rafmacalaba/datause-encoder-data huggingface dataset:
the Lerner index are not consistent with results using concentration measures as proxies for competition. They conclude that concentration is not a good measure of competition.

As described above, existing studies on the link between competition and financial access either analyze cross-sectional data or are only able to look at multi-year data for a single country. In contrast, our paper combines multi-year firm-level data with panel country-level data on bank competition. One advantage of our dataset is that it contains repeated cross-sections of firms for the countries in our sample. This allows us to control for unobserved differences between countries, using country fixed effects in our estimations. Such unobserved differences may be correlated with both access to finance and the extent of competition. Thus, our methodology isolates within country variation in competition and access. This is an improvement over previous cross-country studies, such as Beck, Demirguc-Kunt, and Maksimovic (2004). Furthermore, in contrast to US and cross-country studies that measure competition with concentration indicators, we also present results using the Lerner index, a direct measure of bank pricing behavior. Several studies have argued theoretically and empirically that pricing behavior measures such as the Lerner index are superior to concentration measures as indicators of competition.<sup>8</sup> Concentration is a measure of market structure, while competition is a measure of market conduct. There can be competition in concentrated markets, if there is a credible threat of entry and exit (i.e., if markets are contestable). A contribution of our paper is the ability to distinguish the impact of concentration and competition in a multi-country setting. Also, the fact that we offer cross-country evidence using the Lerner index allows for more general results relative to those that focus on individual countries.

We find that low competition, as proxied by high levels of the Lerner index, is associated with diminished access to finance by firms, while concentration has a less robust relationship with access. We use different weighting schemes to account for differences in the number of firms across countries and the variance of the estimated Lerner index. Overall, our results support the market power hypothesis. Furthermore, our results confirm that concentration measures are not reliable predictors of firms’ access to finance, which is in line with previous contradictory evidence.

> 8 See among others Cetorelli (1999), Claessens and Laeven (2004), Demirguc-Kunt et al. (2004), and Carbo-Verde et al. (20009).

4
effectively restrict local governments' ability to engage in anti-competitive activities must be found.

The rest of this section is organized as follows. Subsection 4.1 briefly describes the current division of regulatory powers between the centrl and the local govemments in the areas of investment project approval, industrial planning, pricing, marketing, international trade management, etc. Subsection 4.2 examines the three main areas where the decentralized centrallocal relations have negatively impacted the functioning of markets. Subsection 4.3 sketches a framework that can help limit the local governments' ability to restrict market competition. The three basic elements in this framework are: (1) a Interregional Commerce Clause and related laws that prohibit local governments' restrictions on resource flows: (2) a Fair Trade Commission that ensures all regulations issued by government agencies are compedtion compatible; (3) reform of the government' administrative structure in the direction of reducing ani-competitive government interventions.

# 4.1. Selected Ases of Division of ers: Who Controls Wat?

In China, state-owned enterprises (SOEs) and collectively-owned enterprises (COEs) account for about 89 percent of the total industrial output." Each state-owned enteprise is administratively affiliated with a supervisory agency, or its 'department in charge.* Most COEs are supervised by the local governments. For example, the township and village enterprises, the bulk of COEs, are under the control of township or village governments. Whether an enterprise's 'deprtment in charge is the central government or a local government 1 rgely determines who has the power to regulate the activities of the firm. If a firm's supervisory agency is a local government (usually represented by one of its industrial bureau), it is referred to be "owned' by the local government; if its supervisory agency is a line ministry under the State Council, it is referd to be "owned" by the central government. Over the past forty years, the ownership (referring to who supervises the firm) of the state enterprises have been decentralized and recentralized several times. Since the early 1980s, the main trend has been a decentralization ofownership from the central government to the local governments. By 1985, **e** centrally-own d nterprises accounted for only 20 percent of the total industil output from all enterprises at and above township level.

How do these ownership rights originate? The traditional rule is that investment confers ownership. The level of govenment that supplied the fixed capital for the enterprise obtains ownership rights over that enterprise. The customary mle summed up in the expression 'shei jian, shei guan, shei yong' (who builds the enterprise, administers it, and has control over the output). Iherefore, enterprises owned by the central government are those established with central government funds or those taken over from private owners after 1949. Regional levels I' See State Statistical Bureau (1992). **The** rest include foreign-invest **e** d nterprises, privately-owned enterprises, and jointly-owned enterprises.

49
- 19 -

1985. The design of that *urvey and its implementation is thoroughly described in Ains%orth and Munoz (1986). The survey attempted to collect information on household income sources, consumption patterns, employment activities, housing conditions and several other subjects. On the expenditure side data were collected on regularly purchased nonfood items (fuel, cigarettes for example), on irregularly purchased clothing, household goods and maintenance, food expenditures, durable goods ownership, housing rents and the value of home produced and consumed food.

In Table 2 one can see the expenditure shares of different consumption categories and the range of values for the weighted income elasticities. Most food items and, as well, tobacco products, are incomeinelastic but as the welfare weights attach more importance to low-income consumption the income elasticity approaches unity for rice, millet, bread, casava and a casava derivative called attieke. Income elasticities for public transportation, education expenses and alcoholic beverages hover slightly above unity. Consumption of gasoline, telephone-telegraph and electricity, on the other hand, all exhibit income elasticities substantially larger than one.

On the basis of the income elasticity information contained in Table 2 numerous pairwise tax comparisons can be made to identify instances of welfare dominance. It would appear, for example, that, as alternative commodity tax bases, telephones dominate electricity and gasoline, electricity dominates public transportation and gasoline dominates alcoholic beverages. Figures 2-5, which exhibit the form of the empirical DCC curve for each comparison, confirm these suspicions.

HMuellA/Welfare Dominanco **& tho** Design of **excise Taxation** in **the** Coto **D'** voLre/WThirsk eb/5S/18/8
30 Table 6: Prospective effects of completing GM cotton adoption globally post-2001 on national economic welfare and net cotton farm incomes, without and with cotton subsidies and tariffs removed

||Without cotto<br>tariff r|n subsidy and<br>eform|With cotton<br>subsidies and<br>tariffs first<br>d d|With<br>simultaneous<br>cotton<br>bid/iff|
|---|---|---|---|---|
||||remove, an|susytar|
|**_(a) Effects on welfare_**<br>**_(US$m) of:_**|without<br>SSAfrican<br>GM adoption|with<br>SSAfrican<br>GM adoption|then GM<br>catch-up|<br>removal and<br>GM catch-up|
|**_All high-income countries_**|**_318_**|**_366_**|**_279_**|**_744_**|
|Australia|-14|-28|-58|80|
|United States|61|57|-25|404|
|**_All developing countries_**|**_1701_**|**_1957_**|**_2043_**|**_1866_**|
|E. Europe and Central Asia|325|317|317|303|
|China|113|100|94|144|
|Other Southeast Asia|31|63|83|-48|
|India|817|822|855|771|
|Other South Asia|147|148|151|140|
|Middle East & Nth Africa|157|175|211|194|
|Sub-Saharan Africa|-13|199|223|370|
|Latin American & Carib.|124|135|146|-8|
|**World**|**2018**|**2323**|**2322**|**2610**|
|**_(b) Effects on net cotton f_**<br>**_incomes (% change) in:_**|**_arm_**||||
|**_All high-income countries_**|**_-2.7_**|**_-4.5_**|**_-5.0_**|**_-19.3_**|
|Australia|-5.6|-9.3|-10.3|9.6|
|United States|-2.7|-3.9|-3.7|-20.9|
|**_All developing countries_**|**_-2.7_**|**_-2.2_**|**_-2.2_**|**_2.0_**|
|E. Europe and Central Asia|-2.3|-3.1|-3.5|-0.3|
|China|-1.7|-1.9|-2.0|-0.5|
|Other Southeast Asia|-1.6|-1.9|-2.0|3.1|
|India|-3.6|-3.9|-4.1|-4.5|
|Other South Asia|-2.1|-2.5|-2.7|1.8|
|Middle East & Nth Africa|-2.7|-4.5|-5.2|0.6|
|Sub-Saharan Africa|-7.2|10.0|9.0|41.6|
|Latin American & Carib.|-1.7|-3.4|-3.7|5.3|
|**World**|**-2.7**|**-2.9**|**-2.9**|**-4.6**|

Source: Anderson, Valenzuela and Jackson (2006)
Amid annual fluctuations, total national opium poppy cultivated area has shown a generally rising trend since the early to mid-1990s, interrupted by the Taliban ban which almost wiped out the 2001 harvest, and reaching new peaks in 2006 and 2007. Estimated opium production shows broadly similar trends, although percentage changes differ, reflecting fluctuations in estimated opium yields. Estimated gross income per hectare and gross farm income rose very sharply after the Taliban ban (reflecting a supply shockinduced spike in prices, shown in Figure 2 later in this paper), and even after half a decade remain considerably higher than in the 1990s. This may reflect in part a higher risk premium in farm-gate prices as a result of criminalization of the opium economy and intensified (albeit fragmented and uneven) counter-narcotics efforts.

National cultivation trends mask major diversity across provinces, selected examples of which are shown in Figure 1. Cultivation estimates for some provinces tend to move together (at least fluctuating in the same direction), often with somewhat offsetting changes from year to year. In other cases, fluctuations across provinces are partially offsetting within a year. In 2005 for example, the year in which cultivation in Nangarhar Province declined by 96% due to a largely effective ban on cultivation imposed by the provincial authorities, cultivation in Kandahar and Balkh rose sharply and in Farah it more than quadrupled, largely offsetting the impressive decline in Nangarhar. There is also great diversity at local (district) level and, as demonstrated by extensive fieldwork, across households, although regularities in the parameters influencing decisions on opium poppy cultivation are evident

**Figure 1: Opium Poppy Cultivation in Selected Provinces, 2003-2007 (ha)**

<!-- Start of picture text --> 30000<br>25000<br>Badakhshan<br>Balkh<br>20000<br>Farah<br>15000 Ghor<br>Kandahar<br>10000<br>Nangarhar<br>5000 Uruzgan<br>0<br>2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source: UNODC (2003, 2004, 2006, 2007)._

# **Trade and Processing**

Less is known about the trade in opium in and around Afghanistan, its conversion into refined products (morphine and heroin), and trade in these products. Nevertheless a rough picture can be gleaned from field research and interviews with (mostly smaller) 7
the student’s family. In addition, it would have been more likely that the best students would have moved out of PAE schools, which, if anything, would suggest the impact of the program could have been stronger than suggested by this study. Third, school directors could have changed their managerial practices in PAE schools. There is some evidence, from self-reported surveys, that directors in PAE schools improve monitoring of teacher’s attendance and punctuality, visited classrooms more often, and had meetings to discuss learning outcomes between 2009 and 2010. Finally, principals and teachers may have focused on teaching to the test. Curriculum and ENLACE are linked by design and ENLACE was used to show the weakness areas in the classrooms of the low-performing schools. With the existing information, we cannot rule out the possibility that teachers used this information to teach the subjects that were more closely connected to the test (math and Spanish).

# **4.3 Heterogeneous effects**

The average positive and significant effect of PAE could hide important heterogeneous impacts within schools. The program could have a differentiated effect among boys and girls or among students with adverse initial conditions versus relatively well-off students. Between 2009 and 2010, the student-level dispersion of ENLACE results in PAE schools experienced a larger increase vis-a-vis what was observed for the state as a whole. For instance, between 2009 and 2010, the standard deviation in math test scores among all schools in Colima increased by 4 ENLACE points while the increase was equal to 10 points among PAE schools.

To explore possible heterogeneous effects of the program, separate specifications are estimated for boys and girls and for students identified as having an age-grade distortion and those who do not. Figure 8 shows PAE’s learning outcome effects in math and Spanish, differentiated by gender using the two alternative methodologies, DID and RD. The results show that although the program had some heterogeneous impacts across boys and girls with boys experiencing larger improvements in math and girls in Spanish, we cannot reject the null hypothesis of equality of coefficients between gender-specific equations.

Students with an age-grade distortion in Mexico are largely the outcome of late enrollment in the education system. As shown by Manacorda (2012) age-grade distortions have significant and long-lasting negative effects on learning outcomes. In 2009, more than 17 percent of the students in Colima’s public primary schools had an age-grade distortion and close to 40 percent of them fell in the “insufficient” level in ENLACE compared to 20 percent among students without an age-grade distortion. Figure (9) shows PAE’s learning outcome effects in math and Spanish, for students with an age-grade distortion and those who are on the right grade based on their age, using the two alternative methodologies, 16
appropriate analysis – in other words, Latin America (since the majority of the evaluated interventions were implemented in Latin America) and Africa.

Finally, we used Egger’s tests to assess whether the results discussed above reveal any indication of publication bias.<sup>17</sup> The first column in table 3 shows the results for the three primary outcomes.

Table <u>3 – Egger’s Test for Publication Bias</u>

||Firm Performance|EmploymentCreation|Labor Productivity|
|---|---|---|---|
|Slope|0.055|-0.20**|0.20**|
|(s.e.)|(0.03)|(0.08)|(0.07)|
|_p-value_|_0.109_|_0.028_|_0.027_|
|Bias|1.82|7.14*|-3.24|
|(s.e.)|(1.07)|(3.82)|(1.96)|
|_p-value_|_0.104_|_0.084_|_0.148_|

Note: Standard errors (s.e.) in parenthesis. **, * Statistically significant at 5 and 10 percent respectively.

The coefficient of the variable _bias_ is positive but only statistically significant at 11 percent (p-value = 0.104) for firm performance indicators. This result indicates weak evidence of publication bias towards studies showing positive effects of business support on SME performance indicators. The evidence of publication bias is stronger for employment creation as can be seen in the second column of table 3. The coefficient of the variable bias is positive (7.14) and statistically significant at 9 percent (p-value = 0.084). We found no evidence of publication bias for labor productivity.

# **Concluding Remarks**

This systematic review summarizes the evidence of the impact of SME-support programs that used rigorous evaluation techniques to identify the causal effect of an intervention on SME outcomes.

> 17 We used the _metabias_ command in Stata.

29
41 Empirical Results.

The number of lags k is determined by two methods-the Akaike (1974) Information Criterion Statistic (AIC), and Hall's (1992) General-to Specific method, where a general lag structure is fitted (4 in this paper) and an F test is used to test backward.L2y The optimal lag length k is found to be three. Equation (6) was estimated by OLS for twenty countries over the period 1971-1991- with pooled timeseries and cross-section data.33L The regression is tested and corrected for hetroskedasticity.4/ The results are: ARERt= 0.05A(RER),1 +0.88(NDEV), -0.05(NDEV)1<sup>.</sup> 1<sup>-0.05(NDEV),.</sup> 2<sup>-0.04(NDEV)</sup> 1<sup>.,</sup> (1.34) (44.61)* (-1.42) (-2.31)' (-2.358)* -0.03(ZNFTAX) -0.Ol(QMtP1,J) + 0.02(rJ. (-24.66)' (-1.50) (0.62) R<sup>2</sup> = 0.88 F= 295.9{T N=339.

The t ratios are in parentheses. The estimated coefficient on ARER.1 is significantly different which indicates that there is no significant persistent in real devaluations. The estimated coefficient 'yb gives the contemporaneous relationship between nominal devaluation and real depreciations and is equal to 0.88. It is found to be statistically significant at the 5 percent level. This coefficient implies that a 10 percent noniinal devaluation at time t yields about 8.8 percent real depreciation at time t, other things remain unchanged The other estimated coefficients 71, y7, and 73 are all negative (-0.05, -0.05, and -0.04, respectively) and (with the exception of y,) statistically significant at the _5_ percent level. Their magnitudes, however, are very small. We test the null hypothesis that the sum of all contemporaneous and lagged nominal devaluation terms is equal to zero, (H: li-. oy y=0). The F value is found to be 32/ We test for the significance of the fourth lag, then the fourth and the third, the fourth, third, and the second, and finally, all four lags together.

**_331_** The list of countries included in the sample are in footnote 2 except for Rwanda and Lesotho, where complete time series data cannot be found.

34! The Breusch-Pagan and the White tests are used to test for hetroskedasticity. The correction is accomplished by scaling all variables including the intewrept and instnuments in the regression by the estimated standard deviations of the residuals for each country.
Hausmann, R., & B. Klinger (2006) “Structural transformation and patterns of comparative advantage in the product space.” RWP06-041 Faculty Research Working Papers Series, JFK School of Government, Harvard University.

Hausmann, R. and D. Rodrik (2003) “Economic development as self-discovery.” _Journal of Development Economics_ . 72: 603-633.

Jaffe, A. (1986). “Technological Opportunity and Spillovers of R&D: Evidence from Firm’s Patents, Profits and Market Value”. American Economic Review 76(5): 984-1001.

Jaffe A., Trajtenberg M. and Henderson R. (1993). “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Querterly Journal of Economics (108(3): 577-98.

Kremer M. (1993). “The O-ring Theory of Economic Development.” Quarterly Journal of Economics 108:551-75.

Kremer M. and Maskin E. (1994). “Segregation by skill and the rise in quality.” Harvard University, Mimeo.

Leamer, E. (1984). Sources of Comparative Advantage: Theory and Evidence. Cambridge MA, the MIT Press.

Levine, R., Loayza, N. and Beck, Thorsten (2000) “Financial Intermediation and Growth: Causality and Causes”, Journal of Monetary Economics, 46(1) 37-77.

Loayza, N., Fajnzylber, P. and Calderon, C. (2004) “Economic Growth in Latin America and the Caribbean: Stylized Factas, Explanations, and Forecasts” Central Bank of Chile Working Papers N° 265, June.

Matsuyama, K. (1991). “Increasing Returns, Industrialization and Indeterminacy of Equilibrium” Quarterly Journal of Economics 106(2): 617-650.

Michaely, M. (1984). Trade, Income Levels and Dependence, North Holland, Amsterdam.

Morris S. and Shin H. (2000). “Rethinking Multiple Equilibria in Macroeconomic Modeling” NBER Macroeconomics annual, pp. 139-161.

Murphy K., Schleifer A. and Vishny R. (1989). “Industrialization and the Big Push.” Journal of Political Economy 97:1003-26.

Rodrik, D. (2006) “What’s so Special about China’s Exports?” China & World Economy, forthcoming. NBER Working Paper No. 11947 Rodrik, D. (2004) “Industrial Policies for the Twenty-First Century” Unpublished paper, Harvard University, September.

Roodman, D. (2006) “How to Do xtabond2: An Introduction to “Difference” and “System” GMM in Stata” Center for Global Development, Working Paper N. 103, December.

Sala-i-Martin, Xavier, and Arvind Subramanian, (2003) “Addressing the Natural Resource Curse: An illustration from Nigeria,” IMF Working Paper No. 03139.

Schott, P. (2004) “Across-product Versus Within-Product Specialization in International Trade.” The Quarterly Journal of Economics, vol. 119, issue 2, pages 646-677 Young, A. (1991) “Learning by Doing and the Dynamic Effects of International Trade”, The Quarterly Journal of Economics, Vol. 106, No. 2, May, pp. 369-405.

41
# **Quality Education and the Efficiency of Public Expenditure: A Cross‐Country Comparative Analysis**

Élisé Wendlassida Miningou<sup>*</sup>

**Keywords:** Education, Public Expenditure, Efficiency

**JEL Codes:** O15, I25

> * World Bank, Human Development Practice Group, 1850 H Street, NW, Suite 650, Washington, DC, 20006, USA Email: eminingou@worldbank.org
determinant of e-commerce. This suggests that competition induces organizational and management changes in the use of internet, although not in the case of e-commerce. In general, the impact of competition from informal firms is not significant, suggesting that either ICT adoption is not an option to compete against these firms, or given the significant impact of size, that ICT adopters are not the firms that face the strongest competition from informal firms.

_<u>Table 14 Base model estimations - Pooled sample</u>_

|**Pooled sample**|**(1)**<br>**Log(sales/l**<br>**)**|**(2)**<br>**Product**<br>**innovation**|**(3)**<br>**Process**<br>**innovation**|**(4)**<br>**R&D+ per**<br>**worker**|**(5)**<br>**ICT total**<br>**index**|
|---|---|---|---|---|---|
|Log(K/L)|0.1869***<br>(0.028)|||||
|Log(L)|0.3014***|0.0121|0.0229**|0.1152|0.2006***|
||(0.054)|(0.010)|(0.010)|(0.088)|(0.016)|
|Product Innovation|-0.0214|||||
||(0.121)|||||
|Process Innovation|<br>0.0811|||||
||(0.122)|||||
|ICT Index||0.0922***|0.1057***|||
|||(0.015)|(0.015)|||
|RD+/L||<br>0.0076*|<br>0.0166***|||
|||(0.004)|(0.004)|||
|Log(age)||<br>-0.0133|<br>0.0097|0.1893|-0.0522**|
|||(0.015)|(0.015)|(0.120)|(0.022)|
|Informal sector competition||<br>-0.0151|<br>0.0550***|<br>-0.0950|<br>-0.0064|
|||(0.020)|(0.021)|(0.077)|(0.011)|
|Education obstacle||<br>0.0213|<br>-0.0169|||
|||(0.024)|(0.024)|||
|Exporter||0.0124|-0.0178|0.1197|-0.0426|
|||(0.025)|(0.025)|(0.249)|(0.036)|
|Demand increasing||0.0348*|0.0298|||
|||(0.020)|(0.021)|||
|Certification||<br>-0.0182|<br>0.0287|-0.0839|0.1914***|
|||(0.026)|(0.026)|(0.246)|(0.043)|
|Market share change||||<br>-0.0519|<br>-0.0705**|
|||||(0.171)|(0.032)|
|Foreign Status > 25% ownership||||<br>0.7003***|<br>0.0755*|
|||||(0.261)|(0.044)|
|Importer||||<br>0.7358***|<br>0.1614***|
|||||(0.281)|(0.054)|
|New Capital in Previous Year||||<br>0.8290***|<br>0.0877***|
|||||<br>(0.185)|<br>(0.027)|
|Percentage of High School||||<br>0.0081*|<br>0.0028***|
|||||(0.004)|(0.000)|
|Access to Finance obstacle||||-0.1588**|-0.0279**|
|||||(0.076)|(0.011)|
|Foreign Technology||||<br>-0.2687|<br>0.1074**|
|||||(0.287)|(0.053)|
|Cov(product-process)||0.0043<br>||||
|||(0.004)||||
|Constant|6.4948***|<br>0.1799***|0.1590***|-1.6098***|-0.8723***|
||(0.359)|(0.060)|(0.060)|(0.543)|(0.089)|
|Sector fixed effects|YES|YES|YES|YES|YES|
|Country fixed effects|YES|YES|YES|YES|YES|

23