rafmacalaba/datause-encoder-data huggingface dataset:
the regulated business is reminded of its obligations to ensure continued output provision into the longer term. A number of approaches exist to allow the regulator to critically assess the information it has been presented with, including independent audit examinations, shadow network modelling and so forth.

As regulation evolves from its focus on cost minimization to one of network integrity and output delivery, the proper definition of outputs that can form the basis of incentive regulation is the major task ahead for regulators and companies.

# **If a comparative incentive regime is used, it should not apply to both costs and service provision, but only to one of these**

Yardstick competition has significant efficiency benefits, but the application of a yardstick mechanism to both costs and quality leads to an inherent circularity in the regulatory arrangements, and multiple equilibria. In these situations, regulators should focus on whether they wish to promote cost reduction, subject to quality safeguards that cannot be breached, or quality enhancement on a comparative basis, with costs determined as a residual. In our view, the former approach is likely to be less complex to administer, provides clarity to companies and yet at the same time provides the necessary safeguards on output provision.

# **Incentive based regulation should avoid distortion of the firm's input choices**

Regulators should seek to evaluate and reward operating and capital efficiency on the same basis in order to avoid distorting input use. One way of doing this is to develop a total cost model of the businesses to be benchmarked, and uprate prices on the basis of total costs. The construction of a total cost variable has been described in Frontier Economics (2003)

# **Regulation should promote balanced incentives across different regulated products**

This should ensure that total cost is minimized, not just the cost of producing the output with the highest-powered regulation.

30
Policy Research Working Paper 8803

# **Abstract**

This paper offers new evidence on the relationship between contractual institutions, family management, and aggregate performance. The study creates a new firm-level database on management and ownership structures spanning 134 regions in 11 European countries. To guide the empirical analysis, it develops a model of industry equilibrium in which heterogeneous firms decide between family and professional management when the latter are subject to contracting frictions. The paper tests the model’s predictions using regional variation in trust within countries.

Consistent with the model, the finding show that there is sorting of firms across management modes, in which smaller firms and those in regions with worse contracting environments are more likely to be family managed. These firms are on average 25 percent less productive than professionally managed firms, and moving from the country with the least reliable contracting environment to the most increases total factor productivity by 21.6 percent. Family management rather than ownership drives these results.

This paper is a product of the Equitable Growth, Finance and Institutions Vice Presidency. 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://www.worldbank.org/research. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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
# **Program Performance**

This section reviews program performance as measured by sustainability and outreach. Sustainability is measured by the program's arrears rate, its profitability, and the size of the subsidy required to sustain operations. Outreach is measured by the volume of annual lending and savings activities (scope) and the population it serves (depth of market penetration).

# _Sustainahility._ This analysis estimates arrears rates

assuming that units wrote off 100 percent of their loans in default each year.<sup>3</sup> 3 If the arrears rate is recalculated in this way, the volume of loans in arrears is equal to 20 percent of net outstanding loans in 1995 for the system as a whole (excluding one unit for which data are not available). In all, one unit has maintained consistently low arrears rates since 1992. The other three units had Table 16 Adjusted Annual Arrears Rates" for LKP Units in **Dompu District (Percent)**

|Units<br>|19921<br>|**1993**<br>|**1994**<br>|**1995**<br>|
|---|---|---|---|---|
|Total|16|30|NA<br>_|2____|
|Hu'u|20|78|NA|NA|
|Monitabaru|4|4|NA|7|
|Sariutu|23<br>|23|NA|37|
|Bada|*<br>8 i|<br>22|NA|22|

a/ (Volumc of Loans in Arrears in Year I - Volume of Loans in Default in Year 0)/ (Outstanding Loan Volume in Year I - Loan Volume in Default in Year 0).

b/ Excludes the Hu'u unit for w.lhich 1995 data were not available.

loans in arrears equal to 18 percent of total loans over the period. For Hu'u, loans in arrears reached 78 percent of total loans in 1993. The program did not have arrears data for the unit thereafter (Table 16). The program's arrears rate is significantly higher than that which would be considered sound by international microcredit standards.3<sup>4</sup> 33 The LKP system defines loans in default as being more than 90 days late in payment.

- 34 The Committee of Donor Agencies for Small Enterprise Development (1995) set as an acceptable standard for microenterprise lending, that 10 percent or less of total loans should have late payments of 30 days or more. BKK's arrears rate in 1995 includes loans that are one day late or more. so it is not directly comparable. However, it is twice as high as the standard, and did not include arrears information for the program's most troubled unit.

45
<!-- Start of picture text --> No Project |_| __ 0.00<br>urcoe |§ {| TT.<br>Project Feasibility<br>Project Feasibility :<br><!-- End of picture text -->

<!-- Start of picture text --> Project Feasibility<br>ct<br>100% Budget, Limited $<br>cs)<br><<br>75% Budget, Limited<br>3<br>a<br>50% Budget, Limited 5<br>40 60 80 100 0 1 2<br>90th Percentile of Monthly Met Demand (% of Total) Cost of Portfolio (US$, Billions)<br><!-- End of picture text -->
**Table 7: Partial correlations of expanding permanent employment with other indicators of firm productivity and growth**

||expand_p log_lprod<br>tf|p<br>|rd<br>|newPP<br>s|ales_MNCe|du_topmaed|u_prodWft|rain|pct_prod_worker|s_train|
|---|---|---|---|---|---|---|---|---|---|---|
|expand_p|.<br>Neg||Pos|Pos|Pos|Pos||Pos||Expanding permanent employment|
|N|7,791|6,625|8,903|9,375|2,290|10,731|9,509|11,299|6,859||
|log_lprod|Neg<br>.|Pos|Pos|Pos|Pos|Pos|Pos|Pos|Pos|Log VA/L|
|N|7,791|6,437|6,006|6,373|2,095|7,252|6,476|7,383|4,339||
|tfp|Pos|.|Pos|Pos|Pos|Pos|||Pos|TFP|
|N|6,625<br>6,437||5,545|5,511|1,835|6,410|5,701|6,321|4,098||
|rd|Pos<br>Pos|Pos|.|Pos|Pos|Pos||Pos|Pos|Firm does R&D|
|N|8,903<br>6,006|5,545||7,751|2,228|8,642|8,171|8,870|6,065||
|newPP|Pos<br>Pos|Pos|Pos|.|Pos|Pos|Pos|Pos||Introduced new product/process|
|N|9,375<br>6,373|5,511|7,751||2,216|8,930|8,291|9,033|5,902||
|sales_MNC|Pos<br>Pos|Pos|Pos|Pos|.|Pos||Pos|Pos|Percent of sales to MNCs|
|N|2,290<br>2,095|1,835|2,228|2,216||2,271|2,140|2,274|362||
|edu_topma|Pos<br>Pos|Pos|Pos|Pos|Pos|.|Pos|Pos||Education level of top manager in years|
|N|10,731<br>7,252|6,410|8,642|8,930|2,271||9,000|10,378|6,527||
|edu_prodW|Pos|||Pos||Pos|.|Pos||Average education level of the production workforce in years|
|N|9,509<br>6,476|5,701|8,171|8,291|2,140|9,000||9,477|6,114||
|ftrain|Pos<br>Pos||Pos|Pos|Pos|Pos|Pos|.|Pos|Training offered|
|N|11,299<br>7,383|6,321|8,870|9,033|2,274|10,378|9,477||6,540||
|pct_prod_w|Pos||Pos|||Pos||Pos|.|% of production workers that were trained|
|N|6,859<br>4,339|4,098|6,065|5,902|362|6,527|6,114|6,540|||

Notes: Controls are: country fixed effects; log employment (size); new firm; services; foreign; exporter; importer; missing importer indicator; small city. “Pos” and green indicates a positive correlation significant at the 5% level. “Neg” and red indicates a negative correlation significant at the 5% level.

43
On the revenue side of the economy, if labor does not change (a fair assumption if we assume that workers displaced in the fossil fuel industry get absorbed by the renewable industry -– i.e., perfect labor mobility), then this approach implies that the cost should be reflected in capital rents ( _Rt_ ) of<sup>12</sup> Since we are substituting out one form of capital for another, _Rt_ is the only variable that can shift in this case. The cost is thus only equal to the difference between one source of energy compared to another. While nominal GDP is likely increasing in this scenario, real GDP should be falling due to higher costs. Given that the stock of productive capital is the same in the two cases, total energy costs would then be equal to: Unfortunately, the capital stock and valuation of electricity asset data is not readily available, hence the methodology we propose through investment deflator.

# 3.2.3 Approach through quantities

For this approach, one will need to modify the production function in the case of viewing – it as an investment. GDP will increase in the short run (due to the investment values — not because there are more laborers or more productive capital generated i.e., the total electricity output is the same before and after the transition). However, we know that if we increase investment, then capital stock will also increase. This will lead to an increase in potential GDP. To ensure that we do not have an increase in potential GDP (since both sources of electricity yield the same output) we need to modify TFP as follows: where ∆is the change in capital due to additional investment in alternative energy sources. This modification will ensure that potential GDP remains as before (i.e., capital stock increases but TFP falls). A lower TFP will result in a higher marginal cost and hence lead to higher prices, but a lower real wage. Hence this change will have Hicks-neutral properties and will not alter revenue distribution. It is important to note the differences in

> 12We note that labor is a more intensive resource in renewables compared to fossil fuel electricity production.

18
The SBM impact estimate of 4.2 in the total score can be translated into a change of pupil teacher or classroom ratio that is required to bring about NAT changes in the same magnitude. Table A2 shows the estimated effects of changes in the pupil teacher ratio, pupil classroom ratio and per-pupil teacher salary on NAT scores (using the 2005-2010 panel data). Though the estimates of the PCR and PTR effects are likely subject to upward bias (therefore, the impacts are underestimated), the results strikingly show that the impact of SBM, measured in a three-year period, is sufficiently large in terms of the impacts of school resource reallocations.<sup>18</sup>

# **7. Conclusions**

This paper showed a significant SBM impact on student test scores over three years. SBM was implemented earlier among schools that had experienced teachers but a large number of students per teacher.

Our estimate of the SBM impact over three years is larger than the previously available estimate from the Philippines (Khattri, Ling and Jha, 2012). There are several reasons. First, their estimate is based on a comparison between the first and second batch schools, scheduled during TEEP. In reality, however, interventions to the two groups occurred almost simultaneously. Second, TEEP not only introduced SBM but included other investments such as textbooks, school buildings, and teachers’ training. Though they attempted to control those non-SBM investments, their PSM method faced a serious challenge since those investments are also endogenous as part of the intervention package. In our case, SBM was the only major change experienced among schools in our sample.

> 18 Investments in classrooms and teachers during the period of 2005 to 2010 could be positively correlated with the initial shock to test score in 2005 (e.g., schools that performed well in NAT 2005 could receive more allocations from the government). In this case, the estimates in Table A2 and, therefore, the simulated changes of PCR and PTR are upwardly biased.

15
**Table 1 (Continued) Descriptive Statistics By Birth Cohort** (Standard Deviation in Parentheses)

|||Birth Year Cohort<br>||All<br>|
|---|---|---|---|---|
||1940 to 1947|1948 to 1963|1964 to 1978|1940 to 1978|
|Father was a Technician|0.066|0.107|0.203|0.138|
||(0.249)|(0.310)|(0.402)|(0.345)|
|Father was a High Administrator|0.022|0.057|0.076|0.061|
||(0.147)|(0.233)|(0.266)|(0.240)|
|Father had High Technician Status|0.055|0.069|0.150|0.096|
||(0.228)|(0.254)|(0.357)|(0.295)|
|Mother was a Farmer|0.275|0.149|0.137|0.154|
||(0.447)|(0.356)|(0.344)|(0.361)|
|Mother was a Worker|0.253|0.554|0.529|0.522|
||(0.435)|(0.497)|(0.499)|(0.499)|
|Mother was Self-Employed|0.018|0.013|0.018|0.015|
||(0.134)|(0.115)|(0.133)|(0.123)|
|Mother was a Private Business Owner|0.007|0.001|0.001|0.001|
||(0.085)|(0.037)|(0.039)|(0.043)|
|Mother is/was an Administrator|0.007|0.022|0.041|0.027|
||(0.085)|(0.148)|(0.198)|(0.164)|
|Mother is/was a Technician|0.029|0.058|0.146|0.086|
||(0.169)|(0.234)|(0.353)|(0.281)|
|**School Characteristics**<br>|||||
|Attended Vocational Technical High|||||
|School|0.029|0.029|0.109|0.057|
||(0.169)|(0.170)|(0.311)|(0.232)|
|Attended Magnet High School|0.136|0.041|0.125|0.078|
||(0.343)|(0.199)|(0.331)|(0.268)|
|In Magnet Class of Regular High<br>School|0.040|0.064|0.109|0.078|
||(0.197)|(0.245)|(0.312)|(0.269)|
|Elementary School in County Seat|0.040|0.036|0.050|0.041|
||(0.197)|(0.186)|(0.219)|(0.199)|
|Elementary School in Town or Village|0.183|0.092|0.132|0.113|
||(0.388)|(0.290)|(0.339)|(0.317)|
|Middle School in County Seat|0.055|0.038|0.059|0.047|
||(0.228)|(0.192)|(0.236)|(0.211)|
|Middle School in Township or Village|0.117|0.071|0.107|0.087|
||(0.322)|(0.257)|(0.309)|(0.282)|
|High School in County Seat|0.040|0.027|0.063|0.041|
||(0.197)|(0.164)|(0.243)|(0.198)|
|High School in Township|0.025|0.027|0.039|0.031|
||(0.158)|(0.164)|(0.194)|(0.175)|
|Observations|535|2294|785|3614|

33
**Figure 5.4  Moran Index (MI) and Sigma Convergence for Municipalities in Rio Grande do Norte’s per-capita Income (1970-96)**

<!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1970 1975 1980 1985 1990 1996<br>moran std log(y)<br><!-- End of picture text --> _Source_ : authors calculations.

Figure 5.4, furthermore, suggests a negative correlation between Moran Index and the standard errors of per-capita income over time. According to Rey and Montouri (1999), _apud_ Mossi _et al._ (2001): “this co-movement may reflect a dynamic characteristic of regional clustering and two possible explanations may support this fact. On the one hand, an increase in spatial dependence could be due to regions in each cluster becoming more similar. On the other hand, it could be due to newly formed clusters emerging during a period of increasing income dispersion”. That is, when rich (poor) municipalities are surrounded by rich (poor) municipalities, in a cluster, the income dispersion increases among municipalities.

# **5.4 Transitional Dynamics**

Recent literature on economic growth has used the transition matrix to deal with transitional dynamic, see Quah (1993). According to Kremer (2001), this methodology allows a more flexible relationship between the level of income and growth rate of income than the standard convergence approach in which countries’ or regions’ growth rate are assumed to be a linear function of their income levels.

16
the distribution has not yet settled into the stationary one. The protracted pace of convergence in the distribution feeds into the dynamics of aggregate _TFP._ When the reforms lift entry distortions, the drivers of the speed of transition are reversed. With exogenous innovation (bottom right), the immediate eect of the reform is to create a burst in the entry of new entrepreneurs that makes the distribution of rms across productivity be almost entirely dominated by the distribution of entrants (line labeled _t_ = 1 ). Thereafter the distribution converges to the stationary one at a pace dictated by the exogenous stochastic process of rm dynamics. When allowing for endogenous innovation (bottom left), the burst in entry also leads to a contraction in the right tail of the distribution. However, as rms increase their expenses in innovation, the convergence of the distribution is accelerated.

Figure B.3: Innovation Proles and Productivity Distributions <!-- Start of picture text --> Idiosyncratic Distortions Endo. Innov. Idiosyncratic Distortions Exo. Innov.<br>SST SST<br>SS0 SS0<br>t=1 t=1<br>t=10 t=10<br>1 2 3 4 1 2 3 4<br>Log Z Log Z<br>Entry Distortions Endo. Innov. Entry Distortions Exo. Innov.<br>SST SST<br>SS0 SS0<br>t=1 t=1<br>t=10 t=10<br>1 2 3 4 1 2 3 4<br>Log Z Log Z<br>0.20 0.20<br>0.15 0.15<br>Pdf 0.10 Pdf 0.10<br>0.05 0.05<br>0.00 0.00<br>0.20 0.20<br>0.15 0.15<br>Pdf 0.10 Pdf 0.10<br>0.05 0.05<br>0.00 0.00<br><!-- End of picture text --> The top two panels illustrate the _pdf_ of the distribution of physical productivity ( _TFP_ ) at various points of the transition for economies with endogenous (top left) and exogenous (top right) innovation, under a reform eliminating idiosyncratic distortions. The bottom gures illustrate the same objects for the case of reforms reversing entry distortions.

The dierence in the behavior of _TFP_ help rationalize the behavior of investment dynamics. With exogenous innovation and idiosyncratic distortions, the immediate jump in _TFP_ induces a neoclassical-shaped response of investment, increasing on impact and converging to the steady state level from above.<sup>22</sup> In the case of entry

> 22Recall that the idiosyncratic distortions were calibrated so that the capital to output ratio was undistorted. Thus, the behavior of investment dynamics cannot be attributed to an investment specic component of idiosyncratic distortions.

45
-6-

cyclical fluctuations of production do. The income elasticity of imports will be significantly higher in the short run; as the evidence in the studies by Khan and Ross (1975) and Clavijo and Faini (1988) indicates. Thus the assumptton of a unit income elasticity, which is often made in the context of short and medium-run projections, proves to be inadequate.

The income elasticity estimates for developing countries reported here are quite close to estimates for developed countries. In their survey Goldstein and Khan (1985) report the results of a number of studies that estimate import activity elasticit!.es for 14 developed countries. The cross-country averages of the estimates from the four studies with broadest coverage ranged from 1.22 to 1.63. The central tendency of the previous estimates for developing countries is also above one. The modal value of Bahmani-Oskooee's3 (1986) six-country estimates is 1.16.

Price elasticities were generally less precisely estimated. The average price elasticity of -.57, while a non-negligible price responsiveness, is less than one. Only 14 of the 28 countries had long-run price elasticities significantly different than zero (even at the 10 percent significance level). Only 2 countries had estimated price elasticities significantly greater than one, whereas 9 of the 28 countries have price elasticities significantly less than one. Hcst countries had price elasticities in the zero to one range, indicating that although price responsiveness is not entirely absent, it is not large, perhaps, as noticed earlier, because of the existence of controls. Some regional patterns of price elasticity are striking. For example, six of the eight South American countries in this study are reported to have price elasticities in the narrow range of 0.3 to 0.6. All of the four Sub-Saharan African
Table 4 shows that the null hypothesis of no targeting is confidently rejected in the direction of targeting toward the poor (i.e., a positive targeting coefficient). Despite the increase of leaking to the well off as the program expanded, the targeting coefficient increased from 24 to 35. Although the levels of coverage and leakage are lower when we evaluate targeting based on poverty among all households, not just those with young children, their relative magnitudes do not differ much. As such, the positive targeting coefficients are quite similar.

**Table 2: Vaso de Leche Coverage Rates for Tier I Households**

|||_Quintilesof_|_percapita_|_incomea_||_Transferspercapita_<br>_(1997Soles)_|
|---|---|---|---|---|---|---|
|Quintiles|ENNIV<br>1994<br>(percent)|ENNIV<br>1997<br>(percent)|Enaho<br>1998<br>(percent)|Enaho<br>1999<br>(percent)|Enaho<br>2000<br>(percent)|ENNIV<br>1997|
|Poorest|39.3|60.5|65.5|59.4|68.2|26|
|2|37.0|52.4|61.5|50.0|66.9|30|
|3|34.3|44.6|48.2|39.4|49.4|19|
|4|20.1|30.7|36.0|29.3|37.3|22|
|Richest|7.8|15.8|20.2|15.8|15.2|7|
|**Total**|27.7|**40.8**|**46.3**|**38.8**|**47.5**|**21**|

_Note:_ Domain is the set of households with at least one child of age 6 and under. a. Consumption for ENNIV 1994 and 1997.

Table **3: Percentage of All Households Benefiting from Vaso de Leche Transfers**

|Quintiles|_Quin_<br>ENNIV<br>1994<br>(percent)|_tilesofwei_<br>ENNIV<br>1997<br>(percent)|_ghtedperc _<br>Enaho<br>1998<br>(percent)|_apitaincom_<br>Enaho<br>1999<br>(percent)|<br>_ea_<br>Enaho<br>2000<br>(percent)|_Transferspercapita_<br>_(1997Soles)_<br>ENNIV<br>1997|
|---|---|---|---|---|---|---|
|Poorest|35.0|52.1|55.8|56.1|57.6|23|
|2|29.3|43.4|43.7|45.2|48.6|25|
|3|20.9|30.4|32.1|34.0|26.1|20|
|4|9.3|19.3|20.7|24.9|19.3|16|
|Richest|4.3|5.9|9.2|8.2|8.7|4|
|**Total**|**19.7**|**30.2**|**32.3**|**33.7**|**32.1**|**18**|

_Note:_ Domain is the set of households in the samples.

a. Consumption for ENNIV 1994 and 1997.

_12_
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