced Form: A Simple Approach to Inference with Weak Instruments”. Economics Letters. 100(1):67‐71.

Dabla‐Norris, Era, Kalpana Kochhar, Frantisek Ricka, Nujin Suphaphiphat, and Evridiki Tsounta (2015). “Causes and Consequences of Income Inequality: A Global Perspective”. IMF Staff Discussion Note No. 15/13.

Dollar, David and Aart Kraay (2003). “Institutions, Trade and Growth: Revisiting the Evidence”. World Bank Policy Research Department Working Paper No. 3004.

Easterly, William (2007). “Inequality Does Cause Underdevelopment: Insights from a New Instrument”. Journal of Development Economics. 84(2):755‐766.

Finlay, K., Magnusson, L.M., Schaffer, M.E. (2013). “weakiv: Weak‐instrument‐robust tests and confidence intervals for instrumental‐variable (IV) estimation of linear, probit and tobit models”. <u>http://ideas.repec.org/c/boc/bocode/s457684.html.</u> Forbes, Kristin (2000). “A Reassessment of the Relationship Between Inequality and Growth”. American Economic Review. 90(4):869‐87.

Halter, Daniel, Manuel Oechslin, and Josef Zweimüller (2014). “Inequality and Growth: The Neglected Time Dimension”. Journal of Economic Growth. 19:81‐104.

25
**Figure 14: Sifted flour, loose flour and maize grain prices (left panel), and Sifted flour margins over maize grain prices (right panel)**

_Source: KNBS, author calculations._

**Although no evidence was found to conclude that the CMA was operating as a cartel, a small, unexplained drop in sifted flour prices following the investigation could be indicative of a response to pressures for setting more competitive prices.** Immediately after investigation began in 2009, sifted flour prices started falling despite the fact that _grain prices were still rising_ , as was the price of posho flour.  This is illustrated in the left hand panel of Figure 14, where the vertical line indicates March 2009.  Similarly the right hand panel of Figure 14 shows the margin of sifted flour over grain prices – to the right of the vertical line (March 2009), the margins of sifted flour dropped. Over the five months following March 2009 (April – October), the average margin of sifted flour over the price of grain was only 24.6 percent as compared to the average over the previous five years of 30.9 percent<sup>95</sup> .   It is not the fact that this margin dropped that is unusual – as is clear from the margin history, drops of this size have happened in the past – but rather the fact that this occurred during a period while grain prices were still rising.  This _might_ be an indication of collusion, and investigation by the commission prompted a change from the millers. However, there could well be other reasons that account for this change, and available evidence is certainly not sufficient to conclude that there were in fact cartel activities being carried out.

**The very large increase in margins starting in late 2009 should not necessarily be interpreted as anticompetitive.** Sifted flour prices actually fell across the entire period of this margin spike.  This increase in margins was caused by grain prices _falling_ very rapidly back towards the historical average (see the left hand panel of Figure 14), while sifted flour prices fell more slowly.  The slower fall of sifted flour prices may have been due to either (a) stocks of higher priced grain already purchased by millers, or (b) wariness about further grain price fluctuations given the unprecedented price shifts over the previous months.  The higher margins were not maintained during the price spikes that started in early 2011, during which margins dropped once again.  After the end of the unusual margin spike, margins have remained slightly higher than historical levels (the average from April 2011 through October 2012 was 36 percent as opposed to the average prior to March 2009 of 32 percent). Further data and analysis would be required to assess whether this reflects anything other than the costs of managing risks associated with much more volatile grain prices.

> 95 Margin calculated as the price per kg of sifted flour over the price per kg of maize grain.

29
Rasiah (2006, p. 22) has pointed out, “in Penang, the supply of R&D engineers and technicians is too small for the [MNCs] to upgrade further into R&D activities.”<sup>19</sup> While firms find the quality of students graduating from local public universities to be adequate (see Table 9), they would welcome improvement in the level of skills.

**Table 9: Assessment of quality of students graduating from local public universities in Penang**

|Assessment|Number of Firms|%|
|---|---|---|
|very poor|0|0.0|
|poor|5|7.3|
|fairly good|50|72.5|
|very good|14|20.3|

Source: Authors’ calculations based on investment climate survey data Few Malaysian firms – whether in Penang or elsewhere - have thus far sought to cement university industry linkages for the purposes of product upgrading and innovation.<sup>20</sup> The interaction is limited to consulting arrangements and troubleshooting contracts. This is not unusual for three reasons. First, firms, especially small and medium sized ones, conduct a minimal amount of R&D and when they outsource, seldom seek assistance from universities. Second, for larger firms also, universities are ranked low as sources of technology. Third, most firms are specialized in electronics and electrical engineering, which are research intensive fields but not ones noted for university industry collaboration. Few universities anywhere in the world, do much downstream applied research in electronics, and USM is no exception. Fourth, USM has limited research capacity and is not in a position to significantly assist firms even if the demand were there.<sup>21</sup> The absence of major research institutions whether affiliated with

> product designs.  Agilent employs 500 R&D engineers in Penang, mainly on development work.  Many of the MNCs identified the low number of engineering and science Ph.D. and MA produced by universities as a significant bottleneck. Shortages also contribute to the high turnover among experienced engineers (currently 16 percent annually) working for MNCs.

> 19 In 2000, there were more than 5,000 scientists and engineers in R&D per one million people in Japan. The comparable number for China was 545 (in 2000) and 160 in Malaysia (in 1998) (Fan and Watanabe 2006).

> 20 One exception is Hovid Pharmaceutical that has an R&D center located in USM.

> 21 USM was granted 10 patents between 1995 and 2005. Its R&D spending is concentrated in biochemistry, energy technology, and process technology and engineering (Yusuf and Nabeshima 2008).

28